Jose Marti


Relevant Thesis-Based Degree Programs


Graduate Student Supervision

Doctoral Student Supervision

Dissertations completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest dissertations.

Hybrid shifted frequency analysis-electromagnetic transients multirate simulation of power systems (2023)

Traditional power systems have high inertia, and disturbances can be represented with relatively large discretization steps. However, power electronic interfaced generation requires time steps, at least two orders of magnitude smaller to represent the switching operations. In addition, the increased penetration of these devices reduces the total inertia of the system, and a single-time step size transient stability (TS) solution that considers the details of the power electronic devices would take two orders of magnitude longer (that is, electromagnetic transients (EMT) simulators time step sizes). Hybrid TS-EMT simulators aim at using large time steps for the traditional (slow) part of the system and small time steps for the power electronic devices. However, in the current state-of-the-art, the proposed hybrid solutions are limited in a number of ways. Most of them introduce a one TS time step interfacing delay between solutions, which reduces accuracy and can create numerical oscillations. Other approaches use the travelling time of transmission lines to tear the system and interface the solutions, but this limits the flexibility of connecting power electronic components anywhere in the system.The hybrid solution presented in this work combines a Shifted Frequency Analysis (SFA) solution that is more accurate than traditional phasor solutions to follow transient stability trajectories in lower inertia systems with the traditional EMT solution for the fast power electronic converters. In order to interface the SFA and EMT solutions, a new hybrid protocol has been developed that overcomes the disadvantages of the current hybrid solutions. The two subsystems are joined using the Multi-Area Thévenin Equivalent (MATE) solution framework, and introducing the novel concept of employing a second parallel EMT simulation. Illustrative examples demonstrate the validity and accuracy of the SFA-EMT multirate protocol. The new protocol achieves significant computational savings when compared with an all-EMT solution. A simulator operating with the proposed protocol will allow improved accuracy when conducting power system transient studies with high penetration of renewable generation without considerably increasing the computational time.

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Design concept for an IoT-based earthquake early warning platform (2022)

This dissertation aims to present a full-stack concept design for an earthquake early warning platform utilizing a dense array of sensors embedded in consumer electronic devices. The proposed design includes four systems: observation, estimation, prediction, and decision systems, each with a specific type of intelligence.A novel change detection technique, specially devised for the observation system, is responsible for detecting and estimating the arrival time. Knowing the geographical location and arrival times collected from various sensors allows the platform to compute an approximate location of the event through an optimization method. The performance results on 732 ground motions indicate that the error in arrival time estimation is less than 1.5 ?, on average. Meanwhile, the event location estimation average error is less than 16 ??, with a 99% confidence level.Furthermore, a novel earthquake intensity prediction model based on a neural network structure is responsible for evaluating the size of the event. The proposed prediction model yields 57% standard error over 4691 carefully selected ground motions. In contrast, the performance results of the voting process responsible for updating the alarm status indicate the overall accuracy of 75% for the platform; that is, three-quarters of the ground motions are correctly classified, on average. However, the average false alarm rates are 20% to 30%. Accordingly, investigating the effect of the threshold value on the false alarm rates illustrates that consideration of the practicality in the design concept allows the selection of an optimal alarm threshold.Utilizing the Internet of Things (IoT) infrastructure for earthquake early warning is a compelling and challenging alternative compared to conventional platforms. Future works require collaboration between academia and industry to devise guidelines for reconfiguring IoT infrastructure and improving the performance of individual earthquake early warning systems.

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Resilience enhancement for interdependent critical infrastructures (2020)

Modern societies depend on the proper and resilient functioning of their critical infrastructures (CIs) to support quality of life to their population. The interdependencies of the CI systems, however, make the CIs increasingly vulnerable to several threats; as a result, natural or human-made disasters can cause significant physical, economic, and social disruptions. Since total beforehand protection cannot be guaranteed, CI protection strategies should focus on resilience enhancement at both the pre- and the post-disaster phase for a better response. This thesis first proposes a resilience assessment framework that provides quantitative means to assess infrastructure resilience for interdependent CI systems. The framework is tested within the Infrastructure Interdependencies Simulator (i2SIM) that models and simulates the CI interdependencies. A coupled Cyber-Physical System (CPS) is modelled within the i2SIM framework to study the process of cascading failures. Resilience enhancement for the pre-disaster preparedness is done with a risk evaluation approach and is proposed by considering the profile of a hurricane to calculate the probability of failure of the network components. Based on the results of the evaluation, a resource allocation optimization is formulated using mixed-integer nonlinear programming (MINLP). For post-disaster resilience enhancement, an optimal reconfiguration algorithm, together with a hybrid load shedding strategy, is developed to find alternative paths to maintain supply to the most critical loads. A modified shortest path search that we call the Optimal Recovery Sequencing (ORS) is used to optimize the repair sequences. The obtained numerical results validate that the recovery ability of the coupled system, and as a result its resilience, is increased with the proposed optimization. Finally, to reduce the computational complexity for very large scenarios and for real-time response, this thesis uses a Soft-Hard Optimal Convergence (SHOC) methodology. Machine learning techniques are used to train an artificial intelligence (AI) agent with thousands of off-line scenarios to provide the initial estimate for the SHOC algorithm. After the disaster occurs, hard optimization methods are used on a small subset of solutions identified by the AI agent. Using this methodology, solution time speedups of 800 times for a 70-bus test system with 30 simultaneous faults are achieved.

