Issues in integrating existing multi-agent systems for power engineering applications

Multi-agent systems (MAS) have proven to be an effective platform for diagnostic and condition monitoring applications in the power industry. For example, a multi-agent system architecture, entitled condition monitoring multi-agent system (COMMAS) (McArthur et al., 2004), has been applied to the ultra high frequency (UHF) monitoring of partial discharge activity inside transformers. Additionally, a multi-agent system, entitled protection engineering diagnostic agents (PEDA) (Hossack et al., 2003), has demonstrated the use of MAS technology for automated and enhanced post-fault analysis of power systems disturbances based on SCADA and digital fault recorder (DFR) data. In this paper, the authors propose the integration of COMMAS and PEDA as a means of offering enhanced decision support to engineers tasked with managing transformer assets. By providing automatically interpreted data related to condition monitoring and power system disturbances, the proposed integrated system offer engineers a more comprehensive picture of the health of a given transformer. Defects and deterioration in performance can be correlated with the operating conditions it experiences. The integration of COMMAS and PEDA has highlighted the issues inherent to the inter-operation of existing multi-agent systems and, in particular, the issues surrounding the use of differing ontologies. The authors believe that these issues need to be addressed if there is to be widespread deployment of MAS technology within the power industry. This paper presents research undertaken to integrate the two MAS and to deal with ontology issues

A frequency-based RF partial discharge detector for low-power wireless sensing

Partial discharge (PD) monitoring has been the subject of significant research in recent years, which has given rise to a range of well-established PD detection and measurement techniques, such as acoustic and RF, on which condition monitoring systems for highvoltage equipment have been based. This paper presents a novel approach to partial discharge monitoring by using a low-cost, low-power RF detector. The detector employs a frequency-based technique that can distinguish between multiple partial discharge events and other impulsive noise sources within a substation, tracking defect severity over time and providing information pertaining to plant health. The detector is designed to operate as part of a wireless condition monitoring network, removing the need for additional wiring to be installed into substations whilst still gaining the benefits of the RF technique. This novel approach to PD detection not only provides a low-cost solution to on-line partial discharge monitoring, but also presents a means to deploy wide-scale RF monitoring without the associated costs of wide-band monitoring systems

Partial Discharge Location within a Transformer Winding using Principal Component Analysis

Partial discharge (PD) may occur in a transformer winding due to ageing processes, operational over stressing or defects introduced during manufacture. The presence of PD does not necessarily indicate imminent failure of the transformer but it will lead to serious degradation and ageing mechanisms which can be considered as a precursor of transformer failure. A necessary step is required in order to prevent degradation due to PD activity which may ultimately lead to failure. PD might occur anywhere along the transformer winding, the discharge signal can propagate along the winding to the bushing and neutral to earth connections. As far as maintenance and replacement processes are concerned, it is important to identify the location of PD activity so any repair or replace decision is assured to be cost effective. Therefore, identification of a PD source as well as its location along the transformer winding is of great interest to both manufacturers and system operators. The proposed method for locating PD sources in windings is based on wavelet filtering and principal component analysis. An experiment has been developed based on a high voltage winding section that has been used to produce PD measurement data and to investigate the feasibility of the proposed approach

Development of an integrated low-power RF partial discharge detector

This paper presents the results from integrating a low-power partial discharge detector with a wireless sensor node designed for operating as part of an IEEE 802.15.4 sensor network, and applying an on-line classifier capable of classifying partial discharges in real-time. Such a system is of benefit to monitoring engineers as it provides a means to exploit the RF technique using a low-cost device while circumventing the need for any additional cabling associated with new condition monitoring systems. The detector uses a frequency-based technique to differentiate between multiple defects, and has been integrated with a SunSPOT wireless sensor node hosting an agent-based monitoring platform, which includes a data capture agent and rule induction agent trained using experimental data. The results of laboratory system verification are discussed, and the requirements for a fully robust and flexible system are outlined

