Design Of Passive Power Filters Research Articles

Optimal Design of Passive Power Filters Using the MRFO Algorithm and a Practical Harmonic Analysis Approach including Uncertainties in Distribution Networks

The design of Passive Power Filters (PPFs) has been widely acknowledged as an optimization problem. This paper addresses the PPF parameters design problem using the novel Manta Ray Foraging Optimization (MRFO) algorithm. Moreover, an analytical method based on Monte Carlo Simulation (MCS) is proposed to investigate the harmonic performance of such an optimally designed PPF with variations in power networks. The MRFO algorithm has shown a superior solution-finding ability, but a relatively higher computational effort in comparison with other recently proposed algorithms. The harmonic performance of the optimal PPF solution with uncertainties was analyzed using the proposed method. The results imply that the optimally designed PPF can effectively attenuate the high-order harmonics and improved the system performance parameters over different operating conditions to continually comply with the standard limits. The proposed MCS method showed that the optimally designed PPF reduced the voltage and current distortions by roughly 54% and 30%, respectively, and improved the network hosting capacity by 10% for the worst-case scenario.

Multi-Objective Artificial Bee Colony Algorithm with Minimum Manhattan Distance for Passive Power Filter Optimization Problems

Passive power filters (PPFs) are most effective in mitigating harmonic pollution from power systems; however, the design of PPFs involves several objectives, which makes them a complex multiple-objective optimization problem. This study proposes a method to achieve an optimal design of PPFs. We have developed a new multi-objective optimization method based on an artificial bee colony (ABC) algorithm with a minimum Manhattan distance. Four different types of PPFs, namely, single-tuned, second-order damped, third-order damped, and C-type damped order filters, and their characteristics were considered in this study. A series of case studies have been presented to prove the efficiency and better performance of the proposed method over previous well-known algorithms.

Optimal design of passive power filter for enhancing the harmonic-constrained hosting capacity of renewable DG systems

This paper investigates the application of a passive harmonic filtering to improve the harmonic-constrained hosting capacity (HCHC) of renewable distributed generation (DG) systems in harmonically polluted distribution systems. Concurrently, the optimal parameters of a C-type passive filter (CTPF) and the rating of the DG system are set. The optimum HC of the distorted grid for DG is achieved while reducing total filter cost (FC) to a minimum. Using the newly suggested multi-objective Pareto-based bat algorithm (pb-MOBA), a best-of-breed solution is obtained. The formulated problem of multi-objective optimization (MOO) is dealt with under a number of system performance indices (PIs) constraints such as individual as well as total harmonic distortion (THD) in the line current and the point of common coupling's (PCC) voltage, distribution line ampacity under harmonic current overloading, steady-state voltage profile, load power factor (PF), and so on and a handful that is linked to the passive power filter (PPF) itself. The Pareto optimal front is produced using the MOBA extension, and the trade-off between the objectives is examined. The efficiency and accuracy of pb-MOBA, in dealing with the designated MOO problem, is confirmed by making a comparison of the acquired solution and three computed PIs viz. diversity metric (DM), generational distance (GD), and convergence metric (CM) with those achieved by Multi-objective Pareto-based PSO (pb-MOPSO), Non-dominated Sorting Genetic Algorithm (NSGA).

Multi-Objective Teaching–Learning-Based Optimization with Pareto Front for Optimal Design of Passive Power Filters

This paper proposes an optimal design method to suppress critical harmonics and improve the power factor by using passive power filters (PPFs). The main objectives include (1) minimizing the total harmonic distortion of voltage and current, (2) minimizing the initial investment cost, and (3) maximizing the total fundamental reactive power compensation. A methodology based on teaching–learning-based optimization (TLBO) and Pareto optimality is proposed and used to solve this multi-objective PPF design problem. The proposed method is integrated with both external archive and fuzzy decision making. The sub-group search strategy and teacher selection strategy are used to improve the diversity of non-dominated solutions (NDSs). In addition, a selection mechanism for topology combinations for PPFs is proposed. A series of case studies are also conducted to demonstrate the performance and effectiveness of the proposed method. With the proposed selection mechanisms for the topology combinations and parameters for PPFs, the best compromise solution for a complete PPF design is achieved.

Optimal Design of Passive Power Filter Using Multi-Objective Pareto-Based Firefly Algorithm and Analysis Under Background and Load-Side’s Nonlinearity

