Electric Power ›› 2024, Vol. 57 ›› Issue (7): 188-195.DOI: 10.11930/j.issn.1004-9649.202401026
• Power System • Previous Articles Next Articles
Bo TIAN1(), Yue ZHANG2(
), Fei MENG1, Lianfei SHAN2, Haiyang GAO1, Kun TIAN1, Yongtian QIAO2
Received:
2024-01-03
Accepted:
2024-04-02
Online:
2024-07-23
Published:
2024-07-28
Supported by:
Bo TIAN, Yue ZHANG, Fei MENG, Lianfei SHAN, Haiyang GAO, Kun TIAN, Yongtian QIAO. Adaptive Understanding Framework and Key Technology of Power Grid Fault Disposal Information[J]. Electric Power, 2024, 57(7): 188-195.
序号 | 操作意图 | 操作意图表述 | ||
1 | 调阅电网负荷曲线 | 查询宁夏电网负荷曲线 | ||
2 | 查询有功出力 | 查询常胜变有功 | ||
3 | 查询机组备用出力 | 查询大坝电厂#1机备用出力 | ||
4 | 打开有功曲线 | 打开中宁二厂有功曲线 | ||
··· | ··· | ··· | ||
22 | 打开厂站图 | 打开常胜变厂站图 | ||
23 | 查询事故预案 | 查询大侯双回线跳闸预案 | ||
24 | 打开潮流图 | 打开宁夏电网潮流图 |
Table 1 Partial fault disposal test data
序号 | 操作意图 | 操作意图表述 | ||
1 | 调阅电网负荷曲线 | 查询宁夏电网负荷曲线 | ||
2 | 查询有功出力 | 查询常胜变有功 | ||
3 | 查询机组备用出力 | 查询大坝电厂#1机备用出力 | ||
4 | 打开有功曲线 | 打开中宁二厂有功曲线 | ||
··· | ··· | ··· | ||
22 | 打开厂站图 | 打开常胜变厂站图 | ||
23 | 查询事故预案 | 查询大侯双回线跳闸预案 | ||
24 | 打开潮流图 | 打开宁夏电网潮流图 |
序号 | 操作意图表述 | 标记意图 | 标记槽位 | |||
1 | 查询大侯I线有功曲线 | open_active_power_ output_curve | 大侯I线 | |||
2 | 查询宁夏电网功率曲线 | open_grid_load_curve | 宁夏电网 | |||
··· | ··· | ··· | ··· | |||
17560 | 查询昭沂直流潮流 | query_trend_size | 昭沂直流 |
Table 2 Partial fault disposal information label data
序号 | 操作意图表述 | 标记意图 | 标记槽位 | |||
1 | 查询大侯I线有功曲线 | open_active_power_ output_curve | 大侯I线 | |||
2 | 查询宁夏电网功率曲线 | open_grid_load_curve | 宁夏电网 | |||
··· | ··· | ··· | ··· | |||
17560 | 查询昭沂直流潮流 | query_trend_size | 昭沂直流 |
样本集 | 精准率 | 召回率 | F1值 | |||
训练样本 | 99.96 | 99.96 | 99.96 | |||
验证样本 | 99.95 | 99.95 | 99.95 | |||
测试样本 | 99.02 | 99.31 | 99.16 |
Table 3 Recognition effect of fault disposal information multi-task collaborative understanding model 单位:%
样本集 | 精准率 | 召回率 | F1值 | |||
训练样本 | 99.96 | 99.96 | 99.96 | |||
验证样本 | 99.95 | 99.95 | 99.95 | |||
测试样本 | 99.02 | 99.31 | 99.16 |
模型 | 精准率 | 召回率 | F1值 | |||
BERT-CRF | 99.02 | 99.31 | 99.16 | |||
BiLSTM-CRF | 97.99 | 98.40 | 98.19 | |||
DPCNN+IDCNN | 91.47 | 92.21 | 91.84 | |||
TextCNN+BiLSTM-CRF | 97.80 | 98.14 | 97.97 |
Table 4 Calculation results of evaluation indicators for each model 单位:%
模型 | 精准率 | 召回率 | F1值 | |||
BERT-CRF | 99.02 | 99.31 | 99.16 | |||
BiLSTM-CRF | 97.99 | 98.40 | 98.19 | |||
DPCNN+IDCNN | 91.47 | 92.21 | 91.84 | |||
TextCNN+BiLSTM-CRF | 97.80 | 98.14 | 97.97 |
1 | 莫石, 徐秋实, 卢子敬, 等. 模糊分割多目标风险框架下电网连锁故障运行风险评估[J]. 中国电力, 2024, 57 (2): 41- 48. |
