Artificial Intelligence Based Optimal Placement of PMU
Authors/Creators
- 1. Department of Electrical Engineering, Shri Govindram Seksaria Institute of Technology And Science Indore (M.P), India.
- 2. Department of Electrical Engineering, Shri Govindram Seksaria Institute of Technology and Science Indore (M.P), India.
Contributors
Contact person:
- 1. Department of Electrical Engineering, Shri Govindram Seksaria Institute of Technology And Science Indore (M.P), India.
Description
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.
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Additional details
Related works
- Is cited by
- Journal article: 2319-6378 (ISSN)
References
- Jiangxia Zhong," Phasor Measurement Unit (PMU) Placement Optimisation in Power Transmission Network based on Hybrid Approach", School of Electrical and Computer Engineering RMIT University August 2012.
- DIGVIJAY SINGH,"OPTIMIZATION OF PHASOR MEASUREMENT UNIT", Lovely Professional University Phagwara Punjab,(2017).
- https://www.geeksforgeeks.org/artificial-intelligence- an-introduction/?ref=rp
- https://www.datacamp.com/community/tutorials/intro duction-reinforcement-learning
- Pedro Emanuel Almeida Cardoso," Deep Learning Applied to PMU Data in Power Systems", FACULDADE DE ENGENHARIA DA UNIVERSIDADE DO PORTO
- Tapas Kumar Maji," Multiple solution of optimal placement using exponential binary PSO algorithm,"2015, IEEE INDICON 1570183738
Subjects
- ISSN: 2319-6378 (Online)
- https://portal.issn.org/resource/ISSN/2319-6378#
- Retrieval Number: 100.1/ijese.I254109101022
- https://www.ijese.org/portfolio-item/i254109101022/
- Journal Website: www.ijese.org
- https://www.ijese.org
- Publisher: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP)
- https://www.blueeyesintelligence.org