Partial discharge classification on xlpe cable joints under different noise levels using artificial intelligence techniques / Wong Jee Keen Raymond

Cross linked polyethylene (XLPE) cables are widely used in power industries due to their good electrical and mechanical properties. Cable joints are the weakest point in the XLPE cables and most susceptible to insulation failures. Any cable joint insulation breakdown may cause a huge loss to power companies. Therefore, it is vital to diagnose the insulation quality to detect early signs of insulation failure. Partial discharge (PD) measurement is a vital tool for assessing the insulation quality at cable joints. Since the past, there have been many pattern recognition methods to classify PD, where each method has its own strengths and weaknesses. Although many works have been done on PD pattern recognition, it is usually performed in a noise-free environment. Also, works on PD pattern recognition are mostly done on lab fabricated insulators, where works using actual cable joints are less likely to be found in literature. Therefore, in this work, classification of real cable joint defect types using partial discharge measurement under noisy environment was performed. Five cross-linked polyethylene (XLPE) cable joints with artificially created defects were prepared based on the defects commonly encountered on site. A novel high noise tolerance principal component analysis (PCA)-based feature extraction was proposed and compared against conventional input features such as statistical features and fractal features. These input features were used to train the classifiers to classify each PD defect type. Classifications were performed using three different artificial intelligence classifiers, which include Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM). The performance of each classifier and feature extraction method was evaluated. It was found that SVM and ANN performed well while ANFIS classification accuracy was the weakest. As for input features, the proposed PCA features displayed highest noise tolerance with the least performance degradation compared to other input features

Classification of Partial Discharge Measured under Different Levels of Noise Contamination.

Cable joint insulation breakdown may cause a huge loss to power companies. Therefore, it is vital to diagnose the insulation quality to detect early signs of insulation failure. It is well known that there is a correlation between Partial discharge (PD) and the insulation quality. Although many works have been done on PD pattern recognition, it is usually performed in a noise free environment. Also, works on PD pattern recognition in actual cable joint are less likely to be found in literature. Therefore, in this work, classifications of actual cable joint defect types from partial discharge data contaminated by noise were performed. Five cross-linked polyethylene (XLPE) cable joints with artificially created defects were prepared based on the defects commonly encountered on site. Three different types of input feature were extracted from the PD pattern under artificially created noisy environment. These include statistical features, fractal features and principal component analysis (PCA) features. These input features were used to train the classifiers to classify each PD defect types. Classifications were performed using three different artificial intelligence classifiers, which include Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM). It was found that the classification accuracy decreases with higher noise level but PCA features used in SVM and ANN showed the strongest tolerance against noise contamination

Extracted PCA features.

<p>Extracted PCA features.</p

Extracted fractal features.

<p>Extracted fractal features.</p

PRPD patterns from samples of different defects; (a) Insulation incision defect (b) axial direction shift defect, (c), semiconductor layer tip defect (d) metal particle on XLPE defect and (e) semiconductor layer air gap defect.

<p>PRPD patterns from samples of different defects; (a) Insulation incision defect (b) axial direction shift defect, (c), semiconductor layer tip defect (d) metal particle on XLPE defect and (e) semiconductor layer air gap defect.</p

Training time vs feature size for PD classifiers.

<p>Training time vs feature size for PD classifiers.</p

PRPD patterns of different noise duration; (a) 15 seconds, (b) 30 seconds, (c), 45 seconds, and (d) 60 seconds.

<p>PRPD patterns of different noise duration; (a) 15 seconds, (b) 30 seconds, (c), 45 seconds, and (d) 60 seconds.</p

Noise tolerance of; (a) ANN, (b) ANFIS, and (c) SVM.

<p>Noise tolerance of; (a) ANN, (b) ANFIS, and (c) SVM.</p

Unsupervised Feature-Preserving CycleGAN for Fault Diagnosis of Rolling Bearings Using Unbalanced Infrared Thermal Imaging Sample

The fault diagnosis of rolling bearing is of great significance in industrial safety. The method of infrared thermal image combined with neural network can diagnose the fault of rolling bearing in a non-contact manner, however its data in different scenes are often unbalanced and difficult to obtain. The generative adversarial networks can solve this problem by generating data with the required features. In this paper, an unsupervised learning framework named Feature-Preserving Cycle-Consistent Generative Adversarial Networks (FP-CycleGAN) is designed for defect detection in unbalanced rolling bearing infrared thermography sample. Since the classical Cycle-Consistent Generative Adversarial Networks (CycleGAN) often must balance the weights between generation, discrimination and consistency loss when doing the feature conversion from source domain to target domain, and the process often results in pattern collapse or feature loss. To avoid this problem, a new discriminator is designed to identify whether the generated image A and B belong to two different classes, and a new class loss are proposed. In order to better extract fault features and perform features migration, the new generator is reconstructed based on the U-Network structure, the convtraspose method of the up-sampling network is replaced by Bicubic Interpolation to effectively avoid the checkerboard effect of the generated images. The defect detection of the expanded dataset was performed using Residual Network and compared with the pre-expansion data to demonstrate the usability of the generated data and the superiority of the proposed FP-CycleGAN method for rolling bearing defect detection in small sample of infrared thermal images

Classification accuracy results using noise-free PD data.

<p>Classification accuracy results using noise-free PD data.</p