Load Shedding in Microgrids with Dual Neural Networks and AHP Algorithm
This paper proposes a new load shedding method based on the application of a Dual Neural Network (NN). The combination of a Back-Propagation Neural Network (BPNN) and of Particle Swarm Optimization (PSO) aims to quickly predict and propose a load shedding strategy when a fault occurs in the microgri...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
D. G. Pylarinos
2022-02-01
|
| Series: | Engineering, Technology & Applied Science Research |
| Subjects: | |
| Online Access: | https://etasr.com/index.php/ETASR/article/view/4652 |
| _version_ | 1797694727206731776 |
|---|---|
| author | L. T. H. Nhung T. T. Phung H. M. V. Nguyen T. N. Le T. A. Nguyen T. D. Vo |
| author_facet | L. T. H. Nhung T. T. Phung H. M. V. Nguyen T. N. Le T. A. Nguyen T. D. Vo |
| author_sort | L. T. H. Nhung |
| collection | DOAJ |
| description | This paper proposes a new load shedding method based on the application of a Dual Neural Network (NN). The combination of a Back-Propagation Neural Network (BPNN) and of Particle Swarm Optimization (PSO) aims to quickly predict and propose a load shedding strategy when a fault occurs in the microgrid (MG) system. The PSO algorithm has the ability to search and compare multiple points, so the proposed NN training method helps determine the link weights faster and stronger. As a result, the proposed method saves training time and achieves higher accuracy. The Analytic Hierarchy Process (AHP) algorithm is applied to rank the loads based on their importance factor. The results of the ratings of the loads serve as a basis for constructing the load shedding strategies of a NN combined with the PSO algorithm (ANN-PSO). The proposed load shedding method is tested on an IEEE 25-bus 8-generator MG power system. The simulation results show that the frequency recovery of the power system is positive. The proposed neural network adapts well to the simulated data of the system and achieves high performance in fault prediction.
|
| first_indexed | 2024-03-12T03:02:00Z |
| format | Article |
| id | doaj.art-4b128d2e24354ac0939a3b2fd8825ce6 |
| institution | Directory Open Access Journal |
| issn | 2241-4487 1792-8036 |
| language | English |
| last_indexed | 2024-03-12T03:02:00Z |
| publishDate | 2022-02-01 |
| publisher | D. G. Pylarinos |
| record_format | Article |
| series | Engineering, Technology & Applied Science Research |
| spelling | doaj.art-4b128d2e24354ac0939a3b2fd8825ce62023-09-03T14:40:43ZengD. G. PylarinosEngineering, Technology & Applied Science Research2241-44871792-80362022-02-0112110.48084/etasr.4652Load Shedding in Microgrids with Dual Neural Networks and AHP AlgorithmL. T. H. Nhung0T. T. Phung1H. M. V. Nguyen2T. N. Le3T. A. Nguyen4T. D. Vo5Electrical and Electronics Department, HCMC University of Technology and Education, VietnamElectrical and Electronics Department, Cao Thang Technical College, VietnamUrban Engineering Department, HCMC University of Architecture, VietnamDepartment of Electrical and Electronics Engineering, HCMC University of Technology and Education, VietnamElectrical and Electronics Department, Cao Thang Technical College, VietnamDepartment of Electrical and Electronics Engineering, HCMC University of Technology and Education, VietnamThis paper proposes a new load shedding method based on the application of a Dual Neural Network (NN). The combination of a Back-Propagation Neural Network (BPNN) and of Particle Swarm Optimization (PSO) aims to quickly predict and propose a load shedding strategy when a fault occurs in the microgrid (MG) system. The PSO algorithm has the ability to search and compare multiple points, so the proposed NN training method helps determine the link weights faster and stronger. As a result, the proposed method saves training time and achieves higher accuracy. The Analytic Hierarchy Process (AHP) algorithm is applied to rank the loads based on their importance factor. The results of the ratings of the loads serve as a basis for constructing the load shedding strategies of a NN combined with the PSO algorithm (ANN-PSO). The proposed load shedding method is tested on an IEEE 25-bus 8-generator MG power system. The simulation results show that the frequency recovery of the power system is positive. The proposed neural network adapts well to the simulated data of the system and achieves high performance in fault prediction. https://etasr.com/index.php/ETASR/article/view/4652load sheddingANN-PSOBPNNDual Neural NetworkAHP |
| spellingShingle | L. T. H. Nhung T. T. Phung H. M. V. Nguyen T. N. Le T. A. Nguyen T. D. Vo Load Shedding in Microgrids with Dual Neural Networks and AHP Algorithm Engineering, Technology & Applied Science Research load shedding ANN-PSO BPNN Dual Neural Network AHP |
| title | Load Shedding in Microgrids with Dual Neural Networks and AHP Algorithm |
| title_full | Load Shedding in Microgrids with Dual Neural Networks and AHP Algorithm |
| title_fullStr | Load Shedding in Microgrids with Dual Neural Networks and AHP Algorithm |
| title_full_unstemmed | Load Shedding in Microgrids with Dual Neural Networks and AHP Algorithm |
| title_short | Load Shedding in Microgrids with Dual Neural Networks and AHP Algorithm |
| title_sort | load shedding in microgrids with dual neural networks and ahp algorithm |
| topic | load shedding ANN-PSO BPNN Dual Neural Network AHP |
| url | https://etasr.com/index.php/ETASR/article/view/4652 |
| work_keys_str_mv | AT lthnhung loadsheddinginmicrogridswithdualneuralnetworksandahpalgorithm AT ttphung loadsheddinginmicrogridswithdualneuralnetworksandahpalgorithm AT hmvnguyen loadsheddinginmicrogridswithdualneuralnetworksandahpalgorithm AT tnle loadsheddinginmicrogridswithdualneuralnetworksandahpalgorithm AT tanguyen loadsheddinginmicrogridswithdualneuralnetworksandahpalgorithm AT tdvo loadsheddinginmicrogridswithdualneuralnetworksandahpalgorithm |