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

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

Fuzzy Logic

Fuzzy Logic is becoming an essential method of solving problems in all domains. It gives tremendous impact on the design of autonomous intelligent systems. The purpose of this book is to introduce Hybrid Algorithms, Techniques, and Implementations of Fuzzy Logic. The book consists of thirteen chapters highlighting models and principles of fuzzy logic and issues on its techniques and implementations. The intended readers of this book are engineers, researchers, and graduate students interested in fuzzy logic systems

Advanced fault diagnosis techniques and their role in preventing cascading blackouts

This dissertation studied new transmission line fault diagnosis approaches using new technologies and proposed a scheme to apply those techniques in preventing and mitigating cascading blackouts. The new fault diagnosis approaches are based on two time-domain techniques: neural network based, and synchronized sampling based. For a neural network based fault diagnosis approach, a specially designed fuzzy Adaptive Resonance Theory (ART) neural network algorithm was used. Several ap- plication issues were solved by coordinating multiple neural networks and improving the feature extraction method. A new boundary protection scheme was designed by using a wavelet transform and fuzzy ART neural network. By extracting the fault gen- erated high frequency signal, the new scheme can solve the difficulty of the traditional method to differentiate the internal faults from the external using one end transmis- sion line data only. The fault diagnosis based on synchronized sampling utilizes the Global Positioning System of satellites to synchronize data samples from the two ends of the transmission line. The effort has been made to extend the fault location scheme to a complete fault detection, classification and location scheme. Without an extra data requirement, the new approach enhances the functions of fault diagnosis and improves the performance. Two fault diagnosis techniques using neural network and synchronized sampling are combined as an integrated real time fault analysis tool to be used as a reference of traditional protective relay. They work with an event analysis tool based on event tree analysis (ETA) in a proposed local relay monitoring tool. An interactive monitoring and control scheme for preventing and mitigating cascading blackouts is proposed. The local relay monitoring tool was coordinated with the system-wide monitoring and control tool to enable a better understanding of the system disturbances. Case studies were presented to demonstrate the proposed scheme. An improved simulation software using MATLAB and EMTP/ATP was devel- oped to study the proposed fault diagnosis techniques. Comprehensive performance studies were implemented and the test results validated the enhanced performance of the proposed approaches over the traditional fault diagnosis performed by the transmission line distance relay

Accurate Fault Classifier and Locator for EHV Transmission Lines Based on Artificial Neural Networks

The ability to identify the fault type and to locate the fault in extra high voltage transmission lines is very important for the economic operation of modern power systems. Accurate algorithms for fault classification and location based on artificial neural network are suggested in this paper. Two fault classification algorithms are presented; the first one uses the single ANN approach and the second one uses the modular ANN approach. A comparative study of two classifiers is done in order to choose which ANN fault classifier structure leads to the best performance. Design and implementation of modular ANN-based fault locator are presented. Three fault locators are proposed and a comparative study of the three fault locators is carried out in order to determine which fault locator architecture leads to the accurate fault location. Instantaneous current and/or voltage samples were used as inputs to ANNs. For fault classification, only the pre-fault and post-fault samples of three-phase currents were used. For fault location, pre-fault and post-fault samples of three-phase currents and/or voltages were used. The proposed algorithms were evaluated under different fault scenarios. Studied simulation results which are presented confirm the effectiveness of the proposed algorithms

Condition monitoring of hydraulic systems using neural network for data analysis

Condition monitoring of engineering processes or equipment has become of paramount importance as there is a growing need for improved performance, reliability, safety and more efficient maintenance. Condition monitoring in railway industry as a whole covers a very wide field. To restrict the field we have confined ourselves to the non-intrusive monitoring of hydraulic systems. This thesis is mainly concerned with the investigation of the non-intrusive method based on ultrasonic concepts and neural networks for rapid condition monitoring and/or fault diagnosis of the hydraulic systems.A comparison between diagnosing hydraulic systems and electric systems is made. The location of faults in hydraulic systems is more difficult. The key to fault finding in hydraulic systems is the location of pressure. The development of pressure measurement instruments is reviewed. In case of trouble-shooting hydraulic systems, pressure readings are often required to be taken at several temporary locations. Since the hydraulic system is fully sealed, the direct measurement instruments can not be practically utilised for this purpose unless they are built-in during the production stage of the system. Instead, the indirect pressure measurement systems can be very helpful for rapid diagnosis of hydraulic systems. The new approach is a combination of the acoustic effect of the fully sealed oil inside the pipe and the penetrating capability of the ultrasonic waves. The ultrasonic wave energy enters the interior of the hydraulic piping and passes through the contained fluid, of which the pressure is being measured.Two modelling approaches for this non-intrusive pressure monitoring system have been presented based on FLNN and MLP respectively. They offer the ability to establish the direct and inverse models. For both methods the maximum relative error (%FS) achieved for either the direct model or the inverse model is well within 2 %FS in our case studies. However, compared to the MLP, the FLNN provides a reduced cost of computational complexity.The novel non-intrusive measurement of hydraulic pressure based on ultrasonic concepts offers the capability of making pressure measurements for trouble-shooting without intruding into the pipe. It is specifically designed for rapid diagnosis of hydraulic equipment, where the conventional measurement instruments fail to make the necessary pressure readings within the sealed pipes. This has the advantage of not having an effect on the condition of the sealed hydraulic system and also of assisting rapid trouble-shooting to save time and cost. Testing the pipes with such a non-intrusive technique is of great interest to all metal pipe related industries for the provision of no disruption to pipe operations

Protection of Future Electricity Systems

The electrical energy industry is undergoing dramatic changes: massive deployment of renewables, increasing share of DC networks at transmission and distribution levels, and at the same time, a continuing reduction in conventional synchronous generation, all contribute to a situation where a variety of technical and economic challenges emerge. As the society’s reliance on electrical power continues to increase as a result of international decarbonisation commitments, the need for secure and uninterrupted delivery of electrical energy to all customers has never been greater. Power system protection plays an important enabling role in future decarbonized energy systems. This book includes ten papers covering a wide range of topics related to protection system problems and solutions, such as adaptive protection, protection of HVDC and LVDC systems, unconventional or enhanced protection methods, protection of superconducting transmission cables, and high voltage lightning protection. This volume has been edited by Adam Dyśko, Senior Lecturer at the University of Strathclyde, UK, and Dimitrios Tzelepis, Research Fellow at the University of Strathclyde