Pareto optimal allocation of resistive‐type fault current limiters in active distribution networks with inverter‐interfaced and synchronous distributed generators

Abstract

To efficiently reduce the fault currents of active distribution networks (ADNs), this paper proposes a novel methodology for Pareto optimal allocation of resistive‐type fault current limiters (R‐FCLs). The proposed approach enables to simultaneously optimize the number, location, and size of the R‐FCLs in the ADNs with inverter‐interfaced and synchronous distributed generators (DGs). The sensitivity analysis is introduced to rank candidate locations, and a constrained multiobjective function is created to minimize the cost of the R‐FCLs and the fault currents of the ADNs. An improved multiobjective particle swarm optimization (MOPSO) algorithm and a multiobjective artificial bee colony (MOABC) algorithm are designed to obtain the Pareto optimal solution set. The proposed approach is verified using the modified IEEE 33‐node and 69‐node distribution systems, where both centralized and dispersed access of DGs are considered. The numerical results demonstrate that: (a) The fault currents in all nodes of the two testing systems are limited to the permissible levels using the least capital cost of the R‐FCLs, and (b) the improved MOPSO outperforms the MOABC to achieve a better Pareto front and decrease the number of iterative computation. In consequence, the feasibility and superiority of the proposed approach are well validated.

A review of swarm-based metaheuristic optimization techniques and their application to doubly fed induction generator

“…The proposed algorithm is employed on the optimal distribution of flexible fault current limiters and applied to the revised IEEE 33-BUS distribution systems with distributed generation and IEEE 30-BUS benchmark system. The proposed method produced optimal configuration of the system and displayed an improved accuracy when compared to a non-dominated sorting genetic algorithm, as well as a Multi Objective Particle Swarm Optimization which are shown in [121] and [122] respectively. However, the algorithm was not compared to the conventional BA.…”

Pareto Optimal Allocation of Flexible Fault Current Limiter Based on Multi-Objective Improved Bat Algorithm

“…Here, Z CHB−FCL is the equivalent impedance of CHB-FCL. When CHB-FCL is inserted in the branch ij, the modified self-impedance Z ii is derived as follows [14]:…”

“…Along with the objective dimension increasing, multiobjective optimization algorithms exhibit more excellent performance than single-objective optimization algorithm in handling such problems. In [14], the Pareto optimal solution set of resistive-type FCLs are obtained by using an improved multi-objective particle swarm optimization (MOPSO) and a multi-objective artificial bee colony. In [15], a nondominated sorting genetic algorithm (NSGA-II) is applied to optimize the allocation of DGs and FCLs to reduce fault negative effects on distribution networks.…”

Risk‐based many‐objective configuration of power system fault current limiters utilising NSGA‐III

“…The quenching process is determined by the superconducting materials and the dynamic response process of R‐FCL [29] is modelled as shown in Fig. 6.…”