International Journal of Applied Power Engineering (IJAPE) Vol. 14, No. 1, March 2025, pp. 155~162 ISSN: 2252-8792, DOI: 10.11591/ijape.v14.i1.pp155-162  155 Journal homepage: http://ijape.iaescore.com Optimal distributed generator placement for loss reduction using fuzzy and adaptive grey wolf algorithm Daruru Sarika 1 , Palepu Suresh Babu 1 , Pasala Gopi 1 , Manubolu Damodar Reddy 2 , Suresh Babu Potladurty 3 1 Department of Electrical and Electronics Engineering, Annamacharya University, Rajampet, India 2 Department of Electrical and Electronics Engineering, Sri Venkateswara University College of Engineering, Tirupati, India 3 Department of Electronics and Communication Engineering, Sri Venkateswara College of Engineering, Tirupati, India Article Info ABSTRACT Article history: Received Feb 6, 2024 Revised Sep 9, 2024 Accepted Oct 23, 2024 This research provides a new methodology for locating distributed generation (DG) units in distribution electrical networks utilizing the fuzzy and adaptive grey wolf optimization algorithm (AGWOA) to decrease power losses and enhance the voltage profile. Everyday living relies heavily on electrical energy. The promotion of generating electrical power from renewable energy sources such as wind, tidal wave, and solar energy has arisen due to the significant value placed on all prospective energy sources capable of producing it. There has been substantial research on integrating distributed generation into the electricity system due to the growing interest in renewable sources in recent years. The primary reason for adding distributed generation sources for the network is to supply a net quantity of power, lowering power losses. Determining the amount and location of local generation is crucial for reducing the line losses of power systems. Numerous studies have been conducted to determine the best location for distributed generation. In this study, DG unit placement is determined using a fuzzy technique. In contrast, photovoltaic (PV) and capacitor placement and size are determined simultaneously using an adaptive grey wolf technique based on the cunning behavior of wolves. The proposed method is developed using the MATLAB programming language; the results are then provided after testing on test systems with 33-bus and 15-bus. Keywords: Allocation of DG Fuzzy technique Grey wolf technique Power loss Voltage-profile This is an open access article under the CC BY-SA license. Corresponding Author: Palepu Suresh Babu Department of Electrical and Electronics Engineering, Annamacharya University Rajampet, Andhra Pradesh, India Email: sureshram48@gmail.com 1. INTRODUCTION Small-scale generation situated at or close to the load centers is referred to as "distributed generation" [1]. In the evolving landscape of power systems, distributed generation (DG) has emerged as a significant component in enhancing the reliability, efficiency, and sustainability of electricity networks. DG involves the placement of small-scale power generation units close to the load centers, providing numerous benefits including loss reduction, voltage improvement, and deferral of system upgrades. However, the optimal placement and sizing of these generators are crucial to maximize their potential benefits. Distributed energy, decentralized energy, embedded energy, on-site generation, scattered generation, and dispersed energy have all been other names. There are numerous small-scale power generation methods used for distributed generation. Regardless of whether these technologies are linked to the electrical network, to increase the effectiveness of