In power system networks, automatic fault diagnosis techniques of switchgears with high accuracy and less time consuming are important. In this work, classification of abnormal location in switchgears is proposed using hybrid gravitational search algorithm (GSA)-artificial intelligence (AI) techniques. The measurement data were obtained from ultrasound, transient earth voltage, temperature and sound sensors. The AI classifiers used include artificial neural network (ANN) and support vector machine (SVM). The performance of both classifiers was optimized by an optimization technique, GSA. The advantages of GSA classification on AI in classifying the abnormal location in switchgears are easy implementation, fast convergence and low computational cost. For performance comparison, several well-known metaheuristic techniques were also applied on the AI classifiers. From the comparison between ANN and SVM without optimization by GSA, SVM yields 2% higher accuracy than ANN. However, ANN yields slightly higher accuracy than SVM after combining with GSA, which is in the range of 97%-99% compared to 95%-97% for SVM. On the other hand, GSA-SVM converges faster than GSA-ANN. Overall, it was found that combination of both AI classifiers with GSA yields better results than several well-known metaheuristic techniques.
Classification of Streaming Data has been recently recognized as an important research area. It is different from conventional techniques of classification because we prefer to have a single pass over each data item. Moreover, unlike conventional classification, the true labels of the data are not obtained immediately during the training process. This paper proposes ILEP, a novel instance-based technique for classification of streaming data with a modifiable reference set based on the concept of Emerging Patterns. Emerging Patterns (EPs) have been successfully used to catch important data items for addition to the reference set, hence resulting in an increase in classification accuracy as well as restricting the size of the reference set.
IEEE Std 100-2000 defines corona as a luminous discharge due to ionization of the air surrounding a conductor caused by a voltage gradient exceeding a certain critical value. It occurs when the insulating material begins to ionize or conduct due to voltage stress. Corona brings a lot of damages such as corrosion, loss inoverhead transmission lines and electromagnetic interference. Monitoring of corona may reduce the maintenance and replacement cost of electrical equipment. The motivation of this experiment is to calibrate corona detector antennas in the future. The error obtained will determine the efficiency of the antenna to detect and locate potential coronas in electrical equipment in a substation with switchgears or transformers. The operation bandwidth of the antenna is 320MHz to 1.20GHz making it useful to detect and corona. The measurement method of utilizing delay between signals first peak is effective with average 4.76% error with maximum 10.0% error recorded. This may be used to develop a corona online measuring system in the future.
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