Power System Chaos Prediction Based on DLSTM with Genetic Attention Mechanism

doi:10.19596/j.cnki.1001-246x.8400

Abstract

Abstract:

A deep learning algorithm using deep long short-term memory and genetic attention mechanism (DLSTM-GA) is proposed for the prediction of chaotic behavior of power system. With shared parameters, attention mechanism is added to optimize DLSTM model based on genetic algorithm. One can find potential characteristics in time sequence and avoid the local optimization. Inspired by evolutionary computation of optimization method, the method is a good way to learn parameters in the attention layer. It shows that the trained DLSTM-GA network not only has higher prediction accuracy than the reference model, but also has long-term prediction ability.

Key words: power system, deep learning, deep long short-term memory, chaos prediction, attention mechanism

Yingdong LU, Duqu WEI. Power System Chaos Prediction Based on DLSTM with Genetic Attention Mechanism[J]. Chinese Journal of Computational Physics, 2022, 39(3): 371-378.

Figures/Tables 8

Fig.1 Illustration of DLSTM using genetic algorithm with optimize attention mechanism

Table 1 Symbols and values of excitation-limited power system parameters

符号	参数	数值	符号	参数	数值
x_d	发电机d轴同步电抗	1	E_{fd_max}	励磁控制器输出上限	5
x′_d	发电机d轴瞬变电抗	0.4	E_{fd_min}	励磁控制器输出下限	0
T′_d0	发电机d轴励磁绕组时间常数	10	E_fd0	限制器输入参考电压	2
M	发电机转动惯量	10	T_A	励磁控制器时间常数	1

Fig.2 Time series of test set ω(t) of a power system considering excitation constraints

Fig.3 The training process of DLSTM-GA with genetic algorithm

Fig.4 Heat maps of the distribution of attention

Fig.5 Test data and forecast data of DLSTM-GA

Fig.6 The performance of long-term prediction of DLSTM-GA

Table 2 Best results of DLSTM-GA, DLSTM-Attention, DLSTM, Multi-RNN, DGRU, DA-RNN, LSTM-Attention and LSTM

模型	RMSE	MAE
DLSTM-GA	0.006 765	0.005 720
DLSTM-Attention	0.010 023	0.008 612
DLSTM	0.019 374	0.0126 98
Multi-RNN	0.028 503	0.018 181
DGRU	0.063 732	0.039 145
DA-RNN	0.052 671	0.032 445
LSTM-Attention	0.108 452	0.082 951
LSTM	0.186 159	0.151 927

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