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Optimal Micro-PMU Placement Using Mutual Information Theory in Distribution Networks

doi: 10.3390/en11071917
Optimal Micro-PMU Placement Using Mutual Information Theory in Distribution Networks
Micro-phasor measurement unit (μPMU) is under fast development and becoming more and more important for application in future distribution networks. It is unrealistic and unaffordable to place all buses with μPMUs because of the high costs, leading to the necessity of determining optimal placement with minimal numbers of μPMUs in the distribution system. An optimal μPMU placement (OPP) based on the information entropy evaluation and node selection strategy (IENS) using greedy algorithm is presented in this paper. The uncertainties of distributed generations (DGs) and pseudo measurements are taken into consideration, and the two-point estimation method (2PEM) is utilized for solving stochastic state estimation problems. The set of buses selected by improved IENS, which can minimize the uncertainties of network and obtain system observability is considered as the optimal deployment of μPMUs. The proposed method utilizes the measurements of smart meters and pseudo measurements of load powers in the distribution systems to reduce the number of μPMUs and enhance the observability of the network. The results of the simulations prove the effectiveness of the proposed algorithm with the comparison of traditional topological methods for the OPP problem. The improved IENS method can obtain the optimal complete and incomplete μPMU placement in the distribution systems.
- Southeast University China (People's Republic of)
- Electric Power Research Institute China (People's Republic of)
- Southeast University China (People's Republic of)
- State Grid Shanxi Electric Power Company Electric Power Research Institute China (People's Republic of)
- State Grid Shanxi Electric Power Company Electric Power Research Institute China (People's Republic of)
stochastic state estimation, Technology, T, mutual information theory, micro-phasor measurement unit, two-point estimation method
stochastic state estimation, Technology, T, mutual information theory, micro-phasor measurement unit, two-point estimation method
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