The focus of preventive maintenance (PM) programmes in industry is shifting from a pure statistical basis to online condition monitoring. Examines the shortcomings of statistical‐based PM which are contributing to this shift, and the potential benefits of and current research issues within condition‐based PM. Notes that statistics and quality control techniques will continue to play a critical role in this evolution.

Statistical‐based or condition‐based preventive maintenance?

Modern meta-heuristics based on nonlinear physics processes: A review of models and design procedures

A systematic and mathematically sound model and a refined genetic algorithm (RGA) based method for fault-section estimation in power systems is proposed. First, the probabilistic causality relationship among section fault, protective relay action and circuit breaker trip is formulated as a probabilistic causality matrix. Secondly, the well-developed parsimonious set covering theory is applied to the fault-section estimation problem, and a 0-1 integer programming model is then obtained. Thirdly, a RGA-based method for fault-section estimation is developed by using information on operations of protective relays and circuit breakers. The proposed method is versatile and can deal with uncertainties in fault-section estimation, such as protective relay failures and/or malfunction and circuit breaker failures and/or malfunction. Test results for a sample power system have shown that the probabilistic approach developed for fault-section estimation is feasible and efficient.

Probabilistic approach for fault-section estimation in power systems based on a refined genetic algorithm

A survey on intelligent system application to fault diagnosis in electric power system transmission lines

A scalable event identification system for power quality events is proposed. Unlike ANN-based approaches where the system is not scalable and not "debug-able" without retraining, the proposed approach is particularly advantageous compared to those of ANN's since it is scalable, debug-able and easily modified. This approach is adopted from artificial intelligence's rule-based approach and attempts to mimic power engineers thought process in identifying PQ events. This paper describes prerequisites in constructing such a scalable system. Examples of rules to identify power quality event are also presented. The prototype of the system is built and tested using 770 field-measured voltage waveforms which covers ten types of PQ events. The accuracy rate is nearly 95% with less than 6% of rejection rate. Potential applications of the proposed system in PQ community are also described.

A scalable PQ event identification system

Knowledge-based fault diagnosis systems are classified by the method of inference utilized in the knowledge-based systems for fault diagnosis of power systems. The characteristics of each class and corresponding issues as well as the state-of-the-art techniques for improving their performance are presented. Additional topics covered are user interfaces, interfaces with energy management systems, and expert system development tools for fault diagnosis. Results and evaluation of actual operation in the field are also discussed. >

C. Fukui

Papers

Fault diagnosis of power systems