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Islanding Detection Using Empirical Mode Decomposition and Artificial Neural Network for Inverter Interfaced Distributed Generation
https://orcid.org/http://orcid.org/0000-0001-7759-4343
The empirical mode decomposition (EMD) and artificial neural network (ANN) are used in this paper to solve an islanding detection problem. In light of this, the voltage signal parameter is obtained or measured at the inverter interfaced distributed generation (IIDG) point of common coupling (PCC) and EMD signal processing technique is used to obtain the attributes. Accordingly, various intrinsic mode functions (IMFs) are generated by breaking down the signal parameter. The neural network receives statistical attributes as input after being extracted from these IMFs. Using an Artificial Neural Network (ANN), islanding can be identified. Several islanding scenarios, such as mismatches in active power and reactive power, and various non-islanding scenarios, such as fault conditions, switching of capacitors, and switching of loads, are simulated. The suggested algorithm is tested to demarcate between an islanding scenario and a non-islanding scenario.
Islanding Detection Using Empirical Mode Decomposition and Artificial Neural Network for Inverter Interfaced Distributed Generation
https://orcid.org/http://orcid.org/0000-0001-7759-4343
The empirical mode decomposition (EMD) and artificial neural network (ANN) are used in this paper to solve an islanding detection problem. In light of this, the voltage signal parameter is obtained or measured at the inverter interfaced distributed generation (IIDG) point of common coupling (PCC) and EMD signal processing technique is used to obtain the attributes. Accordingly, various intrinsic mode functions (IMFs) are generated by breaking down the signal parameter. The neural network receives statistical attributes as input after being extracted from these IMFs. Using an Artificial Neural Network (ANN), islanding can be identified. Several islanding scenarios, such as mismatches in active power and reactive power, and various non-islanding scenarios, such as fault conditions, switching of capacitors, and switching of loads, are simulated. The suggested algorithm is tested to demarcate between an islanding scenario and a non-islanding scenario.
Islanding Detection Using Empirical Mode Decomposition and Artificial Neural Network for Inverter Interfaced Distributed Generation
https://orcid.org/http://orcid.org/0000-0001-7759-4343
The empirical mode decomposition (EMD) and artificial neural network (ANN) are used in this paper to solve an islanding detection problem. In light of this, the voltage signal parameter is obtained or measured at the inverter interfaced distributed generation (IIDG) point of common coupling (PCC) and EMD signal processing technique is used to obtain the attributes. Accordingly, various intrinsic mode functions (IMFs) are generated by breaking down the signal parameter. The neural network receives statistical attributes as input after being extracted from these IMFs. Using an Artificial Neural Network (ANN), islanding can be identified. Several islanding scenarios, such as mismatches in active power and reactive power, and various non-islanding scenarios, such as fault conditions, switching of capacitors, and switching of loads, are simulated. The suggested algorithm is tested to demarcate between an islanding scenario and a non-islanding scenario.
Islanding Detection Using Empirical Mode Decomposition and Artificial Neural Network for Inverter Interfaced Distributed Generation
J. Inst. Eng. India Ser. B
Farhan, M. A. Aneesa (Autor:in) / Soni, Kushal (Autor:in) / Thelegthoti, Maria K. (Autor:in) / Rana, Ankur Singh (Autor:in)
01.02.2025
16 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
| British Library Online Contents | 2016
| British Library Online Contents | 2016
| American Institute of Physics | 2015