PMU optimal placement method for distribution network based on improved grey wolf algorithm

By constructing an optimized PMU configuration model through an improved gray wolf algorithm, the problems of slow PMU configuration calculation speed and poor universality in existing technologies are solved, realizing efficient and reliable PMU configuration in distributed power generation access to the distribution network, and ensuring the complete observability of the system under different scenarios.

CN115511160BActive Publication Date: 2026-04-28CHINA THREE GORGES UNIV
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Patent Information

Application Number
CN202211078764.3
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2022-09-05
Publication Date
2026-04-28
Estimated Expiration
2042-09-05

AI Technical Summary

Technical Problem

Existing PMU optimization configuration methods suffer from slow calculation speed and poor universality in distribution networks. In particular, when dealing with the randomness and volatility brought about by distributed power source access, it is difficult to achieve efficient PMU configuration and data monitoring.

Method used

An improved gray wolf algorithm is adopted to construct a fully observable mathematical model of the system. It considers scenarios such as zero-injection nodes, single PMU interruption, and single-line power outage. The PMU configuration scheme is optimized by improving population initialization, nonlinear convergence factor and position update weight coefficient.

Benefits of technology

It improves the optimization effect and system reliability of PMU configuration optimization, ensures the complete observability of the system under different scenarios, reduces the number of PMUs required, and improves the optimization accuracy and speed of the algorithm.

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Abstract

Based on the improved grey wolf optimization algorithm, the PMU optimal configuration method for distribution network is proposed. Under the condition of system full observability, the PMU optimal configuration mathematical model is established. Considering the influence of zero injection node and the system observability under single PMU interruption and single line outage, the constraint conditions of the established PMU optimal configuration mathematical model are modified. On the basis of grey wolf optimization algorithm, the population initialization, nonlinear convergence factor and position update weight coefficient are improved. The improved grey wolf optimization algorithm is used to solve the PMU configuration model under different scenarios. The method builds the optimal configuration model under different scenarios to achieve the goal of system full observability, and uses the improved grey wolf optimization algorithm to solve the PMU optimal configuration scheme. Compared with other algorithms, the method has better optimization effect.
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