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光伏发电技术 ★ 4.0

验证光伏组件实测性能与质保条件的方法论

Methodology to validate measured performance and warranty conditions of PV modules

作者 Rana Badr · Yehea I. Ismail
期刊 Solar Energy
出版日期 2025年1月
卷/期 第 295 卷
技术分类 光伏发电技术
相关度评分 ★★★★ 4.0 / 5.0
关键词 PV Panels typically have a 10-25-year warranty to function above a certain percentage of the maximum power under STC.
语言:

中文摘要

摘要 本文提出了一种新颖、高效且精确的方法,用于将电流-电压(I-V)和功率-电压(P-V)曲线从实测条件转换至产品说明书中的标准测试条件(STC),以验证光伏(PV)组件的性能和质保条款。为此,本文介绍了一种方法论,该方法利用任意给定测试条件下光伏组件的实测数据:(1)采用单二极管模型对数据进行建模;(2)通过牛顿-拉夫森法求解一组非线性方程,提取二极管模型参数;(3)求解该模型以生成I-V和P-V曲线;(4)将实测曲线从测量条件映射至STC,并将结果与说明书中的技术指标进行比较。仿真模型通过实测数据进行了验证,能够准确表征光伏组件的电气特性,最大相对误差仅为1.37%。该模型在某一光伏组件上进行了测试,结果表明该组件的性能符合说明书所列的质保标准,在运行前两年后,被测光伏组件的平均功率衰减为−4.88%。

English Abstract

Abstract This paper introduces a new, efficient, and accurate way to transform current–voltage (I-V) and power-voltage (P-V) curves from measurement conditions to the datasheet’s Standard Test Conditions (STC) for validating the performance and warranty of PV modules. To achieve this, a methodology is presented that uses measurement data of a PV module for any given test condition: (1) models it using the single diode model, (2) extracts the diode-model parameters from a set of non-linear equations using the Newton-Raphson method, and (3) solves the model to generate the I-V and P-V curves and (4) maps the measurement curves from measurement conditions to the STC and compares results to datasheet metrics. The simulation model is validated with measurement data and accurately represents the PV module’s electrical characteristics with a maximum relative error of 1.37%. The model is tested on a PV module, and results show that the module’s performance is consistent with the datasheet warranty standards, where the PV module under test has experienced an average power degradation of −4.88% after the first two years of operation.
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SunView 深度解读

该光伏组件性能验证方法对阳光电源SG系列逆变器及iSolarCloud平台具有重要应用价值。通过单二极管模型和牛顿-拉夫逊法精确提取I-V特性曲线,可优化MPPT算法的追踪精度,将误差控制在1.37%以内。该方法可集成至iSolarCloud智能运维平台,实现组件衰减率实时监测(-4.88%/两年),为质保验证和预测性维护提供数据支撑,提升电站全生命周期发电效率和资产管理水平,强化阳光电源端到端解决方案竞争力。