Medium-voltage (MV) cables often experience a shortened lifespan attributed to insulation breakdown resulting from accelerated aging and anomalous operational and environmental stresses. While partial discharge (PD) measurements serve as valuable tools for assessing the insulation state, complexity arises from the presence of diverse discharge sources, making the evaluation of PD data challenging. The reliability of diagnostics for MV cables hinges on the precise interpretation of PD activity. To streamline the repair and maintenance of cables, it becomes crucial to discern and categorize PD types accurately. This paper presents a comprehensive review encompassing the realms of detection, feature extraction, artificial intelligence, and optimization techniques employed in the classification of PD signals/sources. Its exploration encompasses a variety of sensors utilized for PD detection, data processing methodologies for efficient feature extraction, optimization techniques dedicated to selecting optimal features, and artificial intelligence-based approaches for the classification of PD sources. This synthesized review not only serves as a valuable reference for researchers engaged in the application of methods for PD signal classification but also sheds light on potential avenues for future developments of techniques within the context of MV cables.

Partial discharge (PD) diagnostic is a crucial tool for condition monitoring of power system equipment (e.g. switchgear, cable) in the medium voltage (MV) network, which is degraded by the gradual deterioration of insulation elements, ageing, and various operational and environmental stresses. In the MV network, different types of PD faults are generated from different sources and to know the impact of an individual PD fault on the health of MV equipment, classification plays an important role. This paper aims to provide suitable techniques for classifying PD faults. The data is collected from an experimental investigation of three different types of PD faults from MV switchgear and classified using features extraction, dimensionality reduction and clustering techniques. To identify the best classification technique, dimensionality reduction techniques (principal component analysis and t-distributed stochastic neighbour embedding) are used, and their results are compared using the confusion matrix after applying k-means clustering technique.

Partial discharge (PD) diagnosis is an effective tool to track the condition of electrical insulation in the medium voltage (MV) power components. Machine Learning Algorithms (MLAs) promote automated diagnosis solutions for large scale and reliable maintenance strategy. This paper aims to investigate the performance of two MLAs: Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) for the classification of different types of PD sources. Suitable features are extracted by applying statistical parameters on the coefficients of discrete wavelet transform (DWT) for observing the performance of both MLAs. The performance of the algorithms is evaluated using key performance indicators (KPIs); accuracy, prediction speed and training time. Besides KPIs, a confusion matrix is presented to highlight the accurately classified and misclassified PD signals for the SVM algorithm. Comparative study of both algorithms demonstrates that SVM provides better results as compared to the KNN algorithm. The proposed solution can be valuable for the development of automated classification.

This study compares the performance of different artificial intelligence (AI) based algorithms/ classifiers used for partial discharge (PD) classification during insulation diagnostics in power components. During PD measurements, a considerable amount of data is collected, and processing such a huge amount of data is time-consuming and expensive. The useful PD signals can be extracted from the measurements, and AI-based algorithms can be used to process those signals for classification and diagnostics purposes. In this work, the data is collected from three different PD sources, namely, corona, internal, and surface in the high voltage laboratory. Each measurement consists of the PD activity captured in the form of power frequency cycles. The single PD pulses are extracted from the measured signals using the segmentation method. For features extraction, at first discrete wavelet transform (DWT) technique is applied on single pulses, and then statistical parameters (mean, standard deviation, skewness, and kurtosis) are applied to the extracted features. To classify different PD sources, two different classifiers, support vector machine (SVM) and k-nearest neighbors (KNN) with their types, are applied to extracted features. The performance of each classifier is evaluated using the accuracy performance indicator by varying the amount of input PD data from each PD source. The developed understanding will enable researchers/asset managers to extract the required amount of data from the field measurements.

The objective of this paper is to show the characteristics of smart meters enabling to monitor and analyze the low-voltage (LV) network. This is achieved by developing use cases, where power quality and outage data are transferred from smart meters through distribution network to the control center. To visualize the monitoring process of LV network, the use cases are mapped into smart grid architecture model. The paper proposes a solution to analyze the LV network interruption and power quality problems (over-voltage, under-voltage, voltage sags, and swells). Thus, this paper provides a smart platform for monitoring LV network.

Aging and abnormal stresses cause insulation degradation in underground cables, reducing their in-service lifetime. Partial discharge (PD) monitoring is an effective tool to monitor the insulation condition. For growing networks, monitoring solutions need more efficient diagnostics, particularly to classify PD activity by source type. One bottleneck here is feature extraction, for which many computationally expensive techniques have been proposed. This paper presents a more efficient approach, enabling real-time PD classification at high classification performance. It is applied to phase resolved PD cycles, measured on a medium voltage cable in a laboratory environment, containing either internal, corona, or surface discharge activity.