Efficiency Improvement of Solar Panels Through Parasitic Parameters Extraction and Maximum Power Improvement with Enhanced Slime Mold Optimization Under Partial Shading Conditions

Abstract

Abstract Solar energy offers several environmental, economic, and energy security advantages. Parasitic parameters and shading on solar panels can reduce efficiency. This paper presents a bio-inspired Enhanced Slime Mold (ESM) algorithm search strategy to find the optimal power point by simulating the behaviour of slime molds in a virtual environment. In a solar panel, proposed ESM provides not only for parameter extraction but also serves as Maximum Power Point Tracking (MPPT) during Partial Shading Conditions (PSC). Proposed ESM dynamic behaviour is examined under solar irradiation and various temperature conditions. The effectiveness of proposed technique has been validated by extracting parameters from conventional polycrystalline and monocrystalline modules in the form of a 5S-5P arrangement. In the instance of MPPT operation, the proposed ESM algorithm is compared with Ant Bee Colony and Perturb& Observe (ABC-PO) to determine its efficacy. Moreover, during extraction of unknown parameters of solar cell ESM is compared with existing optimization algorithms such as Artificial Bee Swarm Optimization (ABC SO), Genetic Algorithm (GA), Covariant Matrix (CM), Ant Bee Colony (ABC), and Advanced Particle Swarm Optimization (APSO). In this connection, proposed ESM algorithm is superior to above-mentioned algorithms due to high accuracy, a smaller number of computations, and minimum computational time.