含需求响应资源的电力系统稳定性智能化评估方法
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摘要
首先,阐述了需求响应资源的概率分布特性,并以IES运行成本为优化目标,综合考虑电力系统、天然气系统运行约束及能量耦合约束。建立IES最优能量流(optimal energy flow,OEF)模型,用于求取发电机和耦合环节功率,并将其作为电力系统稳定器的输入;其次,通过搭建不同负荷水平下的暂态仿真模型,得到故障情况下的系统稳定情况;然后,提出基于堆栈降噪自动编码器(stacked denoising auto-encoders,SDAE)的电力系统稳定性评估器的训练方法;最后,在IEEE-39节点电力系统和修改的比利时20节点天然气系统组成的IES中,进行电力系统稳定性智能化评估的算例分析。仿真结果表明,基于SDAE的电力系统稳定性评估器识别精度较高,同时计算效率也较优。
Firstly, the probability distribution characteristics of demand response resources are expounded. The IES(integrated energy system)operating cost is taken as the optimization goal and the power system, natural gas system operation constraints and energy coupling constraints are considered. The IES optimal energy flow(OFF)model is established, which is used as the input of the power system stability estimator to calculate the generator and coupling link power. Secondly, by setting up the transient simulation model under different load levels, the system stability under fault conditions is obtained. The training of power system stability estimator based on stack denoising auto-encoders(SDAE)is proposed. Finally, the IES comprised of the IEEE-39 node power system and the modified Belgian 20-node natural gas system is used to analyze the intelligent evaluation of power system stability. The simulation results show that the SDAE-based power system stability estimator has high recognition accuracy and excellent computational efficiency.
Firstly, the probability distribution characteristics of demand response resources are expounded. The IES(integrated energy system)operating cost is taken as the optimization goal and the power system, natural gas system operation constraints and energy coupling constraints are considered. The IES optimal energy flow(OFF)model is established, which is used as the input of the power system stability estimator to calculate the generator and coupling link power. Secondly, by setting up the transient simulation model under different load levels, the system stability under fault conditions is obtained. The training of power system stability estimator based on stack denoising auto-encoders(SDAE)is proposed. Finally, the IES comprised of the IEEE-39 node power system and the modified Belgian 20-node natural gas system is used to analyze the intelligent evaluation of power system stability. The simulation results show that the SDAE-based power system stability estimator has high recognition accuracy and excellent computational efficiency.
引文
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[15]赖联有,许伟坚.基于零相位误差滤波的局部放电故障识别[J].电力工程技术,2018,37(3):73-77.LAI Lianyou, XU Weijian. Recognition of partial discharge faults using zero phase error filtering[J]. Electric Power Engineering Technology, 2018, 37(3):73-77.
[2] LECUN Y, BENGIO Y, HINTON G. Deep learning[J].Nature, 2015, 521(7 553):436-444.
[3]薛禹胜,赖业宁.大能源思维与大数据思维的融合(一)大数据与电力大数据[J].电力系统自动化,2016,40(1):1-8.XUE Yusheng, LAI Yening. Integration of macro energy thinking and big data thinking:part one big data and power big data[J]. Automation of Electric Power Systems,2016,40(1):1-8.
[4]汤奕,崔晗,李峰,等.人工智能在电力系统暂态问题中的应用综述[J].中国电机工程学报,2019,39(1):2-13.TANG Yi, CUI Han, LI Feng, et al. Review on artificial intelligence in power system transient stability analysis[J].Proceedings of the CSEE, 2019, 39(1):2-13.
[5]顾雪平,曹绍杰,张文勤.人工神经网络和短时仿真结合的暂态安全评估事故筛选方法[J].电力系统自动化,1999,23(8):16-19.GU Xueping, CAO Shajie, ZHANG Wenqing. Integration of ANNs and short-duration numerical simulation for contingencyscreeningoftransientsecurityassessment[J].AutomationofElectricPowerSystems,1999,23(8):16-19.
[6]刘艳,顾雪平,李军.用于暂态稳定评估的人工神经网络输入特征离散化方法[J].中国电机工程学报,2005,25(15):56-61.LIU Yan, GU Xueping, LI Jun. Discretization in artificial neural networks used for transient stability assessment[J].ProceedingsoftheCSEE, 2005,25(15):56-61.
[7] HE M, ZHANG J, VITTAL V. Robust online dynamic security assessment using adaptive ensemble decision-tree learning[J]. IEEE Transactions on Power Systems, 2013,28(4):4 089-4 098.
[8]瞿寒冰,朱英刚,尹茂林,等.计及风电并网的机组组合方法研究[J].电力系统保护与控制,2017,45(16):95-98.QU Hanbing, ZHU Yinggang, YIN Maolin, et al. Study on the unit commitment considering wind power paralleling in the power system[J]. Power System Protection and Control, 2017, 45(16):95-98.
[9]张昕,张勇,钱伟杰,等.基于简化零空间内点法VSCHVDC离散化最优潮流的研究[J].电力系统保护与控制,2017(18):15-23.ZHANG Xin, ZHANG Yong, QIAN Weijie, et al. Research on the optimal power flow of VSC-HVDC based on the simplified null space interior point method[J]. Power System Protection and Control, 2017, 45(18):15-23.
[10] HE Y, YAN M, SHAHIDEHPOUR M, et al. Decentralized optimization of multi-area electricity-natural gas flows based on cone reformulation[J]. IEEE Transactions onPowerSystems,2018,33(4):4531-4542.
[11]余娟,杨燕,杨知方,等.基于深度学习的概率能量流快速计算方法[J].中国电机工程学报,2019,39(1):22-30.YU Juan, YANG Yan, YANG Zhifang, et al. Fast probabilistic energy flow analysis based on deep learning[J].Proceeding of the CSEE, 2019, 39(1):22-30.
[12] GLOROT X, BORDES A, BENGIO Y. Deep sparse rectifier neural networks[C]//Proceedings of the 14th International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, USA, IEEE, 2011:315-323.
[13]韩佶,苗世洪,李超,等.计及相关性的电-气-热综合能源系统概率最优能量流[J].电工技术学报,2019,34(5):1 055-1 067.HANJi,MIAOShihong,LIChao,etal.Probabilisticoptimal energy flow of electricity-gas-heat integrated energy system considering correlation[J]. Transactions of China ElectrotechnicalSociety, 2019,34(5):1055-1067.
[14]郑一鸣,何文林,孙翔,等.基于油色谱超立方映射的电力变压器缺陷援例诊断模型[J].电力工程技术,2017,36(4):48-53.ZHENG Yiming, HE Wenlin, SUN Xiang, et al. Case basedpowertransformerdefeatsdiagnosemodelusinghypercube mapping of oil chromatography[J]. Electric PowerEngineeringTechnology,2017,36(4):48-53.
[15]赖联有,许伟坚.基于零相位误差滤波的局部放电故障识别[J].电力工程技术,2018,37(3):73-77.LAI Lianyou, XU Weijian. Recognition of partial discharge faults using zero phase error filtering[J]. Electric Power Engineering Technology, 2018, 37(3):73-77.