Risk management and analysis of risk analysis in photovoltaic systems installations with fuzzy systems

Photovoltaic (PV) systems are technologies that directly convert sunlight into electricity. These systems are becoming increasingly popular due to their environmentally friendly and sustainable energy provision. However, various risks may arise during the installation phase of photovoltaic systems. Identifying and effectively managing these risks is vital for the reliability and performance of the systems. Fuzzy logic is a powerful tool for modeling and analyzing systems with uncertainty. Traditional risk analysis methods often categorize risks into clearly defined categories. However, some risks during the installation process of photovoltaic systems may contain uncertainties that cannot be precisely defined. Fuzzy logic offers an ideal solution to address these uncertainties. It provides a framework in which each risk factor can be defined by specific degrees of membership functions, allowing uncertainties to be expressed more accurately. For example, the risk of "weather conditions" can be categorized as "very good," "good," "moderate," "bad," and "very bad" to a certain degree. This approach makes risk analysis more flexible and allows for modeling that is closer to real-world conditions. In this study, a two-input single-output fuzzy logic model was created using the Fuzzy Logic Toolbox available in MATLAB. The model was based on twenty-five rules, utilizing triangular membership functions and Gaussian membership functions, alongside a fuzzy rule table. The fuzzy inference system was defuzzified using the centroid method of the Mamdani type. As a result of the study, a significant difference was observed between the data obtained through traditional methods and those obtained using fuzzy logic methods. The risk assessment studies conducted through fuzzy analysis can assist decision-makers in developing more comprehensive and balanced risk management strategies.