DG Source Allocation by Fuzzy and Clonal Selection Algorithm for Minimum Loss in Distribution System

Abstract

Distributed Generation (DG) is a promising solution to many powersystem problems such as voltage regulation, power loss, etc. This articlepresents a new methodology using Fuzzy and Artificial Immune System(AIS) for the placement of Distributed Generators (DGs) in a radial dis-tribution system to reduce the real power losses and to improve the volt-age profile. A two-stage methodology is used for the optimal DG place-ment. In the first stage, the Fuzzy Set approach is used to find the optimalDG locations and in the second stage, Clonal Selection algorithm of AISis used to size the DGs corresponding to maximum loss reduction. Thisalgorithm is a new, population based, optimization method inspired bythe cloning principle of the human body immune system. The advantageof this algorithm is the population size is dynamic and it is determinedby the fitness values of the population. The proposed method is testedon standard IEEE-33 based bus test system. Net, the results are com-pared with different approaches available in the literature. The proposedmethod outperforms the other methods in terms of the quality of solu-tion and computational efficiency.

Optimal allocation of distributed generation with the presence of photovoltaic and battery energy storage system using improved barnacles mating optimizer

“…It can be detected from Table 12 that using the CQOBMO_7, a loss reduction (LR) in the power loss quals 65.26% is achieved, which is higher than those obtained by LSF 69 (59.72%), Fuzzy‐IAS 74 which is 42.45%, 57.76% given in BSOA, 75 65.14% in BFOA, 76 TLBO 83 which reaches 64.20%, 64.88% in QOTLBO 83 …”

“…The base case power flow results verified that the active power loss is 210.98 kW, where the reactive power losses are 143.14 kVAR. BMO, CBMO, QOBMO, and CQOBMO are applied to find the best solutions (sizes and locations) of the three DG units for reducing the power loss (objective function), and the results are given in Table 12.It can be detected from Table 12 that using the CQOBMO_7, a loss reduction (LR) in the power loss quals 65.26% is achieved, which is higher than those obtained by LSF 69 (59.72%), Fuzzy‐IAS 74 which is 42.45%, 57.76% given in BSOA, 75 65.14% in BFOA, 76 TLBO 83 which reaches 64.20%, 64.88% in QOTLBO 83 10.…”

“…68,72 Optimization algorithms based on artificial intelligence have been used to evaluate the optimum location for multiple DGs in Hung and Colleagues. 69,73 An algorithm for fuzzy and clonal selection has been developed for DG allocation in Lalitha et al 74 Recently, a variety of nature-inspired optimization algorithms were used in the DG allocation problem, such as the backtracking optimization algorithm (BSOA), 75 bacterial foraging optimization algorithm (BFOA), 76 stud krill herd algorithm (SKHA), 77 whale optimization algorithm (WOA), 78 and chaotic sine cosine (CSCA). 4…”

Optimal Placement and Sizing Problem for Power Loss Minimization and Voltage Profile Improvement of Distribution Networks under Seasonal Loads Using Harris Hawks Optimizer

“…AI (artificial intelligence)-based optimization techniques have been introduced in [8,9] for determining the optimal allocation of multiple DG units. e fuzzy-based technique has been used in [10] for the optimal allocation of DGs. Various nature-inspired optimization methods have been employed in the recent literature for the optimal placement of DGs.…”

“…In the literature studies, researchers have also analyzed DG allocation using IEEE 33 [10] and 69 bus RDNs [23] to minimize power losses and VDI.…”