Optimal design of passive power filter for enhancing the harmonic-constrained hosting capacity of renewable DG systems. (January 2022)

• Record Type: Journal Article
• Authors: Bajaj, Mohit; Singh, Amit Kumar
• Is Part Of: Computers & electrical engineering. Volume 97(2022)
• Publication Date: 2022-01
• Subjects: Hosting capacity -- Harmonic distortion -- Power quality -- Passive power filters -- Renewable energy; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107646
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: 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).