Fault Section Estimation In Power Systems Research Articles

Implementing Fuzzy Reasoning Petri-Nets for Fault Section Estimation

Fuzzy reasoning Petri-nets (PNs) is a promising technique to tackle the complexities of power system fault section estimation. This paper addresses several key issues in implementing fuzzy reasoning PNs for fault section estimation, which include optimal design of structure of diagnosis models to avoid large matrix size, utilization of fuzzy logic parameters to effectively handle uncertainties, realization of matrix execution algorithm to achieve parallel reasoning and adaptability, and integration of more reliable input data to enhance estimation accuracy. Case studies are presented to demonstrate the estimation capability under complex scenarios. An implementation solution residing in a control center is proposed.

Fault section estimation in power systems using a novel decision support system

This paper presents a novel decision support system (DSS) using circuit breaker information for online fault section estimation in power systems. The new representation of knowledge and approach to resolving fault diagnosis problems are included in the proposed system. The DSS automatically creates rules for knowledge representation and develops an efficient diagnosis procedure. After the fault section is estimated, the logical reasoning approach uses the relay information to further validate the candidate fault section and circuit breaker status. Test results verify that the DSS can obtain rapid and accurate diagnosis results with flexibility and portability for fault diagnosis of diverse power systems. Those results further demonstrate the feasibility of applying the proposed DSS to actual power system fault section estimation.

Fault Section Estimation in Power Systems Using a Novel Decision Support System

This paper presents a novel decision support system (DSS) using circuit breaker information for online fault section estimation in power systems. The new representation of knowledge and approach to resolving fault diagnosis problems are included in the proposed system. The DSS automatically creates rules for knowledge representation and develops an efficient diagnosis procedure. After the fault section is estimated, the logical reasoning approach uses the relay information to further validate the candidate fault section and circuit breaker status. Test results verify that the DSS can obtain rapid and accurate diagnosis results with flexibility and portability for fault diagnosis of diverse power systems. Those results further demonstrate the feasibility of applying the proposed DSS to actual power system fault section estimation.

Efficient multiway graph partitioning method for fault section estimation in large-scale power networks

Fault section estimation (FSE) of large-scale power networks can be implemented effectively by the distributed artificial intelligence (AI) technique. In this paper, an efficient multiway graph partitioning method is proposed to partition the large-scale power networks into the desired number of connected subnetworks with balanced working burdens in performing FSE. The number of elements at the frontier of each subnetwork is also minimised in the method. The suggested method consists of three basic steps: forming the weighted depth-first-search tree of the studied power network; partitioning the network into connected, balanced subnetworks and minimising the number of the frontier nodes of the subnetworks through iterations so as to reduce the interaction of FSE in adjacent subnetworks. The relevant mathematical model and partitioning procedure are presented. The method has been implemented with the sparse storage technique and tested in the IEEE 14-bus, 30-bus and 118-bus systems, respectively. Computer simulation results show that the proposed multiway graph partitioning method is effective for the large-scale power system FSE using the distributed AI technique.

Comparison between evolutionary programming and a genetic algorithm for fault-section estimation

The paper proposes an application of evolutionary programming (EP) to fault-section estimation in power systems. Several techniques have been employed to solve this problem so far. A genetic algorithm (GA) has been reported to be one of these techniques. In order to measure the efficiency of EP and make comparisons, a GA has also been used to solve the same problem. Different parameters which affect the EP convergence have been investigated. Two object-oriented software codes have been developed to implement the algorithms. A sample power system is used to examine the algorithms. It shows that EP is superior to the GA for the type of coding strategy and evolution as defined for the GA.

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

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.

A new neural networks approach to on-line fault section estimation using information of protective relays and circuit breakers

This paper proposes a new neural network diagnostic system for online power system fault section estimation using information of relays and circuit breakers. This system has a similar profile of an expert system, but can be constructed much more easily from elemental samples. These samples associate fault section with its primary, local and/or remote protective relays and breakers. The diagnostic system can be applicable to the power system control center for single or multiple fault sections estimation, even in the cases of failure operation of relays and breakers, or error-existent data transmission. The proposed approach has been practically verified by testing on a model power system. The test results, although preliminary, suggest this system can be implemented by various electric utilities with relatively low customization effort. >

Expert system for fault section estimation of power systems using time-sequence information

Rapid and correct fault section estimation is vital for power system restoration. This is particularly true for complicated multiple faults. This paper presents an expert system for power system fault section estimation using information including operating times of actuated relays and tripped circuit breakers. The inference is executed by transmitting timed-event markers in the cause-effect network proposed in an earlier paper. Since the proposed system can handle information on the operations of relays and circuit breakers with the aid of time sequence information, it can infer fault sections correctly even for complicated multiple faults. In addition, since the number of generated fault section candidates can be decreased, the inference speed is improved significantly. The proposed system has been tested for two examples. It has been shown that the inference speed and accuracy of the method proposed in this paper are much better than those of the one proposed earlier which does not use time information. The inference speed is also fast enough for real-time use.