Abstract
Enhancing the reliability of photovoltaic (PV) systems is of paramount importance, given their expanding role in sustainable energy production, carbon emissions reduction, and supporting industrial growth. However, PV panels commonly encounter issues that significantly impact their performance. Specifically, the accumulation of dust and the rise in internal temperature lead to a drop in energy production efficiency. The primary issue addressed in this paper is using mathematical modeling to determine the optimal cleaning frequency. This paper first focuses on stochastic modeling for dust accumulation and temperature changes in PV panels, considering varying environmental conditions and proposing a model-based approach to determine the optimal cleaning frequency. Dust accumulation is described using a Non-homogeneous compound Poisson process (NHCPP), while temperature evolution is modeled using Markov chains. Within this framework, we consider the impact of wind speed and rainfall on dust accumulation and temperature. These factors, treated as covariates, are modeled using a two-dimensional time-continuous Markov chain with a finite state space. A Condition-based cleaning policy is proposed and assessed based on the degradation model. Optimal preventive cleaning thresholds and cleaning frequency (periodic and non-periodic) are determined to minimize the long-term average maintenance cost. The gain achieved by non-periodic inspections compared to periodic inspections ranges from 3.83% to 9.37%. Numerical experiments demonstrate the performance of the proposed cleaning policy, highlighting its potential to improve PV system efficiency and reliability.