Research on the Photovoltaic MPPT Method Based on Improved BP-SVM-ELM Combination Prediction
In order to solve the problems of maximum power point tracking(MPPT) such as power oscillation and slow tracking speed when using traditional methods, this paper proposed a combined prediction algorithm that considered variables affecting photovoltaic output characteristics. The algorithm uses genet...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Control and Information Technology
2019-01-01
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| Series: | Kongzhi Yu Xinxi Jishu |
| Subjects: | |
| Online Access: | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2019.01.009 |
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| Summary: | In order to solve the problems of maximum power point tracking(MPPT) such as power oscillation and slow tracking speed when using traditional methods, this paper proposed a combined prediction algorithm that considered variables affecting photovoltaic output characteristics. The algorithm uses genetic algorithm to optimize BP neural network, least squares support vector machine and extreme learning machine (ELM) to predict the voltage of maximum power point respectively, and then adopts variance-covariance(VC) weight dynamic allocation method to combine the predictions. Through experimental simulation analysis, the combined forecasting method can use the advantages of each algorithm, effectively avoiding their deficiencies, and fundamentally improve the performance of the predictive model. Compared with the traditional disturbance observation method, the combined prediction algorithm not only ensures the stable operation of photovoltaic array at the maximum power point, but also effectively saves the time for tracking the maximum power point, which is of great significance to improve the efficiency of photovoltaic power generation system. |
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| ISSN: | 2096-5427 |