上海交通大学学报

上海交通大学学报 >

2023 , Vol. 57 >Issue 11: 1477 - 1491

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2022.157

新型电力系统与综合能源

基于堆叠式降噪自动编码器和深度神经网络的风电调频逐步惯性智能控制

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  • 1.南京理工大学 自动化学院, 南京 210094
    2.东南大学 电气工程学院,南京 210096
    3.国网电力科学研究院有限公司 智能电网保护和运行控制国家重点实验室,南京 211106
王亚伦(1997-),硕士生,从事电力系统频率稳定与控制研究.

收稿日期: 2022-05-13

  修回日期: 2022-09-08

  录用日期: 2022-11-04

  网络出版日期: 2023-03-10

基金资助

江苏省自然科学基金青年项目(BK20220216);智能电网保护和运行控制国家重点实验室开放课题(SGNR0000KJJS2200305)

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Stepwise Inertial Intelligent Control of Wind Power for Frequency Regulation Based on Stacked Denoising Autoencoder and Deep Neural Network

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  • 1. School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China
    2. School of Electrical Engineering, Southeast University, Nanjing 210096, China
    3. National Key Laboratory for Smart Grid Protection and Operation Control, State Grid Electric Power Research Institute Co., Ltd., Nanjing 211106, China

Received date: 2022-05-13

  Revised date: 2022-09-08

  Accepted date: 2022-11-04

  Online published: 2023-03-10

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摘要

风电调频的逐步惯性控制(SIC)策略在负荷波动后提供一个阶跃式功率增发,能够有效阻止系统频率下降,保障电网频率安全.但在其功率恢复阶段,容易出现二次频率跌落现象,需优化SIC以获得更好的调频效果.传统方法存在计算维度高和耗时较长的弊端,难以满足不同场景下快速提供最优控制效果的需求.为实现负荷扰动事件下风电调频的最优逐步惯性快速控制,引入深度学习算法,提出一种基于堆叠式降噪自动编码器(SDAE)和深度神经网络(DNN)的风电调频逐步惯性智能控制方法.首先,使用麻雀搜索算法获得最优参数,使用SDAE高效提取数据特征;随后,基于DNN对数据特征进行学习,并引入加速自适应矩估计优化网络参数,提升网络全局最优参数;最后,应用SDAE-DNN联合方法实现扰动事件后风电调频的逐步惯性在线控制.在IEEE 30节点测试系统中分别对单台风力机和风电场进行仿真分析,与传统方法、浅层反向传播神经网络及原始DNN所得结果对比发现,所提网络结构具有更优的预测精度和泛化能力,该方法能够实现良好的逐步惯性调频效果.

关键词： 逐步惯性控制; 二次频率跌落; 麻雀搜索算法; 堆叠式降噪自动编码器; 深度神经网络

本文引用格式

王亚伦, 周涛, 陈中, 王毅, 权浩 . 基于堆叠式降噪自动编码器和深度神经网络的风电调频逐步惯性智能控制[J]. 上海交通大学学报, 2023 , 57(11) : 1477 -1491 . DOI: 10.16183/j.cnki.jsjtu.2022.157

Abstract

Stepwise inertial control (SIC) provides a step-increase of power after load fluctuation, which can effectively prevent system frequency decline and ensure the safety of grid frequency. However, in the power recovery stage, secondary frequency drop (SFD) is easy to occur. Therefore, it is necessary to optimize SIC to obtain a better frequency regulation effect. The traditional method has the disadvantages of high calculation dimension and long consuming time, which is difficult to meet the requirements of providing the optimal control effect in different scenarios. In order to realize the optimal stepwise inertial fast control of wind power frequency regulation in load disturbance events, this paper introduces the deep learning algorithm and proposes a stepwise inertial intelligent control of wind power for frequency regulation based on stacked denoising autoencoder(SDAE) and deep neural network(DNN). First, sparrow search algorithm (SSA) is used to obtain the optimal parameters, and SDAE is used to extract the data features efficiently. Then, DNN is used to learn the data features, and the accelerated adaptive moment estimation is introduced to optimize the network parameters to improve the global optimal parameters of the network. Finally, the stepwise inertial online control of wind power frequency regulation after disturbance event is realized according to SDAE-DNN. The simulation analysis is conducted for a single wind turbine and a wind farm in the IEEE 30-bus test system. Compared with the results obtained by the traditional method, shallow BP neural network and original DNN network, it is found that the proposed network structure has a better prediction accuracy and generalization ability, and the proposed method can achieve a great effect of stepwise inertia frequency regulation.

Key words： stepwise inertial control (SIC); secondary frequency drop (SFD); sparrow search algorithm (SSA); stacked denoising autoencoder (SDAE); deep neural network (DNN)

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