Stochastic Programming-Based Fault Diagnosis in Power Systems Under Imperfect and Incomplete Information

Abstract

When a fault occurs in a section or a component of a given power system, the malfunctioning of protective relays (PRs) and circuit breakers (CBs), and the false and missing alarms, may manifestly complicate the fault diagnosis procedure. It is necessary to develop a methodologically appropriate framework for this application. As a branch of stochastic programming, the well-developed chance-constrained programming approach provides an efficient way to solve programming problems fraught with uncertainties. In this work, a novel fault diagnosis analytic model is developed with the ability of accommodating the malfunctioning of PRs and CBs, as well as the false and/or missing alarms. The genetic algorithm combined with Monte Carlo simulations are then employed to solve the optimization model. The feasibility and efficiency of the developed model and method are verified by a real fault scenario in an actual power system. In addition, it is demonstrated by simulation results that the computation speed of the developed method meets the requirements for the on-line fault diagnosis of actual power systems.

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

“…Under normal condition, the A-phase ARRs of the subsystem S 1 and S 2 can be described according to Kirchhoff's voltage law as the following: Correspondingly, the B-phase ARRs of the subsystem S 1 and S 2 are described as follows: In the same way, the C-phase ARRs of the subsystem S 1 and S 2 are described as follows: When faults occur in phase A of subsystem S 1 and phase B of subsystem S 2 , the corresponding AARs are presented in equations (10) and (13) When faults occur in phase B of subsystem S 1 and phase A of subsystem S 2 , the corresponding AARs are as in equations (12) and (11) When faults occur in phase C of subsystem S 1 and phase A of subsystem S 2 , the corresponding AARs are as in (14) and (11) (14) and (13) During faults occur in phase A and B of subsystem S 1 , the corresponding AARs are as in (10) and (12) It can be concluded that there is no intersection between MCSs within a single subsystem. Therefore, the MCSs of the distribution network fault diagnosis possess the characteristic of no intersection between all the MCSs.…”

“…Among them, the explicit methods [3]- [8] such as the analytical models [3], [4] and the implicit methods [9]- [17] The associate editor coordinating the review of this manuscript and approving it for publication was Ruqiang Yan. such as the expert systems [14], [15]. Although these methods offer incredible solutions to the fault diagnosis, it has some imperfections.…”

“…Accordingly, the faults outside experience are difficult to be diagnosed. Moreover, the time of response of these methods are normally not pertinent to a real-time environment because of the traditional knowledge representation and inference mechanism [14].…”

Fault diagnosis and identification of malfunctioning protection devices in a power system via time series similarity matching

“…For fault diagnosis based on the analytical model paradigm, several practical methods have been proposed and implemented in actual power systems. For example, a method based on chance constrained programming is presented in [5] to address the uncertainties associated with alarm messages and the reliabilities of power system components; here, strong fault tolerance capability is achieved. In [6], an analytic integer linear programming model is established to identify the suspected fault sections and false alarms generated by directional fault indicators.…”

Identification Technology of Grid Monitoring Alarm Event Based on Natural Language Processing and Deep Learning in China

“…The analytic model-based method describes the fault diagnosis as an unconstrained 0-1 integer programming problem, and the optimization algorithm is used to minimize the objective function, with the optimal solution as the fault diagnosis result. In reference [15], an analytic model based on chance-constrained programming technology is introduced, and a genetic algorithm based on the Monte Carlo simulation is used to resolve the objective function. In reference [16], an analytic method based on the topological description is proposed and the mapping relationship between protection device and section is built according to an event matrix.…”