Multi-Objective Optimization Algorithm Based Bidirectional Long Short Term Memory Network Model for Optimum Sizing of Distributed Generators and Shunt Capacitors for Distribution Systems
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 distrib...
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2024-11-01
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| author | Amarendra Alluri Srinivasa Rao Gampa Balaji Gutta Mahesh Babu Basam Kiran Jasthi Nibir Baran Roy Debapriya Das |
| author_facet | Amarendra Alluri Srinivasa Rao Gampa Balaji Gutta Mahesh Babu Basam Kiran Jasthi Nibir Baran Roy Debapriya Das |
| author_sort | Amarendra Alluri |
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| description | 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. |
| format | Article |
| id | doaj-art-8f277dd42f7f4e5d83983751517f6425 |
| institution | Kabale University |
| issn | 2411-5134 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Inventions |
| spelling | doaj-art-8f277dd42f7f4e5d83983751517f64252024-12-27T14:31:35ZengMDPI AGInventions2411-51342024-11-019611410.3390/inventions9060114Multi-Objective Optimization Algorithm Based Bidirectional Long Short Term Memory Network Model for Optimum Sizing of Distributed Generators and Shunt Capacitors for Distribution SystemsAmarendra Alluri0Srinivasa Rao Gampa1Balaji Gutta2Mahesh Babu Basam3Kiran Jasthi4Nibir Baran Roy5Debapriya Das6Department of Electrical & Electronics Engineering, Seshadri Rao Gudlavalleru Engineering College, Gudlavalleru, Vijayawada 521356, Andhra Pradesh, IndiaDepartment of Electrical & Electronics Engineering, Seshadri Rao Gudlavalleru Engineering College, Gudlavalleru, Vijayawada 521356, Andhra Pradesh, IndiaDepartment of Electrical & Electronics Engineering, Seshadri Rao Gudlavalleru Engineering College, Gudlavalleru, Vijayawada 521356, Andhra Pradesh, IndiaDepartment of Electrical & Electronics Engineering, Seshadri Rao Gudlavalleru Engineering College, Gudlavalleru, Vijayawada 521356, Andhra Pradesh, IndiaDepartment of Electrical & Electronics Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad 500090, Telangana, IndiaDepartment of Electrical Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, IndiaDepartment of Electrical Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, IndiaIn 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.https://www.mdpi.com/2411-5134/9/6/114distributed generatorsshunt capacitorsmachine learninggrey wolf optimizationBiLSTM modelmulti-objective optimization |
| spellingShingle | Amarendra Alluri Srinivasa Rao Gampa Balaji Gutta Mahesh Babu Basam Kiran Jasthi Nibir Baran Roy Debapriya Das 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 distributed generators shunt capacitors machine learning grey wolf optimization BiLSTM model multi-objective optimization |
| title | Multi-Objective Optimization Algorithm Based Bidirectional Long Short Term Memory Network Model for Optimum Sizing of Distributed Generators and Shunt Capacitors for Distribution Systems |
| title_full | Multi-Objective Optimization Algorithm Based Bidirectional Long Short Term Memory Network Model for Optimum Sizing of Distributed Generators and Shunt Capacitors for Distribution Systems |
| title_fullStr | Multi-Objective Optimization Algorithm Based Bidirectional Long Short Term Memory Network Model for Optimum Sizing of Distributed Generators and Shunt Capacitors for Distribution Systems |
| title_full_unstemmed | Multi-Objective Optimization Algorithm Based Bidirectional Long Short Term Memory Network Model for Optimum Sizing of Distributed Generators and Shunt Capacitors for Distribution Systems |
| title_short | Multi-Objective Optimization Algorithm Based Bidirectional Long Short Term Memory Network Model for Optimum Sizing of Distributed Generators and Shunt Capacitors for Distribution Systems |
| title_sort | multi objective optimization algorithm based bidirectional long short term memory network model for optimum sizing of distributed generators and shunt capacitors for distribution systems |
| topic | distributed generators shunt capacitors machine learning grey wolf optimization BiLSTM model multi-objective optimization |
| url | https://www.mdpi.com/2411-5134/9/6/114 |
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