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Distributed reinforcement learning in emergency response simulation (2019)

In this thesis we present the implementation of a coordinated decision-making agent for emergency response scenarios. The agent’s implementation uses Reinforcement Learning (RL). RL is a machine learning technique that enables an agent to learn from experimenting. The agent’s learning is based on rewards, feedback signals proportional to how good its actions are. The simulation platform used was i2Sim, the Infrastructure Interdependencies Simulator, in which, we have tested the suitability of the approach in previous studies. In this work, we have added new features, for increasing the speed of convergence and enabling distributed processing capabilities. These additions include enhanced reward and exploration schemes and a scheduler for orchestrating the distributed training. We include two test cases. The first case is a compact model with 4 critical infrastructures. In this model, the agent’s training required only 10% of the attempts as compared to references given by past studies done in our group. Improvements in convergence come from the enhanced shaping reward and exploration schemes. We trained the agent across 24 simultaneous configurations of our model (scenarios). The complete distributing training process needed 4 minutes. The second case is an extended model, a more detailed representation of the first case. This extended case included additional infrastructures and a higher level of resolution. By adding more infrastructures, the dimensionality of the problem grew four thousand times. This dimensionality growth did not affect performance and the training had an even faster convergence. We ran 96 parallel instances of the extended model and the process completed in 2.87 minutes. The results show a fast and stable convergence framework with a wide range of applicability. This agent could help during multiple stages of emergency response including real time situations.

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A frequency-dependent multiconductor transmission line model with collocated voltage and current propagation (2017)

This research contributes to developing a time domain and a frequency domain formulationsto solve electromagnetic transients in power system with multiconductoroverhead transmission lines.The time domain solution introduces a frequency dependent transmission linemodel “FDLM”. For the development of the FDLM a fundamental constraint isadded to the classical line equations to maintain the symmetry between electric andmagnetic fields. As a result, voltage waves and current waves travel together andthe characteristic impedance remains uniform along the line. With this premise, aconstant real transformation matrix can be obtained to diagonalize the line functionswith high accuracy. This feature can greatly facilitate the line modelling asopposed to the existing line models which require complex frequency dependenttransformation matrices for their diagonalization. The use of a single constant realtransformation matrix for the voltage and current waves which is exact over the frequencyrange enables FDLM to provide higher accuracy and numerical efficiencythan the existing line models while it complies with the physical system.The accuracy of the FDLM is assessed through comparisons with a newly developedDiscrete Time Fourier Series frequency domain solution. This methodologyis based on the correct specification of the time window and frequency windowwidths. Guidelines are provided for this set up which avoids the typical Gibbs andaliasing errors related to the classical frequency domain solutions. The proposedfrequency domain solution is simpler to implement than the most commonly usednumerical Laplace transform solution while it does not require further considerationsto use damping factors or windowing functions.

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Decision support for emergency response in interdependent infrastructure systems (2017)

In recent years, extreme events, such as hurricanes, earthquakes, floods and fires, occur more frequently and at a higher intensity. The growing complexity and interdependence of modern infrastructure systems makes them vulnerable to such events. Emergency response is the process of implementing appropriate actions to reduce human and economic losses following these events. Efficient response requires an understanding of the existing infrastructure systems and their interdependencies. In this thesis, we propose a decision support system for helping emergency responders in making efficient decisions during extreme events. Fires are chosen as an example of the extreme events and firefighting operations as the emergency response to these events. Everyday, fire managers are faced with making increasingly complex manpower decisions; trying to minimize costs and risk levels. The effectiveness of firefighting operations is crucial in minimizing both cost of suppression and economic losses. The contributions of this thesis focus on two levels of fire management plans: operational and strategic. We first develop a methodology to optimize the allocation process of firefighting resources in multiple-fire incidents. The developed methodology employs reinforcement learning, a machine learning algorithm that optimizes the allocation of firefighting units to minimize the total economic losses in the long run. To consider the concept of infrastructure interdependencies in evaluating the economic impact of the incidents, we model a large petrochemical complex using the Infrastructure Interdependency Simulator (i2Sim). In addition, a capacity planning methodology is developed to investigate the impact of manpower investment on the effectiveness of firefighting operations. The developed methodology aims at finding the optimal number of firefighters to be recruited to contain fires and effectively extinguish them. It performs an economic analysis to evaluate the efficiency fire management plans. Finally, we propose a methodology to evaluate the effectiveness of emergency response plans in improving infrastructure resilience. This methodology focuses on two dimensions of resilience: resourcefulness and rapidity. These dimensions are measured by the optimality of allocating firefighting units and by minimizing economic losses. The proposed methodologies are tested using a case study of a large petrochemical complex and promising results are achieved.

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Planning and operation of active smart grids (2017)

Future smart grids will be operated actively in the presence of distributed generators and topological reconfigurations. Distributed Storage Systems (DSS) will also become a viable solution for balancing the load and the intermittent generation of renewable energy sources. The DSS can also provide the smart grid operator with various other benefi ts including peak load shaving, resilience enhancement, power loss reduction, and arbitrage gain.The active nature of future smart grids calls for an accurate state estimation mechanism to serve as a building block for many operational tasks. To that end, the first part of the present thesis leverages the concept of submodularity to solve the problem of robust meter placement for state estimation in reconfigurable smart grids. Next, the thesis proposes a methodology for optimal planning of DSS in smart grids with high penetration of renewable sources. The presented methodology accounts for various advantages of energy storage in smart grids and seeks the optimal trade-off between the investment cost and the expected discounted reward of DSS installation. Finally, the thesis focuses on the problem of Volt-VAR Optimization (VVO) in activesmart grids. The optimal joint operation of reconfiguration switches, energy storage units, under load tap changers, and shunt capacitors is investigated in the presented VVO methodology. The proposed methodologies in this thesis have been tested on sample distribution systems and their effectiveness is validated using real data of smart meters and renewable energy sources.

