Precise PMU locations on distribution system considering power system disruptions for elegant state estimation

Puvikko Chezhian  Vgopal; Subramanian  Srikrishna; Ganesan  Sivarajan; Naveenkumar  Kaliyan

doi:10.59400/sv1728

Precise PMU locations on distribution system considering power system disruptions for elegant state estimation

Puvikko Chezhian Vgopal Department of Electrical Engineering, Annamalai University, Tamil Nadu 608002, India
Subramanian Srikrishna Department of Electrical Engineering, Annamalai University, Tamil Nadu 608002, India
Ganesan Sivarajan Department of Electrical and Electronics Engineering, Government College of Engineering, Tamil Nadu 636011, India
Naveenkumar Kaliyan Department of Instrumentation and Control Engineering, Sri Manakula Vinayagar Engineering College, Puducherry 605107, India

Ariticle ID: 1728

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DOI: https://doi.org/10.59400/sv1728

Keywords: PMU; state estimation; fault detection; optimization; distribution system; machine learning

Abstract

This study presents a novel approach to achieve complete system observability by optimizing the placement of Phasor Measuring Units (PMUs), reducing the risk of fault identification. The process considers both the redundancy and the cost of installation. The proposed solution methodology improves upon existing algorithms by utilizing the Butterfly Optimization Algorithm (BOA), which identifies optimal PMU locations. Resilient fault detection techniques are employed to detect and mitigate disruptions in the power grid swiftly. Addressing transmission line faults, the research integrates a Deep Learning Network (DLN) to enhance the state estimation process during fault conditions. Simulations of fault transients, including LG (Line-to-Ground), LLG (Line-to-Line-to-Ground), and LL (Line-to-Line) faults, are conducted using MATLAB Software. The Neural Network (NN) response is evaluated based on two key hyperparameters—the number of hidden layers and the number of neurons utilized for feature extraction. Results demonstrate the superiority of the proposed method, with approximately 85% fault detection accuracy and a system performance metric of 90%. Additionally, the processing time required for training the network is small in the order of micro seconds.

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Published

2024-11-15

How to Cite

Vgopal, P. C., Srikrishna, S., Sivarajan, G., & Kaliyan, N. (2024). Precise PMU locations on distribution system considering power system disruptions for elegant state estimation. Sound & Vibration, 59(1), 1728. https://doi.org/10.59400/sv1728

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