Optimal Placement of μPMUs in Distribution Networks with Adaptive Topology Changes

Abstract

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 (μPMUs) focus on achieving observability and efficient monitoring. These algorithms aim to minimize the number of μPMUs 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 μPMUs 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 μPMUs 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 μPMUs compared to the state-of-the-art. By strategically placing μPMUs, 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 μPMUs by providing practical and effective solutions to improve distribution network reliability, resilience, and performance in the face of changing dynamics.

Events Classification in Power Systems with Distributed Generation Sources Using an LSTM-Based Method with Multi-Input Tensor Approach

“…For this aim, the development of new technologies for sensing and processing the data from a DGS has opened new opportunities for event classification. In this sense, measurement devices such as µPMUs are being placed at strategic sensing points along DGSs [32], increasing the availability of relevant information necessary to determine the type of event produced in a DGS.…”

Load Margin Assessment of Power Systems Using Physics-Informed Neural Network with Optimized Parameters

“…Technological advances over the years have allowed the development of Synchronized Phasor Measurement Systems that provide voltage and current magnitude and phase measurements in real time with time synchronization and high sampling rates acquired by Phasor Measurement Units (PMUs) installed on system buses [5]. Data from PMUs have led to the development of different and promising tools for monitoring [6][7][8][9], protecting [10][11][12] and controlling [13][14][15][16][17][18] power systems.…”