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.

Recent Trends in Power Quality Improvement: Review of the Unified Power Quality Conditioner

The electrical power sector is currently focusing on power quality (PQ) issues and their mitigation. Industrial automation and the use of power electronic converters for the integration of distributed generation create various PQ problems. This necessitates PQ enhancement, which in turn helps to improve the life span of the equipment as well as the reliability of supply for feeding critical loads within the system. This paper presents a comprehensive review of the Unified Power Quality Conditioner (UPQC) and its widespread application in the distribution system. The UPQC belongs to the family of active power filters. It contributes to the alleviation of voltage and current-related PQ issues along with power factor correction and the integration of renewable energy systems in the distribution network. This paper discusses various topologies, compensation methods, control theories, and the technological developments in recent years. More than 160 research papers have summarized the features of UPQC for further applications. Based on the outcomes of the investigation, the future direction of the UPQC is discussed. This paper is expected to play a major role in guiding research scholars in the application of the UPQC.

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.