基于动态Gamma过程与参数自适应调整的光伏组件剩余寿命预测方法

陈伟; 吴阳; 裴婷婷; 魏智; 吴燕

doi:10.19912/j.0254-0096.tynxb.2024-2395

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

基于动态Gamma过程与参数自适应调整的光伏组件剩余寿命预测方法

更新时间：2026-07-21

- 基于动态Gamma过程与参数自适应调整的光伏组件剩余寿命预测方法
- Vol. 47, Issue 5, Pages: 697-704(2026)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.19912/j.0254-0096.tynxb.2024-2395
  CLC： TM615
- Online First：04 June 2026，
  
  Published：2026
- 稿件说明：
移动端阅览
陈伟, 吴阳, 裴婷婷, et al. 基于动态Gamma过程与参数自适应调整的光伏组件剩余寿命预测方法[J]. 2026, 47(5): 697-704.
DOI：

陈伟, 吴阳, 裴婷婷, et al. 基于动态Gamma过程与参数自适应调整的光伏组件剩余寿命预测方法[J]. 2026, 47(5): 697-704. DOI： 10.19912/j.0254-0096.tynxb.2024-2395.

摘要

针对光伏组件退化过程的动态变化特性及对剩余寿命自适应预测的需求

建立基于动态Gamma过程的退化模型（DGDPM）

并提出一种结合退化模型与参数自适应调整的光伏组件剩余寿命预测方法。首先

构建基于DGDPM的光伏组件功率退化模型

刻画组件退化过程的时间变化规律;然后

利用退化数据对Gamma过程分布参数进行动态调整

基于插值法提高数据分辨率

并使用滑动窗口法进行实时更新

以优化预测结果。通过对不同阶段、不同组件退化数据的建模与分析

验证了所提方法在光伏组件剩余寿命实时预测中的准确性和优越性。

Abstract

To address the dynamic characteristics of photovoltaic （PV） module degradation and the need for adaptive remaining useful life （RUL） prediction

a Dynamic Gamma Degradation Process Model （DGDPM） is established

and a PV module RUL prediction method integrating degradation modeling with adaptive parameter updating is proposed. First

a power degradation model for PV modules based on the DGDPM is constructed to characterize the time-varying behavior of the degradation process. Subsequently

the distribution parameters of the Gamma process are dynamically adjusted using real-time degradation data. An interpolation technique is introduced to enhance data resolution

while a sliding window mechanism is employed to enable real-time parameter updating

thereby improving prediction accuracy and robustness. Through modeling and analysis of degradation data from different stages and different PV modules

the proposed method is validated in terms of its accuracy and superiority for real-time RUL prediction of PV modules.

关键词

Keywords

references

NARENDRA A, VENKATARAMANA NAIK N, PANDA A K, et al.A comprehensive review of PV driven electrical motors[J]. Solar energy, 2020, 195: 278-303.

VOSWINCKEL S, MIKOLAJICK T, WESSELAK V.Influence of the active leakage current pathway on the potential induced degradation of CIGS thin film solar modules[J]. Solar energy, 2020, 197: 455-461.

PARK S H, KIM J H.Lifetime estimation of LED lamp using gamma process model[J]. Microelectronics reliability, 2016, 57: 71-78.

张波, 胡昌华, 张浩, 等. 机器学习应用于随机退化设备剩余寿命预测的综述[J]. 哈尔滨工程大学学报, 2024, 45(9): 1783-1790.

毛黎明, 杨先振. 故障物理分析与机械可靠性(五): 机械可靠性设计与试验[J]. 航空标准化与质量, 2000(2): 40-43.

王平, 杜炜, 张海宁, 等. 表面积灰影响光伏组件泄漏电流与衰减寿命的研究[J]. 太阳能学报, 2019, 40(1): 119-125.

KOEHL M, HOFFMANN S, WIESMEIER S.Evaluation of damp-heat testing of photovoltaic modules[J]. Progress in photovoltaics: research and applications, 2017, 25(2): 175-183.

CHIN V J, SALAM Z, ISHAQUE K.An accurate modelling of the two-diode model of PV module using a hybrid solution based on differential evolution[J]. Energy conversion and management, 2016, 124: 42-50.

HUANG C, WANG L.Simulation study on the degradation process of photovoltaic modules[J]. Energy conversion and management, 2018, 165: 236-243.