MO Shi, XU Qiushi, LU Zijing, et al. Fuzzy partitioned multi-objective risk framework based operational risk assessment of cascading failure for power grid[J]. Electric Power, 2024, 57 (2): 41- 48. | |
2 | 皮俊波, 齐世雄, 孙文多, 等. 基于UIE框架的电网故障处置预案实体和事件识别方法[J]. 中国电力, 2023, 56 (12): 138- 146. |
PI Junbo, QI Shixiong, SUN Wenduo, et al. Entity and event recognition method for power grid fault handling plan based on UIE framework[J]. Electric Power, 2023, 56 (12): 138- 146. | |
3 |
毕超豪, 史艳刚, 付志超, 等. 基于MMC的多端直流电网通用计算方法及拓扑优化研究[J]. 智慧电力, 2023, 51 (4): 99- 106.
DOI |
BI Chaohao, SHI Yangang, FU Zhichao, et al. MMC-based general calculation method and topology optimization of DC power grid[J]. Smart Power, 2023, 51 (4): 99- 106.
DOI |
|
4 | 袁泉, 周海峰, 黄金满, 等. 电网故障诊断解析模型的改进二进制增益共享知识算法求解[J]. 电力系统保护与控制, 2023, 51 (24): 175- 187. |
YUAN Quan, ZHOU Haifeng, HUANG Jinman, et al. An improved binary Gaining-sharing knowledge-based algorithm for solving the analytic model of power grid fault diagnosis[J]. Power System Protection and Control, 2023, 51 (24): 175- 187. | |
5 |
程学珍, 庄学山, 孟璐莎. 基于加权模糊的时序约束Petri网电网故障诊断方法[J]. 智慧电力, 2023, 51 (11): 83- 90.
DOI |
CHENG Xuezhen, ZHUANG Xueshan, MENG Lusha. Fault diagnosis method of power grid based on weighted fuzzy temporal constraint petri net[J]. Smart Power, 2023, 51 (11): 83- 90.
DOI |
|
6 | 王洪彬, 周念成, 黄睿灵, 等. 基于深度学习的110kV电网监控信号语义解析及态势感知模型[J]. 电力系统保护与控制, 2023, 51 (2): 160- 168. |
WANG Hongbin, ZHOU Niancheng, HUANG Ruiling, et al. 110 kV signal semantic analysis and situation awareness model based on deep learning theory for a power system monitoring system[J]. Power System Protection and Control, 2023, 51 (2): 160- 168. | |
7 | 孙端航, 李本新. 考虑风电不确定性的电网状态检修策略[J]. 东北电力大学学报, 2023, 43 (4): 65- 73. |
SUN Duanhang, LI Benxin. Condition-based maintenance scheduling for power transmission system considering wind power uncertainty[J]. Journal of Northeast Electric Power University, 2023, 43 (4): 65- 73. | |
8 | 刘育权, 宋禹飞, 梁锦照, 等. 电力设备数字化标准一体化支撑智能制造[J]. 南方电网技术, 2022, 16 (12): 46- 53. |
LIU Yuquan, SONG Yufei, LIANG Jinzhao, et al. Digital standardization integration on power equipment for intelligent manufacturing[J]. Southern Power System Technology, 2022, 16 (12): 46- 53. | |
9 | 王中行, 周元贵, 张学广. 基于人工智能算法的风电机组状态监测和故障诊断技术研究综述[J]. 东北电力大学学报, 2024, 44 (1): 42- 51. |
WANG Zhongxing, ZHOU Yuangui, ZHANG Xueguang. Review of artificial intelligence algorithms-based wind turbine condition monitoring and fault diagnosis techniques[J]. Journal of Northeast Electric Power University, 2024, 44 (1): 42- 51. | |
10 |
王福贺, 海威, 张越, 等. 电网线路故障处置智能调度机器人研究及应用[J]. 电气自动化, 2021, 43 (3): 1- 3, 23.
