基于神经网络分位数的分布式光伏发电功率异常识别方法

doi:10.16183/j.cnki.jsjtu.2023.412

摘要/Abstract

摘要：

分布式光伏发电系统点多面广,缺乏科学规范的运维管理体系,同时可用数据匮乏,难以准确识别由气象波动干扰导致的光伏设备异常.针对分布式光伏的运维现状和数据特征,提出一种基于神经网络分位数回归(QRNN)的光伏发电功率异常检测方法.首先分析晴天的太阳辐照度特性,利用晴朗日筛选方法排除阴雨天气的干扰影响;然后对不同电站的出力相关性进行分析,以获取出力相关性高的光伏电站作为横向参考;再将待测电站在不同晴朗日的出力曲线进行纵向对比,排除天气与环境条件等干扰因素.将排除干扰的计量出力有功功率数据输入QRNN模型,得到光伏正常有功功率出力区间,以正常出力区间的功率阈值识别光伏发电功率的异常.对某实际光伏系统数据进行仿真分析,结果表明:该方法能排除气象因素的干扰,准确识别出存在故障的光伏系统,推动分布式光伏的精细化运维.

关键词: 分布式光伏发电, 功率异常检测, 晴朗日筛选, 神经网络分位数, 出力相关性

Abstract:

The distributed photovoltaic power generation system is widely dispersed and lacks a scientific and standardized operation and maintenance management system. Due to the limited availability of data, it is difficult to accurately detect abnormal conditions in photovoltaic devices caused by fluctuations in weather. In this paper, according to the operation and maintenance status and data characteristics of distributed photovoltaic, a quantile regression neural network (QRNN)-based method is proposed for detecting photovoltaic power outliers. First, the solar irradiance characteristics of sunny days are analyzed, and the influence of rainy weather is excluded by using a sunny day screening method. Then, the power output correlation of different power stations is analyzed to identify the photovoltaic stations with high power output correlation, which is used as a horizontal reference. Subsequently, the curves of the power output of the stations tested on different sunny days are compared vertically to eliminate the interfering factors such as weather and environmental conditions. The measured active power data is fed into the QRNN model to establish the normal active power range for the photovoltaic system, whose threshold is used to detect photovoltaic power outliers. The simulation results of actual photovoltaic system data show that the method proposed can eliminate the meteorological influence, accurately identify the faulty photovoltaic system, and promote the fine operation and maintenance of distributed photovoltaic.

Key words: distributed photovoltaic power generation, power outlier detection, sunny day screening, quantile regression neural network (QRNN), power output correlation

中图分类号:

TM615

王晓倩, 周羽生, 毛源军, 李彬, 周文晴, 苏盛. 基于神经网络分位数的分布式光伏发电功率异常识别方法[J]. 上海交通大学学报, 2025, 59(6): 836-844.

WANG Xiaoqian, ZHOU Yusheng, MAO Yuanjun, LI Bin, ZHOU Wenqing, SU Sheng. Distributed Photovoltaic Power Outlier Detection Based on Quantile Regression Neural Network[J]. Journal of Shanghai Jiao Tong University, 2025, 59(6): 836-844.

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异常状态	具体原因	出力数据表征
故障	接线错误,阵列间短路开路	等比例或随机下降
老化	组件和线路电阻老化	等比例下降
遮挡	灰尘、树叶、建筑、树木等	等比例或随机下降
扩容	用户私自扩容骗取发电补贴	增加
限电	对新能源上网消纳能力有限	降低

置信度/%	气候	算法	可靠性/%	敏锐性/W
90	晴朗日	QRNN	99.90	67.3
	晴朗日	QRLSTM	74.40	184.9
	未筛选晴朗日	QRNN	73.78	204.5
	未筛选晴朗日	QRLSTM	80.36	315.8
80	晴朗日	QRNN	92.36	24.7
	晴朗日	QRLSTM	51.60	121.1
	未筛选晴朗日	QRNN	63.31	85.0
	未筛选晴朗日	QRLSTM	57.73	161.6

气候	算法	R_FP/%	R_D/%
晴朗日	QRNN	2.25	95.15
未筛选晴朗日	QRNN	10.26	89.31
晴朗日	iForest	39.60	91.42
未筛选晴朗日	iForest	44.34	85.13
晴朗日	LOF	15.79	92.53
未筛选晴朗日	LOF	7.62	45.63