An Innovative Minimum Hitting Set Algorithm for Model-Based Fault Diagnosis in Power Distribution Network

Abstract

The distribution network plays a great role in the power system, and any fault in it may threaten the safe and stable operation of the power system. Hence, fault diagnosis has an important role in protecting the distribution network and maintaining power system stability. The model-based diagnosis (MBD) is one of the diagnostic methods that have some advantages compared with the common diagnosis methods. The latter, such as expert diagnosis systems depend on the professional experience and fault information. However, the inefficiency and incomplete calculation of the minimum hitting set (MHS) limits the performance of the MBD. In this paper, to overcome these limitations, an innovative MHS algorithm which considers the nature and characteristics of the distribution network is proposed. In the proposed algorithm, a new fitness function is constructed by a weighted combination method, which allows particles to move directly toward the MHS without the effect of non-hitting set particles. Finally, three cases studies and discussion on the distribution network are introduced to validate the advantages of the proposed algorithm, in terms of the computation efficiency and accuracy. INDEX TERMS Distribution network, fault diagnosis, model based diagnosis, minimum hitting set algorithm, power system stability.

Multi-Branch Fault Line Location Method Based on Time Difference Matrix Fitting

“…The method described in reference [18] is not effective for practical applications due to the small harmonic component, which cannot accurately describe the transient electrical characteristics of the system. In contrast, reference [19] improved positioning efficiency and accuracy by combining the genetic algorithm and particle swarm optimization algorithm using the minimum collision set criterion. References [16][17][18][19] all relied on self-modeling for section positioning or improving positioning accuracy.…”

“…In contrast, reference [19] improved positioning efficiency and accuracy by combining the genetic algorithm and particle swarm optimization algorithm using the minimum collision set criterion. References [16][17][18][19] all relied on self-modeling for section positioning or improving positioning accuracy. However, these methods face challenges such as difficulty in collecting fault samples, significant discrepancies between simulation and real-world scenarios, and limited practicality.…”

A Fault Signal Processing Method Based on An Improved Prony Algorithm

“…An accurate assessment plays an indispensable role in the safety active control systems (Shen et al, 2021a;Shen and Raksincharoensak, 2021a;Shen and Raksincharoensak, 2021b). Similarly, it is significant to study an effective fault signal analysis method for safe and stable operation in power systems (WangJin et al, 2019;Yang et al, 2019;Yang et al, 2021a;Zhang et al, 2021). Although the technology of fault analysis has already been developed in the existing literature, there are still obstacles to fault identification using the electrical parameters of recording signals.…”

A Progressive Search Algorithm for the Minimum Hitting Set Problem

“…Genetic algorithms and their variations [14], particle swarm optimization [15] and improved differential evaluation algorithm [16] were initially proposed to compute the minimum hitting set using a fitness function. The hybrid versions of these algorithms [17] and parallel hybrid algorithms are also proposed [18]. However, all of these algorithms have a major drawback in the sense that they may not guarantee exact minimum hitting sets.…”