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Optimization of Phasor Measurement Unit (PMU) Placement in Supervisory Control and Data Acquisition (SCADA)-Based Power System for Better State-Estimation Performance

Author

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  • Mohammad Shoaib Shahriar

    (Department of Electrical Engineering, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi Arabia)

  • Ibrahim Omar Habiballah

    (Department of Electrical Engineering, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi Arabia)

  • Huthaifa Hussein

    (Department of Electrical Engineering, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi Arabia)

Abstract

Present-day power systems are mostly equipped with conventional meters and intended for the installation of highly accurate phasor measurement units (PMUs) to ensure better protection, monitoring and control of the network. PMU is a deliberate choice due to its unique capacity in providing accurate phasor readings of bus voltages and currents. However, due to the high expense and a requirement for communication facilities, the installation of a limited number of PMUs in a network is common practice. This paper presents an optimal approach to selecting the locations of PMUs to be installed with the objective of ensuring maximum accuracy of the state estimation (SE). The optimization technique ensures that the critical locations of the system will be covered by PMU meters which lower the negative impact of bad data on state-estimation performance. One of the well-known intelligent optimization techniques, the genetic algorithm (GA), is used to search for the optimal set of PMUs. The proposed technique is compared with a heuristic approach of PMU placement. The weighted least square (WLS), with a modified Jacobian to deal with the phasor quantities, is used to compute the estimation accuracy. IEEE 30-bus and 118-bus systems are used to demonstrate the suggested technique.

Suggested Citation

  • Mohammad Shoaib Shahriar & Ibrahim Omar Habiballah & Huthaifa Hussein, 2018. "Optimization of Phasor Measurement Unit (PMU) Placement in Supervisory Control and Data Acquisition (SCADA)-Based Power System for Better State-Estimation Performance," Energies, MDPI, vol. 11(3), pages 1-15, March.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:3:p:570-:d:134976
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    References listed on IDEAS

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    1. Nazari-Heris, M. & Mohammadi-Ivatloo, B., 2015. "Application of heuristic algorithms to optimal PMU placement in electric power systems: An updated review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 214-228.
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    Cited by:

    1. István Táczi & Bálint Sinkovics & István Vokony & Bálint Hartmann, 2021. "The Challenges of Low Voltage Distribution System State Estimation—An Application Oriented Review," Energies, MDPI, vol. 14(17), pages 1-17, August.
    2. Weijia Wen & Xiao Ling & Jianxin Sui & Junjie Lin, 2023. "Data-Driven Dynamic Stability Assessment in Large-Scale Power Grid Based on Deep Transfer Learning," Energies, MDPI, vol. 16(3), pages 1-15, January.
    3. Ziad M. Ali & Seyed-Ehsan Razavi & Mohammad Sadegh Javadi & Foad H. Gandoman & Shady H.E. Abdel Aleem, 2018. "Dual Enhancement of Power System Monitoring: Improved Probabilistic Multi-Stage PMU Placement with an Increased Search Space & Mathematical Linear Expansion to Consider Zero-Injection Bus," Energies, MDPI, vol. 11(6), pages 1-17, June.
    4. Nikolaos P. Theodorakatos & Miltiadis Lytras & Rohit Babu, 2020. "Towards Smart Energy Grids: A Box-Constrained Nonlinear Underdetermined Model for Power System Observability Using Recursive Quadratic Programming," Energies, MDPI, vol. 13(7), pages 1-17, April.
    5. Fahad M. Almasoudi, 2023. "Grid Distribution Fault Occurrence and Remedial Measures Prediction/Forecasting through Different Deep Learning Neural Networks by Using Real Time Data from Tabuk City Power Grid," Energies, MDPI, vol. 16(3), pages 1-20, January.
    6. Rafael Cisneros-Magaña & Aurelio Medina & Olimpo Anaya-Lara, 2018. "Time-Domain Voltage Sag State Estimation Based on the Unscented Kalman Filter for Power Systems with Nonlinear Components," Energies, MDPI, vol. 11(6), pages 1-20, June.
    7. Zoran Zbunjak & Igor Kuzle, 2019. "System Integrity Protection Scheme (SIPS) Development and an Optimal Bus-Splitting Scheme Supported by Phasor Measurement Units (PMUs)," Energies, MDPI, vol. 12(17), pages 1-21, September.

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