基于GRU-贝叶斯的分布式光伏功率异常检测方法

王耀龙; 吴裕宙; 刘韵艺; 李彬; 苏盛

doi:10.19912/j.0254-0096.tynxb.2023-0465

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太阳能学报 ›› 2024, Vol. 45 ›› Issue (7) : 494-501. DOI: 10.19912/j.0254-0096.tynxb.2023-0465

基于GRU-贝叶斯的分布式光伏功率异常检测方法

王耀龙¹, 吴裕宙², 刘韵艺², 李彬¹, 苏盛¹

作者信息 +

GRU-BAYESIAN BASED METHOD FOR DISTRIBUTED PHOTOVOLTAIC POWER ANOMALY DETECTION

Wang Yaolong¹, Wu Yuzhou², Liu Yunyi², Li Bin¹, Su Sheng¹

Author information +

文章历史 +

摘要

为有效识别分布式光伏故障系统,提出一种基于GRU-贝叶斯神经网络的分布式光伏功率异常检测方法。首先,选取晴天为检测场景,降低天气因素的干扰;然后,引入灰色绝对关联度算法,利用同地区光伏系统出力的相似性,筛除不合格光伏出力数据,构建光伏用户正常的光伏出力数据集。使用GRU-贝叶斯神经网络训练得到用户正常的光伏功率区间再进行检测。最后,用实际数据进行算例分析,表明所提方法的可行性和有效性。

Abstract

For the effective identification of faults in distributed photovoltaic（PV） systems, this study proposes a GRU-Bayesian neural network-based method for anomaly detection in the power output of distributed PV systems.Firstly, sunny days are selected as the detection scene to reduce the interference of weather factors. Then, the gray absolute correlation degree algorithm is introduced to screen out unqualified PV output data by utilizing the similarity of PV system output in the same region and construct a dataset of normal PV output for users. The GRU-Bayesian neural network is used to train and obtain the normal PV power interval for detection. Finally, actual data is used for case analysis, demonstrating the feasibility and effectiveness of the proposed method.

导出引用

王耀龙, 吴裕宙, 刘韵艺, 李彬, 苏盛. 基于GRU-贝叶斯的分布式光伏功率异常检测方法[J]. 太阳能学报. 2024, 45(7): 494-501 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0465

Wang Yaolong, Wu Yuzhou, Liu Yunyi, Li Bin, Su Sheng. GRU-BAYESIAN BASED METHOD FOR DISTRIBUTED PHOTOVOLTAIC POWER ANOMALY DETECTION[J]. Acta Energiae Solaris Sinica. 2024, 45(7): 494-501 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0465