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Dynamic and static voltage stability analysis of distribution systems in the presence of distributed generation (2016)

In this research, static and dynamic voltage stability of distribution systems are studied, with a focus on systems with distributed generation. A voltage dependent model is used for distribution loads, which converts the static voltage stability analysis into a linear problem and allows for real-time solutions. For static voltage stability studies, an index is defined that contains critical data on load and system characteristics. The static voltage behavior of distribution systems, either with or without DG, is studied by analyzing the P-V curves and the proposed voltage stability index at each node of the system. Besides identifying the weakest buses of the system in terms of voltage profile and voltage stability, this approach also allows the extension of the classical voltage stability solution to the increasingly important case of distributed generation placement - where the system is more likely to face voltage transients. The method can also be used in resilience studies. For dynamic voltage stability studies, the Shifted Frequency Analysis (SFA) method is used to evaluate the system transients and its dynamic voltage behavior during and right after being subjected to a change or disturbance in the system. Various scenarios are discussed, including scenarios in which the voltage transients due to DG cause voltage instabilities in the system. SFA and EMTP solutions are compared with each other, and with the static analysis results. SFA and EMTP results also verify the validity of the proposed voltage stability index. The proposed voltage assessment method facilitates real-time decision making, topological reconfiguration to strengthen voltage stability robustness, and DG placement. Different scenarios and distribution system topologies such as looped or meshed distribution systems, as well as microgrids and autonomous/islanded energy grids can be included in the solution.

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Improving critical infrastructure resilience with application to power distribution networks (2016)

Our modern societies are dependent on the functioning of infrastructure systems that support economic prosperity and quality of life. These infrastructure systems face an increasingly set of threats, natural or man-made disasters, that can cause significant physical, economic, and social disruptions. Recent extreme events have shown that total protection can not be accomplished. Therefore, Critical Infrastructure Protection strategies should focus not only on the prevention of these events but also on the response and recovery following them. This shift is realized by the concept of infrastructure resilience. In this thesis, we address the problem of assessing and improving infrastructure resilience. The contributions of this thesis focus on modelling, simulation, and optimization of infrastructure systems with respect to their resilience to extreme events. We first develop a resilience assessment framework for interdependent infrastructure systems. The developed framework provides a quantitative means to assess infrastructure resilience by introducing a generalized resilience index. To account for the inherent complexity due to infrastructure interdependencies, we use the i2Sim framework for modelling and simulating the studied infrastructure. The resilience improvement problem is formulated using the proposed resilience index as a resources allocation optimization problem. The problem aims at finding the best allocation of available resources such as power and water to mitigate the consequences of a disaster. Two solutions algorithm are proposed to solve the problem: the first one uses a simulation-optimization approach based on the Ordinal Optimization theory, and the second one uses a Linear Programming formulation. Results of both algorithms show that infrastructure resilience can be greatly improved by efficient allocations of available resources. In addition, a prioritization methodology is developed to assess decision makers to direct resilience investment to the most important components in the infrastructure. Finally, an optimal power distribution network reconfiguration algorithm is developed to complement the two resources allocation algorithms by solving the technical feasibility problem of the power distribution network. A heuristic computationally inexpensive optimization algorithm is developed based on Graph theory for solving this problem. The proposed algorithms are tested using different test cases and promising results are achieved.

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Distribution Systems Analysis and Optimization (2015)

Distribution systems (DS) are the last stage of any large power system, delivering electricity to the end-users. Conventionally, simplicity of DS operation has been a priority over its optimality. However, with the recent advancements in the automation and measurement infrastructures, it is now possible to improve the efficiency of DS operation. In this dissertation, a load modeling procedure is proposed which takes advantage of the data available at the smart meters. An algorithm is proposed to decompose the load at each customer level using the smart meter measurements. The proposed load model represents the voltage dependence of loads according to the load composition. Based on the voltage-dependent load model, a linear power flow formulation is developed for DS analysis. The linear current flow equations are then proposed which calculate the branch flows directly without requiring the nodal voltages. Sensitivity factors in terms of current transfer and branch outage distribution factors are also derived using the linear power flow concept. The advantages of having a set of linear equations describing the system statics are demonstrated in a variety of DS optimization problems, such as topological reconfiguration, capacitor placement, and volt-VAR optimization. Using the linear current flow equations, the mixed-integer nonlinear programming problem of DS reconfiguration is reformulated into a mixed-integer quadratic/linear programming problem, which substantially reduces the computational burden of the nonlinear combinatorial problem. Besides developing a direct mathematical optimization approach, a fast heuristic method is also developed here for the minimum-loss network reconfiguration based on the minimum spanning tree problem. This heuristic method provides a good suboptimal solution to initialize the direct mathematical optimization approaches such as branch-and-cut algorithm used for solving combinatorial problems. Based on planar graph theory, an efficient mathematical formulation for the representation of the radiality constraint in reconfiguration problems is introduced. It is shown that this formulation is advantageous over the available methods in terms of accuracy and computational efficiency. The proposed algorithms are tested using a variety of DS benchmarks and promising results are achieved.

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Resource allocation optimziation of a dirupted interdependent system using machine learning (2014)

National safety and homeland security of an urban community rely heavily on a system of interconnected critical infrastructures (CI’s). The interdependencies in such complex systems give rise to vulnerabilities, which must be accounted for by a proper disaster management. It is a proactive step that is needed to address and mitigate any major interruption in a timely manner. Only then will the management of CI’s be able to appropriately reallocate and distribute the available scarce resources of an existing interdependent system.In this research, we propose an intelligent decision making system that optimizes the allocation of the available resources following an infrastructure disruption. The novelty of our suggested model is based upon the application of a well-known Machine Learning (ML) technique called Reinforcement Learning (RL). This learning method is capable of using experience from a massive number of simulations to discover underlying statistical regularities. Two alternative approaches to intelligent decision-making are studied, learning by Temporal Differences (TD) and Monte Carlo (MC) based estimation. The learning paradigms are explored within the context of competing designs composed of simulators and learning agents architected either independently or together. The results indicate the best learning performance is obtained using MC within a homogeneous system. The goal here is to maximize the number of discharged patients from emergency units by intelligently utilizing the existing limited resources. We show that such a learning agent, through interactions with an environment of simulated catastrophic scenarios (i2Sim-infrastrucutre interdependency simulator), is capable of making informed decisions in a reasonable time.

