基于深度置信网络状态最优反馈的智能发电控制策略

doi:10.12204/j.issn.1000-7229.2021.10.009

电力建设 ›› 2021, Vol. 42 ›› Issue (10): 78-88.doi: 10.12204/j.issn.1000-7229.2021.10.009

基于深度置信网络状态最优反馈的智能发电控制策略

戚焕兴¹, 殷林飞², 万俊¹, 黄阳龙¹

1.广西电网北海供电局,广西壮族自治区北海市536000
2.广西大学电气工程学院,南宁市530004

收稿日期:2021-03-08 出版日期:2021-10-01 发布日期:2021-09-30
通讯作者: 戚焕兴
作者简介:殷林飞(1990),男,博士,助理教授,主要研究方向为电力系统智能发电控制技术和人工智能技术。
万俊(1990),男,本科,技师,主要从事变电检修工作, 主要研究方向为变电检修技术优化。
黄阳龙(1991),男,硕士,正值调度员,工程师,主要从事电力调度与控制工作,主要研究方向为电力系统调度自动化。
基金资助:
国家自然科学基金(52107081);广西自然科学基金(AD19245001);广西自然科学基金(2020GXNSFBA159025)

Smart Generation Control Strategy Based on State Optimal Feedback of Deep Belief Network

QI Huanxing¹, YIN Linfei², WAN Jun¹, HUANG Yanglong¹

1. Beihai Power Supply Bureau, Guangxi Power Grid, Beihai 536000, Guangxi Zhuang Autonomous Region, China
2. College of Electrical Engineering, Guangxi University, Naning 530004, China

Received:2021-03-08 Online:2021-10-01 Published:2021-09-30
Contact: QI Huanxing
Supported by:
National Natural Scienle Foundation of China(52107081);Natural Scienle Foundation of Guangxi(AD19245001);Natural Scienle Foundation of Guangxi(2020GXNSFBA159025)

摘要/Abstract

摘要：

针对自动发电控制问题中的状态信息测取和最优发电控制策略的难题,结合深度置信网络与最优控制理论,提出了一种应用于自动发电控制领域的深度置信网络状态最优反馈算法。首先,设计了一种适用于发电控制问题的全状态最优反馈控制策略,并引入了深度置信网络学习全状态最优反馈控制特性,以深度置信网络的非线性表达能力弥补传统线性控制的不足,进而实现了非全状态信息反馈下的近似最优发电控制,有效解决了最优发电控制问题中的状态信息测取难题,同时提升了自动发电控制性能。在IEEE标准两区域系统的算例仿真结果表明,所提算法可以实现由自区域频率偏差与传输功率偏差组合反馈下的系统近似最优发电控制,验证了所提方法的有效性、可行性和强鲁棒性。

关键词: 自动发电控制, 深度置信网络, 状态最优反馈, 近似最优, 状态信息, 互联电力系统

Abstract:

According to the theory of deep belief network (DBN) and optimal control, this paper proposes a DBN state optimal feedback algorithm, which is applied to the field of automatic generation control. Firstly, a full-state optimal feedback control strategy suitable for power generation control problem is designed, and the deep-belief network is introduced to learn the characteristics of full-state optimal feedback control. The nonlinear expression ability of deep-belief network is used to make up for the deficiency of traditional linear control, thus the approximate optimal power generation control with incomplete state information feedback is realized, which effectively solves the problem of state information measurement in optimal generation control, and improves the performance of automatic generation control. The simulation results show that the the DBN state optimal feedback algorithm is able to realize the approximate optimal generation control under the combined feedback of self-area frequency deviation and transmission power deviation. The effectiveness, feasibility and strong robustness of the proposed algorithm are verified.

Key words: automatic generation control, deep-belief network, state optimal feedback, approximate optimal, state information, interconnected power systems

中图分类号:

TM761

戚焕兴, 殷林飞, 万俊, 黄阳龙. 基于深度置信网络状态最优反馈的智能发电控制策略[J]. 电力建设, 2021, 42(10): 78-88.

QI Huanxing, YIN Linfei, WAN Jun, HUANG Yanglong. Smart Generation Control Strategy Based on State Optimal Feedback of Deep Belief Network[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(10): 78-88.

