[1] 赵启承, 虞雁凌. 基于长短期记忆神经网络的生命体触电电流检测 [J]. 传感器与微系统, 2022, 41(1): 142-145. DOI:  10.13873/J.1000-9787(2022)01-0142-04.

ZHAO Q C, YU Y L. Biological body electrocution current detection based on LSTM neural networks [J]. Transducer and microsystem technologies, 2022, 41(1): 142-145. DOI:  10.13873/J.1000-9787(2022)01-0142-04.
[2] 劳永钊, 吴任博, 肖健, 等. 中压配电网线损实时同步监测系统设计方案研究 [J]. 南方能源建设, 2022, 9(增刊1): 139-146. DOI:  10.16516/j.gedi.issn2095-8676.2022.S1.021.

LAO Y Z, WU R B, XIAO J, et al. Research on design scheme of real-time synchronous monitoring system for line loss of medium voltage distribution network [J]. Southern energy construction, 2022, 9(Suppl.1): 139-146. DOI:  10.16516/j.gedi.issn2095-8676.2022.S1.021.
[3] 蔡智萍, 郭谋发, 魏正峰. 基于BP神经网络的低压配电网生命体触电识别方法研究 [J]. 电网技术, 2022, 46(4): 1614-1623. DOI:  10.13335/j.1000-3673.pst.2021.0742.

CAI Z P, GUO M F, WEI Z F. Research on recognition method of living body shock in low-voltage distribution network based on BP neural network [J]. Power system technology, 2022, 46(4): 1614-1623. DOI:  10.13335/j.1000-3673.pst.2021.0742.
[4] 侯瑞. 基于粒子群算法的区域综合能源系统规划及运行优化 [J]. 内蒙古电力技术, 2019, 37(4): 43-48. DOI:  10.3969/j.issn.1008-6218.2019.04.003.

HOU R. Planning of reginal integrated energy system based on particle swarm optimization and its operation optimization [J]. Inner Mongolia electric power, 2019, 37(4): 43-48. DOI:  10.3969/j.issn.1008-6218.2019.04.003.
[5] 关海鸥, 杜松怀, 苏娟, 等. 一种触电信号的自动快速检测模型 [J]. 电网技术, 2013, 37(8): 2328-2335. DOI:  10.13335/j.1000-3673.pst.2013.08.008.

GUAN H O, DU S H, SU J, et al. An automatic and quick detection model of electric shock signals [J]. Power system technology, 2013, 37(8): 2328-2335. DOI:  10.13335/j.1000-3673.pst.2013.08.008.
[6] 陈航宇, 李天友, 杨智奇. 低压配网剩余电流保护运行现状及相关措施分析 [J]. 电气技术, 2021, 22(1): 104-108. DOI:  10.3969/j.issn.1673-3800.2021.01.021.

CHEN H Y, LI T Y, YANG Z Q. Analysis of current operation status and relevant measures of low-voltage distribution network residual current protection [J]. Electrical engineering, 2021, 22(1): 104-108. DOI:  10.3969/j.issn.1673-3800.2021.01.021.
[7] 韩晓慧, 杜松怀, 苏娟, 等. 触电信号暂态特征提取及故障类型识别方法 [J]. 电网技术, 2016, 40(11): 3591-3596. DOI:  10.13335/j.1000-3673.pst.2016.11.046.

HAN X H, DU S H, SU J, et al. Fault transient feature extraction and fault type identification for electrical shock signals [J]. Power system technology, 2016, 40(11): 3591-3596. DOI:  10.13335/j.1000-3673.pst.2016.11.046.
[8] 关海鸥, 刘梦, 李春兰, 等. 基于小波包变换和量子神经网络的触电故障类型识别模型 [J]. 农业工程学报, 2018, 34(5): 183-190. DOI:  10.11975/j.issn.1002-6819.2018.05.024.

