Efficiency improvement of the maximum power point tracking for PV systems using support vector machine technique
The aim of this paper is to improve efficiency of maximum power point tracking (MPPT) for PV systems. The Support Vector Machine (SVM) was proposed to achieve the MPPT controller. The theoretical, the perturbation and observation (P&O), and incremental conductance (IC) algorithms were used to co...
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| Main Authors: | , |
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| Format: | Article |
| Language: | en_US |
| Published: |
2017
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| Summary: | The aim of this paper is to improve efficiency of maximum power point tracking (MPPT) for PV systems. The Support Vector Machine (SVM) was proposed to achieve the MPPT controller. The theoretical, the perturbation and observation (P&O), and incremental conductance (IC) algorithms were used to compare with proposed SVM algorithm. MATLAB models for PV module, theoretical, SVM, P&O, and IC algorithms are implemented. The improved MPPT uses the SVM method to predict the optimum voltage of the PV system in order to extract the maximum power point (MPP). The SVM technique used two inputs which are solar radiation and ambient temperature of the modeled PV module. The results show that the proposed SVM technique has less Root Mean Square Error (RMSE) and higher efficiency than P&O and IC methods. © Published under licence by IOP Publishing Ltd. |
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