近年來積極推動智慧製造與工業4.0的發展，增進人工智慧技術廣泛地應用於電力系統的每一環節，使得電力設備故障診斷朝向數位化、智能化與預測化之需求趨勢，憑藉採集設備運作之訊號數據，搭配人工智慧技術，便能快速且準確地篩選電力設備出現的各種故障類型，掌握電力系統運轉狀態，針對存在問題的設備得以提早發現某些徵兆，及時進行設備維修保養與零件更換，避免無預警的異常事故發生。因此，電力設備應用人工智慧技術實現故障預測與診斷，有助於提升電力系統運轉效率及供電穩定度。
為了探討人工智慧神經網路於電力設備故障診斷，本文選用電力電容器、XLPE電力電纜與風力發電機齒輪箱作為研究對象，首先分別建構三種電力設備之不同故障瑕疵模型，其次利用經驗模態分析法與離散小波轉換來濾波處理檢測故障訊號所隱含雜訊成分，然後透過混沌同步檢測法與對稱點圖像分析擷取出具有意義特徵圖像，最後則以卷積神經網路，以及本文改良卷積神經網路進而發展的卷積機率神經網路與卷積可拓神經網路進行特徵圖像訓練與辨識。
經由實測結果得知，本文提出的三種神經網路辨識方法均能夠檢測故障訊號變化，且準確地診斷出電力設備故障類型，其中電力電容器故障診斷採用卷積可拓神經網路與卷積機率神經網路辨識方法之總辨識準確率皆高達98.33%，在雜訊容錯能力總辨識準確率可達95%以上，XLPE電力電纜與風力發電機齒輪箱故障診斷採用卷積機率神經網路辨識方法之總辨識準確率分別可達97.14%與98.89%，在雜訊容錯能力總辨識準確率分別可達96%與97.63%以上，證實本文所創建卷積機率神經網路與卷積可拓神經網路之辨識準確率與抗雜訊容錯能力相較於卷積神經網路更加出色表現，未來亦可延伸運用於其他電力設備故障診斷與朝向硬體電路實現電力設備故障診斷分析儀表。

In recent years, there has been an active promotion of smart manufacturing and Industry 4.0, enhancing the widespread application of artificial intelligence technology in every aspect of the power system. This has led to a growing demand for digitization, intelligence, and predictability in the field of power apparatus fault diagnosis. By collecting operational data from apparatus and leveraging artificial intelligence technology, it becomes possible to rapidly and accurately identify various types of faults in power apparatus. This allows for better monitoring of the power system's operational status and early detection of signs of issues in specific apparatus. Timely maintenance and part replacement can then be carried out to prevent unexpected incidents. Therefore, the application of artificial intelligence technology in power apparatus for fault prediction and diagnosis contributes to improving the efficiency of power system operations and the stability of power supply.
To explore the application of artificial intelligence neural networks in the diagnosis of power apparatus faults, this paper selects power capacitors, XLPE power cables, and wind turbine gearbox gears as the research subjects. Firstly, distinct fault models for these three types of power apparatus are constructed. Subsequently, the empirical mode decomposition (EMD) analysis and discrete wavelet transform (DWT) are used to filter and process the fault signals, removing hidden noise. Then, meaningful feature images are extracted using chaotic synchronization detection and symmetrized dot pattern (SDP). Finally, convolutional neural network (CNN), along with the convolutional probabilistic neural network (CPNN) and convolutional extension neural network (CENN) developed in this paper as enhancements to the original convolutional neural network, are employed for the training and recognition of feature images.
Based on the experimental results, it is revealed that the three neural network recognition methods proposed in this paper can effectively detect variations in fault signals and accurately diagnose the types of faults in power apparatus. Specifically, for the fault diagnosis of power capacitor, the recognition methods using CENN and CPNN achieve a remarkable overall recognition accuracy of 98.33%, with a fault tolerance accuracy exceeding 95%. For the fault diagnosis of XLPE power cable and wind turbine gearbox, the recognition method using CPNN achieves overall recognition accuracies of 97.14% and 98.89%, respectively, with fault tolerance accuracies exceeding 96% and 97.63%.
These findings validate that the recognition accuracy and noise tolerance of the convolutional probabilistic neural network and convolutional extension neural network created in this paper outperform those of the traditional convolutional neural network. In the future, these models can be further applied to the diagnosis of faults in other power apparatus and extended towards the implementation of hardware circuits for power apparatus fault diagnosis analysis instruments.

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