GUAN H O, LIU M, LI C L, et al. Classification recognition model of electric shock fault based on wavelet packet transformation and quantum neural network [J]. Transactions of the Chinese society of agricultural engineering, 2018, 34(5): 183-190. DOI:  10.11975/j.issn.1002-6819.2018.05.024.
[9] 胡文堂, 高胜友, 余绍峰, 等. 统计参数在变压器局部放电模式识别中的应用 [J]. 高电压技术, 2009, 35(2): 277-281. DOI:  10.13336/j.1003-6520.hve.2009.02.007.

HU W T, GAO S Y, YU S F, et al. Application of statistic parameters in recognition of partial discharge in transformers [J]. High voltage engineering, 2009, 35(2): 277-281. DOI:  10.13336/j.1003-6520.hve.2009.02.007.
[10]

CHEN L, HAN W, HUANG Y H, et al. Online fault diagnosis for photovoltaic modules based on probabilistic neural network [J]. European journal of electrical engineering, 2019, 21(3): 317-325. DOI:  10.18280/ejee.210309.
[11]

WANG H H, WANG P, LIU T. Power quality disturbance classification using the S-transform and probabilistic neural network [J]. Energies, 2017, 10(1): 107. DOI: 10.3390/en1001 0107.
[12] 李春兰, 罗杰, 石砦, 等. 基于小波分析和概率神经网络的触电事故识别方法 [J]. 江苏大学学报(自然科学版), 2023, 44(1): 75-81, 88. DOI:  10.3969/j.issn.1671-7775.2023.01.011.

LI C L, LUO J, SHI Z, et al. Electric shock identification method based on probabilistic neural network and wavelet analysis [J]. Journal of Jiangsu University (Natural Science Edition), 2023, 44(1): 75-81, 88. DOI:  10.3969/j.issn.1671-7775.2023.01.011.
[13] 罗杰. 基于生物触电特征的触电事故识别方法研究 [D]. 乌鲁木齐: 新疆农业大学, 2021. DOI:  10.27431/d.cnki.gxnyu.2021.000173.

LUO J. Research on recognition method of electric shock accident based oil biological electric shock characteristics [D]. Urumqi: Xinjiang Agricultural University, 2021. DOI:  10.27431/d.cnki.gxnyu.2021.000173.
[14] 周松林, 茆美琴, 苏建徽. 基于主成分分析与人工神经网络的风电功率预测 [J]. 电网技术, 2011, 35(9): 128-132. DOI:  10.13335/j.1000-3673.pst.2011.09.004.

ZHOU S L, MAO M Q, SU J H. Prediction of wind power based on principal component analysis and artificial neural network [J]. Power system technology, 2011, 35(9): 128-132. DOI:  10.13335/j.1000-3673.pst.2011.09.004.
[15] 马瑞, 康仁, 罗斌, 等. 基于改进主成分分析法的火电机组能耗特征识别方法 [J]. 电网技术, 2013, 37(5): 1196-1201. DOI:  10.13335/j.1000-3673.pst.2013.05.012.

MA R, KANG R, LUO B, et al. An improved principal component analysis based recognition method for energy consumption characteristics of thermal generation unit [J]. Power system technology, 2013, 37(5): 1196-1201. DOI:  10.13335/j.1000-3673.pst.2013.05.012.
[16] 肖颍涛, 王化全, 俞海峰, 等. 基于主成分分析法和模糊综合评价法的配电网评估 [J]. 南方能源建设, 2019, 6(3): 105-112. DOI:  10.16516/j.gedi.issn2095-8676.2019.03.018.

XIAO Y T, WANG H Q, YU H F, et al. Evaluation of distribution network status based on principal component analysis and correspondence analysis [J]. Southern energy construction, 2019, 6(3): 105-112. DOI:  10.16516/j.gedi.issn2095-8676.2019.03.018.
[17] 黄子敬. 基于循环神经网络的内部威胁检测方法研究 [D]. 哈尔滨: 哈尔滨工业大学, 2020. DOI:  10.27061/d.cnki.ghgdu.2020.000634.

