An adaptive neural network approach for the estimation of power system frequency |
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| Affiliation: | 1. Centre for Intelligent Systems, Department of Electrical Engineering, Regional Engineering College, Rourkela, India;2. Department of Electrical Engineering, National University of Singapore, Singapore-0511, Singapore;1. Faculty of Mathematics and Statistics, Hubei Key Laboratory of Applied Mathematics, Hubei University, Wuhan 430062, China;2. Beijing Research Institute of Uranium Geology, Beijing 100029, China;3. Research Center for Eco-Environment Sciences, Chinese Academy of Sciences, Beijing 100085, China;4. Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;5. CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, China;1. Department of Energy, Information Engineering and Mathematical Models (DEIM), University of Palermo, Viale delle Scienze, 90128 Palermo, Italy;2. Department of Electrical Engineering and Information Technology, University of Naples Federico II, Via Claudio, 21, 80125 Naples, Italy;1. East China Normal University, Shanghai 200241, China;2. State Key Laboratory of Hydroscience and Engineering of Tsinghua University, Beijing 100084, China;3. Nanjing Hydraulic Research Institute, Nanjing 210029, China;1. College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029, China;2. Department of Chemical Engineering & Materials Science, University of California, Davis, CA 95616, USA |
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| Abstract: | A new approach to the estimation of power system frequency using an adaptive neural network is presented in this paper. This approach uses a linear adaptive neuron or an adaptive linear combiner called “Adaline” to identify the parameters of a discrete signal model of the power system voltage. Here, the learning parameters are adjusted to force the error between the actual and the computed signal samples to satisfy a stable difference error equation, rather than to minimize an error function. The proposed algorithm shows a high degree of robustness and estimation accuracy over a wide range of frequency changes. The technique is shown to be capable of tracking power system conditions and is immune to the effects of harmonics and random noise. |
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