图/表 13

图1 两区域互联电力系统负荷-频率控制模型

Fig.1 LFC model of two-area interconnected power system

表1 两区域互联电力系统参数值

Table 1 Parameter values of two-area interconnected power system

图2 基于全状态最优反馈的两区域互联电力系统负荷-频率控制系统

Fig.2 LFC system of Two-area interconnected power system applying FSOF

图3 深度置信网络模型

Fig.3 Model of deep belief network

图4 深度置信网络状态最优反馈算法

Fig.4 DBN state optimal feedback control algorithm

图5 深度置信网络训练过程

Fig.5 DBN training process

图6 DBNSOF控制下的两区域互联系统负荷-频率控制模型

Fig.6 LFC model of two-area interconnected power system under DBNSOF control

图7 阶跃负荷扰动下的自动发电控制测试

Fig.7 Automatic generation control test under step load disturbance

表2 阶跃负荷扰动下的AGC性能指标

Table 2 AGC performance index under step load disturbance

图8 标称参数系统的自动发电控制仿真结果

Fig.8 AGC simulation results of nominal parameter system

图9 参数摄动系统的自动发电控制仿真结果

Fig.9 AGC simulation results of system with uncertain parameters

表3 标称参数系统的AGC仿真结果统计表

Table 3 AGC simulative statistical results of nominal parameter system

表4 参数摄动系统的AGC仿真结果统计表

Table 4 AGC simulative statistical results of system with uncertain parameters

参考文献 27

[1]	SZNAIER M, BIANCHINI F. Mixed L^∞/H_∞ suboptimal controllers for SISO continuous-time systems[J]. IEEE Transactions on Automatic Control, 2016, 40(11): 1831-1840. doi: 10.1109/TAC.1995.7469037 URL
[2]	JOSÉ C G, GABRIEL G W. Optimal H∞ state feedback sampled-data control design of Markov jump linear systems[J]. Automatica, 2015, 54: 182-188. doi: 10.1016/j.automatica.2015.02.011 URL
[3]	HADDAD W M, L’AFFLITTO A. Finite-time stabilization and optimal feedback control[J]. IEEE Transactions on Automatic Control, 2016, 61(4): 1069-1074. doi: 10.1109/TAC.2015.2454891 URL
[4]	白莎, 潘晨玥, 熊思宇, 等. 考虑带外干扰的电力系统频率测量算法[J]. 电网技术, 2019, 43(3): 762-768.
	BAI Sha, PAN Chenyin, XIONG Siyu, et al. A frequency estimation algorithm of power system considering out-of-band interference[J]. Power System Technology, 2019, 43(3): 762-768.
[5]	QI Q Y, ZHANG H S, JI Z J. Further results on stabilization for NCSs with packet losses and transmission delay: UDP case[J]. Journal of the Franklin Institute, 2019, 356(8): 4601-4621. doi: 10.1016/j.jfranklin.2019.04.022 URL
[6]	TAHA A, BAZRAFSHAN M, NUGROHO S A, et al. Robust control for renewable-integrated power networks considering input bound constraints and worst case uncertainty measure[J]. IEEE Transactions on Control of Network Systems, 2019, 6(99): 1210-1222. doi: 10.1109/TCNS.2019.2930452 URL
[7]	SONDHI S, HOTE Y V. Fractional order PID controller for load frequency control[J]. Energy Conversion and Management, 2014, 85(9): 343-353. doi: 10.1016/j.enconman.2014.05.091 URL
[8]	IBRAHEEM N, KUMAR P, HASAN N, et al. Sub-optimal automatic generation control of interconnected power system using constrained feedback control strategy[J]. Electric Machines & Power Systems, 2012, 40(9): 977-994.
[9]	颜伟, 赵瑞锋, 赵霞, 等. 自动发电控制中控制策略的研究发展综述[J]. 电力系统保护与控制, 2013, 41(8): 154-160.
	YAN Wei, ZHAO Ruifeng, ZHAO Xia, et al. Review on control strategies in automatic generation control[J]. Power System Protection and Control, 2013, 41(8): 154-160.
[10]	VARGAS S A, TELLES ESTEVES G R, MACAIRA P M, et al. Wind power generation: A review and a research agenda[J]. Journal of Cleaner Production, 2019, 218(1): 850-870. doi: 10.1016/j.jclepro.2019.02.015 URL
[11]	丁明, 王伟胜, 王秀丽, 等. 大规模光伏发电对电力系统影响综述[J]. 中国电机工程学报, 2014, 34(1): 1-14.
	DING Ming, WANG Weisheng, WANG Xiuli, et al. A review on the effect of large-scale PV generation on power systems[J]. Proceedings of the CSEE, 2014, 34(1): 1-14.
[12]	胡泽春, 罗浩成. 大规模可再生能源接入背景下自动发电控制研究现状与展望[J]. 电力系统自动化, 2018, 42(8): 2-15.
	HU Zechun, LUO Haocheng. Research status and prospect of automatic generation control with integration of large-scale renewable energy[J]. Automation of Electric Power Systems, 2018, 42(8): 2-15.
[13]	邵汉桥, 张籍, 张维. 分布式光伏发电经济性及政策分析[J]. 电力建设, 2014, 35(7): 51-57.
	SHAO Hanjiao, ZHANG Ji, ZHANG Wei. Economy and policy analysis of distributed photovoltaic generation[J]. Electric Power Construction, 2014, 35(7): 51-57.
[14]	胡泽春, 占恺峤, 徐智威, 等. 电动汽车与电网互动的关键问题分析与展望[J]. 电力建设, 2015, 36(7): 6-13.
	HU Zechun, ZHAN Kaiqiao, XU Zhiwei, et al. Analysis and outlook on the key problems of electric vehicle and power grid interaction[J]. Electric Power Construction, 2015, 36(7): 6-13.
[15]	李扬, 韦钢, 马钰, 等. 含电动汽车和分布式电源的主动配电网动态重构[J]. 电力系统自动化, 2018, 42(5): 102-110.
	LI Yang, WEI Gang, MA Yu, et al. Dynamic reconfiguration of active distribution network considering electric vehicles and distributed generations[J]. Automation of Electric Power Systems, 2018, 42(5): 102-110.
[16]	CHEN G G, LI Z J, Zhang Z Z, et al. An improved ACO algorithm optimized fuzzy pid controller for load frequency control in multi area interconnected power systems[J]. IEEE Access, 2020, 8: 6429-6447. doi: 10.1109/ACCESS.2019.2960380 URL
[17]	殷林飞, 余涛. 基于深度Q学习的强鲁棒性智能发电控制器设计[J]. 电力自动化设备, 2018, 38(5): 12-19.
	YIN Linfei, YU Tao. Design of strong robust smart generation controller based on deep Q learning[J]. Electric Power Automation Equipment, 2018, 38(5): 12-19.
[18]	Yin L F, Zhang B, Yu T, et al. Adaptive distributed consensus protocol for automatic generation control of large-scale interconnected power systems[J]. IEEE Access, 2019, 7: 48167-48174. doi: 10.1109/ACCESS.2019.2910567 URL
[19]	李彬, 张洁, 田世明, 等. 智能电网用户域标准化最新进展及发展趋势[J]. 电力建设, 2018, 39 (3): 12-22.
	LI Bin, ZHANG Jie, TIAN Shiming, et al. Recent progress and trend analysis of standardization of smart grid user domain[j]. electric power construction, 2018, 39 (3): 12-22.
[20]	翁毅选, 邓长虹, 黄文涛, 等. 基于最优动态闭环控制的水火电互联大电网自动发电控制策略[J]. 电力自动化设备, 2013, 33(3): 66-71.
	WENG Yixuan, DENG Changhong, HUANG Wentao, et al. AGC based on optimal dynamic closed-loop control for interconnected power grid[J]. Electric Power Automation Equipment, 2013, 33(3): 66-71.
[21]	KHAMPARIA A, SINGH K M. A systematic review on deep learning architectures and applications[J]. Expert Systems, 2019, 36(3): 1-22.
[22]	熊伟丽, 孙文心, 史旭东. 基于曲率信息的改进BP算法及其在FNN中的应用[J]. 系统仿真学报, 2020, 32(1): 1-8. doi: 10.16182/j.issn1004731x.joss.17-0461
	XIONG Weili, SUN Wenxin, SHI Xudong. Curvature-based BP algorithm optimization and its application in FNN[J]. Journal of System Simulation, 2020, 32(1): 1-8.
[23]	MERINO E R, CASTRILLEJO F M, PIN J D, et al. Weighted contrastive divergence[J]. Neural Networks, 2018, 106: 147-156.
[24]	HINTON G E, OSINDERO S, TEH Y W. A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006, 18(7): 1527-1554. doi: 10.1162/neco.2006.18.7.1527 URL
[25]	杨永强, 巴宇, 吴玉琼, 等. 联络线功率控制性能评价标准的实施方式[J]. 电力系统自动化, 2015, 39(10): 100-106.
	YANG Yongqiang, BA Yu, WU Yuqiong, et al. Implementation mode of tie-line power control performance assessment standard[J]. Automation of Electric Power Systems, 2015, 39(10): 100-106.
[26]	JALEELI N, VANSLYCK L S. NERC’s new control performance standards[J]. Power systems IEEE trans on power systems, 1999, 14(3): 1092-1099.
[27]	WANG J, ZHANG B, SUN Z, et al. A novel conjugate gradient method with generalized Armijo search for efficient training of feedforward neural networks[J]. Neurocomputing, 2017, 275(31): 308-316. doi: 10.1016/j.neucom.2017.08.037 URL

