Yu Christine Chen
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Affiliations to Research Centres, Institutes & Clusters
Graduate Student Supervision
Doctoral Student Supervision (Jan 2008 - April 2022)
Motivated by environmental concerns, DERs are progressively replacing conventional fossil fuel-based generators in modern power systems. This transition towards renewable generation lowers the overall system inertia and introduces variability and uncertainty that are unprecedented in conventional power systems. As a result, future power systems will have reshaped dynamics and increased risk of instability. To mitigate these challenges, we develop operational monitoring and control schemes that ensure reliable and efficient operation of power systems. These schemes enable real-time detection and identification of disturbances, offer tractable computational efficiency for fast dynamic contingency analysis, and utilize DERs and existing resources through optimal control strategies.Using a reduced-order system dynamic model, we develop an optimization-based method to detect, identify the location, and estimate the magnitude of load disturbances in the network. The proposed method requires measurements of only synchronous generator rotor speed variations which is enabled by leveraging the sparsity structure of load-change disturbances. Furthermore, a convex relaxation of the problem ensures that it can be solved online in a computationally efficient manner.Rapid variations in renewable generation are likely to cause large excursions away from the nominal operating point and frequent operational limit violations. To address this, we leverage a transfer-function representation of a reduced-order aggregate system model to derive analytical closed-form expressions that estimate bus frequencies and predict them under what-if contingency scenarios. Furthermore, we derive a dynamic version of distribution factors to predict dynamic line flows throughout the post-contingency transient period following a disturbance. These expressions offer advancement in dynamic contingency analysis by eliminating the need for repeated time-domain simulations.The widespread integration of DERs offers tremendous potential to provide ancillary services required to operate the bulk power system reliably. To this extent, we develop optimal controllers that aim to maximize the utilization of inverter-interfaced DERs and controllable loads. First, we propose a decentralized optimal controller to regulate line active-power flows while maintaining the nominal system frequency. Next, we propose an optimal controller to track feeder-head active- and reactive-power flows in distribution networks. The proposed control schemes offer intuitive tuning of design parameters and are susceptible to disturbances and measurement noise.
Ongoing efforts toward environmentally sustainable electricity generation give rise to gradual displacement of synchronous generators by renewable energy resources (RESs). This paradigm shift reshapes power system dynamics and presents numerous challenges to reliable and efficient grid operations. For example, our power system will have reduced inertia and be at higher risk of instability, since the RESs generally interconnect to the grid via power-electronic converters with less or no inertia. Motivated by these challenges arising from RES integration, the concept of virtual synchronous generator (VSG) has been proposed to provide virtual inertia by emulating the SG dynamics in the RES controller. Among all VSG designs, synchronverter is a representative one with concise structure. However, this dissertation finds that conventional synchronverter designs lack in control degrees of freedom, require trial-and-error tuning process, synchronize with the grid slowly, and suffer from output-power coupling. Also, their active-power transfer capacity has not been studied, especially under weak-grid conditions. In order to address these problems and integrate more RESs into our system, my dissertation has five major contributions ranging from control design to tuning method to operation characteristics. First, in order to improve the synchronverter control degrees of freedom, I augment the synchronverter with a damping correction loop, which freely adjusts its response speed without affecting the steady-state performance. In order to simplify the tuning process, I propose a tuning method that evaluates the feasible pole-placement region and directly computes synchronverter parameters to achieve desired dynamics. My proposed tuning method completely avoids the trial-and-error tuning process and thus has overwhelming advantages over conventional tuning methods. Next, in order to synchronize the synchronverter quickly to the grid and enable the flexible ``plug-and-play" operation of RESs, this dissertation proposes a self-synchronizing synchronverter design with both fast self-synchronization speed and easily tuneable parameters. Then, to further improve the tracking performance, I propose a design with reduced output-power coupling. Finally, in order to integrate synchronverter-based RESs in weak grid, this dissertation analytically studies its active power transfer capacity and proposes two countermeasures to improve it. All my proposed designs and analyses are verified through extensive numerical or experimental studies.
