Abstract
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.
Highlights
Accepted: 17 November 2021Energy conversion is increasingly drawing attention due to the goals of the KyotoProtocol and Paris Climate Agreement
Generate population: The Nig combinations are chosen at random from a set of N C Nm, where N is the total number of buses and Nm is the number of phasor measurement units (PMUs)-installed buses
Cases of pseudo-measurements generated from optimal placement (OP), and worst placement (WP) and pseudo-measurements generated through load profile (LP), were compared
Summary
Energy conversion is increasingly drawing attention due to the goals of the Kyoto. Protocol and Paris Climate Agreement. In [19], an extreme learning machine-based pseudo-measurement modeling technique is proposed that uses injection power measured by the supervisory control and data acquisition as input, and real and imaginary parts of the bus voltage as output. This technique improves the accuracy of the state estimation, and significantly reduces the computational time. The main contributions of the proposed approach are: (i) Provides an approach for optimal PMU placement for supervised learning-based pseudo-measurement modeling techniques, which previous studies have not focused on.
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