摘要:
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's 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's 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