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Temporal-spatial discretization and fractional latency techniques for wave propagation in heterogeneous media (2010)

This thesis presents the development of a novel, transient wave propagation simulator using time-decoupled transmission line models. The models are based on the electro-magnetic transient program (EMTP) power system transient analysis tools, extended to two dimensions. The new tool is targeted at acoustic wave propagation phenomena. The method, called TINA for transient insular nodal analysis, uses temporal interpolation and fractional latency to maintain synchronicity in heterogeneous media. The fractional latency method allows the model cells to operate at a local simulation time step which can be a non-integer ratio of the global simulation time step. This simplifies synchronicity and saves computation time and memory. Thévenin equivalents are used to interface the mesh cells and provide an abstraction of the cell content. Numerically, the method is of the transmission-line matrix (TLM) family. In the thesis, loss-less and distortion-less models are considered. The loss-less transmission line models are studied for their stability and numerical error, for which analytical expressions are derived based on the simulation parameters. A number of new relations were discovered and discussed. The TINA method is evaluated in 2D using acoustic experiments, and also a new method is proposed for obtaining impulse responses in time-domain simulation, based on a periodic, band-limited impulse signals.

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A multi-layer neural network approach to identification of mechanical damage in power transformer windings (2009)

Power transformers are among the most critical of assets for electric utilities, in the financialimpact that their failure can bring. Asset Managers need to be able to determine theright time for replacement, refurbishment or relocation of these devices, with an increasingdegree of confidence, in order to minimize the total cost of operation over the equipments’life. This has brought a change from scheduled maintenance to condition based monitoring(CBM), where the state of the transformer is continuously monitored to evaluate its workingcondition.A key method of transformer CBM, which effectively detects mechanical damage to thestructure of the transformer windings, is Frequency Response Analysis (FRA). FRA relieson comparison of electrical admittance signatures to determine if the winding has becomedeformed. One of the major problems it still faces is the interpretation of differences inthe signatures. To date, experts are needed to analyse graphs, drawing from experience inorder to produce educated guesses as to what the differences in admittance functions denote.However, in the recent past, there has been some headway in programming computer basedsolutions for the problem of interpretation.The use of Artificial Neural Networks (ANNs) has perhaps been the most promising in thisrespect. ANNs perform in the same way that human experts do, drawing upon experience tomap a change in shape of a signature to a physical change in the winding system. However,one of the major drawbacks of these methods is the large training data-sets required for theneural network to learn.The work reported in this thesis seeks to address this problem by generating training datasetsfrom analytical models of the transformer. Due to the large number of simulations thatneed to be performed a customized solution method was developed to speed up computations.A combination of back propagation and radial basis function networks were then usedto classify the type, location and severity of winding movement. The results showed thatthe neural network approach was not only accurate but tolerant to high noise levels.

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Modelling and simulation of interdependencies between the communication and information technology infrastructure and other critical infrastructures (2009)

Critical infrastructures are the lifelines of modern societies. The Communication and Information Technology Infrastructure (CITI) provides the basic mechanisms for sharing control and decision-making information among different critical infrastructures. Failures in CITI, either due to an accident or malicious action can propagate to other infrastructures and degrade or disrupt their functionality. Conversely, failures in other infrastructures can also propagate to CITI and hence disrupt the operation of many of the interconnected systems. For reliable and consistent operation of critical infrastructure networks, it is important to have tools and techniques to model and simulate CITI related interdependencies. This research is focusing on developing such methods and tools for CITI interdependency modelling and simulation. Our approach is based on system engineering techniques, where critical infrastructures are viewed as a system of systems. Interdependencies between different system components are captured using precise mathematical functions. As such, our approach goes beyond the limitations of agent-based modelling and simulation paradigms, where interdependencies are considered an emergent behavior. In this research, we have used predictive modelling techniques commonly used in power systems, data communication networks and information systems. The approach is based on results from real CITI interdependency related data. In our model, we used these data to identify the origin of different types of CITI failure and their impacts on critical infrastructures. Following that, we developed techniques to estimate interdependencies between CITI and other critical infrastructures. Finally, we developed techniques to simulate CITI interdependencies in a critical infrastructures simulator. The simulation results were validated against real-life failure cases. Our approach gives a comprehensive solution to CITI interdependency modelling and simulation problems and hence is an important step in the critical infrastructure related research. Even though our techniques are developed for CITI interdependency, they will be useful for other critical infrastructure networks as well.

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Negative sequence impedance measurement for distributed generator islanding detection (2009)

This thesis presents a method of detecting electrical islands in low voltage distributed generator networks by measuring negative sequence impedance differences between islanded and utility connections. Extensive testing was conducted on a commercial building and 25 kV distributed generator fed network by measuring naturally occurring and artificially injected negative sequence components. Similarly, this technique was tested using the IEEE 399-1990 bus test case using the EMTP software. The practical measurements have been matched to simulations where further system performance characteristics of detecting power system islands has been successfully demonstrated. Measured results indicate that unbalanced load conditions are naturally occurring and readily measurable while deliberately unbalanced loads can increase the accuracy of negative sequence impedance islanding detection. The typically low negative sequence impedance of induction motors was found to have only a small effect in low voltage busses, though large machines can effect the threshold settings. Careful placement of the island detector is required in these situations. The negative sequence impedance measurement method is an improvement on previous impedance measurement techniques for islanding detection due to its accuracy, and distinctly large threshold window which have challenged previous impedance based islanding detection techniques.

