A Linear Integer Programming Model for Fault Diagnosis in Active Distribution Systems With Bi-Directional Fault Monitoring Devices Installed

Abstract

With the extensive installation of intelligent electronic devices with bi-directional fault monitoring capabilities, richer fault direction information can be collected and utilized to achieve an accurate fault diagnosis. In this paper, we consider the fault diagnosis problem in active distribution systems with distributed generators connected, such as rotating electrical machine power sources and centralized inverter interfaced renewable energy resources. The fault diagnosis problem is modeled as a linear integer programming problem with an objective to minimize the numbers of fault zones and false alarms. A novel functional form is derived to capture the expected alarms sent by bi-directional fault monitoring devices to be compared with the actual alarms received by the dispatch center. Uncertainties in both the monitoring and communication stages are considered in the model by formulating the numbers of false alarms in the objective function. Three types of suspected false alarms can be detected: "missing alarms", "distorted alarms", and "reverse alarms". By solving the developed optimization model for fault diagnosis, false alarms and suspected fault zones can be found. Case studies in a modified IEEE 33-bus system and a 55-bus system in Guangzhou, China are carried out in several different scenarios with multiple faults to demonstrate the performance of the proposed model. Numerical tests show that the proposed approach is superior in computational time such that it can be used for real-time fault diagnosis in active distribution systems. INDEX TERMS Active distribution system, bi-directional fault monitoring devices, fault diagnosis, linear integer programming.

Applications of Stochastic Mixed-Integer Second-Order Cone Optimization

“…We emphasize that Model (18,19) accepts only binary variables and is intractable for an extensive form formulation. Therefore, a solution for such a model is now possible by applying the decomposition algorithm developed in [54].…”

“…For example, the (deterministic) mixed-integer secondorder cone programming (DMISOCP) models pre-sented in [12] (see also [13]- [15]) have proved to be useful in dealing with a variety of applications that involve integrality and conicity. For another example, stochastic mixed-integer linear programming (SMILP) [16] has been demonstrated to be effective in many applications involving integrality and uncertainty (see also [17]- [19] and the references contained therein). For a third example, the stochastic second-order cone programming (SSOCP) models described in [20] and the stochastic semidefinite programming models described in [21] have proved to be useful in dealing with uncertainty and conicity in many applications (see also [22] and [23]).…”

A Review of Real-Time Fault Diagnosis Methods for Industrial Smart Manufacturing

“…In addition, real-time detection, identification, and diagnosis of faults in safetycritical systems that need to ensure human safety, environmental health, and economic security are particularly important [12]. This includes medical and surgical equipment, aviation and air traffic control, hazardous and toxic chemical processes, large-scale power systems [13][14][15][16][17][18][19] and transmission lines [20][21][22].…”

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

“…The development and maintenance of a large knowledge base is not easy for a large‐scale system with rapidly varying parameters, which is characteristic of an actual power system. Power system fault diagnosis methods that are based on analytical models [2–6] consider logical relationships among faulty components, protective relays, and circuit breakers, and they then formulate the fault diagnosis problem as a 0–1 integer programming problem, and seek the fault diagnosis result using an optimization algorithm. 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. In [7], a fault diagnosis model is reported for a distribution system with integrated distributed generation.…”