基于UIE框架的电网故障处置预案实体和事件识别方法

doi:10.11930/j.issn.1004-9649.202307076

中国电力 ›› 2023, Vol. 56 ›› Issue (12): 138-146.DOI: 10.11930/j.issn.1004-9649.202307076

• 电力系统弹性提升关键技术 • 上一篇下一篇

基于UIE框架的电网故障处置预案实体和事件识别方法

皮俊波¹(), 齐世雄¹(), 孙文多², 楼贤嗣², 沃建栋³, 张越⁴^,⁵(), 姜涛⁴^,⁵, 单连飞⁴^,⁵

1. 国家电网有限公司国家电力调度控制中心，北京　100031
2. 国网浙江电力调度控制中心，浙江杭州　310007
3. 国网浙江省电力有限公司金华供电公司，浙江金华　321016
4. 南瑞集团有限公司（国网电力科学研究院有限公司），江苏南京　211106
5. 北京科东电力控制系统有限责任公司，北京　100192

收稿日期:2023-07-19 接受日期:2023-11-07 出版日期:2023-12-28 发布日期:2023-12-28
作者简介:皮俊波（1984—），男，硕士，高级工程师，从事大电网调控运行控制及管理工作，E-mail： pi-junbo@sgcc.com.cn
齐世雄（1994—），男，硕士，工程师，从事电力系统优化调度、大电网运行控制研究，E-mail： qi-shixiong@sgcc.com.cn
张越（1989—），男，通信作者，硕士，工程师，从事人工智能技术在电力系统的应用研究，E-mail： zhangyue3655@163.com
基金资助:
国家电网有限公司科技项目（5108-202319067A-1-1-ZN）。

Entity and Event Recognition Method for Power Grid Fault Handling Plan Based on UIE Framework

Junbo PI¹(), Shixiong QI¹(), Wenduo SUN², Xiansi LOU², Jiandong WO³, Yue ZHANG⁴^,⁵(), Tao JIANG⁴^,⁵, Lianfei SHAN⁴^,⁵

1. National Electric Power Dispatching and Control Center of State Grid Corporation of China, Beijing 100031, China
2. State Grid Zhejiang Electric Power Dispatching and Control Center, Hangzhou 310007, China
3. Jinhua Power Supply Company of State Grid Zhejiang Electric Power Co., Ltd., Jinhua 321016, China
4. NARI Group Corporation Co., Ltd. (State Grid Electric Power Research Institute Co., Ltd.), Nanjing 211106, China
5. Beijing KeDong Electric Power Control System Co., Ltd., Beijing 100192, China

Received:2023-07-19 Accepted:2023-11-07 Online:2023-12-28 Published:2023-12-28
Supported by:
This work is supported by Science and Technology Project of SGCC (No.5108-202319067A-1-1-ZN).

摘要/Abstract

摘要：

电网故障处置预案区域差异化明显且实体嵌套复杂，仅凭实体类型识别难以准确地将其结构化。提出一种基于通用信息抽取（universal information extraction，UIE）框架的故障处置预案实体和事件识别方法。首先，提出基于句法分析的故障处置预案实体标记方法，生成故障处置预案标记实体；然后，接入ERNIE 3.0编码及双指针解码模块替代UIE框架中生成式模型，通过调节超参数训练故障处置预案实体与实体标签在高维空间的映射关系及实体嵌套关系；最后，基于句法结构组合嵌套复杂的预案实体得到预案事件。通过不同区域电网调控中心预案验证，与其他算法相比，所提方法对故障处置预案具有较高的实体和事件识别精度，能够准确识别预案中故障处置策略和恢复策略，为故障情况下区域电网弹性提升提供支撑。

关键词: 故障处置预案, 通用信息抽取框架, 实体识别, 事件抽取

Abstract:

The power grid fault handling plan is obvious in regional differentiation and the nested entities are complex, so it is difficult to accurately structure the fault handling plan only by entity type identification. An entity and event recognition method for power grid fault handling plan based on an universal information extraction (UIE) framework is proposed. Firstly, an entity marking method for fault handling plan based on syntactic analysis is proposed to generate fault handling plan marking entity. Then, the ERNIE 3.0 encoding and double-pointer decoding module are used to replace the generative model in the UIE framework, and the mapping relationship between entity and entity labels of fault handling plan and the nested entity relationship are trained through adjusting the hyper-parameters of the UIE framework. Finally, the complex nested plan entities are combined to obtain the plan event based on the syntactic structure. Through verification by the plans of different regional power grid dispatch and control centers, the proposed method has higher entity and event recognition accuracy for fault handling plans than other algorithms. It can accurately identify the fault handling strategies and recovery strategies in the plan, and provide support for the improvement of the regional power grid elasticity in the case of fault.

