With the increasing integration of energy sources and the growing complexity of distribution networks, it is crucial to monitor and early detection of topological changes to ensure grid stability and resilience. Current methods, for optimizing the placement of micro Phasor Measurement Units ((Formula presented.) PMUs) focus on achieving observability and efficient monitoring. These algorithms aim to minimize the number of (Formula presented.) PMUs needed while maintaining system observability or meeting criteria for observability. However, they may not consider all real-world constraints and uncertainties. In this study, we introduce a strategy for placing (Formula presented.) PMUs with the objective of enhancing observability and monitoring capabilities. Our proposed algorithm employs a technique that makes optimal decisions at each step to approximate the global optimum. To determine the locations for (Formula presented.) PMUs our algorithm takes into account parameters such as network structure, key nodes, and system stability. One distinguishing feature is its adaptability to distribution networks, including changes, in topology or potential device failures. Unlike classical approaches, our algorithm can continuously provide optimal placement solutions even in evolving network conditions. We have demonstrated that our suggested method achieves better results in terms of observability value and the required number of (Formula presented.) PMUs compared to the state-of-the-art. By strategically placing (Formula presented.) PMUs, operators can improve system observability, quickly detect and locate faults, and make informed decisions for effective network operations. This research helps improve optimal placement strategies for (Formula presented.) PMUs by providing practical and effective solutions to improve distribution network reliability, resilience, and performance in the face of changing dynamics.
CITATION STYLE
Hassini, K., Fakhfakh, A., & Derbel, F. (2023). Optimal Placement of μPMUs in Distribution Networks with Adaptive Topology Changes. Energies, 16(20). https://doi.org/10.3390/en16207047
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