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

doi:10.11930/j.issn.1004-9649.202307076

Abstract

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

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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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202307076

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

Figures/Tables 9

References 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