Artificial Intelligence Based Optimal Placement of PMU
Rachana Pandey1, H.K. Verma2, Arun Parakh3, Cheshta Jain Khare4
1Rachana Pandey, Department of Electrical Engineering, Shri Govindram Seksaria Institute of Technology And Science Indore (M.P), India.
2Dr. H.K. Verma, Department of Electrical Engineering, Shri Govindram Seksaria Institute of Technology and Science Indore (M.P), India.
3Dr. Arun Parakh, Department of Electrical Engineering, Shri Govindram Seksaria Institute of Technology and Science Indore (M.P), India.
4Dr. Cheshta Jain Khare, Department of Electrical Engineering, Shri Govindram Seksaria Institute of Technology and Science Indore (M.P), India.
Manuscript received on 22 August 2022 | Revised Manuscript received on 01 October 2022 | Manuscript Accepted on 15 October 2022 | Manuscript published on 30 October 2022 | PP: 1-5 | Volume-10 Issue-11, October, 2022. | Retrieval Number: 100.1/ijese.I254109101022 | DOI: 10.35940/ijese.I2541.10101122
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Abstract: The investigation of power system disturbances is critical for ensuring the supply’s dependability and security. Phasor Measurement Unit (PMU) is an important device of our power network, installed on system to enable the power system monitoring and control. By givingsynchronised measurements at high sample rates, Phasor Measurement Units have the potential to record quick transients with high precision. PMUs are gradually being integrated into power systems because they give important phasor information for power system protection and control in both normal and abnormal situations. Placement of PMU on every bus of the network is not easy to implement, either because of expense or because communication facilities in some portions of the system are limited. Different ways for placing PMUs have been researched to improve the robustness of state estimate. The paper proposes unique phasor measurement unit optimal placement methodologies. With full network observability, the suggested methods will assure optimal PMU placement. The proposed algorithm will be thoroughly tested using IEEE 7, 9, 14, and 24 standard test systems, with the results compared to existing approaches.
Keywords: Phase Measurement Unit, Optimization, Observability, deep Q Learning, Reinforcement Learning
Scope of the Article: Artificial Intelligence and machine learning