Abstract
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.
Highlights
Harmonic distortion of current and voltage may lead to adverse effects, such as increased power loss and equipment damage [3,4]
This paper proposes a multi-objective optimization methodology based on teaching–learning-based optimization (TLBO) with Pareto optimality for solving passive power filters (PPFs) planning problems
Harmonic loads were regarded as harmonic sources
Summary
Non-linear loads that produce harmonics [1,2] are generally used in modern power systems. Harmonic distortion of current and voltage may lead to adverse effects, such as increased power loss and equipment damage [3,4]. Many studies have concentrated on planning and designing passive power filters (PPFs) [8,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34] to reduce harmonic distortion effects in the past two decades. More constraints and detuning mechanisms are required to achieve an optimal solution Another method was proposed by Wang et al [36], employing a tuning filtering process. Determining the optimum parameters is an open question for most evolutionary optimization algorithms
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