Crack detection in a rotating shaft using artificial neural networks and PSD characterisation

Peer reviewedPostprin

Classification of EMI discharge sources using time–frequency features and multi-class support vector machine

This paper introduces the first application of feature extraction and machine learning to Electromagnetic Interference (EMI) signals for discharge sources classification in high voltage power generating plants. This work presents an investigation on signals that represent different discharge sources, which are measured using EMI techniques from operating electrical machines within power plant. The analysis involves Time-Frequency image calculation of EMI signals using General Linear Chirplet Analysis (GLCT) which reveals both time and frequency varying characteristics. Histograms of uniform Local Binary Patterns (LBP) are implemented as a feature reduction and extraction technique for the classification of discharge sources using Multi-Class Support Vector Machine (MCSVM). The novelty that this paper introduces is the combination of GLCT and LBP applications to develop a new feature extraction algorithm applied to EMI signals classification. The proposed algorithm is demonstrated to be successful with excellent classification accuracy being achieved. For the first time, this work transfers expert's knowledge on EMI faults to an intelligent system which could potentially be exploited to develop an automatic condition monitoring system

Deep residual neural network for EMI event classification using bispectrum representation

This paper presents a novel method for condition monitoring of High Voltage (HV) power plant equipment through analysis of discharge signals. These discharge signals are measured using the Electromagnetic Interference (EMI) method and processed using third order Higher-Order Statistics (HOS) to obtain a Bispectrum representation. By mapping the time-domain signal to a Bispectrum image representations the problem can be approached as an image classification task. This allows for the novel application of a Deep Residual Neural Network (ResNet) to the classification of HV discharge signals. The network is trained on signals into 9 classes and achieves high classification accuracy in each category, improving upon our previous work on this task

A Literature Review of Fault Diagnosis Based on Ensemble Learning

The accuracy of fault diagnosis is an important indicator to ensure the reliability of key equipment systems. Ensemble learning integrates different weak learning methods to obtain stronger learning and has achieved remarkable results in the field of fault diagnosis. This paper reviews the recent research on ensemble learning from both technical and field application perspectives. The paper summarizes 87 journals in recent web of science and other academic resources, with a total of 209 papers. It summarizes 78 different ensemble learning based fault diagnosis methods, involving 18 public datasets and more than 20 different equipment systems. In detail, the paper summarizes the accuracy rates, fault classification types, fault datasets, used data signals, learners (traditional machine learning or deep learning-based learners), ensemble learning methods (bagging, boosting, stacking and other ensemble models) of these fault diagnosis models. The paper uses accuracy of fault diagnosis as the main evaluation metrics supplemented by generalization and imbalanced data processing ability to evaluate the performance of those ensemble learning methods. The discussion and evaluation of these methods lead to valuable research references in identifying and developing appropriate intelligent fault diagnosis models for various equipment. This paper also discusses and explores the technical challenges, lessons learned from the review and future development directions in the field of ensemble learning based fault diagnosis and intelligent maintenance

Failure analysis informing intelligent asset management

With increasing demands on the UK’s power grid it has become increasingly important to reform the methods of asset management used to maintain it. The science of Prognostics and Health Management (PHM) presents interesting possibilities by allowing the online diagnosis of faults in a component and the dynamic trending of its remaining useful life (RUL). Before a PHM system can be developed an extensive failure analysis must be conducted on the asset in question to determine the mechanisms of failure and their associated data precursors that precede them. In order to gain experience in the development of prognostic systems we have conducted a study of commercial power relays, using a data capture regime that revealed precursors to relay failure. We were able to determine important failure precursors for both stuck open failures caused by contact erosion and stuck closed failures caused by material transfer and are in a position to develop a more detailed prognostic system from this base. This research when expanded and applied to a system such as the power grid, presents an opportunity for more efficient asset management when compared to maintenance based upon time to replacement or purely on condition