Fault diagnosis and identification in the distribution network using the fuzzy expert system

Abstract

In this paper, a fuzzy expert off-line system has been developed for fault diagnosis in the distribution network based on the structural and functional operation of the relay and circuit breakers. Functional operations (correct operation, false operation and failure to operate) of the relays and circuit breakers are described by fuzzy logic. Input data for the proposed fuzzy expert fault diagnosis system (FDS) are status and time stamps of the alarms, associated with relays and circuit breakers. The diagnostic system from a huge number of alarms sets, logically organizes and quantifies the diagnosis. FDS can diagnose correct operation, false operation and failure to operate of the relays and circuit breakers. Also, it can identify and quantify fault location based on the Hamacher’s operator of a fuzzy union. The additional contribution of this paper is in modeling unknown information using linear fuzzy membership function. Statuses of certain components may be unknown due to telemetry failures or are simply unavailable to the operator and proposed FDS can make diagnosis in such a situation. Developed fuzzy expert FDS is tested on the two examples of faults in real life distribution network

Analysis of Cable Screen Currents for Diagnostics Purposes

“…High occurrences of failures and the increasing cost of non-supplied energy allow one to expect that the proposed method will be used in the future [35]. It is believed that in the initial period, the proposed diagnostic method will help to increase the competence of workers, and in the next decade, the solution will be used for online control of cable screens of important cable lines [36]. The proposed method could be a part of an expert system, e.g., a fuzzy expert system that offers additional functionalities, e.g., localization of phase to ground faults or identification of failing components [37,38].…”

Dynamic Analysis Method for Fault Propagation Behaviour of Machining Centres

“…As expressed in Equation ( 10), the degree of correlation between components obtained from the perspective of the reliability function is represented by θ and as expressed in Equation (11), F(t) = 1 − R(t). Therefore, the fault influence degree of the edge at the same level in a machining centre is I v i , v j = 1 − θ and the greater the value is, the greater the fault influence degree of the edge will be.…”

“…The current fault diagnosis methods can be summarized into four categories [8,9]: knowledge-based fault diagnosis [10][11][12], model-based fault diagnosis [13][14][15], signalbased fault diagnosis [16][17][18], and hybrid method-based fault diagnosis (a method that combines two or more methods) [19][20][21][22]. Fault diagnosis for machining centres mainly include diagnosis methods based on fault information monitoring, training models, and fault trees.…”

Entropy Weight-Logarithmic Fuzzy Multiobjective Programming Method for Evaluating Emergency Evacuation in Crowded Places: A Case Study of a University Teaching Building

“…Triangular fuzzy numbers are mainly represented by three parameters l, m, u, and are denoted as (l, m, u), l and u represent the lower and upper bounds of triangular fuzzy Numbers, respectively, and they represent the degree of fuzziness. The larger the interval, the stronger the degree of fuzziness, and m represents the optimal value [38], [39]. A function of a triangular fuzzy number has the following form:…”