中图分类号： TM615

参考文献

[1] 黎博, 陈民铀, 钟海旺, 等. 高比例可再生能源新型电力系统长期规划综述[J]. 中国电机工程学报, 2023, 43(2): 555-581.
LI B, CHEN M Y, ZHONG H W, et al.A review of long-term planning of new power systems with large share of renewable energy[J]. Proceedings of the CSEE, 2023, 43(2): 555-581.
[2] 王彪, 吕洋, 陈中, 等. 考虑信息时移的分布式光伏机理-数据混合驱动短期功率预测[J]. 电力系统自动化, 2022, 46(11): 67-74.
WANG B, LYU Y, CHEN Z, et al.Hybrid mechanism-data-driven short-term power forecasting of distributed photovoltaic considering information time shift[J]. Automation of electric power systems, 2022, 46(11): 67-74.
[3] 徐可寒, 张哲, 刘慧媛, 等. 光伏电源故障特性研究及影响因素分析[J]. 电工技术学报, 2020, 35(2): 359-371.
XU K H, ZHANG Z, LIU H Y, et al.Study on fault characteristics and its related impact factors of photovoltaic generator[J]. Transactions of China Electrotechnical Society, 2020, 35(2): 359-371.
[4] BOGGARAPU P K, MANICKAM C, LEHMAN B, et al.Identification of pre-existing/undetected line-to-line faults in PV array based on preturn on/off condition of the PV inverter[J]. IEEE transactions on power electronics, 2020, 35(11): 11865-11878.
[5] VERGURA S, MARINO F.Quantitative and computer-aided thermography-based diagnostics for PV devices: part I:framework[J]. IEEE journal of photovoltaics, 2017, 7(3): 822-827.
[6] 刘强, 郭珂, 毛明轩, 等. 一种基于串联等效电阻的光伏故障检测方法[J]. 太阳能学报, 2020, 41(10): 119-126.
LIU Q, GUO K, MAO M X, et al.A photovoltaic fault detection method based on series equivalent resistance[J]. Acta energiae solaris sinica, 2020, 41(10): 119-126.
[7] ANSARI S, SAMET H, GHANBARI T.Fault location in solar farms[J]. IEEE systems journal, 2021, 15(3): 4003-4012.
[8] 马铭遥, 张志祥, 刘恒, 等. 基于I-V特性分析的晶硅光伏组件故障诊断[J]. 太阳能学报, 2021, 42(6): 130-137.
MA M Y, ZHANG Z X, LIU H, et al.Fault diagnosis of crystalline silicon photovoltaic module based on I-V characteristic analysis[J]. Acta energiae solaris sinica, 2021, 42(6): 130-137.
[9] CHEN L A, LI S, WANG X D.Quickest fault detection in photovoltaic systems[J]. IEEE transactions on smart grid, 2018, 9(3): 1835-1847.
[10] RAO S, MUNIRAJU G, TEPEDELENLIOGLU C, et al.Dropout and pruned neural networks for fault classification in photovoltaic arrays[J]. IEEE access, 2021, 9: 120034-120042.
[11] 王康达, 张保会. 远方集中式与就地分布式光伏供电经济性比较[J]. 电力系统自动化, 2017, 41(16): 179-186.
WANG K D, ZHANG B H.Economy comparison of distant ground photovoltaic stations and distributed photovoltaic stations[J]. Automation of electric power systems, 2017, 41(16): 179-186.
[12] 尹德扬, 梅飞, 郑建勇, 等. 分布式光伏系统最优运维周期确定方法[J]. 电力自动化设备, 2022, 42(5): 135-141.
YIN D Y, MEI F, ZHENG J Y, et al.Determination method of optimal operation and maintenance cycles for distributed photovoltaic system[J]. Electric power automation equipment, 2022, 42(5): 135-141.
[13] 马铭遥, 王海松, 马文婷, 等. 基于S-V特性分析的晶硅光伏组件阴影遮挡故障诊断[J]. 太阳能学报, 2022, 43(9): 64-72.
MA M Y, WANG H S, MA W T, et al.Partial shadow fault diagnosis of crystalline silicon photovoltaic module based on S-V characteristic analysis[J]. Acta energiae solaris sinica, 2022, 43(9): 64-72.
[14] GB/T 41734. 3—2022, 公共气象服务, 天气图形符号[S].
GB/T 41734. 3—2022, Public meteorological service, weather graphic symbols[S].
[15] 王飞, 米增强, 甄钊, 等. 基于天气状态模式识别的光伏电站发电功率分类预测方法[J]. 中国电机工程学报, 2013, 33(34): 75-82, 14.
WANG F, MI Z Q, ZHEN Z, et al.A classified forecasting approach of power generation for photovoltaic plants based on weather condition pattern recognition[J]. Proceedings of the CSEE, 2013, 33(34): 75-82, 14.
[16] 国家市场监督管理总局, 国家标准化管理委员会. 分布式光伏发电系统集中运维技术规范: GB/T 38946—2020[S]. 北京: 中国标准出版社, 2020.
State Administration for Market Regulation, Standardization Administration of the People's Republic of China. Specification of centralized operation and maintenance for distributed photovoltaic power system: GB/T 38946—2020[S]. Beijing: Standards Press of China, 2020.
[17] 郑可轲, 牛玉广. 大规模新能源发电基地出力特性研究[J]. 太阳能学报, 2018, 39(9): 2591-2598.
ZHENG K K, NIU Y G.Research on renewable power basement output characteristics[J]. Acta energiae solaris sinica, 2018, 39(9): 2591-2598.
[18] 刘晓艳, 王珏, 姚铁锤, 等. 基于卫星遥感的超短期分布式光伏功率预测[J]. 电工技术学报, 2022, 37(7): 1800-1809.
LIU X Y, WANG J, YAO T C, et al.Ultra short-term distributed photovoltaic power prediction based on satellite remote sensing[J]. Transactions of China Electrotechnical Society, 2022, 37(7): 1800-1809.
[19] 乔颖, 孙荣富, 丁然, 等. 基于数据增强的分布式光伏电站群短期功率预测(二): 网格化预测[J]. 电网技术, 2021, 45(6): 2210-2218.
QIAO Y, SUN R F, DING R, et al.Distributed photovoltaic station cluster short-term power forecasting part Ⅱ: gridding prediction[J]. Power system technology, 2021, 45(6): 2210-2218.
[20] 丁坤, 陈富东, 翁帅, 等. 基于I-V特性灰色关联分析的光伏阵列健康状态评估[J]. 电网技术, 2021, 45(8): 3087-3095.
DING K, CHEN F D, WENG S, et al.Health state evaluation of photovoltaic array based on I-V characteristics and grey relational analysis[J]. Power system technology, 2021, 45(8): 3087-3095.
[21] 王开艳, 杜浩东, 贾嵘, 等. 基于相似日聚类和QR-CNN-BiLSTM模型的光伏功率短期区间概率预测[J]. 高电压技术, 2022, 48(11): 4372-4388.
WANG K Y, DU H D, JIA R, et al.Short-term interval probability prediction of photovoltaic power based on similar daily clustering and QR-CNN-BiLSTM model[J]. High voltage engineering, 2022, 48(11): 4372-4388.
[22] 赵康宁, 蒲天骄, 王新迎, 等. 基于改进贝叶斯神经网络的光伏出力概率预测[J]. 电网技术, 2019, 43(12): 4377-4386.
ZHAO K N, PU T J, WANG X Y, et al.Probabilistic forecasting for photovoltaic power based on improved Bayesian neural network[J]. Power system technology, 2019, 43(12): 4377-4386.
[23] 王鑫, 李慧, 叶林, 等. 考虑风速波动特性的VMD-GRU短期风电功率预测[J]. 电力科学与技术学报, 2021, 36(4): 20-28.
WANG X, LI H, YE L, et al.VMD-GRU based short-term wind power forecast considering wind speed fluctuation characteristics[J]. Journal of electric power science and technology, 2021, 36(4): 20-28.
[24] WAN C, XU Z, PINSON P.Direct interval forecasting of wind power[J]. IEEE transactions on power systems, 2013, 28(4): 4877-4878.
[25] NING Y, LIU Y F, JI Q. Bayesian - BP Neural Network based Short-term Load Forecasting for power system[C]//2010 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE). Chengdu, China, 2010: V2-89-V2-93.
[26] LIU F, TAO Q, YANG D C, et al.Bidirectional gated recurrent unit-based lower upper bound estimation method for wind power interval prediction[J]. IEEE transactions on artificial intelligence, 2022, 3(3): 461-469.