An Efficient Scheme for Determining the Power Loss in Wind-PV Based on Deep Learning
Power loss is a bottleneck in every power system and it has been in focus of majority of the researchers and industry. This paper proposes a new method for determining the power loss in wind-solar power system based on deep learning. The main idea of the proposed scheme is to freeze the feature extr...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9302579/ |
| _version_ | 1818737358867005440 |
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| author | Muhyaddin Rawa Salem Alkhalaf Lucian Mihet-Popa Raef Aboelsaud Tahir Khurshaid Abdurrahman Shuaibu Hassan Muhammad Faizan Tahir Ziad M. Ali |
| author_facet | Muhyaddin Rawa Salem Alkhalaf Lucian Mihet-Popa Raef Aboelsaud Tahir Khurshaid Abdurrahman Shuaibu Hassan Muhammad Faizan Tahir Ziad M. Ali |
| author_sort | Muhyaddin Rawa |
| collection | DOAJ |
| description | Power loss is a bottleneck in every power system and it has been in focus of majority of the researchers and industry. This paper proposes a new method for determining the power loss in wind-solar power system based on deep learning. The main idea of the proposed scheme is to freeze the feature extraction layer of the deep Boltzmann network and deploy deep learning training model as the source model. The sample data with closer distribution with the data under consideration is selected by defining the maximum mean discrepancy contribution coefficient. The power loss calculation model is developed by configuring the deep neural network through the sample data. The deep learning model is deployed to simulate the non-linear mapping relationship between the load data, power supply data, bus voltage data and the grid loss rate during power grid operation. The proposed algorithm is applied to an actual power grid to evaluate its effectiveness. Simulation results show that the proposed algorithm effectively improved the system performance in terms of accuracy, fault tolerance, nonlinear fitting and timeliness as compared with existing schemes. |
| first_indexed | 2024-12-18T00:51:47Z |
| format | Article |
| id | doaj.art-3373900e5e414a328bfc94bfcb57346b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-18T00:51:47Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-3373900e5e414a328bfc94bfcb57346b2022-12-21T21:26:38ZengIEEEIEEE Access2169-35362021-01-0199481949210.1109/ACCESS.2020.30466879302579An Efficient Scheme for Determining the Power Loss in Wind-PV Based on Deep LearningMuhyaddin Rawa0Salem Alkhalaf1Lucian Mihet-Popa2https://orcid.org/0000-0002-4556-2774Raef Aboelsaud3Tahir Khurshaid4Abdurrahman Shuaibu Hassan5Muhammad Faizan Tahir6Ziad M. Ali7https://orcid.org/0000-0001-7315-0040Center of Research Excellence in Renewable Energy and Power Systems, King Abdulaziz University, Jeddah, Saudi ArabiaComputer Science Department, Arrass College of Science and Arts, Qassim University, Qassim, Saudi ArabiaFaculty of Engineering, Østfold University College, Halden, NorwayDepartment of Electrical Engineering, National Research Tomsk Polytechnique University, Tomsk, RussiaDepartment of Electrical Engineering, Yeungnam University, Gyeongsan, South KoreaDepartment of Electrical and Electronics Engineering Science, University of Johannesburg, Auckland Park, South AfricaSchool of Electric Power, South China University of Technology, Guangzhou, ChinaCollege of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Wadi Addawaser, Saudi ArabiaPower loss is a bottleneck in every power system and it has been in focus of majority of the researchers and industry. This paper proposes a new method for determining the power loss in wind-solar power system based on deep learning. The main idea of the proposed scheme is to freeze the feature extraction layer of the deep Boltzmann network and deploy deep learning training model as the source model. The sample data with closer distribution with the data under consideration is selected by defining the maximum mean discrepancy contribution coefficient. The power loss calculation model is developed by configuring the deep neural network through the sample data. The deep learning model is deployed to simulate the non-linear mapping relationship between the load data, power supply data, bus voltage data and the grid loss rate during power grid operation. The proposed algorithm is applied to an actual power grid to evaluate its effectiveness. Simulation results show that the proposed algorithm effectively improved the system performance in terms of accuracy, fault tolerance, nonlinear fitting and timeliness as compared with existing schemes.https://ieeexplore.ieee.org/document/9302579/Renewable energyPVoptimizationdeep learningpower loss |
| spellingShingle | Muhyaddin Rawa Salem Alkhalaf Lucian Mihet-Popa Raef Aboelsaud Tahir Khurshaid Abdurrahman Shuaibu Hassan Muhammad Faizan Tahir Ziad M. Ali An Efficient Scheme for Determining the Power Loss in Wind-PV Based on Deep Learning IEEE Access Renewable energy PV optimization deep learning power loss |
| title | An Efficient Scheme for Determining the Power Loss in Wind-PV Based on Deep Learning |
| title_full | An Efficient Scheme for Determining the Power Loss in Wind-PV Based on Deep Learning |
| title_fullStr | An Efficient Scheme for Determining the Power Loss in Wind-PV Based on Deep Learning |
| title_full_unstemmed | An Efficient Scheme for Determining the Power Loss in Wind-PV Based on Deep Learning |
| title_short | An Efficient Scheme for Determining the Power Loss in Wind-PV Based on Deep Learning |
| title_sort | efficient scheme for determining the power loss in wind pv based on deep learning |
| topic | Renewable energy PV optimization deep learning power loss |
| url | https://ieeexplore.ieee.org/document/9302579/ |
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