Congestion Management Using an Optimized Deep Convolution Neural Network in Deregulated Environment
The technical issue of congestion, which is predominantly found in deregulated power systems, is caused by the failure of transmission networks to satisfy load power demands. This failure is primarily caused due to an increase in loads or loss of transmission lines or generators in modern restructur...
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| Format: | Article |
| Language: | English |
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Academy of Sciences of Moldova
2023-08-01
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| Series: | Problems of the Regional Energetics |
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| Online Access: | https://journal.ie.asm.md/assets/files/11_03_59_2023.pdf |
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| author | Dhanadeepika B. Vanithasri M. Chakravarthi M. |
| author_facet | Dhanadeepika B. Vanithasri M. Chakravarthi M. |
| author_sort | Dhanadeepika B. |
| collection | DOAJ |
| description | The technical issue of congestion, which is predominantly found in deregulated power systems, is caused by the failure of transmission networks to satisfy load power demands. This failure is primarily caused due to an increase in loads or loss of transmission lines or generators in modern restructured power networks. This work introduces a CM approach using Deep Convolution Neural Network (DCNN) for minimizing congestion and supporting Independent System Operators (ISOs). The purpose of the work is to generate enhanced prediction outputs for congestion management with reduced error values. These objectives were achieved through the actual power rescheduling of generators. The proposed work adopts DCNN which is optimized using an Improved Lion Algorithm (LA) and aids in providing significant outcomes for congestion management with reduced error. By implementing customized IEEE 57-bus, IEEE 30-bus, and IEEE 118-bus test systems, the suggested approach has been successfully verified for its performance on test systems of varied sizes. This analysis incorporates restrictions such as line loads, bus voltage influence, generator, line limits, etc. The most important results for the test system indicating convergence profile, congestion cost, and change in real-power and voltage magnitude are obtained by the simulation in MATLAB, and on the basis of the obtained simulation outcomes, it is evident that the proposed Improved Lion Algorithm optimized Deep Convolution Neural Network displays phenomenal computation performance in minimizing congestion losses at minimum congestion costs. When compared to several contemporary optimization techniques, the suggested technique performs better in terms of congestion cost and losses by generating improved prediction outputs with reduced errors. |
| format | Article |
| id | doaj-art-0aa0eb8dbb394feb8b672ea4bdfbcdd1 |
| institution | Matheson Library |
| issn | 1857-0070 |
| language | English |
| publishDate | 2023-08-01 |
| publisher | Academy of Sciences of Moldova |
| record_format | Article |
| series | Problems of the Regional Energetics |
| spelling | doaj-art-0aa0eb8dbb394feb8b672ea4bdfbcdd12025-08-02T01:04:52ZengAcademy of Sciences of MoldovaProblems of the Regional Energetics1857-00702023-08-0159312213710.52254/1857-0070.2023.3-59.11Congestion Management Using an Optimized Deep Convolution Neural Network in Deregulated EnvironmentDhanadeepika B.0Vanithasri M.1Chakravarthi M.2Annamalai University, India, Tamil NaduAnnamalai University, India, Tamil NaduVasavi College of Engineering, Telangana, IndiaThe technical issue of congestion, which is predominantly found in deregulated power systems, is caused by the failure of transmission networks to satisfy load power demands. This failure is primarily caused due to an increase in loads or loss of transmission lines or generators in modern restructured power networks. This work introduces a CM approach using Deep Convolution Neural Network (DCNN) for minimizing congestion and supporting Independent System Operators (ISOs). The purpose of the work is to generate enhanced prediction outputs for congestion management with reduced error values. These objectives were achieved through the actual power rescheduling of generators. The proposed work adopts DCNN which is optimized using an Improved Lion Algorithm (LA) and aids in providing significant outcomes for congestion management with reduced error. By implementing customized IEEE 57-bus, IEEE 30-bus, and IEEE 118-bus test systems, the suggested approach has been successfully verified for its performance on test systems of varied sizes. This analysis incorporates restrictions such as line loads, bus voltage influence, generator, line limits, etc. The most important results for the test system indicating convergence profile, congestion cost, and change in real-power and voltage magnitude are obtained by the simulation in MATLAB, and on the basis of the obtained simulation outcomes, it is evident that the proposed Improved Lion Algorithm optimized Deep Convolution Neural Network displays phenomenal computation performance in minimizing congestion losses at minimum congestion costs. When compared to several contemporary optimization techniques, the suggested technique performs better in terms of congestion cost and losses by generating improved prediction outputs with reduced errors.https://journal.ie.asm.md/assets/files/11_03_59_2023.pdfcongestion managementdcnnisoimproved lion algorithmderegulated power. |
| spellingShingle | Dhanadeepika B. Vanithasri M. Chakravarthi M. Congestion Management Using an Optimized Deep Convolution Neural Network in Deregulated Environment Problems of the Regional Energetics congestion management dcnn iso improved lion algorithm deregulated power. |
| title | Congestion Management Using an Optimized Deep Convolution Neural Network in Deregulated Environment |
| title_full | Congestion Management Using an Optimized Deep Convolution Neural Network in Deregulated Environment |
| title_fullStr | Congestion Management Using an Optimized Deep Convolution Neural Network in Deregulated Environment |
| title_full_unstemmed | Congestion Management Using an Optimized Deep Convolution Neural Network in Deregulated Environment |
| title_short | Congestion Management Using an Optimized Deep Convolution Neural Network in Deregulated Environment |
| title_sort | congestion management using an optimized deep convolution neural network in deregulated environment |
| topic | congestion management dcnn iso improved lion algorithm deregulated power. |
| url | https://journal.ie.asm.md/assets/files/11_03_59_2023.pdf |
| work_keys_str_mv | AT dhanadeepikab congestionmanagementusinganoptimizeddeepconvolutionneuralnetworkinderegulatedenvironment AT vanithasrim congestionmanagementusinganoptimizeddeepconvolutionneuralnetworkinderegulatedenvironment AT chakravarthim congestionmanagementusinganoptimizeddeepconvolutionneuralnetworkinderegulatedenvironment |