An Anti-Islanding Protection Technique Using a Wavelet Packet Transform and a Probabilistic Neural Network

Masoud Ahmadipour,Mohammad Lutfi Othman,Hashim Hizam,Mohd Amran Mohd Radzi

doi:10.3390/en11102701

Masoud Ahmadipour, Mohammad Lutfi Othman + Show 2 more

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Journal: Energies	Publication Date: Oct 11, 2018
Citations: 26	License type: CC BY 4.0

Affiliation: Universiti Putra Malaysia

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This paper proposes a new islanding detection technique based on the combination of a wavelet packet transform (WPT) and a probabilistic neural network (PNN) for grid-tied photovoltaic systems. The point of common coupling (PCC) voltage is measured and processed by the WPT to find the normalized Shannon entropy (NSE) and the normalized logarithmic energy entropy (NLEE). Subsequently, the yield feature vectors are fed to the PNN classifier to classify the disturbances. The PNN is trained with different spread factors to obtain better classification accuracy. For the best performance of the proposed method, the precise analysis is done for the selection of the type of input data for the PNN, the type of mother wavelet, and the required transform level which is based on the accuracy, simplicity, specificity, speed, and cost parameters. The results show that, by using normalized Shannon entropy and the normalized logarithmic energy entropy, not only it offers simplicity, specificity and reduced costs, it also has better accuracy compared to other smart and passive methods. Based on the results, the proposed islanding detection technique is highly accurate and does not mal-operate during islanding and non-islanding events.

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The rapid growth of energy demand in recent years has contributed to the popularity of the utilization of renewable energy
((NSE+normalized logarithmic energy entropy (NLEE))-db20-6) technique is compared to a few classifier methods such as Radial Basic Function (RBF) [49], multilayer perceptron (MLP) neural network trained by the Levenberg-Marquardt (LM)
An islanding detection method based on the combination of a wavelet packet transform (WPT) and probabilistic neural network (PNN) was presented

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The rapid growth of energy demand in recent years has contributed to the popularity of the utilization of renewable energy. The environmental and economic benefits of renewable resources e.g., photovoltaic systems, wind turbines, fuel cells, and geothermal energy make them very attractive to researchers and governments around the world [1,2]. In this regard, the developed countries have been trying to reduce their levels of greenhouse gas emissions in order to address the problems of climate change and economic recovery by focusing on the energy resources. In order to achieve these challenging goals, effective renewable energy resource support policies and an effort towards the improvement of energy efficiency are necessary.

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