A Review on the Classification of Partial Discharges in Medium-Voltage Cables : Detection, Feature Extraction, Artificial Intelligence-Based Classification, and Optimization Techniques

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.© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Artificial neural network application for partial discharge recognition: survey and future directions

In order to investigate how artificial neural networks (ANNs) have been applied for partial discharge (PD) pattern recognition, this paper reviews recent progress made on ANN development for PD classification by a literature survey. Contributions from several authors have been presented and discussed. High recognition rate has been recorded for several PD faults, but there are still many factors that hinder correct recognition of PD by the ANN, such as high-amplitude noise or wide spectral content typical from industrial environments, trial and error approaches in determining an optimum ANN, multiple PD sources acting simultaneously, lack of comprehensive and up to date databank of PD faults, and the appropriate selection of the characteristics that allow a correct recognition of the type of source which are currently being addressed by researchers. Several suggestions for improvement are proposed by the authors include: (1) determining the optimum weights in training the ANN; (2) using PD data captured over long stressing period in training the ANN; (3) ANN recognizing different PD degradation levels; (4) using the same resolution sizes of the PD patterns when training and testing the ANN with different PD dataset; (5) understanding the characteristics of multiple concurrent PD faults and effectively recognizing them; and (6) developing techniques in order to shorten the training time for the ANN as applied for PD recognition Finally, this paper critically assesses the suitability of ANNs for both online and offline PD detections outlining the advantages to the practitioners in the field. It is possible for the ANNs to determine the stage of degradation of the PD, thereby giving an indication of the seriousness of the fault

A framework for developing a prognostic model using partial discharge data from electrical trees

Insulation breakdown is a key failure mode of high voltage (HV) equipment, with progressive faults such as electrical treeing leading to potentially catastrophic failure. Electrical treeing proceeds from defects in solid insulation, and cables are particularly affected. Research has shown that diagnosis of the fault can be achieved based on partial discharge (PD) analysis. Nonetheless, after diagnosis of a defect, engineers need to know how long they have to take action. This requires prognosis of remaining insulation life. The progression of a defect is far less well understood than diagnosis, making prognosis a key challenge requiring new approaches to defect modelling. The practical deployment of prognostics for cable monitoring is not currently feasible, due to the lack of understanding of degradation mechanisms and limited data relating defect inception to plant failure. However, this thesis advances the academic state of the art, with an eye towards practical deployment in the future. The expected beneficiaries of this work are therefore researchers in the field of HV condition monitoring in general, and electrical treeing within cables in particular. This research work develops a prognostic model of insulation failure due to the electrical treeing phenomenon by utilising the associated PD data from previous experiment. Both phase-resolved and pulse sequence approaches were employed for PD features extraction. The performance of the PD features as prognostic parameters were evaluated using three metrics, monotonicity, prognosability and trendability. The analysis revealed that features from pulse sequence approach are better than phase-resolved approach in terms of monotonicity and prognosability. The key contributions to knowledge of this work are three-fold: the selection of the most appropriate prognostic parameter for PD in electrical trees, through thorough analysis of the behaviour of a number of candidate parameters; a prognostic modelling approach for this parameter based on curve-fitting; and a generalised framework for prognostic modelling using data-driven techniques.Insulation breakdown is a key failure mode of high voltage (HV) equipment, with progressive faults such as electrical treeing leading to potentially catastrophic failure. Electrical treeing proceeds from defects in solid insulation, and cables are particularly affected. Research has shown that diagnosis of the fault can be achieved based on partial discharge (PD) analysis. Nonetheless, after diagnosis of a defect, engineers need to know how long they have to take action. This requires prognosis of remaining insulation life. The progression of a defect is far less well understood than diagnosis, making prognosis a key challenge requiring new approaches to defect modelling. The practical deployment of prognostics for cable monitoring is not currently feasible, due to the lack of understanding of degradation mechanisms and limited data relating defect inception to plant failure. However, this thesis advances the academic state of the art, with an eye towards practical deployment in the future. The expected beneficiaries of this work are therefore researchers in the field of HV condition monitoring in general, and electrical treeing within cables in particular. This research work develops a prognostic model of insulation failure due to the electrical treeing phenomenon by utilising the associated PD data from previous experiment. Both phase-resolved and pulse sequence approaches were employed for PD features extraction. The performance of the PD features as prognostic parameters were evaluated using three metrics, monotonicity, prognosability and trendability. The analysis revealed that features from pulse sequence approach are better than phase-resolved approach in terms of monotonicity and prognosability. The key contributions to knowledge of this work are three-fold: the selection of the most appropriate prognostic parameter for PD in electrical trees, through thorough analysis of the behaviour of a number of candidate parameters; a prognostic modelling approach for this parameter based on curve-fitting; and a generalised framework for prognostic modelling using data-driven techniques

