Partial Discharge Classification Using Neural Networks and Statistical Parameters
نویسندگان
چکیده
Partial discharge (PD) pattern recognition is an important tool in high-voltage insulation diagnosis of power systems. A PD pattern classification approach of high-voltage power transformers based on a neural network is proposed in this paper. A commercial PD detector is firstly used to measure the 3-D PD patterns of epoxy resin power transformers. Then, the gray intensity histogram extracted from the raw 3-D PD patterns are statistically analyzed for the neural-network-based (NN-based) classification system. The system can quickly and stably learn to categorize input patterns and permit adaptive processes to access significant new information. To demonstrate the effectiveness of the proposed method, the classification ability is investigated on 120 sets of field tested PD patterns of epoxy resin power transformers. Different types of PD within power transformers are identified with rather encouraged results. Key-Words: Partial Discharge, Pattern Classification, Neural Network, Statistical Parameter.
منابع مشابه
Effect of sound classification by neural networks in the recognition of human hearing
In this paper, we focus on two basic issues: (a) the classification of sound by neural networks based on frequency and sound intensity parameters (b) evaluating the health of different human ears as compared to of those a healthy person. Sound classification by a specific feed forward neural network with two inputs as frequency and sound intensity and two hidden layers is proposed. This process...
متن کاملAnalysis and Diagnosis of Partial Discharge of Power Capacitors Using Extension Neural Network Algorithm and Synchronous Detection Based Chaos Theory
Power capacitors are important equipment of the power systems that are being operated in high voltage levels at high temperatures for long periods. As time goes on, their insulation fracture rate increases, and partial discharge is the most important cause of their fracture. Therefore, fast and accurate methods have great importance to accurately diagnosis the partial discharge. Conventional me...
متن کاملPartial discharge pattern classification using multilayer neural networks - Science, Measurement and Technology, IEE Proceedings A
Partial discharge measurement is an important means of assessing the condition and integrity of insulation systems in high voltage power apparatus. Commercially available partial discharge detectors display them as patterns by an elliptic time base. Over the years, experts have been interpreting and recognising the nature and cause of partial discharges by studying these patterns. A way to auto...
متن کاملA heuristic complex probabilistic neural network system for partial discharge pattern classification
Partial discharge (PD) pattern classification has recently become popular since the automated acquisition of PD signals has become vital and cogent. A novel method for identification of defects due to partial discharge is described in this paper. Starting from different PD families of specimen, several sets of characteristic vectors are determined and then used as input variables to the propose...
متن کاملDetermining water quality along the river with using evolutionary artificial neural networks (Case Study, Karoon River , Shahid Abbaspur-Arab Asad reach)
Rivers are important as the main source of supply for drinking, agriculture and industry.However, drinking water quality in terms of qualitative parameters, is the most important variable. Studias and predicting changes in quality parameters along a river, are one of the goals of water resources planners and managers. In this regard, many water quality models in order to maintain better water ...
متن کامل