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于 群,李 浩,屈玉清. 基于深度学习的电网安全态势感知[J]. 科学技术与工程, 2019, 19(35): 273-278.
YU Qun,LI Hao,QU Yu-qing.Security Situational Awareness of Power Grid Based on Deep Learning[J].Science Technology and Engineering,2019,19(35):273-278.
基于深度学习的电网安全态势感知
Security Situational Awareness of Power Grid Based on Deep Learning
投稿时间:2019-04-25  修订日期:2019-07-05
DOI:
中文关键词:  态势感知 指标体系 层次分析法 深度学习 深度神经网络
英文关键词:situational awareness  index system  analytic hierarchy process  deep learning  deep neural network
基金项目:国家电网公司2018年科技项目“基于多沙堆理论的互联电网停电事故预警技术及系统研发”
        
作者单位
于 群 山东科技大学电气与自动化工程学院
李 浩 山东科技大学电气与自动化工程学院
屈玉清 山东科技大学电气与自动化工程学院
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中文摘要:
      随着电网的发展和技术的进步,电网结构日益复杂,能够及时有效地对电网的安全态势进行感知显得尤为重要。深度学习,近些年在文本、语音、图像等方面取得了巨大进展,同时在人工智能领域也占据着重要地位。将深度学习与电网的安全态势感知相结合,提出了基于深度学习的电网安全态势感知。在态势理解阶段,从电网的静态安全性和动态安全性两个方面出发,构建了一套较完整的电网安全态势评价体系,用来表征电网的运行轨迹。在态势预测阶段,构建深度学习模型,完成对电网安全态势的感知。最后以IEEE39节点系统为例,将其与BP神经网络和RBF神经网络预测模型进行了对比分析,验证了深度学习可以有效地对电网的安全态势进行感知,且预测精度高于传统的神经网络模型。
英文摘要:
      With the development of power grids and advances in technology, the grid structure is increasingly complex, and it is particularly important to be able to sense the security posture of the grid in a timely and effective manner. Deep learning has made great progress in text, voice, and image in recent years, and it also plays an important role in the field of artificial intelligence. Combining deep learning with the security situational awareness of the grid, a power-based situational awareness based on deep learning is proposed. In the situation understanding stage, from the two aspects of static security and dynamic security of the power grid, a complete set of grid security situation evaluation system is constructed to characterize the running trajectory of the power grid. In the situation prediction stage, a deep learning model is constructed to complete the perception of the security posture of the power grid. Finally, the IEEE39 node system is taken as an example to compare it with BP neural network and RBF neural network prediction model. It is verified that deep learning can effectively sense the security situation of the power grid, and the prediction accuracy is higher than the traditional neural network model.
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