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Demand side maximum loading limit ranking based on composite index using ANN

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  • Alok Nath Yadav

    (Gautam Buddha University)

  • Kirti Pal

    (Gautam Buddha University)

Abstract

A power system network becomes more complex and critical with day by day increased demand. The reliability and stability of the power system network also get affected as the network is forces to operate under maximum loading limit. This paper proposes a novel approach to identify the maximum loading limit of each load bus using modified continuous power flow method and then rank the load bus by calculating composite index to identify the most critical load bus. The proposed composite index is calculated under maximum loading limit at collapse of voltage point by using CPF method at each load bus to analyze the stability margin and to rank the most severe bus. To analyze the power system stability only voltage stability index cannot be considered as the main key. For secure analysis of power system stability and reliability the proposed composite index includes active and reactive power flow violation index with voltage stability index. As the power system network is large and complicated neural network can be used for fast and accurate ranking. Here an ANN tool box of MATLAB 2016a is used for training and testing of an IEEE 39 bus system. So that the trained artificial neural network can be directly used for ranking the load buses during on-line monitoring of the system and operator can use the ranking in many security and reliability analysis.

Suggested Citation

  • Alok Nath Yadav & Kirti Pal, 2022. "Demand side maximum loading limit ranking based on composite index using ANN," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(3), pages 1419-1429, June.
    • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:3:d:10.1007_s13198-021-01487-z
    DOI: 10.1007/s13198-021-01487-z
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    References listed on IDEAS

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    1. Walter M. Villa-Acevedo & Jesús M. López-Lezama & Delia G. Colomé, 2020. "Voltage Stability Margin Index Estimation Using a Hybrid Kernel Extreme Learning Machine Approach," Energies, MDPI, vol. 13(4), pages 1-19, February.
    2. Hassan Haes Alhelou & Mohamad Esmail Hamedani-Golshan & Takawira Cuthbert Njenda & Pierluigi Siano, 2019. "A Survey on Power System Blackout and Cascading Events: Research Motivations and Challenges," Energies, MDPI, vol. 12(4), pages 1-28, February.
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