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

Fault Identification of In-Service Power Transformer using Depolarization Current Analysis

Preventive diagnostic testing of in-service power transformers require system outage and expert’s knowledge and experiences in interpreting the measurement results. The chemical oil analysis may cause significant variance to measurement results due to the different practices in oil sampling, storage, handling and transportation. Thus, a cost effective measuring technique by means of a simpler method that is able provide an accurate measurement results is highly required. The extended application of Polarization and Depolarization Current (PDC) measurement for characterization of different faults conditions on in-service power transformer has been presented in this paper. The oil sample from in-service power transformers with normal and 3 different faults type conditions were sampled and tested for Dissolved Gases Analysis (DGA) and PDC measurement. The DGA results was used to confirm type of faults inside the transformer while the PDC pattern of oil with normal, partial discharge, overheating and arcing were correlated to the oil sample conditions. The analysis result shows that depolarization current provides significant information to defferenciate fault types in power transformer. Thus this finding provides a new alternative in identifying incipient faults and such knowledge can be used to avoid catastrophic failures of power transformers

Comparison of the performance of artificial neural networks and fuzzy logic for recognizing different partial discharge sources

This paper compared the capabilities of the artificial neural network (ANN) and the fuzzy logic (FL) approaches for recognizing and discriminating partial discharge (PD) fault classes. The training and testing parameters for the ANN and FL comprise statistical fingerprints from different phase-amplitude-number (phi-q-n) measurements. Two PD fault classes considered are internal discharges in voids and surface discharges. In the void class, there are single voids, serial voids and parallel voids in polyethylene terephthalate (PET), while the surface discharge class comprises four different surface discharge arrangements on pressboard in oil at different voltages and angular positioning of the ground electrode on the respective pressboards. Previously, the ANN and FL have been investigated for PD classification, but there is no work reported in the literature that compares their performance, specifically when applied for real time PD detection problem. As expected, both the ANN and FL can recognize PD defect classes, but the results show that the ANN appears to be more robust as compared to the FL, but these conclusions required to be further investigated with complex PD examples. Finally, both the ANN and FL were assessed as practical PD classification. Despite of the limitations of the ANN, it is concluded that the ANN is better suited for practical PD recognition because of its ability to provide accurate recognition values and the severity level of PD defects

Power transformer faults diagnosis using undestructive methods (Roger and IEC) and artificial neural network for dissolved gas analysis applied on the functional transformer in the Algerian north-eastern: a comparative study

