2024 27th International Conference on Computer and Information Technology (ICCIT) 20-22 December 2024, Cox’s Bazar, Bangladesh 979-8-3315-1909-4/24/$31.00 ©2024 IEEE Stabilization of Photovoltaic Power System Using GA and PSO Techniques Under Varying Solar Irradiance Md. Redowan Habib 1 , Md. Abdullah Nabil 1 , Abu Hena Shatil 1 , Niloy Goswami 1 , and Abu Shufian 1 1 Department of Electrical & Electronic Engineering American International University-Bangladesh, Dhaka, Bangladesh Email- redowanhabib@gmail.com, nabil.abdullah1337@gmail.com, abu.shatil@aiub.edu, ngoswami@aiub.edu, shufian.eee@gmail.com Abstract— This study offers a comparative analysis of two independent optimization techniques: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) in a PID Controller Utilized in a photovoltaic system to enhance power stability under conditions of fluctuating solar irradiation. The study investigates the performance of each method based on their impact on system stability, overshoot, and undershoot during sudden irradiance changes. Case 1 examines a conventional PID controller, which shows significant overshoot (11.963%) and undershoot (78.463%). In contrast, the GA-optimized system in Case 2 exhibits a notably lower overshoot of 2.104% and undershoot of 51.618%, with faster stabilization. Case 3, using PSO, further improves performance with minimal overshoot (2.142%) and undershoot (51.066%), demonstrating superior system stability. The proportional, integral, and derivative parameters for each case are analyzed, with Case 3 emerging as the most balanced approach. This study highlights the effectiveness of advanced optimization algorithms like GA and PSO in enhancing the reliability and efficiency of PV systems. Keywords— Genetic Algorithm, Particle Swarm Optimization, Photovoltaic System, Power Stability, PID Controller, Solar Irradiance. I. INTRODUCTION With the aim of producing electricity from green energy and sustainable sources, renewable energy technologies have played a vital role in transforming the electricity production around the world [1]. Using such technology will reduce environmental pollution, the consumption of fossil fuels, preserve them for the future and enhance the sustainability of environment [2-3]. This is the primary reason for increased investment on this sector and exploration of the sources for renewable energy. Solar power, geothermal power, hydropower, wind power are the main types of renewable energy. Solar energy is a leading and dependable form of green energy globally, characterized by its cleanliness and widespread availability. A year's worth of Earth's energy needs can be met by harnessing the sun's energy in just 90 minutes [4]. It is anticipated that the amount of solar energy that will reach the planet in the year 2035 will be 4,200 times larger than the amount of energy that will be used by the human population in that year [5]. According to another projection, solar photovoltaic power sources might account for as much as 16% of total power consumption by 2050, with an installed capacity of 4,600 GW [6]. This energy is basically limitless, easy to harvest, less overall cost, environmentally friendly and versatile [7-8]. Many researchers have worked on mathematical models to solve this issue. Rakibuzzaman et al. [9], offer a state-space PV system model with variable solar irradiances and investigate stability. They infer that sun irradiation somewhat affects system stability. Shichao et al. [10], propose a probabilistic state-space model utilizing solar irradiance data. Contrary to [9], solar irradiance changes considerably affect the critical eigenvalue, affecting system stability. Other researchers have proposed optimization techniques to solve the problem. In order to regulate the DC nanoGrid's power output, Saurabh et al. applied sophisticated PI and PID controllers based on real-coded GA and binary GA [11]. Ramesh et al. provide a Grey Wolf Optimization (GWO) method for the stability parameters of a multi-machine system, intending to stabilize the power system. This is accomplished by integrating GWO with PSO, the sine-cosine algorithm, and the Crow-Search method [12]. Table I. gives a description of different authors working on power system outputs’ stability by implementing various methods. TABLE I. KEY CONTRIBUTIONS AND FINDINGS FROM PREVIOUS STUDIES Authors, Year [Ref.] Short Review of The Paper Reza et al., 2023 [13] Review on maximum power point tracking (MPPT) under partial shade conditions using the PSO-MPPT method. Ping et al., 2022 [14] Review on enhancing voltage control effectiveness and mitigating low-frequency power oscillation through the integration of GA and PSO. Chandrakant et al., 2022 [15] Review on power stability and maximum power point tracking using Cuckoo Search (CS) and PSO for MPPT under non uniform irradiance. Aliyu et al., 2021 [16] Review on Power System Stability by utilizing Neuro-Fuzzy Controller (NFC). Ying et al., 2021 [17] Review on a PV system’s stability analysis by varying the solar irradiance and cell temperature. Mingxuan et al., 2020 [18] Review on maximum power exploitation of a PV system by utilizing dynamic w factor based salp swarm algorithm (DWSSA).