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Parallel computation of large power system networks using the multi-area Thévenin equivalents (2009)

The requirements of today's power systems are much different from the ones of the systems of the past. Among others, energy market deregulation, proliferation of independent power producers, unusual power transfers, increased complexity and sensitivity of the equipments demand from power systems operators and planners a thorough understanding of the dynamic behaviour of such systems in order to ensure a stable and reliable energy supply.In this context, on-line Dynamic Security Assessment (DSA) plays a fundamental role in helping operators to predict the security level of future operating conditions that the system may undergo. Amongst the tools that compound DSA is the Transient Stability Assessment (TSA) tools, which aim at determining the dynamic stability margins of present and future operating conditions.The systems employed in on-line TSA, however, are very much simplified versions of the actual systems, due to the time-consuming transient stability simulations that are still at the heart of TSA applications. Therefore, there is an increasing need for improved TSA software, which has the capability of simulating bigger and more complex systems in a shorter lapse of time.In order to achieve such a goal, a reformulation of the Multi-Area Thévenin Equivalents (MATE) algorithm is proposed. The intent of such an algorithm is parallelizing the solution of the large sparse linear systems associated with transient stability simulations and, therefore, speeding up the overall on-line TSA cycle. As part of the developed work, the matrix-based MATE algorithm was re-evaluated from an electric network standpoint, which yielded the network-based MATE presently introduced. In addition, a performance model of the proposed algorithm is developed, from which a theoretical speedup limit of p/2 was deduced, where p is the number of subsystems into which a system is torn apart. Applications of the network-based MATE algorithm onto solving actual power systems (about 2,000 and 15,000 buses) showed the attained speedup to closely follow the predictions made with the formulated performance model, even on a commodity cluster built out of inexpensive out-of-the-shelf computers.

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Shifted frequency analysis for EMTP simulation of power system dynamics (2009)

Electromagnetic Transients Program (EMTP) simulators are being widely used in powersystem dynamics studies. However, their capability in real time simulation of power systems iscompromised due to the small time step required resulting in slow simulation speeds.This thesis proposes a Shifted Frequency Analysis (SFA) theory to accelerate EMTPsolutions for simulation of power system operational dynamics. A main advantage of the SFA isthat it allows the use of large time steps in the EMTP solution environment to accuratelysimulate dynamic frequencies within a band centered around the fundamental frequency.The thesis presents a new synchronous machine model based on the SFA theory, whichuses dynamic phasor variables rather than instantaneous time domain variables. Apart from usingcomplex numbers, discrete-time SFA synchronous machine models have the same form as thestandard EMTP models. Dynamic phasors provide envelopes of the time domain waveforms andcan be accurately transformed back to instantaneous time values. When the frequency spectra ofthe signals are close to the fundamental power frequency, the SFA model allows the use of largetime steps without sacrificing accuracy. Speedups of more than fifty times over the traditionalEMTP synchronous machine model were obtained for a case of mechanical torque step changes.This thesis also extends the SFA method to model induction machines in the EMTP. Byanalyzing the relationship between rotor and stator physical variables, a phase-coordinate modelwith lower number of equations is first derived. Based on this, a SFA model is proposed as ageneral purpose model capable of simulating both fast transients and slow dynamics in inductionmachines. Case study results show that the SFA model is in excess of seventy times faster thanthe phase-coordinate EMTP model when simulating the slow dynamics.In order to realize the advantage of SFA models in the context of the simulation of thecomplete electrical network, a dynamic-phasor-based EMTP simulation tool has been developed.

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Master's Student Supervision

Theses completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest theses.

Comparing the SIR and I2SIM models for the COVID-19 virus propagation (2023)

The COVID-19 pandemic has been present for nearly three years; we have acquired significant data about the COVID-19 virus. However, there are still many aspects of the pandemic unknown to us. In particular, it is still unknown what will be the optimal government decision for controlling the pandemic. The interdependencies between the various control policies are complex, with limited information about the relationship each policy has with each other and on controlling the growth of the virus. The I2SIM-RT software is based on the I2SIM concept developed in the UBC Power Research Group under Dr. José R Martí and was designed to simulate system interdependencies. This thesis will use I2SIM-RT to analyze the various protocols and restrictions implemented to counter COVID-19 in two areas in the world affected by the COVID-19 pandemic: British Columbia, Canada, and Hubei, China. We will also use the SIR Model, a model used in epidemiology to model infectious diseases, to assess the various government health policies intended to control the spread of the COVID-19 virus. This project will simulate the various government health policies implemented to counter the COVID-19 pandemic and how they affected the active COVID-19 case counts. We will compare the data reported during the pandemic with the model predictions obtained with the SIR Model implemented using MATLAB and the I2SIM COVID-19 Model using the I2SIM-RT software.A significant aspect of this thesis is to simulate the percentage of people who follow government health policies to see how their actions affect the number of active COVID-19 case counts. We used data from the British Columbia Centre for Disease Control to populate the I2SIM-RT COVID-19 Model. We adjusted different simulation scenarios based on different pandemic policies that will reflect the interdependencies between the policies. The Hubei, China data comes from the Hubei Ministry of Health. We investigated the Hubei government's various restrictions and policies that were used to suppress active COVID-19 cases and prevent new COVID-19 cases from emerging.

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Dense network of sensors applied to earthquake early warning in smart cities (2023)

Earthquakes are unpredictable, unpreventable natural events that cause extensive damage to the impacted area’s infrastructure and inhabitants. Early warning systems are humanity’s way of having some prior knowledge of the onset of earthquakes. Historically, Earthquake Early Warning (EEW) systems have been implemented, but have shown limited success in providing accurate, adequate, and meaningful information to help societies mitigate what is happening. A new approach to EEW is proposed in the Earthquake Early Warning in Smart Cities (EEW-SC) that utilizes modern technologies like 5th generation wireless access telecommunications networks and the Internet of Things (IoT) to achieve much improved seismic behaviour prediction and apply the learned toward next-gen mitigation and prevention strategies for urbanized and industrialized smart cities' protection. The outlined research covers the development of an advanced Dense Network of IoT Sensors (DeNIS) for seismic data collection in real-time and a novel mechanism for data acquisition, data transfer and Asynchronous Sensor Coordination (ASC). A detailed examination of 4th and 5th generation cellular technologies brought much needed understanding into the behaviour of an EEW system operated on a public telecommunications network. The learnings and developments in this work will be further incorporated with the soon-to-be developed and deployed in British Columbia EEW-SC.

