Optimal Placement of PMU to Enhance Supervised Learning-Based Pseudo-Measurement Modelling Accuracy in Distribution Network

Energies

Yun-Su Kim¹  Kyung-Yong Lee¹  Jung-Sung Park² 

[1] Graduate School of Energy Convergence, Gwangju Institute of Science and Technology, Gwangju 61005, Korea;KEPCO Research Institute, 105, Munji-Ro, Yuseong-Gu, Daejeon 34056, Korea;

关键词: metaheuristic algorithms;    optimal placement;    phasor measurement units (PMU);    pseudo-measurement;    state estimation;   

DOI  :  10.3390/en14227767

来源: DOAJ

【 摘 要 】

This paper introduces a framework for optimal placement (OP) of phasor measurement units (PMUs) using metaheuristic algorithms in a distribution network. The voltage magnitude and phase angle obtained from PMUs were selected as the input variables for supervised learning-based pseudo-measurement modeling that outputs the voltage magnitude and phase angle of the unmeasured buses. For three, four, and five PMU installations, the metaheuristic algorithms explored 2000 combinations, corresponding to 40.32%, 5.56%, and 0.99% of all placement combinations in the 33-bus system and 3.99%, 0.25%, and 0.02% in the 69-bus system, respectively. Two metaheuristic algorithms, a genetic algorithm and particle swarm optimization, were applied; the results of the techniques were compared to random search and brute-force algorithms. Subsequently, the effects of pseudo-measurements based on optimal PMU placement were verified by state estimation. The state estimation results were compared among the pseudo-measurements generated by the optimal PMU placement, worst PMU placement, and load profile (LP). State estimation results based on OP were superior to those of LP-based pseudo-measurements. However, when pseudo-measurements based on the worst placement were used as state variables, the results were inferior to those obtained using the LP.

【 授权许可】

Unknown