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Article

Multi-Objective Optimization Algorithm Based Bidirectional Long Short Term Memory Network Model for Optimum Sizing of Distributed Generators and Shunt Capacitors for Distribution Systems

by
Amarendra Alluri
1,
Srinivasa Rao Gampa
1,*,
Balaji Gutta
1,
Mahesh Babu Basam
1,
Kiran Jasthi
2,
Nibir Baran Roy
3 and
Debapriya Das
3
1
Department of Electrical & Electronics Engineering, Seshadri Rao Gudlavalleru Engineering College, Gudlavalleru, Vijayawada 521356, Andhra Pradesh, India
2
Department of Electrical & Electronics Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad 500090, Telangana, India
3
Department of Electrical Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India
*
Author to whom correspondence should be addressed.
Inventions 2024, 9(6), 114; https://doi.org/10.3390/inventions9060114
Submission received: 2 October 2024 / Revised: 31 October 2024 / Accepted: 7 November 2024 / Published: 12 November 2024
(This article belongs to the Special Issue Recent Advances and Challenges in Emerging Power Systems: 2nd Edition)
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Abstract

In this paper, a multi-objective grey wolf optimization (GWO) algorithm based Bidirectional Long Short Term Memory (BiLSTM) network machine learning (ML) model is proposed for finding the optimum sizing of distributed generators (DGs) and shunt capacitors (SHCs) to enhance the performance of distribution systems at any desired load factor. The stochastic traits of evolutionary computing methods necessitate running the algorithm repeatedly to confirm the global optimum. In order to save utility engineers time and effort, this study introduces a BiLSTM network-based machine learning model to directly estimate the optimal values of DGs and SHCs, rather than relying on load flow estimates. At first, a multi-objective grey wolf optimizer determines the most suitable locations and capacities of DGs and SHCs at the unity load factor and the same locations are used to obtain optimum sizing of DGs and SHCs at other load factors also. The base case data sets consisting of substation apparent power, real power load, reactive power load, real power loss, reactive power loss and minimum node voltage at various load factors in per unit values are taken as input training data for the machine learning model. The optimal sizes of the DGs and SHCs for the corresponding load factors obtained using GWO algorithm are taken as target data sets in per unit values for the machine learning model. An adaptive moment estimation (adam) optimization approach is employed to train the BiLSTM ML model for identifying the ideal values of distributed generations and shunt capacitors at different load factors. The efficacy of the proposed ML-based sizing algorithm is demonstrated via simulation studies.
Keywords: distributed generators; shunt capacitors; machine learning; grey wolf optimization; BiLSTM model; multi-objective optimization distributed generators; shunt capacitors; machine learning; grey wolf optimization; BiLSTM model; multi-objective optimization

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MDPI and ACS Style

Alluri, A.; Gampa, S.R.; Gutta, B.; Basam, M.B.; Jasthi, K.; Roy, N.B.; Das, D. Multi-Objective Optimization Algorithm Based Bidirectional Long Short Term Memory Network Model for Optimum Sizing of Distributed Generators and Shunt Capacitors for Distribution Systems. Inventions 2024, 9, 114. https://doi.org/10.3390/inventions9060114

AMA Style

Alluri A, Gampa SR, Gutta B, Basam MB, Jasthi K, Roy NB, Das D. Multi-Objective Optimization Algorithm Based Bidirectional Long Short Term Memory Network Model for Optimum Sizing of Distributed Generators and Shunt Capacitors for Distribution Systems. Inventions. 2024; 9(6):114. https://doi.org/10.3390/inventions9060114

Chicago/Turabian Style

Alluri, Amarendra, Srinivasa Rao Gampa, Balaji Gutta, Mahesh Babu Basam, Kiran Jasthi, Nibir Baran Roy, and Debapriya Das. 2024. "Multi-Objective Optimization Algorithm Based Bidirectional Long Short Term Memory Network Model for Optimum Sizing of Distributed Generators and Shunt Capacitors for Distribution Systems" Inventions 9, no. 6: 114. https://doi.org/10.3390/inventions9060114

APA Style

Alluri, A., Gampa, S. R., Gutta, B., Basam, M. B., Jasthi, K., Roy, N. B., & Das, D. (2024). Multi-Objective Optimization Algorithm Based Bidirectional Long Short Term Memory Network Model for Optimum Sizing of Distributed Generators and Shunt Capacitors for Distribution Systems. Inventions, 9(6), 114. https://doi.org/10.3390/inventions9060114

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