Key words: fault handling plan, universal information extraction framework, entity recognition, event extraction

皮俊波, 齐世雄, 孙文多, 楼贤嗣, 沃建栋, 张越, 姜涛, 单连飞. 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.

Junbo PI, Shixiong QI, Wenduo SUN, Xiansi LOU, Jiandong WO, Yue ZHANG, Tao JIANG, Lianfei SHAN. Entity and Event Recognition Method for Power Grid Fault Handling Plan Based on UIE Framework[J]. Electric Power, 2023, 56(12): 138-146.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202307076

https://www.electricpower.com.cn/CN/Y2023/V56/I12/138

图/表 9

参考文献 26

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参数	设置大小	说明
per_eval_batch_size	32	批处理大小
learning_rate	1×10^–5	最大学习率
num_train_epochs	250	训练周期
attention_dropout	0.1	attention网络失活比例
hidden_act	"gelu"	隐藏层激活函数
hidden_dropout_prob	0.1	隐藏层dropout比例
hidden_size	768	隐藏层神经元个数
initializer_range	0.02	标准偏差
intermediate_size	3072	中间层神经元个数
max_position_emb	2048	序列最大长度
num_attention_heads	12	attention头数
num_hidden_layers	12	transformer层数
pool_act	"tanh"	池化层激活函数
vocab_size	40000	词典大小

参数	设置大小	说明
per_eval_batch_size	32	批处理大小
learning_rate	1×10^–5	最大学习率
num_train_epochs	250	训练周期
attention_dropout	0.1	attention网络失活比例
hidden_act	"gelu"	隐藏层激活函数
hidden_dropout_prob	0.1	隐藏层dropout比例
hidden_size	768	隐藏层神经元个数
initializer_range	0.02	标准偏差
intermediate_size	3072	中间层神经元个数
max_position_emb	2048	序列最大长度
num_attention_heads	12	attention头数
num_hidden_layers	12	transformer层数
pool_act	"tanh"	池化层激活函数
vocab_size	40000	词典大小

序号		事件生成模式
1		act-obj-com-endadv
2		sub-act-obj-com-endadv
3		preadv-sub-act-obj-com-endadv
4		act-preind-ind-ind-endind-com
5		preadv-act-preind-ind-ind-endind-com-endadv
6		sub-act-preind-ind-ind-endind-com-endadv

序号		事件生成模式
1		act-obj-com-endadv
2		sub-act-obj-com-endadv
3		preadv-sub-act-obj-com-endadv
4		act-preind-ind-ind-endind-com
5		preadv-act-preind-ind-ind-endind-com-endadv
6		sub-act-preind-ind-ind-endind-com-endadv

实体类别	精确率/%	召回率/%	F₁/%
sub	98.44	98.44	98.44
act	100.00	98.73	99.36
obj	98.25	98.82	98.53
com	100.00	100.00	100.00
preadv	100.00	100.00	100.00
endadv	94.12	100.00	96.97
preind	92.60	92.60	92.60
endind	97.78	100.00	98.88
ind	97.30	95.58	96.43

基于UIE框架的电网故障处置预案实体和事件识别方法

Entity and Event Recognition Method for Power Grid Fault Handling Plan Based on UIE Framework

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参考文献 26

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模型	精确率/%	召回率/%	F₁值/%
UIE框架	97.61	98.24	97.91
BERT+BiLSTM-CRF	94.69	95.22	94.93
word2vec+BiLSTM-CRF	93.34	92.78	92.98

实体识别		内容展示
输入模板		模板提示 = ['sub'，'act'，'obj'，'com'，'preadv'，'endadv'，'preind'，'endind'，'ind']
输入文本		"xx省调增加xx机组出力，控制xx双回线潮流不超过10万千瓦。"
实体识别结果		'sub':[{'text': 'xx省调', 'start': 0, 'end': 4}] 'act':[{'text': '增加', 'start':4, 'end': 6}, 　　 {'text': '控制', 'start': 13, 'end': 15,}] 'obj':[{'text': 'xx机组', 'start':6, 'end': 10,}, 　　 {'text': 'xx双回线', 'start':15, 'end':20}] 'com':[{'text': '出力', 'start':10, 'end':12 }, 　　 {'text': '潮流', 'start':20, 'end':22}] 'preadv':[] 'endadv':[{'text': '不超过10万千瓦', 'start':22, 'end':30}] 'preind':[] 'endind':[] 'ind':[]

模态框（Modal）标题

基于UIE框架的电网故障处置预案实体和事件识别方法

Entity and Event Recognition Method for Power Grid Fault Handling Plan Based on UIE Framework

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图/表 9

参考文献 26

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