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High impedance fault detection in power distribution systems with impedance-based methods in frequency domain (2016)

High Impedance Faults (HIFs) in electrical distribution systems generally present serious public safety hazards, utility liability problems and equipment damage due to a risk of arcing ignition of fires. However, it is extremely difficult to detect HIFs in electrical distribution systems by conventional overcurrent relays or fuses because they do not generate enough fault current to be detectable. This thesis presents a new detection scheme for HIFs in electrical distribution systems based on observing the harmonic content in the one-sided amplitude spectrum of the impedance. The proposed new HIF detection algorithm can distinguish HIF events from other non-fault events with current waveforms that similar to HIF current waveforms. Four simulation cases have been tested in this thesis to verify the new HIF detection algorithm. The simulation results indicate a potential ability to establish a high impedance fault detection function block in BC Hydro’s distribution system protective devices.

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On-Line Voltage Stability Assessment and Preventive Control Action Recommendations Based on Artificial Neural Network (2016)

Many power systems are being operated close to their security limits, which makes the reliable operation more challenging than ever. Voltage instability has been a major problem faced by many utilities. Many blackouts involved with voltage instability have been reported around the world. There is an increasing demand of accurate and up-to-date assessment for power system voltage stability and recommendations of preventive control actions.On-line voltage stability monitoring tools have been largely matured recently. They are typically integrated with the energy management system (EMS) and assess the voltage stability of the present operation condition based on the load-flow solution generated by state estimator. Preventive control actions to enhance voltage stability against potential contingencies still need to be developed off-line through extensive studies. They are usually presented to the operators in the form of bounds set of key parameters for voltage security monitoring and control action execution. However, these methods are limited by computation cost, extensive simulations, orconservative operation.This thesis proposes an artificial neural network (ANN) based framework to achieve on-line loading limit assessment and preventive control action recommendations for a practical power system. Firstly, an operation knowledge database consisting of interested operation conditions and loading limits is developed offline. Then an ANN model is trained to map the operation conditions with the corresponding loading limits. Finally, the proposed framework is applied in BC Hydro Vancouver Island system operation for on-line loading limit assessment and preventive control action recommendations.

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EMTP-based Load Disaggregation at Feeder Levels (2015)

Power distribution networks play an important role in electricity grid. Distribution system components require becoming smarter and more automated for the sake of improvingtheir reliability and increasing their operational efficiency. Smart meters are one ofthe powerful devices that achieved this goal. However, their data are of minimal use —grid or load information obtained from smart meters are shallowly analysed. This thesistakes advantage of the shortcoming by accurately calculating the load information usingEMTP-based load disaggregation method. The approach is applicable to residential loads at small scale and feeders at large scale. In this thesis we first give our theoretical method for load disaggregation inspired by EMTP computational program. Then with simulation and experimental results, we demonstrate that our work outperforms past solutions by the following advantages:1. EMTP-based load disaggregation is applicable at every point of interest, i.e., from distribution feeder down to the customer’s entry point.2. Unlike other methods, our method employs both transient and steady state load properties.3. Last but not least, our solution is capable of determining load’s electrical parameters.In this thesis we stress on three major eigen-loads: (1) motor, (2) resistive, and (3) purelyinductive. Then we report how much of the load is made of each eigen-load. We exampled our method on a number of PSCAD simulation cases and a few real appliance measurements. Our results prove load disaggregation shall assist power system engineers in evaluating the power flow on accurate load observations.

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Wide-Area Monitoring and Control Utilizing PMU Measurements for a System Protection Scheme (2015)

The ever increasing complexity of the electrical grid has made it difficult to predict and anticipate contingencies. This is mainly due to the advent of deregulated electricity markets, aging transmission infrastructure and the growing penetration of renewable resources. The wave of blackouts in recent years has made utilities much more aware of the need for power system wide monitoring and control. One of the fundamental requirements to achieve that goal is to have common measurement reference. A few technology enablers have emerged which have led to development of a new kind of measurement paradigm; Phasor Measurement Units, or PMUs.PMUs bear high potential for wide-area system monitoring and control as well for conducting advanced engineering analysis. PMUs can provide time-synchronized high-resolution estimates of voltage and currents (both phase amplitude and angle) as well as frequency and rate of change of frequency. Such measurements, alternatively called synchrophasors, can provide visibility of a power system distributed over a wide geographical area and can be utilized in a multitude of applications including real-time monitoring, advanced power system protection, and advanced control schemes.In this thesis, a new special protection scheme (SPS) is proposed based on synchronized measurements provided by PMUs. An existing remedial action scheme (RAS) protecting for contingencies impacting the tie-line interconnecting the Alcan system to B.C. Hydro, using conventional relays is studied, and a new scheme based on time-synchronized, and high-resolution voltage angle measurements from PMU’s in a Wide-are monitoring system (WAMs) is proposed . In this new scheme, the angles of the buses at large power plants in both systems are examined and used to calculate various criteria based on region center of angle and the kinetic energy function to implement RAS. The results of a number of time domain simulations demonstrate that the proposed scheme can lead to faster operation of the SPS and decreased amount of generation and load shedding in the Alcan system. The achieved speed and efficiency of the proposed scheme in comparison to the existing installed scheme further highlight the opportunity in utilizing PMU measurements in online applications for power system protection and monitoring.