In this paper, the optimal designing of passive power filter (PPF) is formulated as a multi-objective optimization (MOO) problem under several constraints of system's performance indices (PIs) such as individual as well as total harmonic distortion (THD) in the line current and the point of common coupling's (PCC) voltage, distribution line's ampacity under harmonic currents overloading, steady-state voltage profile, load power factor (PF) and a few associated with the filter itself. The optimal design parameters of a third-order damped filter are simultaneously determined for achieving maximum PF at the PCC while keeping system's other indices such as total demand distortion (TDD) in the line current, total voltage harmonic distortion (TVHD) at the PCC and total filter cost (FC) incurred at a minimum by obtaining a best-compromised solution using the newly proposed multi-objective Pareto-based firefly algorithm (pb-MOFA). A novel MOO approach inspired by the modified firefly algorithm and Pareto front is established in order to deal with PPF design problems. The extension of MOFA is considered for producing the Pareto optimal front and various conclusions are drawn by analysing the trade-offs among the objectives. The efficiency and accuracy of the proposed pb-MOFA, in solving the concerned MOO problem, is validated by comparing an obtained solution and three computed PIs viz. convergence metric (CM), generational distance (GD) and diversity metric (DM) with those obtained from popular multi-objective Pareto-based PSO (pb-MOPSO), non-dominated sorting genetic algorithm (NSGA-II) and recently introduced multi-objective slime mould algorithm (MOSMA). The need for true Pareto front (TPF) is served by the one obtained by Monte Carlo method. At last, the impacts of different background voltage distortion (BVD) levels and load-side's nonlinearity levels (NLLs) on filter performance are analysed.

Design of passive power filters for battery energy storage system in grid connected and islanded modes

This study presents an improved method to design passive power filters for a battery energy storage system operating in grid connected and islanded modes. The studied system includes appropriate controls according to the selected mode. The global system is composed of two power converters a DC–DC converter and a three phase four wires DC–AC converter. The passive power filters to be designed are the battery inductance, the neutral filter and the LCL filter. Conventionally filter design methods are performed considering only one operating mode parameters. This paper proposes an analysis of the design approaches reported in the literature for each mode separately and then provides a step by step design procedure taking into account constraints of each operation mode. The design is performed based on steady state equations for each mode. An overall system simulation with PSIM software has been developed. Simulation results illustrate the effectiveness of the investigated filter design.

Multi‐objective optimal design of passive power filter for aircraft starter/generator system application

A multi-objective optimisation design method for passive power filter devices is proposed to solve the input current harmonic suppression problem of the aircraft starter/generator system. The parameters of the filter device are optimised by using the particle swarm optimisation algorithm. The proposed algorithm takes harmonic parameters, DC voltage drop, total volume and weight as the optimisation target. With these optimisations, the filter has the minimal volume and weight, while the current harmonic and the DC side voltage drop are able to meet the power quality requirements. This approach improves the filter design procedure with advantages of the reduced amount of calculation. Finally, theoretical analysis and computer simulation validate the effectiveness and viability of the presented algorithm.

Optimal Design of Passive Power Filters Based on Pseudo-parallel Genetic Algorithm

The economic costs together with filter efficiency are taken as targets to optimize the parameter of passive filter. Furthermore, the method of combining pseudo-parallel genetic algorithm with adaptive genetic algorithm is adopted in this paper. In the early stages pseudo-parallel genetic algorithm is introduced to increase the population diversity, and adaptive genetic algorithm is used in the late stages to reduce the workload. At the same time, the migration rate of pseudo-parallel genetic algorithm is improved to change with population diversity adaptively. Simulation results show that the filter designed by the proposed method has better filtering effect with lower economic cost, and can be used in engineering.

Optimal design of passive power filters based on multi-objective bat algorithm and pareto front

This paper proposes an optimal design method for passive power filters (PPFs) in order to suppress critical harmonics and improve power factor. The characteristics of common passive filters, such as single-tuned, second-order, third-order, and C-type damped filters are introduced. In addition, several objective functions and constraints for PPF design problems are constructed. A new multi-objective optimization based on the modified bat algorithm and Pareto front is developed for solving PPF design problems. A case study is also presented to demonstrate the efficiency and superiority of the proposed method.

Multi‐objective bat algorithm with time‐varying inertia weights for optimal design of passive power filters set

In this study, a novel method is developed to optimise the design of passive power filters (PPFs). The proposed method is based on the famous bat algorithm (BA) and is expected to make the PPF design easier and more effective than conventional trial-and-error approaches. Furthermore, the effects of inertia weight variations on the performance of the proposed method are examined. To optimise performance, the most suitable inertia weight, with the best effects, will be selected. For solving multi-objective optimisation problems, a so-called external archive is also integrated into the proposed method. A case study of PPF design is also presented to demonstrate the accuracy, efficiency and superiority of the proposed method.

Optimal design of passive power filters of an asymmetrical system based on genetic algorithm

Hybrid power filter is effective equipment for preventing harmonic pollution and enhancing power quality. The design of passive power filters is one of the important parts of hybrid power filter. Conventional approaches for the design of passive power filters depend on the experiences and single technology criterions, which are difficult to achieve the optimal solution. An optimal design method based on genetic algorithm is proposed in this paper. Two objectives including minimum total harmonics distortion of current and minimum cost for equipments are constructed, and reactive power compensation is used as constraint condition. The objective functions for an asymmetrical powers system are deduced, thus the fitness functions are determined. And the parameters of passive power filters are achieved by adopting genetic algorithm. Passive power filters applied in practical system are designed and the simulation model is given. Simulation results show that the filter designed by the proposed algorithm has better harmonics suppression effect and lower initial investment for equipments than conventional approaches.