Power System Transient Stability Preventive Control Optimization Method Driven by Stacking Ensemble Learning

doi:10.12096/j.2096-4528.pgt.22165

Abstract

Abstract:

Aiming at the contradiction between the rapidity requirement of online calculation of transient stability preventive control and the computational complexity of time-domain equations, a stacking ensemable learning driven optimization method for power system transient stability preventive control was proposed. Firstly, a transient stability estimator based on a stacking ensemble deep belief network was constructed to replace the nonlinear differential algebraic equation solution process required for transient stability determination. Secondly, the trained transient stability estimator was used as a transient stability constraint discriminator, which was embedded in the iterative optimization process of the Aptenodytes Forsteri optimization algorithm. Finally, with the goal of minimizing the cost of preventive control, a stacking ensemble learning driven power system transient stability preventive control optimization algorithm was established. The algorithm realized the efficient judgment of transient stability constraints in preventive control, and improved the decision-making level of preventive control for power generation rescheduling. Based on the IEEE39 bus system, the proposed preventive control method was verified by experiments. The results show that the method has achieved good results in both evaluation accuracy and calculation efficiency.

Key words: power system, stacking ensemble learning, Aptenodytes Forsteri optimization algorithm, transient stability, preventive control

CLC Number:

TM 712

Xiaojie PAN, Youping XU, Zhijun XIE, Yukun WANG, Mujie ZHANG, Mengxuan SHI, Kun MA, Wei HU. Power System Transient Stability Preventive Control Optimization Method Driven by Stacking Ensemble Learning[J]. Power Generation Technology, 2023, 44(6): 865-874.

Figures/Tables 13

Fig. 1 Schematic diagram of stacked ensemble learning

Fig. 2 Transient stability estimator model based on stacked ensemble DBN

Fig. 3 Model structure of SEDBN-AFO

Fig. 4 Calculation flow chart of transient stability preventive control algorithm

Tab. 1 Structure parameters of stacked ensemble DBN model

模型	隐藏层数	隐藏层单元的数目(自左到右)
DBN-1	3	64， 32， 16
DBN-2	4	128， 64， 32， 16
DBN-3	5	256， 128， 64， 32， 16
DBN-4	3	64， 32， 16
DBN-5	4	128， 64， 32， 16
DBN-6	5	256， 128， 64， 32， 16
DBN-7	3	64， 32， 16
DBN-8	4	128， 64， 32， 16
DBN-9	5	256， 128， 64， 32， 16
元学习器DBN	3	64， 32， 16

Fig. 5 Performance index results of transient stability evaluator

Tab. 2 Performance comparison of different models

模型	RF	SVM	CNN	SAE	集成DBN
识别准确率/%	89.92	93.16	95.70	96.34	98.31

Fig. 6 Performance comparison of stacked ensemble DBN transient stability estimator and each sub-estimator

Fig. 7 Fitness comparison results of different models

Fig. 8 Comparison of generator output before and after system preventive control

Tab. 3 Transient stability prevention and control cost

发电机编号	预防控制前出力/MW	预防控制后出力/MW	有功出力调整/MW	单位调节成本/美元	单机调节成本/美元	总调节成本/美元
1	249.57	255.03	5.46	1.0	5.46	127.52
2	687.28	668.87	-18.41	1.0	18.41
3	648.89	664.04	15.15	1.0	15.15
4	630.92	621.99	-8.93	0.5	4.47
5	507.13	497.29	-9.84	0.5	4.92
6	648.89	650.03	1.14	0.5	0.57
7	559.04	616.75	57.71	0.5	28.85
8	539.08	558.03	18.95	1.0	18.95
9	828.58	781.11	-47.47	0.5	23.74
10	998.29	984.29	-14.00	0.5	7.00

Fig. 9 Comparison of power angle curves of generators before and after preventive control of BUS4 and BUS3

Fig. 10 Comparison of power angle curves of generators before and after preventive control of BUS28 and BUS29

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