Kabiru Abubakar Tureta, ALIYU SABO, Yakubu Abdulrazak

A Optimal Placement of Phasor Measurement Units on Shiroro 330kv Grid Network using Binary Grey Wolf Optimization Algorithm

Introduction

A optimal placement of phasor measurement units on shiroro 330kv grid network using binary grey wolf optimization algorithm. Optimize PMU placement on Shiroro 330kV grid using Binary Grey Wolf Optimization (BGWO) to enhance system observability, reliability, and control while minimizing units.

Abstract

Phasor Measurement Units (PMUs) are essential for enhancing the control, monitoring, and observability of modern power systems. This research presents an optimal PMU placement approach for the Shiroro 330 kV grid network using the Binary Grey Wolf Optimization (BGWO) algorithm. The objective is to minimize the number of PMUs while ensuring full system observability under both normal and contingency conditions. The BGWO algorithm, inspired by the hunting behavior of grey wolves, is a powerful metaheuristic technique for solving binary optimization problems. By applying this method to the Shiroro grid, the study demonstrates how optimal PMU placement enhances grid observability and reliability. Compared to alternative optimization techniques, BGWO provides improved accuracy and reduced computational time. The simulation results validate the effectiveness of the proposed approach in achieving a cost-effective and reliable PMU deployment strategy for the 330 kV network.

Review

This paper presents a timely and relevant contribution to the field of power system monitoring and control, focusing on the optimal placement of Phasor Measurement Units (PMUs). The authors tackle the critical challenge of minimizing PMU deployment costs while ensuring full system observability for the Shiroro 330 kV grid network, considering both normal and contingency operating conditions. This objective aligns well with the current demands for enhanced grid reliability and efficiency in modern power systems, making the research topic inherently significant. The methodology leverages the Binary Grey Wolf Optimization (BGWO) algorithm, a metaheuristic technique inspired by the collective hunting behavior of grey wolves, which is well-suited for binary optimization problems like PMU placement. The application of BGWO to a real-world network, the Shiroro 330 kV grid, demonstrates a practical focus. The abstract highlights the algorithm's strengths, claiming improved accuracy and reduced computational time compared to alternative optimization techniques. These claimed advantages, if thoroughly substantiated, would position BGWO as a powerful tool for solving this complex combinatorial problem effectively and efficiently. While the abstract outlines a promising approach, the full manuscript would benefit from further elaboration in several key areas to fully convince the readership. Specifically, it would be crucial to explicitly name the "alternative optimization techniques" used for comparison and present detailed quantitative data to support the claims of improved accuracy and reduced computational time. Additionally, a more in-depth discussion on the specific types of "contingency conditions" considered (e.g., N-1, N-2, or more complex scenarios) and their integration into the optimization model would enhance the robustness of the study. Providing more context on the Shiroro 330 kV grid, such as its topology, size, and specific operational challenges, would also aid in assessing the generalizability and practical implications of the proposed solution.

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