Investigation of state estimation in power systems using crow search algorithm

In modern electric power systems, obtaining accurate and reliable state estimation results is critically important for monitoring, controlling, and managing the system. The voltage magnitudes and phase angles of all buses, which represent the system's state, are estimated using data sets collected from measurement devices. This thesis proposes a novel heuristic method called the Crow Search Algorithm (CSA) as an alternative state estimator. The proposed CSA-based state estimator has been tested on IEEE 9, 14, 30, 57, and 118 bus test systems. The results of the CSA-based state estimator were compared with those of well-known classical heuristic methods in the literature, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Artificial Bee Colony Optimization (ABC), using the Newton-Raphson power flow solution as a reference. The results prove that the CSA-based state estimator outperforms other published methods in terms of accuracy and reliability, as demonstrated through error metrics such as the Mean Absolute Percentage Error (MAPE). Additionally, this study enhances the derived measurement data set by implementing a PMU placement with a limited number of channels, extending the assumptions in the literature. The optimal PMU placement was determined by considering the channel count and the location of the buses where the PMUs were installed. In addition, a chi-square test was applied to the state estimation results to detect and eliminate bad data that may have resulted from cyber-attacks. The chi-square test can identify the presence of bad data in the measurement data set, and the corresponding bad data can be identified and eliminated by examining the normalized residuals. However, the removal of bad data from the measurement dataset may lead to missing data, which can jeopardize the observability conditions of the system. A data mining approach supported by artificial neural networks was proposed to fill in the missing measurement data. This method helps maintain system reliability by improving the accuracy of the CSA-based state estimation, especially in unexpected situations such as cyber-attacks. In conclusion, the CSA-based state estimator enables more reliable and efficient management of power systems, making a significant contribution to the literature in this field. This thesis provides valuable insights and guidance for future research in the analysis, operation, and planning of power systems.