Date of Award
Daniel Felix Ritchie School of Engineering and Computer Science
Jun Zhang, Ph.D.
Power system, Renewable energy, System control, System monitoring
With the evolution of energy and power systems, the emerging Smart Grid (SG) is mainly featured by distributed renewable energy generations, demand-response control and huge amount of heterogeneous data sources. Widely distributed synchrophasor sensors, such as phasor measurement units (PMUs) and fault disturbance recorders (FDRs), can record multi-modal signals, for power system situational awareness and renewable energy integration.
An effective and economical approach is proposed for wide-area security assessment. This approach is based on wavelet analysis for detecting and locating the short-term and long-term faults in SG, using voltage signals collected by distributed synchrophasor sensors.
A data-driven approach for fault detection, identification and location is proposed and studied. This approach is based on matching pursuit decomposition (MPD) using Gaussian atom dictionary, hidden Markov model (HMM) of real-time frequency and voltage variation features, and fault contour maps generated by machine learning algorithms in SG systems. In addition, considering the economic issues, the placement optimization of distributed synchrophasor sensors is studied to reduce the number of the sensors without affecting the accuracy and effectiveness of the proposed approach. Furthermore, because the natural hazards is a critical issue for power system security, this approach is studied under different types of faults caused by natural hazards.
A fast steady-state approach is proposed for voltage security of power systems with a wind power plant connected. The impedance matrix can be calculated by the voltage and current information collected by the PMUs. Based on the impedance matrix, locations in SG can be identified, where cause the greatest impact on the voltage at the wind power plants point of interconnection. Furthermore, because this dynamic voltage security assessment method relies on time-domain simulations of faults at different locations, the proposed approach is feasible, convenient and effective.
Conventionally, wind energy is highly location-dependent. Many desirable wind resources are located in rural areas without direct access to the transmission grid. By connecting MW-scale wind turbines or wind farms to the distributions system of SG, the cost of building long transmission facilities can be avoid and wind power supplied to consumers can be greatly increased. After the effective wide area monitoring (WAM) approach is built, an event-driven control strategy is proposed for renewable energy integration. This approach is based on support vector machine (SVM) predictor and multiple-input and multiple-output (MIMO) model predictive control (MPC) on linear time-invariant (LTI) and linear time-variant (LTV) systems. The voltage condition of the distribution system is predicted by the SVM classifier using synchrophasor measurement data. The controllers equipped with wind turbine generators are triggered by the prediction results. Both transmission level and distribution level are designed based on this proposed approach.
Considering economic issues in the power system, a statistical scheduling approach to economic dispatch and energy reserves is proposed. The proposed approach focuses on minimizing the overall power operating cost with considerations of renewable energy uncertainty and power system security. The hybrid power system scheduling is formulated as a convex programming problem to minimize power operating cost, taking considerations of renewable energy generation, power generation-consumption balance and power system security. A genetic algorithm based approach is used for solving the minimization of the power operating cost.
In addition, with technology development, it can be predicted that the renewable energy such as wind turbine generators and PV panels will be pervasively located in distribution systems. The distribution system is an unbalanced system, which contains single-phase, two-phase and three-phase loads, and distribution lines. The complex configuration brings a challenge to power flow calculation. A topology analysis based iterative approach is used to solve this problem. In this approach, a self-adaptive topology recognition method is used to analyze the distribution system, and the backward/forward sweep algorithm is used to generate the power flow results.
Finally, for the numerical simulations, the IEEE 14-bus, 30-bus, 39-bus and 118-bus systems are studied for fault detection, identification and location. Both transmission level and distribution level models are employed with the proposed control strategy for voltage stability of renewable energy integration. The simulation results demonstrate the effectiveness of the proposed methods. The IEEE 24-bus reliability test system (IEEE-RTS), which is commonly used for evaluating the price stability and reliability of power system, is used as the test bench for verifying and evaluating system performance of the proposed scheduling approach.
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Received from ProQuest
Jiang, Huaiguang, "Synchrophasor Sensing and Processing Based Smart Grid Security Assessment for Renewable Energy Integration" (2015). Electronic Theses and Dissertations. 319.