Introduction. Nowadays, power transformer aging and failures are viewed with great attention in power transmission industry. Dissolved gas analysis (DGA) is classified among the biggest widely used methods used within the context of asset management policy to detect the incipient faults in their earlier stage in power transformers. Up to now, several procedures have been employed for the lecture of DGA results. Among these useful means, we find Key Gases, Rogers Ratios, IEC Ratios, the historical technique less used today Doernenburg Ratios, the two types of Duval Pentagons methods, several versions of the Duval Triangles method and Logarithmic Nomograph. Problem. DGA data extracted from different units in service served to verify the ability and reliability of these methods in assessing the state of health of the power transformer. Aim. An improving the quality of diagnostics of electrical power transformer by artificial neural network tools based on two conventional methods in the case of a functional power transformer at Sétif province in East North of Algeria. Methodology. Design an inelegant tool for power transformer diagnosis using neural networks based on traditional methods IEC and Rogers, which allows to early detection faults, to increase the reliability, of the entire electrical energy system from transport to consumers and improve a continuity and quality of service. Results. The solution of the problem was carried out by using feed-forward back-propagation neural networks implemented in MATLAB-Simulink environment. Four real power transformers working under different environment and climate conditions such as: desert, humid, cold were taken into account. The practical results of the diagnosis of these power transformers by the DGA are presented. Practical value. The structure and specific features of power transformer winding insulation ageing and defect state diagnosis by the application of the artificial neural network (ANN) has been briefly given. MATLAB programs were then developed to automate the evaluation of each method. This paper presents another tool to review the results obtained by the delta X software widely used by the electricity company in Algeria.Вступ. У наш час старіння та несправності силових трансформаторів уважно розглядаються у галузі передачі електричної енергії. Аналіз розчиненого газу  виділяється серед найбільш широко використовуваних методів, що застосовуються в контексті політики управління активами для виявлення початкових несправностей на їх попередній стадії в силових трансформаторах. Дотепер для отримання результатів аналізу розчиненого газу було використано кілька процедур. Серед цих корисних засобів зазначимо такі, як метод основних газів, коефіцієнти Роджерса, коефіцієнти МЕК, історичний підхід, менш використовувані сьогодні коефіцієнти Дерненбурга, два типи методів п’ятикутників Дюваля, кілька варіантів методу трикутників Дюваля та логарифмічний номограф. Проблема. Дані аналізу розчиненого газу, отримані з різних об‘єктів, що експлуатуються, слугували для перевірки здатності та надійності цих методів при оцінці стану працездатності силового трансформатора. Мета. Підвищення якості діагностики електричного силового трансформатора за допомогою штучних нейромережевих інструментів, заснованих на двох звичайних методах, у випадку функціонуючого силового трансформатора в провінції Сетіф на північному сході Алжиру. Методологія. Розробка нетипового засобу для діагностики силових трансформаторів з використанням нейронних мереж на основі традиційних методів МЕК і Роджерса, який дозволяє раннє виявлення несправностей, підвищення надійності всієї електроенергетичної системи від передачі енергії до споживачів та покращення безперервності та якості обслуговування. Результати. Розв‘язання проблеми було здійснено за допомогою нейронних мереж зворотного розповсюдження із зворотним зв'язком, реалізованих в середовищі MATLAB-Simulink. Були враховані чотири діючі силові трансформатори, що працюють в різних умовах оточуючого середовища та клімату, таких як: пустеля, волога, холод. Представлені практичні результати діагностики цих силових трансформаторів з використанням аналізу розчиненого газу. Практичне значення. Стисло наведено структуру та специфічні особливості старіння ізоляції обмоток силових трансформаторів та діагностики стану дефектів за допомогою штучної нейронної мережі. Далі були розроблені програми у MATLAB для автоматизації оцінки кожного методу. Ця стаття представляє ще один засіб для аналізу результатів, отриманих за допомогою програмного забезпечення delta X, що широко використовується електричною компанією в Алжирі

Intelligent diagnosis of defects responsible for partial discharge activity detected in power transformers

This paper describes the application of cluster analysis and classification techniques for the diagnosis of partial discharge defects present in electrical power transformers. The subsequent implementation of an agent-based, decision support system (DSS) incorporating these intelligent techniques is also discussed. Successful defect classification of empirical partial discharge data, using neural networks and rule induction, affirms the application of these techniques as a suitable means of providing reliable decision support for partial discharge defect diagnosis, particularly where expert diagnostic knowledge may be scarce or ambiguous. Through the interaction of intelligent agents the DSS considers the effectiveness and diagnostic contribution of each agent (intelligent technique) before presenting a consolidated diagnosis

A brief review of condition monitoring practices of induction motor

Since the advancement in technology many techniques have been developed to reduce the time needed for correction of faults in an induction motor as a single motor failure in an industrial scenario can lead to breakdown of a whole chain of process. In this regard technologies are needed which can online monitor the faults at an incipient stage thus reducing three downtime required for maintenances and repairs. Analysis of line current for fault detection and other signals such as vibration, power, axial leakage flux and partial discharge have been extensively explored. An effective condition monitoring algorithm using different techniques like Fourier transform, wavelet transform etc. along with the choice of the right parameter to diagnose the various faults in induction motors has been one of the key research area in this field. So keeping in mind the vast scope of this area of research, a review paper compiling the various literatures regarding the different fault monitoring machine techniques and the viable machine parameters used till date is presented. &nbsp