A new approach to fault section estimation in power systems using Ant system

In this paper, a new method to fault section estimation problem in power systems is developed using Ant System. Based on the information from the operated protective relays and tripped circuit breakers, the fault section estimation is first formulated as a combinatorial optimization problem. Then, a new approach known as the Ant System is applied to solve this optimization problem. The Ant System is suitable for solving problems of combinatorial optimization by means of agents that work in parallel, among a population and without a supervisor in a cooperative manner. Finally, a sample power system is used to demonstrate the efficiency of the Ant System based method. From these test results, it appears that the Ant System is of promise for the fault-section estimation problem.     

Application of artificial bee colony-based optimization for fault section estimation in power systems

This paper presents an artificial bee colony algorithm to enhance the fault section estimation performance in power systems. Through mimicking the foraging behaviors of honeybee swarms, the algorithm owns exploitation and exploration procedures to constitute an effective near-optimal search mechanism. This proposed method excels at saving decision on external parameters such as crossover and mutation rates, facilitating the improvement of computation performance. Meanwhile, the method has added a random selection scheme to look for a new source, by which the probability of being trapped into local minimum can be largely reduced, hence serving as beneficial aids of grasping the faulted section more effectively. Through this proposed approach, it benefits the engineers to find the accurate fault section among voluminous alarms, and reduces the possibility of inaccurate diagnosis. To validate the effectiveness of the method, it has been tested on practical power systems with comparisons to published techniques.     

Application of neural-network modules to electric power system fault section estimation

This paper presents a neural system intended to aid the control center operator in the task of fault section estimation. Its analysis is based on information about the operation of protection devices and circuit breakers. In order to allow the diagnosis task, the protection system philosophy of busbars, transmission lines, and transformers are modeled with the use of two types of neural networks: the general regression neural network and the multilayer perceptron neural network. The tool described in this paper can be applied to real bulk power systems and is able to deal with topological changes without having to retrain the neural networks.     

A tabu search approach to generating test sheets for multiple assessment criteria

A computer skill certification test is one of the most important methods for evaluation of a student's computer ability. Since the tests are held frequently, one must efficiently and effectively compose test sheets from a large item bank containing over 10 000 test items. To certify student computer skills fairly, the composed test sheets must meet multiple assessment criteria such as the ratio of relevant concepts to be evaluated, the average discrimination degree, the difficulty degree, and the estimated testing time. One must allow significant time to compose an optimal test sheet from a large item bank by generating and testing each possible combination of test items. To cope with this problem, a Tabu search-based approach is proposed for more efficient composition of near-optimal test sheets from very large item banks, while meeting multiple assessment criteria. Based on the proposed approach, a computer-assisted testing system has been developed, and a series of experiments have been conducted to compare the efficiency and efficacy of this approach with other approaches. The experimental results show that the new approach is desirable for the composition of near-optimal test sheets from large item banks.     

Fault section estimation in power systems by discrimination analysis

Once a fault occurs in a power system, the fault section must be estimated from inadequate information on protective relays and circuit breakers. There have been several studies to develop an automatic estimation method, e.g., knowledge engineering, neural network, etc. However, it was very difficult to obtain a method which is powerful enough and easy to maintain. In this paper, the discrimination analysis theory is applied to the fault section estimation. Using the theory, it is expected that the inference speed can be improved. It is expected also that the knowledge about the fault section estimation can be collected. First, using the small model system, the fault section estimation method with the discrimination analysis theory is described and compared to the method with neural network. Next, it is illustrated that the method can be applied to a large system.     

A classification approach for power distribution systems fault cause identification

Power distribution systems play an important role in modern society. When distribution system outages occur, fast and proper restorations are crucial to improve the quality of services and customer satisfaction. Proper usages of outage root cause identification tools are often essential for effective outage restorations. This paper reports on the investigation and results of two popular classification methods: logistic regression (LR) and artificial neural network (ANN) applied on power distribution fault cause identification. LR is seldom used in power distribution fault diagnosis, while ANN has been extensively used in power system reliability researches. This paper discusses the practical application problems, including data insufficiency, imbalanced data constitution, and threshold setting that are often faced in power distribution fault cause identification problems. Two major distribution fault types, tree and animal contact, are used to illustrate the characteristics and effectiveness of the investigated techniques.     

