An Anti-Islanding Protection Technique Using a Wavelet Packet Transform and a Probabilistic Neural Network
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 Sha...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-10-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1996-1073/11/10/2701 |
| _version_ | 1811306305646755840 |
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| author | Masoud Ahmadipour Hashim Hizam Mohammad Lutfi Othman Mohd Amran Mohd Radzi |
| author_facet | Masoud Ahmadipour Hashim Hizam Mohammad Lutfi Othman Mohd Amran Mohd Radzi |
| author_sort | Masoud Ahmadipour |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T08:43:11Z |
| format | Article |
| id | doaj.art-486af8cacade4c2db3df86a237e549ac |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-13T08:43:11Z |
| publishDate | 2018-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-486af8cacade4c2db3df86a237e549ac2022-12-22T02:53:50ZengMDPI AGEnergies1996-10732018-10-011110270110.3390/en11102701en11102701An Anti-Islanding Protection Technique Using a Wavelet Packet Transform and a Probabilistic Neural NetworkMasoud Ahmadipour0Hashim Hizam1Mohammad Lutfi Othman2Mohd Amran Mohd Radzi3Department of Electrical and Electronic Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, MalaysiaDepartment of Electrical and Electronic Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, MalaysiaDepartment of Electrical and Electronic Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, MalaysiaDepartment of Electrical and Electronic Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, MalaysiaThis 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.http://www.mdpi.com/1996-1073/11/10/2701islanding detectionwavelet packet transformprobabilistic neural networksymmetrical and asymmetrical faultsphotovoltaic system |
| spellingShingle | Masoud Ahmadipour Hashim Hizam Mohammad Lutfi Othman Mohd Amran Mohd Radzi An Anti-Islanding Protection Technique Using a Wavelet Packet Transform and a Probabilistic Neural Network Energies islanding detection wavelet packet transform probabilistic neural network symmetrical and asymmetrical faults photovoltaic system |
| title | An Anti-Islanding Protection Technique Using a Wavelet Packet Transform and a Probabilistic Neural Network |
| title_full | An Anti-Islanding Protection Technique Using a Wavelet Packet Transform and a Probabilistic Neural Network |
| title_fullStr | An Anti-Islanding Protection Technique Using a Wavelet Packet Transform and a Probabilistic Neural Network |
| title_full_unstemmed | An Anti-Islanding Protection Technique Using a Wavelet Packet Transform and a Probabilistic Neural Network |
| title_short | An Anti-Islanding Protection Technique Using a Wavelet Packet Transform and a Probabilistic Neural Network |
| title_sort | anti islanding protection technique using a wavelet packet transform and a probabilistic neural network |
| topic | islanding detection wavelet packet transform probabilistic neural network symmetrical and asymmetrical faults photovoltaic system |
| url | http://www.mdpi.com/1996-1073/11/10/2701 |
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