基于深度置信网络状态最优反馈的智能发电控制策略

Smart Generation Control Strategy Based on State Optimal Feedback of Deep Belief Network

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 13

参考文献 27

相关文章 6

编辑推荐

Metrics

本文评价

[1]	和萍, 刘东哲, 文福拴, 方祺元, 郑明明, 李钊. 确定风电厂最优落点的概率特征值灵敏度方法[J]. 电力建设, 2021, 42(10): 119-128.
[2]	刘辉，谢良峰，谢海敏，阳育德，覃智君. 考虑电动汽车响应AGC的系统网损评估[J]. 电力建设, 2020, 41(6): 52-59.
[3]	荆江平，杨梓俊，陆晓，沈运帷，王子健，宋杰，邵军军，李扬. 计及补偿效果的需求侧资源调频辅助服务市场机制研究[J]. 电力建设, 2020, 41(2): 30-39.
[4]	文芸，周彬，杜振川，张孝顺，余涛. 计及大规模电动汽车接入的AGC功率动态分配深度交互教学优化算法 [J]. 电力建设, 2017, 38(7): 106-.
[5]	武星黄伟. 基于煤泥搀烧的AGC探索[J]. 电力建设, 2011, 32(12): 69-71.
[6]	刘云杰张健. 超临界机组AGC控制系统优化[J]. 电力建设, 2011, 32(10): 61-64.