A tabu search approach to the optimal sequential partitions of directed acyclic graphs

Tabu search is a novel technique for solving combinatorial optimization problems. The process in which the tabu search method seeks to transcend local optimality is based on an evaluation function which chooses the highest-evaluation move in terms of objective function and tabu restrictions. This paper presents a tabu search algorithm for finding a minimum-cost partition of the nodes of a directed acyclic graph into subsets of a given size, subject to the constraint that the precedence relations are satisfied. A standard tabu search approach cannot realize good solutions for this problem, because the problem is a complex multiple partitioning problem in which the number of subsets and the number of nodes in each subset are unsettled. For this problem, we use an appropriate data structure for this method and develop effective neighborhood structure and heuristics. We also assess the effectiveness of the developed algorithm. The results show that this algorithm is effective in obtaining a near-optimal solution to this problem. The running time of the procedure is proportional to the number of nodes in the graph. © 1997 Scripta Technica, Inc. Electr Eng Jpn, 119(4): 42–51, 1997     

Fault section estimation in power systems using a genetic algorithm

In this paper, a new method of fault section estimation in power systems using genetic algorithms (GAs) is presented. The main contributions of this paper include the following three aspects: (a) the fault section estimation problem is formulated as a 0–1 integer programming problem; (b) an efficient method to identify the faulty subnetworks is developed by using information from circuit breakers, and thus the fault diagnosis can be fulfilled in a very short time for large-scale power systems and can be implemented online; (c) a new method based upon a genetic algorithm is used to solve the fault section estimation problem, and the simulation results show that the GA based method can find multiple optimal solutions directly and efficiently in a single run, which is very suitable for complex fault diagnosis problems, especially for situations where protective relays and/or circuit breakers malfunction, because different combinations of fault sections and protective relay and/or circuit breaker malfunctions may give the same alarms.     

Bayesian networks-based approach for power systems fault diagnosis

In this paper, three element-oriented models based on simplified Bayesian networks with Noisy-Or and Noisy-And nodes are proposed to estimate the faulty section of a transmission power system. The three models are used to test if any transmission line, transformer, or busbar within a blackout area is faulty. They can deal with uncertain or incomplete data and knowledge relating to power system diagnosis, so they are flexible. The structures and initial parameters of the Bayesian networks depend on the prior knowledge of the domain experts. The parameters can be revised by using an error back propagation algorithm similar to the back-propagation algorithm for artificial neural networks. The fault diagnosis models do not vary with the change of the network structure, so they can be applied to any transmission power system. Furthermore, they have clear semantics, rapid reasoning, powerful error tolerance ability, and no convergence problem during the diagnosing procedure. Experimental tests show that the approach is feasible and efficient, so the prototype program based on the approach is promising to be used in a large transmission power system for online fault diagnosis.     

一种改进的Tabu Search算法及其在区域电网无功优化中的应用

提出将改进的Tabu(禁忌)搜索算法用于区域电网无功电压优化控制问题的求解.首先根据已知的实际电网的历史数据获得可行的初始解,然后对区域电网采用改进的禁忌搜索方法进行无功优化.在求解的过程中,由于对Tabu表中所记录的"移动"采取"有条件地释放Tabu表中的记录"这一策略,可以使搜索有效地跳出局部极小值点,更好地找到最优解.通过IEEE-14节点算例验证了该算法的有效性.     

An on-line distributed intelligent fault section estimation system for large-scale power networks

In this paper, a novel distributed intelligent system is suggested for on-line fault section estimation (FSE) of large-scale power networks. As the first step, a multi-way graph partitioning method based on weighted minimum degree reordering is proposed for effectively partitioning the original large-scale power network into desired number of connected sub-networks with quasi-balanced FSE burdens and minimum frontier elements. After partitioning, a distributed intelligent system based on Radial Basis Function Neural Network (RBF NN) and companion fuzzy system is suggested for FSE. The relevant theoretical analysis and procedure are presented in the paper. The proposed distributed intelligent FSE method has been implemented with sparse storage technique and tested on the IEEE 14, 30 and 118-bus systems, respectively. Computer simulation results show that the proposed FSE method works successfully for large-scale power networks.     

基于改进禁忌搜索的配电网重构

在配电网的运行中,常需根据负荷的变化情况及时调整网络中的开关状态,以达到整个网络消除过载、损耗最小的目的.为了解决配电网优化重构问题,提出了一种改进禁忌搜索(TS)方法.该方法根据配电网在正常运行的情况下呈辐射状的特点,将同步开关法的思想用于TS优化编码策略中,以系统有功网损最小为目标函数对配电网进行重构.通过对典型系统的计算,结果表明该算法具有更高的搜索效率和可靠性.     

基于改进禁忌搜索的配电网重构

在配电网的运行中,常需根据负荷的变化情况及时调整网络中的开关状态,以达到整个网络消除过载、损耗最小的目的。为了解决配电网优化重构问题,提出了一种改进禁忌搜索(TS)方法。该方法根据配电网在正常运行的情况下呈辐射状的特点,将同步开关法的思想用于TS优化编码策略中,以系统有功网损最小为目标函数对配电网进行重构。通过对典型系统的计算,结果表明该算法具有更高的搜索效率和可靠性。     

Fault section estimation in electric power systems using an optimization immune algorithm

This paper proposes a methodology based on the unconstrained binary programming (UBP) model and an optimization immune algorithm (IA) to estimate fault sections in electric power systems. The UBP model is formulated using the parsimonious set covering theory for associating the alarms of the protective relay functions informed by the SCADA (supervisory control and data acquisition) system and the expected states of the protective relay functions. The IA is developed to minimize the UBP model and to estimate the fault sections as quickly and reliably as possible. The proposed methodology is tested using part of the South-Brazilian electric power system. The control parameters of the IA are set to reach the maximum computational efficiency and reduction of the processing time. The results show the methodology's potential to estimate fault sections in electric power system control centers in real-time.     

