Static and Dynamic State Estimation Applications in Power Systems Protection and Control Engineering

The developed methodologies are proposed to serve as support for control centers and fault analysis engineers. These approaches provide a dependable and effective means of pinpointing and resolving faults, which ultimately enhances power grid reliability. The algorithm uses the Least Absolute Value (LAV) method to estimate the augmented states of the PCB, enabling supervisory monitoring of the system. In addition, the application of statistical analysis based on projection statistics of the system Jacobian as a virtual sensor to detect faults on transmission lines. This approach is particularly valuable for detecting anomalies in transmission line data, such as bad data or other outliers, and leverage points. Through the integration of remote PCB status with virtual sensors, it becomes possible to accurately detect faulted transmission lines within the system. This, in turn, saves valuable troubleshooting time for line engineers, resulting in improved overall efficiency and potentially significant cost savings for the company. When there is a temporary or permanent fault, the generator dynamics will be affected by the transmission line reclosing, which could impact the system\u27s stability and reliability. To address this issue, an unscented Kalman filter (UKF) and optimal performance iterated unscented Kalman filter (IUKF) dynamic state estimation techniques are proposed. These techniques provide an estimate of the dynamic states of synchronous generators, which is crucial for monitoring generator states during transmission lines reclosing for temporary and permanent fault conditions. Several test systems were employed to evaluate reclosing following faults on transmission lines, including the IEEE 14-bus system, Kundur\u27s two-area model, and the reduced Western Electricity Coordinating Council (WECC) model of UTK electrical engineering hardware test bed (HTB). The developed methods offer a comprehensive solution to address the challenges posed by unbalanced faults on transmission lines, such as line-to-line, line-to-line-ground, and line-to-ground faults. Utilities must consider these faults when developing protective settings. The effectiveness of the solution is confirmed by monitoring the reaction of dynamic state variables following transmission lines reclosing after temporary faults and transmission line lockout from permanent faults

Convolutional Neural Network-based RoCoF-Constrained Unit Commitment

The fast growth of inverter-based resources such as wind plants and solar farms will largely replace and reduce conventional synchronous generators in the future renewable energy-dominated power grid. Such transition will make the system operation and control much more complicated; and one key challenge is the low inertia issue that has been widely recognized. However, locational post-contingency rate of change of frequency (RoCoF) requirements to accommodate significant inertia reduction has not been fully investigated in the literature. This paper presents a convolutional neural network (CNN) based RoCoF-constrained unit commitment (CNN-RCUC) model to guarantee RoCoF stability following the worst generator outage event while ensuring operational efficiency. A generic CNN based predictor is first trained to track the highest locational RoCoF based on a high-fidelity simulation dataset. The RoCoF predictor is then formulated as MILP constraints into the unit commitment model. Case studies are carried out on the IEEE 24-bus system, and simulation results obtained with PSS/E indicate that the proposed method can ensure locational post-contingency RoCoF stability without conservativeness

Active Linearized Sparse Neural Network-based Frequency-Constrained Unit Commitment

Conventional synchronous generators are gradually being re-placed by low-inertia inverter-based resources. Such transition introduces more complicated operation conditions, frequency deviation stability and rate-of-change-of-frequency (RoCoF) security are becoming great challenges. This paper presents an active linearized sparse neural network (ALSNN) based frequency-constrained unit commitment (ALSNN-FCUC) model to guarantee frequency stability following the worst generator outage case while ensuring operational efficiency. A generic data-driven predictor is first trained to predict maximal frequency deviation and the highest locational RoCoF simultaneously based on a high-fidelity simulation dataset, and then incorporated into ALSNN-FCUC model. Sparse computation is introduced to avoid dense matrix multiplications. An active data sampling method is proposed to maintain the bindingness of the frequency related constraints. Besides, an active ReLU linearization method is implemented to further improve the algorithm efficiency while retaining solution quality. The effectiveness of proposed ALSNN-FCUC model is demonstrated on the IEEE 24-bus system by conducting time domain simulations using PSS/E

Validation of power system transient stability results

Simulation of the transient stability problem of a power system, which is the assessment of the short term angular and voltage stability of the system following a disturbance, is of vital importance. It is widely known in the industry that different transient stability packages can give substantially different results for the same (or at least similar) system models. The goal of this work is to develop validation methodologies for different transient stability software packages with a focus on Western Electricity Coordinating Council (WECC) system models. We discuss two specific approaches developed and implemented to validate the transient stability results. The sources of discrepancies seen in the results from different packages are investigated. This enables us to identify the differences in the implementation of dynamic models in different transient stability softwares. In this process, we present certain key analyses of the WECC system models for different contingencies

LONG-TERM DYNAMIC SIMULATION OF POWER SYSTEMS USING PYTHON, AGENT BASED MODELING, AND TIME-SEQUENCED POWER FLOWS

Automated controls facilitate reliable and efficient operation of modern power systems. Engineers employ computer simulations to develop, analyze, and tune such controls. Short-term dynamic, or transient, power system simulation is a useful and standardized power industry tool. Researchers have developed effective long-term dynamic (LTD) simulators, but there is not yet an industry standard computational method or software package for LTD simulation. This work introduces a novel LTD simulation tool and provides examples of various engineering applications. The newly created software tool, Power System Long-Term Dynamic Simulator (PSLTDSim), uses a time-sequenced power flow (TSPF) technique to simulate LTD events. The TSPF technique incorporates a number of modeling assumptions that simplify certain engineering calculations. Despite such simplifications, PSLTDSim demonstrates an acceptable amount of accuracy for ramp and small step type perturbations when compared to industry standard transient simulation software. Demonstrated PSLTDSim engineering applications include: investigation of long-term governor deadband effects, automatic generation control tuning, and switched shunt coordination during multi-hour events. Further demonstrated examples consist of user modified turbine speed governor behavior and variable system damping and inertia. This material is based upon work supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, under Award Number DE-SC0012671