A Prediction Method of New Power System Frequency Characteristics Based on Convolutional Neural Network

doi:10.16183/j.cnki.jsjtu.2023.071

Abstract

Abstract:

In order to solve the problems existing in the traditional frequency analysis method for the frequency analysis of grids with a high proportion of new energy, such as the large amount of calculation, the difficulty of modeling, and the prominent contradiction between the calculation speed and the calculation accuracy, this paper proposes a new frequency characteristic prediction method for the new power system based on convolutional neural network (CNN). First, the main frequency indexes of the power system with a high proportion of new energy under power disturbances are predicted using one-dimensional CNN, including the initial frequency change rate, frequency extremum, and frequency steady-state value. The prediction accuracy is improved by setting reasonable input characteristics and optimizing the parameters of the neural network. Then, the impact of disturbance location and disturbance type is further considered, and the power system characteristic data set containing disturbance information is established by the method of data dimensionality reduction. The input characteristics are constructed by using the principle of three primary channels for reference, and the extended two-dimensional CNN is used to predict the frequency security index, which improves the adaptability of CNN in the frequency analysis of grids with a high proportion of new energy. Finally, the method is verified by an example in the improved BPA 10-machine 39-node model, and the results are compared with the prediction results of the recurrent neural network, which proves that the proposed method has a high accuracy and adaptability.

Key words: new power system, convolutional neural network (CNN), frequency safety index, disturbance information, frequency characteristic prediction

CLC Number:

TM716

LU Wen’an, ZHU Qingxiao, LI Zhaowei, LIU Hui, YU Yiping. A Prediction Method of New Power System Frequency Characteristics Based on Convolutional Neural Network[J]. Journal of Shanghai Jiao Tong University, 2024, 58(10): 1500-1512.

Figures/Tables 20

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Comparison of prediction results of different neural network models

网络类型	$ε - M A E$	R²
RNN	0.0423	0.9024
一维CNN	0.0267	0.9400
二维CNN	0.0017	0.9684

Tab.4

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编号	电力系统运行特征量
1	初始时刻发电机节点输出电磁功率
2	初始时刻发电机节点机械功率
3	初始时刻各节点电压幅值
4	初始时刻各节点电压相角
5	初始时刻各节点有功负荷
6	初始时刻新能源出力
7	故障瞬间发电机节点输出电磁功率
8	故障瞬间发电机节点机械功率
9	故障瞬间各节点电压幅值
10	故障瞬间各节点电压相角
11	故障瞬间各节点有功负荷
12	故障瞬间新能源节点新能源出力
13	发电机节点有功备用容量
14	发电机节点惯性时间常数

预测指标	R²	ε_MAE
频率变化率	0.94	0.03
频率极值	0.96	0.03
频率稳态值	0.93	0.02

数据集批次	训练集		验证集
数据集批次	损失函数	R²	损失函数	R²
1	0.0014	0.9747	0.0012	0.9764
2	0.0013	0.9753	0.0014	0.9710
3	0.0013	0.9758	0.0017	0.9674
4	0.0015	0.9718	0.0018	0.9539
5	0.0014	0.9726	0.0013	0.9731