Shen Yaxin, Fouladirad Mitra, Grall Antoine
LIST3N, Université de Technologie de Troyes, Troyes, France.
M2P2, UMR CNRS 7340, Aix Marseille Université, École Centrale Marseille, Marseille, France.
Heliyon. 2024 Aug 2;10(16):e35390. doi: 10.1016/j.heliyon.2024.e35390. eCollection 2024 Aug 30.
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.
鉴于光伏（PV）系统在可持续能源生产、减少碳排放和支持产业增长方面的作用不断扩大，提高其可靠性至关重要。然而，光伏板通常会遇到严重影响其性能的问题。具体而言，灰尘的积累和内部温度的升高会导致能源生产效率下降。本文解决的主要问题是使用数学建模来确定最佳清洁频率。本文首先关注光伏板灰尘积累和温度变化的随机建模，考虑不同的环境条件，并提出一种基于模型的方法来确定最佳清洁频率。灰尘积累用非齐次复合泊松过程（NHCPP）描述，而温度演变用马尔可夫链建模。在此框架内，我们考虑风速和降雨对灰尘积累和温度的影响。这些因素作为协变量，用具有有限状态空间的二维时间连续马尔可夫链建模。基于退化模型提出并评估了基于状态的清洁策略。确定最佳预防性清洁阈值和清洁频率（定期和不定期），以最小化长期平均维护成本。与定期检查相比，不定期检查实现的收益范围为3.83%至9.37%。数值实验证明了所提出清洁策略的性能，突出了其提高光伏系统效率和可靠性的潜力。