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Vulnerability and risk analysis of the Guadeloupe Island for disaster scenarios using the modified I2SIM toolbox (2013)

Relationships between system components can be simple but interdependencies among different systems can sometimes be complex. Interdependencies among systems are the purpose of the I2Sim simulator developed by Dr. José R Martí and the UBC Power Group. As part of this thesis, the I2Sim simulator is used to conduct vulnerabilities and risks analysis for two different test cases in the event of a disaster: The Sendai case in Japan and the Guadeloupe Island case in the French West Indies. The two study cases are part of two separate projects: the DR-NEP project and the MATRIX project, respectively.With the completion of the Sendai study case there was a need to modify the I2Sim toolbox in order to increase its robustness and the flexibilities of its components. A major part of this thesis is the modification of the channel cell and the storage cell in the I2Sim toolbox.The Guadeloupe Island test case is part of a collaborative effort with BRGM of France. In order to define a disaster scenario that is meaningful and realistic we adopted the ARMAGEDOM methodology to determine the magnitude of the earthquake and the effect it had on different areas of the Island. Damage data for infrastructures as well as affected population are generated according to the approach outlined in the RISK-UE project. Combined with data collected through publications the I2Sim model of the Guadeloupe Island was constructed. By running different simulations for different evacuation policies and resource distributions the interdependencies between different systems were revealed. The study also found the vulnerable points in the system and the results are used for risks analysis and assessments for other parts of the project.

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A web-service based disaster response interface for the DR-NEP platform (2012)

The Infrastructure Interdependencies Simulation (I2Sim) team led by Dr. José R. Martí at the University of British Columbia has been researching the hidden interdependencies between complex infrastructures for several years [1]. The I2Sim platform was developed on the foundation of Matlab Simulink and has been significantly improved by researchers and engineers since the first version of the toolbox created in 2007 [2]. The current version of the I2Sim toolbox has versatile capabilities on many applications such as disaster response, resource optimization, financial management, etc. For disaster response application, in particular, the I2Sim team has formed a group of engineers in cooperation with the University of Western Ontario and the University of New Brunswick to develop the Disaster Response Network Enabled Platform (DR NEP). DR NEP is a distributed platform that communicates through an Enterprise Service Bus (ESB)utilizing the state-of-the-art Lightpath services provided by CANARIE.With advanced computing power and high speed network connections, DR NEP is able to integrate I2Sim with other simulators and services, which are physically located all over Canada, to perform real time simulations and provide decision support for emergency responders. To further enhance user experience and improve the user interface for emergency responders, Web services were used in the project to create a web-based platform to display the simulation results on web pages and GIS systems, such as Google Earth. This platform enables responders to update and exchange information from standard web browsers and Google Earth. Simulation experts can use the website to control simulation and view simulation results and feedback from the website. A test case which involves the 2011 Tohoku earthquake incident in Japan is included in this report to demonstrate the simplicity of the user interface and the contribution of the web service to DR NEP. In addition, "what-if" scenarios were conducted on the model to explore better emergency responding strategies. The results from the simulation were studied and analyzed in detail. DR NEP is a fully functioning platform with complete components. With sufficient information provided by emergency responders or local resource management facilities, a complete model can be constructed for simulation and study. The next phase of the development would be model automatic creation, ergonomic user interface design, improvement on role-based access and model validation methodology development. Recommendations to those problems are included accordingly. As a member of the DR NEP team, I have been involved in most of the phases of the platform development. My main contribution to the team includes designing part of the table structures in database schema, implementing the web services for data visualization (Google Earth and the associated web services) and constructing the Japan Sendai City model.

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I2SIM financial model and its application ot UBC's living lab projects (2012)

The Infrastructure Interdependencies Simulator (I2Sim) enables the user to explore the relationship and interdependencies among different infrastructures. Based on the same I2Sim ontology, and derived from a regular production cell, the I2Sim financial production cell (FPC) is designed to simulate and present the financial behaviors.UBC’s living lab model and living lab battery model provided an ideal testing ground for the I2Sim environment and its new financial production cell. In the living lab model, two of the financial production cells were integrated into the system, and provided with real time dynamic inputs. As expected, the financial production cell outputs correctly the cost of purchased resources as well as the ongoing real time costs. The results of this simulation successfully demonstrated the strength of the I2Sim environment and the capabilities of the financial production cell.In the living lab battery model simulation, two cases were tested. In the first test case, with demand peak-shaving as the main objective, an entire battery system model was developed from the ground up and finally integrated with the financial production cell. The simulation provided accurate financial analysis towards three different battery types: Flow batteries, lithium-ion batteries and sodium sulfur batteries. Although the results suggest that the battery system is not the most economical electric energy storage system at the moment, it again proved that the financial production cell is a suitable tool for many different business analysis cases. In the second case, delaying south transmission line’s upgrading process became the main objective; the test results showed that the flow battery was the best choice. The project was proven to be both technically and financially feasible.