PHINIKARIDES A, KINDYNI N, MAKRIDES G, et al.Review of photovoltaic degradation rate methodologies[J]. Renewable and sustainable energy reviews, 2014, 40: 143-152.

JORDAN D C, DELINE C, KURTZ S R, et al.Robust PV degradation methodology and application[J]. IEEE journal of photovoltaics, 2018, 8(2): 525-531.

MEYERS B, DECEGLIE M, DELINE C, et al.Signal processing on PV time-series data: robust degradation analysis without physical models[J]. IEEE journal of photovoltaics, 2020, 10(2): 546-553.

XU Z B, HONG Y L, JIN R.Nonlinear general path models for degradation data with dynamic covariates[J]. Applied stochastic models in business and industry, 2016, 32(2): 153-167.

PAN R, KUITCHE J, TAMIZHMANI G.Degradation analysis of solar photovoltaic modules: Influence of environmental factor[C]//2011 Proceedings-Annual Reliability and Maintainability Symposium. Lake Buena Vista, FL, USA, 2011: 1-5.

PARK S H, KIM J H.Application of gamma process model to estimate the lifetime of photovoltaic modules[J]. Solar energy, 2017, 147: 390-398.

陈伟, 雷欢, 裴婷婷, 等. 基于退化轨迹和Wiener模型的光伏组件剩余寿命预测方法[J]. 太阳能学报, 2023, 44(7): 175-181.

陶海全, 方振雷, 孙勇, 等. 薄膜光伏组件可靠性测试研究[J]. 太阳能学报, 2025, 46(2): 410-415.

FAHIM S R, HASANIEN H M, TURKY R A, et al.A comprehensive review of photovoltaic modules models and algorithms used in parameter extraction[J]. Energies, 2022, 15(23): 8941.

唐圣学, 宋晓, 李明, 等. 光伏组件寿命衰退建模与寿命预测高斯随机过程回归方法研究[J]. 太阳能学报, 2022, 43(12): 41-49.

刘卫东, 文根, 鄢伟安, 等. 基于退化数据和非线性伽马过程的光伏组件现场寿命评估[J]. 计算机集成制造系统, 2021, 27(12): 3494-3502.

王瑞洁, 惠周利, 杨明. 基于间接健康指标的高斯过程回归对锂电池SOH预测[J]. 储能科学与技术, 2023, 12(2): 560-569.

LI N P, WANG M Y, LEI Y G, et al.A nonparametric degradation modeling method for remaining useful life prediction with fragment data[J]. Reliability engineering & system safety, 2024, 249: 110224.

LIM S, LEE T K, LEE S, et al.Adaptive sliding window for hierarchical pose-graph-based SLAM[C]//2012 12th International Conference on Control, Automation and Systems. Jeju, Korea, 2012: 2153-2158.

李奎, 李正广, 段宇, 等. 基于Gamma过程的交流接触器剩余电寿命仿真预测[J]. 电测与仪表, 2018, 55(16): 105-111.

李亚光. 复杂数据多变点分析的若干问题研究[D]. 合肥: 中国科学技术大学, 2018.

KUITCHE J M.A statistical approach to solar photovoltaic module lifetime prediction[D]. Tempe: Arizona State University, 2014.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

RELIABILITY ASSESSMENT OF PHOTOVOLTAIC MODULES WITH INITIAL DEGRADATION RANDOM CHARACTERISTICS

不同坡度地形下光伏组件风荷载特性研究

PHOTOVOLTAIC MODULE DEFECT DETECTION METHOD BASED ON IMPROVED YOLOV5S NETWORK

IMPROVED PERTURBATION OBSERVATION METHOD MPPT CONTROL BASED ON NEURAL NETWORK

基于MFM-CG-DBN模型的高渗透率光伏微电网发电功率短期预测

Related Author

Yan Weian

Kong Wenqi

Liu Weidong

Xiong Yuhui

Zhang Jinjing

王龙威

李如

杨剑新

Related Institution

POWERCHINA Jiangxi Electric Power Engineering Co., Ltd.

College of Mechanical & Electrical Engineering, Nanchang University

College of Transportation and Logistics, East China Jiaotong University

College of Aeronautical Manufacturing Engineering, Nanchang Hangkong University

Shaanxi Joint Laboratory of Artificial Intelligence

AI问答

⁰