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Active power filters (APF) can be employed for harmonic compensation in power systems. In this paper, a fuzzy based method is proposed for identification of probable APF nodes of a radial distribution system. The modified adaptive particle swarm optimization (MAPSO) technique is used for final selection of the APFs size. A combination of Fuzzy-MAPSO method is implemented to determine the optimal allocation and size of APFs. New fuzzy membership functions are formulated where the harmonic current membership is an exponential function of the nodal injecting harmonic current. Harmonic voltage membership has been formulated as a function of the node harmonic voltage. The product operator shows better performance than the AND operator because all harmonics are considered in computing membership function. For evaluating the proposed method, it has been applied to the 5-bus and 18-bus test systems, respectively, which the results appear satisfactorily. The proposed membership functions are new at the APF placement problem so that weighting factors can be changed proportional to objective function.

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An experience oriented-convergence improved gravitational search algorithm (ECGSA) based on two new modifications, searching through the best experiments and using of a dynamic gravitational damping coefficient (α), is introduced in this paper. ECGSA saves its best fitness function evaluations and uses those as the agents’ positions in searching process. In this way, the optimal found trajectories are retained and the search starts from these trajectories, which allow the algorithm to avoid the local optimums. Also, the agents can move faster in search space to obtain better exploration during the first stage of the searching process and they can converge rapidly to the optimal solution at the final stage of the search process by means of the proposed dynamic gravitational damping coefficient. The performance of ECGSA has been evaluated by applying it to eight standard benchmark functions along with six complicated composite test functions. It is also applied to adaptive beamforming problem as a practical issue to improve the weight vectors computed by minimum variance distortionless response (MVDR) beamforming technique. The results of implementation of the proposed algorithm are compared with some well-known heuristic methods and verified the proposed method in both reaching to optimal solutions and robustness.

Purpose The voltage stability is a basic principle in the power system operation. Different short circuits, load growth, generation shortage, and other faults which disturb the voltage stability are serious threats to the system security. The voltage instability causes dispersal of a power system into sub-systems, and leads to blackout as well as heavy damages of the system equipments. Optimum load shedding during contingency situations is one of the most important issues in power system security analysis. Design/methodology/approach In this paper, a New Binary Particle Swarm Optimization technique (NB-PSO) is proposed for solving the integer-valued modeling of under-voltage load shedding (UVLS) problem. The updating mechanisms for the position and velocity of binary particles are amended in the proposed NB-PSO by using a new velocity definition, which has an excellent efficiency for solving complex binary optimization problems. Findings The effectiveness and capability of the proposed NB-PSO optimization algorithm were illustrated according to the simulation results of applying it to the IEEE 118-bus test system. In addition, the performance of the proposed NB-PSO based method was compared with other optimization algorithms, such as the Binary Particle Swarm Optimization (BPSO) and Hybrid Discrete Particle Swarm Optimization (HDPSO) techniques. It was perceived that the NB-PSO performs superior than the BPSO and HDPSO for determining the best location and the minimum level of load shedding in order to prevent voltage instability. Originality/value The proposed NB-PSO has novel modifications and techniques to enhance both exploration and exploitation capabilities to find the optimal feasible solution. The simulation results confirmed the effectiveness of the proposed method in determining the best location and the minimum amount of load shedding for voltage collapse prevention.