摘要
Most manufacturers of solar modules guarantee the minimum performance of their modules for 20 to 25 years, and 30-year warranties have been introduced. The warranty typically guarantees that the modules will perform to at least 90% capacity in the first 10 years and to at least 80% in the following 10 - 15 years. Early degradation resulting from design flaws, materials or processing issues is often apparent from startup to the first few years in service. Importantly, many module failures and performance losses are the result of gradual accumulated damage resulting from long-term outdoor exposure in harsh environments, referred. Many of these processes occur on relatively long time scales and the various degradation processes may be chemical, electrical, thermal or mechanical in nature. These are either initiated or accelerated by the combined stresses of the service environment, in particular solar radiation, temperature and moisture, and other stresses such as salt air, wind and snow. Accelerated Life Testing (ALT) test methodology is normally predicated on first being able to reproduce a specific degradation or failure mode without altering it (correlation);and, second, to produce that result in less than real-time acceleration. Degradation and failure may result when an applied stress exceeds material or product strength. This may be a one-time catastrophic event, the result of cyclic fatigue, or a gradual decline in requisite properties due to ageing mechanisms. Engineers in the manufacturing industries have used accelerated test (AT) experiments for many decades. The purpose of AT experiments is to acquire reliability information quickly. Test units of a material, component, subsystem or entire systems are subjected to higher-than-usual levels of one or more accelerating variables such as temperature or stress. Then the AT results are used to predict life of the units at use conditions. The extrapolation is typically justified (correctly or incorrectly) on the basis of physically motivated models or a combination of empirical model fitting with a sufficient amount of previous experience in testing similar units. The need to extrapolate in both time and the accelerating variables generally necessitates the use of fully parametric models. Statisticians have made important contributions in the development of appropriate stochastic models for AT data [typically a distribution for the response and regression relationships between the parameters of this distribution and the accelerating variable(s)], statistical methods for AT planning (choice of accelerating variable levels and allocation of available test units to those levels) and methods of estimation of suitable reliability metrics. This paper provides a review of many of the AT models that have been used successfully in this area.
Most manufacturers of solar modules guarantee the minimum performance of their modules for 20 to 25 years, and 30-year warranties have been introduced. The warranty typically guarantees that the modules will perform to at least 90% capacity in the first 10 years and to at least 80% in the following 10 - 15 years. Early degradation resulting from design flaws, materials or processing issues is often apparent from startup to the first few years in service. Importantly, many module failures and performance losses are the result of gradual accumulated damage resulting from long-term outdoor exposure in harsh environments, referred. Many of these processes occur on relatively long time scales and the various degradation processes may be chemical, electrical, thermal or mechanical in nature. These are either initiated or accelerated by the combined stresses of the service environment, in particular solar radiation, temperature and moisture, and other stresses such as salt air, wind and snow. Accelerated Life Testing (ALT) test methodology is normally predicated on first being able to reproduce a specific degradation or failure mode without altering it (correlation);and, second, to produce that result in less than real-time acceleration. Degradation and failure may result when an applied stress exceeds material or product strength. This may be a one-time catastrophic event, the result of cyclic fatigue, or a gradual decline in requisite properties due to ageing mechanisms. Engineers in the manufacturing industries have used accelerated test (AT) experiments for many decades. The purpose of AT experiments is to acquire reliability information quickly. Test units of a material, component, subsystem or entire systems are subjected to higher-than-usual levels of one or more accelerating variables such as temperature or stress. Then the AT results are used to predict life of the units at use conditions. The extrapolation is typically justified (correctly or incorrectly) on the basis of physically motivated models or a combination of empirical model fitting with a sufficient amount of previous experience in testing similar units. The need to extrapolate in both time and the accelerating variables generally necessitates the use of fully parametric models. Statisticians have made important contributions in the development of appropriate stochastic models for AT data [typically a distribution for the response and regression relationships between the parameters of this distribution and the accelerating variable(s)], statistical methods for AT planning (choice of accelerating variable levels and allocation of available test units to those levels) and methods of estimation of suitable reliability metrics. This paper provides a review of many of the AT models that have been used successfully in this area.
