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Due to the growing attention for the environmental impacts and power loss minimization, distributed generators (DGs) have been introduced widely into the electric power system. One of the challenges of integrating with the power system is to determine their optimal placement and sizing, which, when not respected, adversely affects the performance of the electrical network. In this paper, three multi‐objective algorithms of particle swarm optimization (PSO), variable constants (VCPSO) and genetic algorithm (GA) are adopted and implemented. The main objectives are to detect the optimum size and location of multiple DGs aiming to reduce the active power loss and improve bus voltage deviations in the distribution networks. The paper conducts a comprehensive review of the optimal size and location of the DG via systematic procedures, including definition, classifications, technologies of DGs. Then, the performances evaluation of the three optimization methods are presented and compared with other methods. The presented optimization methods are tested on the IEEE‐33 bus, 32‐line radial distribution network. Four different scenario‐based studies including base case and different number of DGs are performed to examine the accuracy of the presented algorithms. The obtained results prove that all the three algorithms are suitable for this multi‐objective optimization and VCPSO offers the best solution in terms of convergence and, and it has lowest average computation time. The performance and accuracy of the presented approaches and their improvements in the power loss and bus voltage profile are discussed and presented in detail. The obtained results show that more than 65% of active power loss reduction has been attained with the proper sizing and placement of DG systems. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Dusty weather is a most important disturbance for photovoltaic (PV) systems, that may result in PV panels exposure to partial shading conditions where the PV panel characteristics show several maximum power points. Moreover, dusty weather conditions may lead to rapid changes in solar radiations. Thus, conventional power system tracking algorithms are not being capable to track the global maximum power point (GMPP). This paper provides an advanced maximum power point tracker for partial shading and dusty weather conditions. In the presented technique, scanning and power lock conditions are added to the conventional perturbations and observation technique in photovoltaic system under partial shading or dusty weather conditions. This tracking method scans the PV characteristic in steps and stores all the maximum power points in order to get the GMPP. The proposed tracker is capable to identify the uniform weather conditions to save it from having unnecessary scanning. In addition, this tracker permits dynamic tracking of the GMPP under dusty weather conditions with less fluctuations and stable operation compared to the conventional methods. Accurate and fast conversion to the GMPP during various weather conditions is accomplished using the proposed technique. The proposed modified perturb and observe technique is validated using simulation and practical results. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]