Novel Hybrid MICMAC-TOPSIS based Framework for Solar Photovoltaic Module Cleaning
Аннотация
Solar PV systems need efficient panels and electricity production. In addition to overheating and shadowing, certain locations are prone to PV panel dust. To decrease dust and restore plant performance, certain cleaning processes are required. Maintaining photovoltaic system efficiency requires efficient solar panel cleaning. MIC-MAC and TOPSIS, multi-criteria decision-making methodologies, are used to assess and identify the best cleaning solutions in this research. MICMAC classifies solar panel cleaning factors such surface material, location, tilt, orientation, weather, cleaning frequency, agents, safety, accessibility, cost, efficiency, manufacturer recommendations, and professional services. MICMAC analyzes direct and indirect impacts and dependencies of driving and dependent power factors for solar panel cleaning. TOPSIS rates robot water-based, robot pressure-based, manual, and nano-coating solar panel cleaning techniques. Criteria weights depend on MICMAC relevance. The option matrix is normalized and weighted to produce separation measures from ideal and negative-ideal solutions. Closeness to the finest option sorts cleaning techniques. A full approach shows nano-coating solar panel cleaning is most successful, with a relative closeness of 0.787, followed by two robotic methods and manual cleaning. The MICMAC-TOPSIS system helps choose the optimal solar panel cleaning techniques for performance and sustainability. Research promotes coordinated decision-making for renewable energy technology maintenance optimization.