| Summary: | 碩士 === 國立中正大學 === 電機工程研究所 === 104 === In recent years, the widespread use of power quality (PQ) monitors and research advancements in a new research field named power quality data analytics. Power quality disturbance data is increasingly applied to extract useful information about the conditions of power system, such as monitoring incipient equipment failures, and solve various power system problems based on the information.
In this thesis, abnormalities are detected by comparing with and without disturbances. Kullback-Leibler divergence (KLD) is used to assess the difference of the distributions. An abnormality exists if the KLD is larger than a threshold. The KLD could be used as features. To precisely forecast the incipient fault event, k-nearest neighbors (KNN) and support vector machine (SVM) are used to classify different abnormal waveforms with features of numerous abnormal events. With extend of characteristic of KLD, a diagnosis method is proposed. Results show that the system can provide fast and accurate fault forecast in the power system.
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