DOI |
WANG Fuhe, HAI Wei, ZHANG Yue, et al. Research and application of intelligent dispatching robots handling grid line faults[J]. Electrical Automation, 2021, 43 (3): 1- 3, 23.
DOI |
|
11 | 乔骥, 王新迎, 闵睿, 等. 面向电网调度故障处理的知识图谱框架与关键技术初探[J]. 中国电机工程学报, 2020, 40 (18): 5837- 5849. |
QIAO Ji, WANG Xinying, MIN Rui, et al. Framework and key technologies of knowledge-graph-based fault handling system in power grid[J]. Proceedings of the CSEE, 2020, 40 (18): 5837- 5849. | |
12 | 郭榕, 杨群, 刘绍翰, 等. 电网故障处置知识图谱构建研究与应用[J]. 电网技术, 2021, 45 (6): 2092- 2100. |
GUO Rong, YANG Qun, LIU Shaohan, et al. Construction and application of power grid fault handing knowledge graph[J]. Power System Technology, 2021, 45 (6): 2092- 2100. | |
13 | 王骏东, 杨军, 裴洋舟, 等. 基于知识图谱的配电网故障辅助决策研究[J]. 电网技术, 2021, 45 (6): 2101- 2112. |
WANG Jundong, YANG Jun, PEI Yangzhou, et al. Distribution network fault assistant decision-making based on knowledge graph[J]. Power System Technology, 2021, 45 (6): 2101- 2112. | |
14 |
张晓华, 冯长有, 王永明, 等. 电网调控机器人设计思路[J]. 电力系统自动化, 2019, 43 (13): 1- 8.
DOI |
ZHANG Xiaohua, FENG Changyou, WANG Yongming, et al. Design ideas of robotic dispatcher for power grid[J]. Automation of Electric Power Systems, 2019, 43 (13): 1- 8.
DOI |
|
15 | 李明节, 陶洪铸, 许洪强, 等. 电网调控领域人工智能技术框架与应用展望[J]. 电网技术, 2020, 44 (2): 393- 400. |
LI Mingjie, TAO Hongzhu, XU Hongqiang, et al. The technical framework and application prospect of artificial intelligence application in the field of power grid dispatching and control[J]. Power System Technology, 2020, 44 (2): 393- 400. | |
16 | 殷婕. 基于数据挖掘的输电断面动态极限传输功率在线构建方法[J]. 东北电力大学学报, 2023, 43 (1): 69- 75. |
YIN Jie. Online construction of dynamic total transfer capability of transmission interface based onData mining technology[J]. Journal of Northeast Electric Power University, 2023, 43 (1): 69- 75. | |
17 |
陈郑平, 米为民, 林静怀, 等. 电网调控操作智能助手方案探讨[J]. 电力系统自动化, 2019, 43 (22): 173- 178, 186.
DOI |
CHEN Zhengping, MI Weimin, LIN Jinghuai, et al. Discussion on intelligence assistant scheme of dispatching and control operation in power grid[J]. Automation of Electric Power Systems, 2019, 43 (22): 173- 178, 186.