Unsupervised Monitoring System for Predictive Maintenance of High Voltage Apparatus

The online monitoring of a high voltage apparatus is a crucial aspect for a predictive maintenanceprogram. Partialdischarges(PDs)phenomenaaffecttheinsulationsystemofanelectrical machine and\u2014in the long term\u2014can lead to a breakdown, with a consequent, signi\ufb01cant economic loss; wind turbines provide an excellent example. Embedded solutions are therefore required to monitor the insulation status. The paper presents an online system that adopts unsupervised methodologies for assessing the condition of the monitored machine in real time. The monitoring process does not rely on any prior knowledge about the apparatus; nonetheless, the method can identify the relevant drifts in the machine status. In addition, the system is speci\ufb01cally designed to run on low-cost embedded devices

Compositional modelling of partial discharge pulse spectral characteristics

Partial discharge (PD) monitoring is an established method for insulation health monitoring in high voltage plant. A number of different approaches to PD defect diagnosis have been developed to extract defect-specific information from PD pulse data in both the time and frequency domains. Frequency based PD pulse analysis has previously been demonstrated to offer a low-power approach to PD defect identification, where a mixture of passive and active analog electronics can be used to generate diagnostic features in a low-power device suited to wireless sensor network operation. This paper examines approaches to implementing diagnostic methods for frequency-based PD pulse diagnosis targeted at compositional frequency spectrum features in a computationally efficient manner. Dirichlet and Gaussian distributions are used to demonstrate the complex probabilistic form of fault class decision surfaces, which motivates the proposed application of the log ratio transform to frequency composition data. The results demonstrate that PD defects can be differentiated using these frequency-based methods and that employing the log ratio transform to the compositional frequency content data yields increases in classification accuracy without necessarily resorting to more complex classifiers

Comparison of Algorithms for the AI-Based Fault Diagnostic of Cable Joints in MV Networks

Abstract Electrical utilities and system operators (SOs) are constantly looking for solutions to problems in the management and control of the power network. For this purpose, SOs are exploring new research fields, which might bring contributions to the power system environment. A clear example is the field of computer science, within which artificial intelligence (AI) has been developed and is being applied to many fields. In power systems, AI could support the fault prediction of cable joints. Despite the availability of many legacy methods described in the literature, fault prediction is still critical, and it needs new solutions. For this purpose, in this paper, the authors made a further step in the evaluation of machine learning methods (ML) for cable joint health assessment. Six ML algorithms have been compared and assessed on a consolidated test scenario. It simulates a distributed measurement system which collects measurements from medium-voltage (MV) cable joints. Typical metrics have been applied to compare the performance of the algorithms. The analysis is then completed considering the actual in-field conditions and the SOs’ requirements. The results demonstrate: (i) the pros and cons of each algorithm; (ii) the best-performing algorithm; (iii) the possible benefits from the implementation of ML algorithms

Effect of water on electrical properties of Refined, Bleached, and Deodorized Palm Oil (RBDPO) as electrical insulating material

This paper describes the properties of refined, bleached, deodorized palm oil (RBDPO) as having the potential to be used as insulating liquid. There are several important properties such as electrical breakdown, dielectric dissipation factor, specific gravity, flash point, viscosity and pour point of RBDPO that was measured and compared to commercial mineral oil which is largely in current use as insulating liquid in power transformers. Experimental results of the electrical properties revealed that the average breakdown voltage of the RBDPO sample, without the addition of water at room temperature, is 13.368 kV. The result also revealed that due to effect of water, the breakdown voltage is lower than that of commercial mineral oil (Hyrax). However, the flash point and the pour point of RBDPO is very high compared to mineral oil thus giving it advantageous possibility to be used safely as insulating liquid. The results showed that RBDPO is greatly influenced by water, causing the breakdown voltage to decrease and the dissipation factor to increase; this is attributable to the high amounts of dissolved water

Optimal design of electrical power distribution grid spacers using finite element method

Spacers in the compact power distribution network are essential components for the support, organization, and spacing of conductors. To improve the reliability of these components and have an optimized network design, it is necessary to evaluate the performance of the variation of their geometric parameters. The analysis of these components is fundamental, considering that there are several models available that are validated by the electric power utilities. Due to the various possible design shapes, it is necessary to use an optimized model to reduce the electric potential located in specific sites, improving the reliability in the component, as the higher electrical potential results in a greater chance of failure to occur. The finite element method (FEM) stands out for evaluating the distribution of electrical potential. In this paper, an FEM is used to evaluate variations in vertical and horizontal dimensions in spacers used in the 13.8 kV power grid. The models are analyzed in relation to their behavior regarding the potential distribution on their surface. From the results of these variations, the model is optimized by means of a mixed-integer linear problem (MILP), replacing the FEM output with a ReLU network substitute model, to obtain a spacer with more efficiency to be used in semi-insulated distribution networks.N/