Research on the Harmonic Prediction Method of a PV Plant Based on an Improved Kernel Extreme Learning Machine Model
The harmonics of photovoltaic power plants are affected by various factors including temperature, weather, and light amplitude. Traditional power harmonic prediction methods have weak non-linear mapping and poor generalization capability to unknown time series data. In this paper, a Kernel Extreme L...
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MDPI AG
2023-12-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/13/1/32 |
| _version_ | 1797358960174432256 |
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| author | Zhenghan Liu Quanzheng Li Donglai Wang Guifan Zhang Wei Wang Yan Zhao Rui Guo |
| author_facet | Zhenghan Liu Quanzheng Li Donglai Wang Guifan Zhang Wei Wang Yan Zhao Rui Guo |
| author_sort | Zhenghan Liu |
| collection | DOAJ |
| description | The harmonics of photovoltaic power plants are affected by various factors including temperature, weather, and light amplitude. Traditional power harmonic prediction methods have weak non-linear mapping and poor generalization capability to unknown time series data. In this paper, a Kernel Extreme Learning Machine (KELM) model power harmonic prediction method based on Gray Relational Analysis (GRA) with Variational Mode Decomposition (VMD) coupled with Harris Hawk Optimization (HHO) is proposed. First, the GRA method is used to construct the similar day set in one screening, followed by further using <i>K</i>-means clustering to construct the final similar day set. Then, the VMD method is adopted to decompose the harmonic data of the similar day set, and each decomposition subsequence is input to the HHO-optimized KELM neural network for prediction, respectively. Finally, the prediction results of each subseries are superimposed and numerical evaluation indexes are introduced, and the proposed method is validated by applying the above method in simulation. The results show that the error of the prediction model is reduced by at least 39% compared with the conventional prediction method, so it can satisfy the function of harmonic content prediction of a photovoltaic power plant. |
| first_indexed | 2024-03-08T15:09:42Z |
| format | Article |
| id | doaj.art-bf760cb2f0eb4d718d617561058adb9c |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-08T15:09:42Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-bf760cb2f0eb4d718d617561058adb9c2024-01-10T14:54:11ZengMDPI AGElectronics2079-92922023-12-011313210.3390/electronics13010032Research on the Harmonic Prediction Method of a PV Plant Based on an Improved Kernel Extreme Learning Machine ModelZhenghan Liu0Quanzheng Li1Donglai Wang2Guifan Zhang3Wei Wang4Yan Zhao5Rui Guo6Key Laboratory of Regional Multi-Energy System Integration and Control of Liaoning Province, Shenyang Institute of Engineering, Shenyang 110136, ChinaState Grid Tieling Power Supply Company, State Grid Liaoning Electric Power Co., Ltd., Tieling 112000, ChinaKey Laboratory of Regional Multi-Energy System Integration and Control of Liaoning Province, Shenyang Institute of Engineering, Shenyang 110136, ChinaKey Laboratory of Regional Multi-Energy System Integration and Control of Liaoning Province, Shenyang Institute of Engineering, Shenyang 110136, ChinaKey Laboratory of Regional Multi-Energy System Integration and Control of Liaoning Province, Shenyang Institute of Engineering, Shenyang 110136, ChinaKey Laboratory of Regional Multi-Energy System Integration and Control of Liaoning Province, Shenyang Institute of Engineering, Shenyang 110136, ChinaKey Laboratory of Regional Multi-Energy System Integration and Control of Liaoning Province, Shenyang Institute of Engineering, Shenyang 110136, ChinaThe harmonics of photovoltaic power plants are affected by various factors including temperature, weather, and light amplitude. Traditional power harmonic prediction methods have weak non-linear mapping and poor generalization capability to unknown time series data. In this paper, a Kernel Extreme Learning Machine (KELM) model power harmonic prediction method based on Gray Relational Analysis (GRA) with Variational Mode Decomposition (VMD) coupled with Harris Hawk Optimization (HHO) is proposed. First, the GRA method is used to construct the similar day set in one screening, followed by further using <i>K</i>-means clustering to construct the final similar day set. Then, the VMD method is adopted to decompose the harmonic data of the similar day set, and each decomposition subsequence is input to the HHO-optimized KELM neural network for prediction, respectively. Finally, the prediction results of each subseries are superimposed and numerical evaluation indexes are introduced, and the proposed method is validated by applying the above method in simulation. The results show that the error of the prediction model is reduced by at least 39% compared with the conventional prediction method, so it can satisfy the function of harmonic content prediction of a photovoltaic power plant.https://www.mdpi.com/2079-9292/13/1/32gray relational analysisHarris hawk optimization<i>K</i>-means clusteringkernel extreme learning machinevariational mode decomposition |
| spellingShingle | Zhenghan Liu Quanzheng Li Donglai Wang Guifan Zhang Wei Wang Yan Zhao Rui Guo Research on the Harmonic Prediction Method of a PV Plant Based on an Improved Kernel Extreme Learning Machine Model Electronics gray relational analysis Harris hawk optimization <i>K</i>-means clustering kernel extreme learning machine variational mode decomposition |
| title | Research on the Harmonic Prediction Method of a PV Plant Based on an Improved Kernel Extreme Learning Machine Model |
| title_full | Research on the Harmonic Prediction Method of a PV Plant Based on an Improved Kernel Extreme Learning Machine Model |
| title_fullStr | Research on the Harmonic Prediction Method of a PV Plant Based on an Improved Kernel Extreme Learning Machine Model |
| title_full_unstemmed | Research on the Harmonic Prediction Method of a PV Plant Based on an Improved Kernel Extreme Learning Machine Model |
| title_short | Research on the Harmonic Prediction Method of a PV Plant Based on an Improved Kernel Extreme Learning Machine Model |
| title_sort | research on the harmonic prediction method of a pv plant based on an improved kernel extreme learning machine model |
| topic | gray relational analysis Harris hawk optimization <i>K</i>-means clustering kernel extreme learning machine variational mode decomposition |
| url | https://www.mdpi.com/2079-9292/13/1/32 |
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