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| Intelligent Adjustment Method of Power Grid Operation Mode Based on N/N-1 Power Flow Embedded Graph Convolutional Neural Network |
| Duan Shiqi1, Yu Juan1, Yang Zhifang1, Chen Tao2, Zhu Shengyi2 |
1. State Key Laboratory of Power Transmission Equipment Technology Chongqing University Chongqing 400044 China;
2. State Grid Chongqing Electric Power Research Institute Chongqing 401123 China |
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Abstract The adjustment of the operation mode is crucial for ensuring the safety and stability of the power grid, and manual adjustment is still the main method in current industry practices. However, with the integration of numerous renewable energy sources and the application of power electronic equipment, both the scale and complexity of the power grid continue to increase, resulting in the manual adjustment method that relies on manual experience to adjust repeatedly by trial-and-error method faces the following severe challenges: low efficiency, lack of theoretical guidance. Recently, some methods for operation mode adjustment have been proposed, but most of them cannot provide the transition process and intrinsic connection between the operation modes before and after the adjustment, making it difficult for the staff to explain and analyze the operation mode adjustment issues, which affects the decision-making efficiency. To solve these problems, this paper proposes an intelligent adjustment method of power grid operation mode based on N/N-1 power flow embedded graph convolutional neural network (GCNN).
Firstly, the forward propagation expression of the convolutional module of the design diagram is derived from the N/N-1 power flow physical model, and the convolutional forward propagation strategy based on N/N-1 power flow embedding is proposed to effectively extract the node and branch features of the power system in N/N-1 conditions. Then, taking power flow characteristics in N/N-1 conditions as input/output characteristics, the N/N-1 power flow coupling model based on multi-layer graph convolution and convolutional neural network module collaboration is constructed to characterize the data-driven power flow coupling relationship in N/N-1 conditions. Next, aiming at the operation mode of power flow exceeding the limit under N/N-1 conditions, an intelligent adversarial adjustment method based on N/N-1 power flow coupling relationship is proposed to accurately analyze the adjustment process before and after the adjustment and obtain the effective adjustment strategy for the operation mode, achieving the adjustment of the operation mode of power flow exceeding the limit under N/N-1 conditions to meet the N-1 verification.
The simulation results on the IEEE 30-bus system show that the accuracy of the proposed N/N-1 power flow coupling model for predicting the nodal voltage magnitude, phase angle, branch active power, and reactive power is 99.48%, 99.72%, 99.32%, and 99.81% respectively, which is higher than that of the prediction models constructed based on other neural network architectures (fully connected neural network, convolutional neural network, graph convolutional neural network), indicating that the proposed model can accurately characterize the power flow coupling relationship under N/N-1 conditions. To verify the effectiveness of the proposed intelligent adjustment method for power grid operation mode, the operation mode with power flow limit exceeding in the IEEE 30-bus system and 341-bus system in an area is adjusted, and the adjustment process before and after the adjustment can be accurately analyzed, and the effective adjustment strategy for the operation mode can be obtained. Finally, to prove the convergence of this method, the proposed method is applied to adjust 1000 operation modes with power flow limit exceeding in the two power systems, and the results show that these operation modes can be adjusted to meet the N-1 verification with fewer adjustment times.
The following conclusions can be drawn from the simulation analysis: (1) Compared with the models based on fully connected neural network, convolutional neural network and graph convolutional neural network architectures, the proposed N/N-1 power flow coupling model can more effectively and accurately characterize the power flow coupling relationship under N/N-1 conditions. (2) The proposed intelligent adversarial adjustment method can adjust the operation mode with limit exceeding to meet all static N-1 verification with fewer adjustment times and accurately analyze the adjustment process between the operation modes before and after the adjustment, and it provides the detailed basis for the staff to make the decision of operation mode adjustment.
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Received: 07 October 2024
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2025, Vol. 40 
