This paper presents a state-of-the-art review on machine learning (ML) based intelligent diagnostics that have been applied for partial discharge (PD) detection, localization, and pattern recognition. ML techniques, particularly those developed in the last five years, are examined and classified as conventional ML or deep learning (DL). Important features of each method, such as types of input signal, sampling rate, core methodology, and accuracy, are summarized and compared in detail. Advantages and disadvantages of different ML algorithms are discussed. Moreover, technical roadblocks preventing intelligent PD diagnostics from being applied to industry are identified, such as insufficient/imbalanced dataset, data inconsistency, and difficulties in cost-effective real-time deployment. Finally, potential solutions are proposed, and future research directions are suggested.

Condition Monitoring Based on Partial Discharge Diagnostics Using Machine Learning Methods: A Comprehensive State-of-the-Art Review

Partial discharge (PD) measurement and identification have great importance to condition monitoring of power transformers. In this paper, a new method for recognition of single and multi-source of PD based on extraction of high level image features has been introduced. A database, involving 365 samples of phase-resolved PD (PRPD) data, is developed by measurement carried out on transformer artificial defect models (having different sizes of defect) under a specific applied voltage, to be used for proposed algorithm validation. In the first step, each set of PRPD data is converted into grayscale images to represent different PD defects. Two “image feature extraction” methods, the Local Binary Pattern (LBP), and the Histogram of Oriented Gradient (HOG), are employed to extract features from the obtained gray scale images. Different variants of Support Vector Machine (SVM) are adjusted for optimal classification of PD sources in this process. Impact of the employed parameters in the image processing such as image resolution, random noise, and phase shift, on identification accuracy is investigated and addressed. It is shown that by using HOG-SVM method 99.3% accuracy can be achieved. This is hardly affected by various external factors. Two case studies have been conducted on multi-source PD for evaluating the performance of the proposed algorithm. A void defect is implemented into the transformer model and the resultant recorded signal is used for the study. The DBSCAN algorithm is used as the mean of PD source clustering and sub-PRPD pattern development. It is shown that HOG-SVM method has superior performance in identifying active sources, under sub-PRPD pattern application.

Partial Discharges Pattern Recognition of Transformer Defect Model by LBP & HOG Features

Power utilities are struggling to reduce power failure incidents in substations and their components to operate more reliably and economically [1]. Many power failures are produced directly or indirectly because of the insulation system of utility components [2], [3]. The selection of the insulation should ensure power plant operational continuity along with completely resolving or significantly limiting the actual power system's failures [4]. Gas insulated substations (GIS) have the best insulation performance which ensures achieving minimum failure incidents, although at high installation cost. The most common insulating gas used in GIS is Sulfur hexafluoride (SF 6 ) gas, which is widely used as an effective electrical insulation as well as an arc-quenching medium [5]. Basic GIS and gas insulated transmission lines (GITL or GIL) consist of a conductor supported by solid insulators inside an enclosure filled with SF 6 gas or its mixture [6].

Partial discharge detection and diagnosis in gas insulated switchgear: State of the art

Vibration signal of wind turbine has the non-linear and non-stationary characteristic, thus it is difficult to extract the fault feature In this study, a novel method based on variational mode decomposition (VMD) and Teager energy operator (TEO) is proposed to diagnose the bearing faults of wind turbine First, vibration signal is decomposed into several intrinsic mode function (IMF) components by means of VMD, which is a recently proposed signal decomposition method Then, the most sensitive IMF component is selected according to kurtosis criterion Moreover, TEO is applied to the most sensitive IMF in order to highlight impact signal Finally, spectrum is obtained by applying Fourier transform to Teager energy of the selected IMF, thus extracting the fault feature to diagnose bearing fault The effectiveness of the proposed method for fault diagnosis is validated by simulation and experimental signal analysis results, and comparison studies show its advantage over empirical mode decomposition and conventional spectrum analysis for wind turbine bearing fault diagnosis

Fault diagnosis of wind turbine bearing based on variational mode decomposition and Teager energy operator

This paper introduces a new application of deep belief network (DBN) as an emerging artificial neural network for recognizing and classifying different partial discharge (PD) patterns. Phase resolved PD (PRPD) technique with different window intervals is used to manipulate three PD types, including corona, surface, and internal discharges measured in a high-voltage lab. Four approaches are proposed for extracting features from the raw measured data. In the first approach, the DBN is used as both a feature extractor and a PD classifier. The other three approaches extract discriminatory features using statistical and vector-norm-based operators to train a DBN classifier. The impact of the various phase windows through the PRPD method on the performance of the trained classifier is evaluated to obtain the best window interval. It is shown that the deep architectures are capable of learning important distinguishable features from PD data without any data preprocessing. This eliminates time-consuming feature extraction processes that produce the handcrafted features. Based on a comparison analysis, when the input data are corrupted by noise levels or no feature extraction technique is used to preprocess the data, the proposed approach outperforms other techniques, such as artificial neural networks, fuzzy logic classifiers, and support vector machines.

A Novel Application of Deep Belief Networks in Learning Partial Discharge Patterns for Classifying Corona, Surface, and Internal Discharges

Partial discharge classifications: Review of recent progress

Cable joints are the weakest point in cross-linked polyethylene (XLPE) cables and are susceptible to insulation failures. Partial discharge (PD) analysis is a vital tool for assessing the insulation quality in cable joints. Although many works have been done on PD pattern classification, it is usually performed in a noise-free environment. Also, works on PD pattern classification are mostly done on lab fabricated insulators, where works on actual cable joint defects are less likely to be found in literature. Therefore, in this work, classifications of cable joint defect types from partial discharge measurement under noisy environment were performed. Five XLPE cable joints with artificially created defects were prepared based on the defects commonly encountered on-site. A high noise tolerance principal component analysis (PCA)-based feature extraction was proposed and compared against conventional input features such as statistical and fractal features. These input features were used to train the classifiers to classify each defect type in the cable joint samples. Classifications were performed using Artificial Neural Networks (ANN) and Support Vector Machine (SVM). It was found that the proposed PCA features displayed the highest noise tolerance with the least performance degradation compared to other input features under noisy environment.

High noise tolerance feature extraction for partial discharge classification in XLPE cable joints

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.

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

Complex permittivity measurement using capacitance method from 300 kHz to 50 MHz

In this paper, a pneumonia diagnosis system was developed using convolutional neural network (CNN) based feature extraction. InceptionV3 CNN was used to perform feature extraction from chest X-ray images. The extracted feature was used to train three classification algorithm models to predict the cases of pneumonia from a Kaggle dataset. The three models are K-Nearest Neighbor, Neural Network, and Support Vector Machines. Performance evaluation and confusion matrix were presented to represent the sensitivity, accuracy, precision, and specificity of each of the models. Results show that the Neural Network model achieved the highest sensitivity of 84.1%, followed by Support vector machines (83.5%) and K-Nearest Neighbor Algorithm (83.3%). Among all the classification models, Support vector machines model achieved the highest AUC of 93.1%.

Wong Jee Keen Raymond

Papers

Partial discharge classifications: Review of recent progress

High noise tolerance feature extraction for partial discharge classification in XLPE cable joints

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

Complex permittivity measurement using capacitance method from 300 kHz to 50 MHz

Pneumonia Diagnosis Using Chest X-ray Images and Machine Learning