DOI:10.35833/MPCE.2018.000058 |
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Improved Deep Belief Network and Model Interpretation Method for Power System Transient Stability Assessment |
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Page view: 124
Net amount: 1126 |
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Author:
Shuang Wu1,Le Zheng2,Wei Hu1,Rui Yu3,Baisi Liu3
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Author Affiliation:
1.State Key Laboratory of Power System, Department of Electrical Engineering, Tsinghua University, Beijing 100084, China;2.Stanford University, Stanford, California 94305, USA;3.Southwest Branch, State Grid Corporation of China, Chengdu 610041, China
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Foundation: |
This work was supported by National Natural Science Foundation of China (No. 51777104) and the Science and Technology Project of the State Grid Corporation of China. |
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Abstract: |
The real-time transient stability assessment (TSA) and emergency control are effective measures to suppress accident expansion, prevent system instability, and avoid large-scale power outages in the event of power system failure. However, real-time assessment is extremely demanding on computing speed, and the traditional method is not competent. In this paper, an improved deep belief network (DBN) is proposed for the fast assessment of transient stability, which considers the structural characteristics of power system in the construction of loss function. Deep learning has been effective in many fields, but usually is considered as a black-box model. From the perspective of machine learning interpretation, this paper proposes a local linear interpreter (LLI) model, and tries to give a reasonable interpretation of the relationship between the system features and the assessment result, and illustrates the conversion process from the input feature space to the high-dimension representation space. The proposed method is tested on an IEEE new England test system and demonstrated on a regional power system in China. The result demonstrates that the proposed method has rapidity, high accuracy and good interpretability in transient stability assessment. |
Keywords: |
Transient stability assessment (TSA) ; representation learning ; deep belief network (DBN) ; local linear interpretation (LLI) ; visualization ; emergency control |
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Received:January 26, 2019
Online Time:2020/03/02 |
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