Impact of wind farms on disturbance detection and classification in distributed generation using modified Adaline network and an adaptive neuro-fuzzy information system |
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| Affiliation: | 1. Department of Information Technology, MCKV Institute of Engineering, Liluah, Howrah 711204, India;2. Department of Computer Science and Technology, IIEST, Shibpur, Howrah 711103, India;1. Department of Electronics and Telecommunication Engineering, Veer Surendra Sai University of Technology, Burla 768018, India;2. Department of Electronics and Instrumentation Engineering, Institute of Technical Education and Research, SOA University, Bhubaneswar 751030, India;1. Department of Computer Engineering, Electrical Engineering and Applied Mathematics, University of Salerno, 84084 Fisciano, Italy;2. CORISA, Department of Computer Science, University of Salerno, 84084 Fisciano, Italy;1. Centre for Biomedical Engineering, Transportation Research Alliance, Universiti Teknologi Malaysia, Skudai, Malaysia;2. Faculty of Bioscience and Medical Engineering, Universiti Teknologi Malaysia, Skudai, Malaysia;1. School of Mechanical Engineering, Xiangtan University, Hunan 411105, People?s Republic of China;2. Engineering Research Center for Complex Track Processing Technology and Equipment, Ministry of Education, People''s Republic of China;3. School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, People?s Republic of China;1. Computational Intelligence Technology Center, Industrial Technology Research Institute, Hsinchu 310, Taiwan;2. Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung 811, Taiwan;3. Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung 804, Taiwan;4. Department of Information Management, National University of Kaohsiung, Kaohsiung 811, Taiwan |
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| Abstract: | A new disturbance detection and classification technique based on modified Adaline and adaptive neuro-fuzzy information system (ANFIS) is proposed for a distributed generation system comprising a wind power generating system (DFIG) and a photovoltaic array. The proposed technique is based on a fast Gauss–Newton parameter updating rule rather than the conventional Widrow–Hoff delta rule for the Adaline network. The voltage and current signals near the target distributed generation (DG), particularly the DFIG, whose speed varies from minimum to the maximum cut-off speed, are processed through the modified Adaline network to yield the features like the negative sequence power, harmonic amplification factor (HAF), total harmonic distortion (THD), etc. These features are then used as training sets for the ANFIS, which employs a gradient descent algorithm to update its parameters. The proposed technique distinguishes the islanding condition of the distributed generation system with some other disturbances, such as switching faults, capacitor bank switching, voltage swell, voltage sag, distorted grid voltage, unbalanced load switching, etc. which are referred to as non-islanding cases in this paper. |
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| Keywords: | ANFIS Distributed generation (DG) Hybrid Gauss–Newton linear combiner Islanding events |
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