Enhanced Binary Chimp Optimization For Multiple Topology Error Detection and Identification in Power Systems

Topology errors pose significant challenges to state estimation and subsequent processes in power systems. While numerous techniques for detecting and identifying these errors have been explored in the literature, the majority focus on single topology error scenarios. In contrast, this paper proposes an optimization-based approach that not only improves detection accuracy but also effectively manages multiple topology errors. The study introduces two binary optimization techniques-the binary grey wolf algorithm and an enhanced version of the binary chimp algorithm. These methods are evaluated and compared for their performance and accuracy in detecting and identifying topology errors. To demonstrate their effectiveness, both algorithms were tested on the IEEE 57 bus system, showcasing their robust capabilities in accurately identifying a range of topology error cases.