In this thesis, we focus on two major real-time applications of modern synchrophasor-based wide-area measurement systems , i.e., transient stability assessment (TSA) and fault detection and identification (FDI). First, we develop a tool for real-time TSA based on automatic learning approaches. We use Classification and Regression Tree (CART) as the classification tool and Multivariate Adaptive Regression Splines (MARS) as the regression tool. To train and validate these tools in a practical setting, we conduct test cases on the full Western Electricity Coordinated Council system model, with emphasis on the BC Hydro (BCH) power system. While being mindful of practical field implementations of the proposed methods, our studies assume limited number of phasor measurement units (PMUs) installed, in accordance with existing infrastructure in the BCH system. The trained CART models are tested and show high accuracy rates, and thus, will be able to predict the transient stability issues of the system under study following different contingencies using the synchrophasors obtained from limited number of PMUs in the system. Also, the MARS models, which are proposed to be applied for TSA for the first time, show reasonable prediction accuracy rates. Next, we investigate the possibility of an accurate real-time FDI using synchrophasors received from PMUs installed at the two ends of a transmission line. We apply a new metric called goodness-of- fit (GoF), which is calculated over the time span of measurement and can quantify the credibility of the received synchrophasors. Then, we apply the data to an FDI method to show how accurate and credible the results are. The obtained results show a reasonable relation between the GoF metric, i.e., credibility of the measured sychrophasors, and the accuracy of the obtained results, validating the significance of the proposed method for real-time applications. As it is very rare to have a real power system with all buses and transmission lines equipped with PMUs, we also propose a wide-area real-time FDI approach using a linear observer. Through this wide-area approach, we demonstrate the effectiveness of the proposed method by accurately locating a fault in a small test system.
Master's Student Supervision (2010 - 2021)
We propose an analytically tractable Bayesian method to infer parameters in power system dynamic models from noisy measurements of bus-voltage magnitudes and frequencies as well as active- and reactive-power injections. The proposed method is computationally appealing as it bypasses the large number of system model simulations typically required in sampling-based Bayesian inference. Instead, it relies on analytical linearization of the nonlinear system differential-algebraic-equation model enabled by trajectory sensitivities. Central to the proposed method is the construction of a linearized model with the maximum probability of being (closest to) the actual nonlinear model that gave rise to the measurement data. The linear model together with Gaussian prior leads to a conjugate family where the parameter posterior, model evidence, and their gradients can be computed in closed form, markedly improving scalability for large-scale power systems. We illustrate the effectiveness and key features of the proposed method with numerical case studies for a 3-bus system. Algorithmic scalability is then demonstrated via case studies involving the New England 39-bus test system.
The widespread integration of distributed energy resources (DERs) in the future power system is likely to cause large and frequent excursions from its steady state. Such circumstances call for systematic methods to design DER controllers toward system-level goals and to estimate unexpected changes. We address these challenges via the development of a reduced-order power system dynamical model in this thesis. First, we develop a reduced second-order power-system dynamical model that accounts for locational effects of load disturbances on system frequency. The locational aspects are retained in the proposed model by incorporating linearized power-flow balance into differential equations that describe synchronous-generator dynamics. Individual synchronous-generator frequency dynamics are then combined into a single aggregate frequency state via weighting factors that can be tuned to maximize the accuracy of the reduced-order model. The proposed reduced-order model is general in the sense that its parameters are related to those of the original full-order model in analytical closed form, so that it can be constructed easily for different systems. Time-domain simulations demonstrate the accuracy of the reduced-order model with various choices of weighting factors and highlight the effect of load disturbance location on aggregate-frequency dynamics. The reduced-order model offers the basic foundation on which DER controllers can be systematically designed to meet grid-level aims. Via a transfer-function representation of the reduced-order model, we relate, in analytical closed form, DER controller parameters to system steady-state and dynamic frequency response. Furthermore, time-domain simulations demonstrate that we can design DER controller parameter in order to meet system frequency-response performance requirements. By leveraging the reduced-order power system dynamical model developed earlier, we propose an optimization-based method to detect the occurrence, estimate the magnitude, and identify the location of load changes. The proposed method relies on measurements of only frequency at the output of synchronous generators along with the reduced-order model that captures locational effects of load disturbances on generator frequency dynamics. The sparsity structure of load-change disturbances is leveraged so that fewer measurements are needed to estimate load changes. Furthermore, a convex relaxation of the problem ensures that it can be solved online in a computationally efficient manner.