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Multi-energy systems simulator for hourly management and optimization of CHG emissions and fuel costs (2012)

Many legacy infrastructures are reaching the end of their service life. The necessary replacement of these infrastructures creates an opportunity to replace them with environmentally friendly and innovative systems. The steam plant at the University of British Columbia is one of those cases requiring replacement due to aging. The steam generation boilers are, on average, 53 years old and have short expected remaining service. The boilers process is fed by natural gas as main fuel. It was identified that almost 80% of the CO₂e emissions on campus are produced from the use of gas for heating purposes.UBC is worldwide recognized for being one of the most sustainable university campuses, and the first university in Canada awarded a gold rating in sustainability. UBC’s GHG emissions targets for Kyoto protocol were reached in 2007; at that point, new aggressive reduction targets were established, aiming for 33% by 2015, 67% by 2020 and 100% by 2050. These reductions are expressed in tonnes of CO₂e. The situation described offers an opportunity to explore alternatives for the Steam Plant potential replacements. The Infrastructures Interdependencies Simulator (I2Sim) was selected as simulation platform for this study. The simulator allows real-time resource management using hourly historical operational data. To meet the campus thermal requirements, the system considers biomass cogeneration, heat pump, and excess electricity to offset traditional natural gas fuel sources. All technologies take advantage of real-time management of fuels allocation to reduce GHG emissions. A parallel distribution system based on hot-water is modeled, because of the potential in increasing the overall heating system performance. Four modeling scenarios are constructed, showing that fuel costs can be reduced by 51%, GHG emissions reduced by 76% and overall energy consumption reduced by 29%. The simulator is a first step in integrating all critical infrastructures into a Smart Energy MicroGrid paradigm.

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Intelligent load shedding scheme for frequency control in communities with local alternative generation and limited main grid support (2011)

The number of microgrids in the electric power system is increasing rapidly to meet the increased electrical demand at the distribution level instead of delivering the additional power from centralized generation stations. Due to the above-mentioned transformation and a number of other factors, the utilities find themselves less attracted to investing in the supply lines. As a consequence, the connection points of these, rather self-supplied, microgrids become more vulnerable to any major disturbances in the downstream network. Because microgrids with renewable energy resources do not have a generation reserve, an intelligent load shedding algorithm (based on a smart grid) is proposed, that balances the power demand and generation, and prevents the upstream supply lines from exceeding their capacity at any time. Although the algorithm is accurate, it may not be fast enough to prevent a cascading power outage in the isolated microgrid due to very fast frequency decline. To help maintain the frequency of the microgrid close to the normal level, a supplementary controller is added to the doubly fed induction generator wind turbine. The studied microgrid corresponds to a case study in the University of British Columbia (UBC). A scenario of demand and distributed generation of the campus in the year 2030 are modeled. The proposed algorithm, which combines intelligent load shedding with wind turbine controller, succeeds in managing the power requirements for both grid connected and isolated microgrids. However, the algorithm has a weakness which is its operation delay; for longer delays in deactivating the controller and load shedding, the frequency might drop below the threshold. A number of suggestions are made to overcome this problem.

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Infrastructure interdependencies simulation (I2Sim) system model and toolbox (2010)

The interdependencies between infrastructures have become more complex with thegrowth of the civilization. As a result, the cascading effects on the system caused bya failure of one infrastructure became larger and unpredictable. As seen from the majordisasters such as the Sichuan earthquake in China, understanding infrastructureinterdependencies and allocating resources efficiently in the system can facilitate therecovery process significantly. As a part of Canadian Government’s effort to developinnovative ways to mitigate large disaster situations and grow resiliences of systems,Joint Infrastructure Interdependencies Research Program (JIIRP) has been formed. TheUBC’s Infrastructure Interdependencies Simulation (I2Sim) group led by Dr. Jose Martiparticipated to study decision making for critical linkages in infrastructure networks.At the end of the program period, its achievement was recognized, and the I2Simgroup was selected to develop a simulator for the Vancouver 2010 Winter Olympics.As a part of this research, the concept of cells, channels, and tokens along with componentsconsisting the cells were developed. The core of the cells and the channels areformed by the Human Readable Tables (HRT) which describe the relationship betweenthe inputs and the outputs of the unit. The HRT allows a reasonable prediction of thereal life system even with limited data. As a result, the infrastructure owners do nothave to disclose the confidential operational information of the system. The test casemodels were built based on the UBC Campus first and extended to the Olympics sitesin Vancouver.To simulate the test cases, a customized Matlab/Simulink toolbox called I2Sim wasdeveloped. The I2Sim toolbox follows a graphical drag-and-drop box format. Since theuser of the toolbox only has to deal with the graphical interface, even the non-expertscan build a case easily. The main improved features included in the new version isthat the effect of time delay in the channels and the human flow. The test case resultshelps producing an optimal resource allocation and the restoration priorities duringdisasters.

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Multi-Area Thevenin Equivalent Power Flow Techniques (2010)

This thesis explores the possibility of applying the existing Multi-Area Thévenin Equivalent (MATE) algorithm to the power flow problem. Various theoretical considerations/difficulties of handling link connections in power flow are discussed. The current equation power flow program is examined in hopes of aiding link decoupling by taking advantage of the current equation’s inherent symmetrical links. However, implementation and testing of the current equation program indicated contrary results to recently published material on current equation programs.In an attempt to make MATE viable for power flow, one of MATE’s bottlenecks was examined, the link matrix. It was found that the problem could be alleviated by using a multi-level approach. This approach would allow link computation to be distributed across the subsystems and levels. An existing multi-level MATE algorithm has already been proposed but was implemented for only two levels. This thesis proposes a massively parallel algorithm for a general number of levels. The distribution of the link matrix allows for mass parallelization of the system matrix into very small subsystems. A flop analysis of the proposed multi-level MATE algorithm reveals that the majority of the computation is spent performing independent small matrix multiplication operations.Upon inspection of the strengths of the proposed multi-level MATE algorithm, it appears that the algorithm would benefit from a parallel computing platform such as modern GPUs. Today’s GPUs contain hundreds to thousands of scalar processors providing approximately an order of magnitude in computational power over multi-core CPUs. This has garnered the GPU much attention in many scientific disciplines. To test the feasibility of the MATE’s algorithm on the GPU, the algorithm’s most common operation, small matrix multiplication, was implemented. The test case was arranged to simulate the conditions of a 15,000 node system being factorized. The routine is meant to serve as the algorithm’s BLAS since linear algebra libraries on the GPU are not meant to handle very small matrices. The routine was found to successfully achieve a decent portion of the GPU’s peak flops.

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