DOI |
|
18 | 张中正, 王蓓, 赵建保, 等. 基于NL2SQL实现电力数据智能交互[J]. 电网技术, 2022, 46 (7): 2564- 2571. |
ZHANG Zhongzheng, WANG Bei, ZHAO Jianbao, et al. Realization of power data intelligent interaction based on NL2SQL[J]. Power System Technology, 2022, 46 (7): 2564- 2571. | |
19 | 余建明, 刘赫, 单连飞, 等. 基于ALBERT和RE2融合模型的电网调度意图识别方法[J]. 电力系统保护与控制, 2022, 50 (12): 144- 151. |
YU Jianming, LIU He, SHAN Lianfei, et al. Method of power grid dispatch intention recognition based on ALBERT and RE2 fusion model[J]. Power System Protection and Control, 2022, 50 (12): 144- 151. | |
20 | 田园, 原野, 刘海斌, 等. 基于BERT预训练语言模型的电网设备缺陷文本分类[J]. 南京理工大学学报, 2020, 44 (4): 446- 453. |
TIAN Yuan, YUAN Ye, LIU Haibin, et al. BERT pre-trained language model for defective text classification of power grid equipment[J]. Journal of Nanjing University of Science and Technology, 2020, 44 (4): 446- 453. | |
21 | SU J, DAI Q Y, GUERIN F, et al. BERT-hLSTMs: BERT and hierarchical LSTMs for visual storytelling[J]. Computer Speech & Language, 2021, 67, 101169. |
22 | ARASE Y, TSUJII J. Transfer fine-tuning of BERT with phrasal paraphrases[J]. Computer Speech & Language, 2021, 66, 101164. |
23 | 蒋晨, 王渊, 胡俊华, 等. 基于深度学习的电力实体信息识别方法[J]. 电网技术, 2021, 45 (6): 2141- 2149. |
JIANG Chen, WANG Yuan, HU Junhua, et al. Power entity information recognition based on deep learning[J]. Power System Technology, 2021, 45 (6): 2141- 2149. | |
24 | 刘赫, 皮俊波, 宋鹏程, 等. 基于混合神经网络的电力调度文本事件抽取方法[J]. 中国电力, 2022, 55 (9): 105- 110, 120. |
LIU He, PI Junbo, SONG Pengcheng, et al. An event extraction method for power dispatching text based on hybrid neural network[J]. Electric Power, 2022, 55 (9): 105- 110, 120. | |
25 | 刘东, 张越, 皮俊波, 等. 面向电网调控信息智能检索的知识图谱构建及应用[J]. 中国电力, 2023, 56 (7): 78- 84. |
LIU Dong, ZHANG Yue, PI Junbo, et al. Construction and application of knowledge graph for intelligent retrieval of power grid dispatching and control information[J]. Electric Power, 2023, 56 (7): 78- 84. |
[1] | Jinying ZHANG, Zhefeng WANG, Hua XIE, Changying YAO, Yanli MIN, XINYing WANG. Development and Application of a Knowledge Retrieval and Analysis System for the Power Industry Based on Knowledge Graph and Large Language Model [J]. Electric Power, 2024, 57(12): 198-205. |
[2] | LIU Dong, ZHANG Yue, PI Junbo, SHAN Lianfei, LIU He, SONG Pengcheng, JIANG TAO. Construction and Application of Knowledge Graph for Intelligent Retrieval of Power Grid Dispatching and Control Information [J]. Electric Power, 2023, 56(7): 78-84. |
[3] | MEI Bingxiao, ZHOU Jinhui, SUN Xiang. Analysis of Distribution Network Information Risks Based on Knowledge Graph and Cellular Automata [J]. Electric Power, 2022, 55(10): 23-31. |
[4] | LI Xiaolu, ZUO Xuan, LIU Riliang, LU Yiming, LI Congli, LIN Shunfu. SHACL-Based Validation Method of Knowledge Graph for Power System Model [J]. Electric Power, 2022, 55(1): 119-125,228. |
[5] | LI Xinpeng, XU Jianhang, GUO Ziming, LI Junliang, NING Wenyuan, WANG Zhenxue. Construction and Application of Knowledge Graph of Power Dispatching Automation System [J]. Electric Power, 2019, 52(2): 70-77,157. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||