HUANG Z J. Research on insider threat detection method based on recurrent neural network [D]. Harbin: Harbin Institute of Technology, 2020. DOI:  10.27061/d.cnki.ghgdu.2020.000634.
[18] 王康, 龚文杰, 段晓燕, 等. 基于PSO算法优化GRU神经网络的短期负荷预测 [J]. 广东电力, 2020, 33(4): 90-96. DOI:  10.3969/j.issn.1007-290X.2020.004.012.

WANG K, GONG W J, DUAN X Y, et al. Short-term load forecasting model of power system based on PSO algorithm to optimize GRU neural network [J]. Guangdong electric power, 2020, 33(4): 90-96. DOI:  10.3969/j.issn.1007-290X.2020.004.012.
[19] 周莽, 高僮, 李晨光, 等. GRU神经网络短期电力负荷预测研究 [J]. 科技创新与应用, 2018, 8(33): 52-53, 57. DOI:  10.3969/j.issn.2095-2945.2018.33.021.

ZHOU M, GAO T, LI C G, et al. Research on short-term power load forecasting based on GRU neural network [J]. Technology innovation and application, 2018, 8(33): 52-53, 57. DOI:  10.3969/j.issn.2095-2945.2018.33.021.
[20] 张国豪, 刘波. 采用CNN和Bidirectional GRU的时间序列分类研究 [J]. 计算机科学与探索, 2019, 13(6): 916-927. DOI:  10.3778/j.issn.1673-9418.1812059.

ZHANG G H, LIU B. Research on time series classification using CNN and Bidirectional GRU [J]. Journal of frontiers of computer science and technology, 2019, 13(6): 916-927. DOI:  10.3778/j.issn.1673-9418.1812059.
[21] 牛哲文, 余泽远, 李波, 等. 基于深度门控循环单元神经网络的短期风功率预测模型 [J]. 电力自动化设备, 2018, 38(5): 36-42. DOI:  10.16081/j.issn.1006-6047.2018.05.005.

NIU Z W, YU Z Y, LI B, et al. Short-term wind power forecasting model based on deep gated recurrent unit neural network [J]. Electric power automation equipment, 2018, 38(5): 36-42. DOI:  10.16081/j.issn.1006-6047.2018.05.005.
[22] 陈立国, 张跃冬, 耿光刚, 等. 基于GRU型循环神经网络的随机域名检测 [J]. 计算机系统应用, 2018, 27(8): 198-202. DOI:  10.15888/j.cnki.csa.006466.

CHEN L G, ZHANG Y D, GENG G G, et al. Detection of random generated names using recurrent neural network with gated recurrent unit [J]. Computer systems & applications, 2018, 27(8): 198-202. DOI:  10.15888/j.cnki.csa.006466.
[23] 王增平, 赵兵, 纪维佳, 等. 基于GRU-NN模型的短期负荷预测方法 [J]. 电力系统自动化, 2019, 43(5): 53-58. DOI: 10.7500/AEPS2018062 0003.

WANG Z P, ZHAO B, JI W J, et al. Short-term load forecasting method based on GRU-NN model [J]. Automation of electric power systems, 2019, 43(5): 53-58. DOI: 10.7500/AEPS2018062 0003.
[24] 郭威, 张凯, 魏新杰, 等. 高渗透率分布式光伏接入的新型电力系统净功率预测 [J]. 电测与仪表, 2022, 59(12): 48-55. DOI:  10.19753/j.issn1001-1390.2022.12.006.

GUO W, ZHANG K, WEI X J, et al. Net power prediction for a novel power system with high permeability distributed photovoltaic access [J]. Electrical measurement & instrumentation, 2022, 59(12): 48-55. DOI:  10.19753/j.issn1001-1390.2022.12.006.