A tabu search strategy for an efficient solution of linear systems in electromagnetic problems

A strategy is proposed to enhance the performance of some numerical methods used in the solution of electromagnetic problems. The strategy can be extended to any numerical method based on the partitioning of the spatial domain into elementary cells. Two different implementations of the strategy are compared: modified-reversed Cuthill-McKee and tabu search. While the former is well known, the latter is here applied for the first time to such problems. Their application is shown in conjunction with three different numerical methods: mode matching of complex MW circuits, finite difference solution of PDE and FEM analysis of microstrip lines. The efficiency and versatility of the approach is proved for each of the three numerical methods, reaching a reduction in the working times of up to 20 times. © 1997 John Wiley & Sons, Ltd.     

Power system distributed on-line fault section estimation usingdecision tree based neural nets approach

This paper proposes a distributed neural net decision approach to online estimation of the fault section of a transmission and distribution (T&D) system. The distributed processing alleviates the burden of communication between the control center and local substations, and increases the reliability and flexibility of the diagnosis system. Besides, by using the algorithms of data-driven decision tree induction and direct mapping from the decision tree into neural net, the proposed diagnosis system features parallel processing and easy implementation, overcoming the limitations of overly large and complex systems. The approach has been practically tested on a typical Taiwan Power (Taipower) T&D system. The feasibility of such a diagnosis system is presented     

A fuzzy Petri net based approach for fault diagnosis in power systems considering temporal constraints

The fuzzy Petri net is a promising and efficient approach that can tackle the complexities of power system fault diagnosis. In this work, the temporal constraint between event occurrences in power systems is investigated. Then, it is introduced to a fuzzy Petri net (FPN) for fault diagnosis. The temporal attributes are assigned to the propositions in the Petri net, so that temporal information can be taken into account, which makes the true hypothesis distinguishable from the false ones. The modified matrix execution algorithm can enhance computational efficiency, with a “weighted average” operation included to improve the fault-tolerance. The developed model possesses a modular structure, which is easy to adapt to topology changes, and to accommodate modern protection schemes. A preliminary evaluation of the operating performance of protective devices is also carried out after fault section identification. The testing results on the IEEE 14-bus power system and Zhejiang provincial power system in China demonstrate that the developed model is correct and efficient. Compared with three existing fault diagnosis methods, the proposed one has stronger fault-tolerance with lower computational cost, and is suitable for on-line fault diagnosis in large-scale power systems.     

A tabu search based hybrid optimization approach for a fuzzy modelled unit commitment problem

This article presents a solution model for the unit commitment problem (UCP) using fuzzy logic to address uncertainties in the problem. Hybrid tabu search (TS), particle swarm optimization (PSO) and sequential quadratic programming (SQP) technique (hybrid TS–PSO–SQP) is used to schedule the generating units based on the fuzzy logic decisions. The fitness function for the hybrid TS–PSO–SQP is formulated by combining the objective function of UCP and a penalty calculated from the fuzzy logic decisions. Fuzzy decisions are made based on the statistics of the load demand error and spinning reserve maintained at each hour. TS are used to solve the combinatorial sub-problem of the UCP. An improved random perturbation scheme and a simple method for generating initial feasible commitment schedule are proposed for the TS method. The non-linear programming sub-problem of the UCP is solved using the hybrid PSO–SQP technique. Simulation results on a practical Neyveli Thermal Power Station system (NTPS) in India and several example systems validate, the presented UCP model is reasonable by ensuring quality solution with sufficient level of spinning reserve throughout the scheduling horizon for secure operation of the system.     

一种基于故障录波信息的调度端电网故障诊断系统

顺应电力系统自动化的发展潮流,开发了一种基于故障录波信息的调度端电网故障诊断系统软件,总体上分为数据库模块、系统管理模块、故障诊断模块、故障信息分析模块、保护和开关动作行为评价模块等功能模块.分别介绍了各模块的工作原理,并对故障诊断模块进行了详细的分析.     

一种基于故障录波信息的调度端电网故障诊断系统

顺应电力系统自动化的发展潮流,开发了一种基于故障录波信息的调度端电网故障诊断系统软件,总体上分为数据库模块、系统管理模块、故障诊断模块、故障信息分析模块、保护和开关动作行为评价模块等功能模块。分别介绍了各模块的工作原理,并对故障诊断模块进行了详细的分析。