电力系统综合负荷模型结构研究
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摘要
电力系统仿真在电力系统的分析、规划、设计、运行等一系列过程中起着非常重要的作用,其准确程度关系到电力系统的安全稳定运行。由于电力系统负荷本身时变性、随机性、分布性、多样性及高度非线性等特点,使得负荷建模存在很大困难,负荷模型还不很成熟。
电力系统综合负荷建模研究中,模型结构一直是倍受关注的最基本和最关键的问题之一。本文从两个方面入手对电力系统综合负荷模型结构开展了深入研究。一是机理模型结构的研究,建立了考虑配电网集结等效的综合感应电动机负荷模型。二是非机理模型结构的研究,其中又分为两个部分:第一为神经网络综合负荷模型结构的研究。第二为并行差分方程模型结构研究。
机理型模型是电力系统暂态仿真中应用最为广泛的负荷模型,传统机理模型通常未单独考虑配电网的影响。本文从电力系统综合负荷数据采集及配电网络的结构特点出发,建立了一种新型的考虑配电网络集结等效的综合感应电动机模型,该模型把配电网作为综合负荷的有机组成部分,同时计入了配电无功补偿和变压器分接头的影响,基于变电站实测负荷特性数据的建模检验表明,该模型对综合负荷特性的自描述能力及泛化能力都优于传统机理模型,且辨识所得模型参数分散性显著降低,更加符合综合负荷的实际。
由于ANN理论上可以任意精度逼近任意非线性函数,因而颇受负荷建模研究者的重视,其中选择恰当的神经网络结构应用于负荷建模成为研究的重点。本文深入研究并明确指出了被广泛应用的前向BP ANN应用于负荷动特性描述所存在的缺陷,进而构建了一种基于Elman神经网络结构的综合负荷动态模型;在此基础上,结合传统差分方程对负荷特性具有良好描述能力及神经网络并行计算能力强的优点,提出了并行差分方程的概念,建立了新型的并行差分方程负荷模型,大量现场实测数据的建模结果表明,本文所建立的Elman神经网络和并行差分方程模型不仅能有效减少负荷模型参数,且其描述能力大大优于前向BP ANN的综合负荷模型。
The power system simulation is very important in the analyzing,programming, designing and operating of power system. The accurate degree relate to the security and stability operating of power system. Because the power system load has the characteristics of randomicity, distributivity, multiformity, the load modeling is very difficult, and the load model is not mature.
In the studies of power system load modeling, model structure is one of the basal and important problem. This paper studied the two aspects of model structure. One is mechanism model, the author instruct a new“Synthesis induction-motor model”to describe power system composite load. The other is non-mechanism model, which is divided into two parts: one is neural network composite load model, and the other is parallel difference equations composite load model.
The mechanism model is the most extensive load model used in transient simulation of power system, so it’s necessary to study in-depth. This paper instruct a new“Synthesis induction-motor model”to describe power system composite load. In this model, the distribution network is equivalent to a set of lumping line and transformer, and the influence of OLTC and transient reactive power compensation are considered. Which is more accurate than traditional inductor model.Experiments were carried out to test this model ,some data samples from substation are used in modeling.The result shows that the model has strong desription of the power system composite load, and the model has good identifiability and generalization , whose composite capability is better than tradtional motor model.
ANN is suitable for any nonlinear cure fitting at any precision theroetically and occupies simple structure. So it is widely used as the model of power composite load in past load modeling research.Through the in-depth analysis, the author points out that BP ANN is not suitable for describing the dynamic characteristics of power system composite load.This paper constructed a Elman neural network to model the power system composite load, and based on the study of neural network, the author combined the advantages of traditional difference equation and neural network, and presented a new parallel difference equation, which has good ability of description as difference equation and good ability of together calculation as neural network. The modeling instance to field measured data of power transformer substation shows that the three models presented by author can effectively describe the power system composite load characteristics and the composite load model with excellent performance.
电力系统综合负荷建模研究中,模型结构一直是倍受关注的最基本和最关键的问题之一。本文从两个方面入手对电力系统综合负荷模型结构开展了深入研究。一是机理模型结构的研究,建立了考虑配电网集结等效的综合感应电动机负荷模型。二是非机理模型结构的研究,其中又分为两个部分:第一为神经网络综合负荷模型结构的研究。第二为并行差分方程模型结构研究。
机理型模型是电力系统暂态仿真中应用最为广泛的负荷模型,传统机理模型通常未单独考虑配电网的影响。本文从电力系统综合负荷数据采集及配电网络的结构特点出发,建立了一种新型的考虑配电网络集结等效的综合感应电动机模型,该模型把配电网作为综合负荷的有机组成部分,同时计入了配电无功补偿和变压器分接头的影响,基于变电站实测负荷特性数据的建模检验表明,该模型对综合负荷特性的自描述能力及泛化能力都优于传统机理模型,且辨识所得模型参数分散性显著降低,更加符合综合负荷的实际。
由于ANN理论上可以任意精度逼近任意非线性函数,因而颇受负荷建模研究者的重视,其中选择恰当的神经网络结构应用于负荷建模成为研究的重点。本文深入研究并明确指出了被广泛应用的前向BP ANN应用于负荷动特性描述所存在的缺陷,进而构建了一种基于Elman神经网络结构的综合负荷动态模型;在此基础上,结合传统差分方程对负荷特性具有良好描述能力及神经网络并行计算能力强的优点,提出了并行差分方程的概念,建立了新型的并行差分方程负荷模型,大量现场实测数据的建模结果表明,本文所建立的Elman神经网络和并行差分方程模型不仅能有效减少负荷模型参数,且其描述能力大大优于前向BP ANN的综合负荷模型。
The power system simulation is very important in the analyzing,programming, designing and operating of power system. The accurate degree relate to the security and stability operating of power system. Because the power system load has the characteristics of randomicity, distributivity, multiformity, the load modeling is very difficult, and the load model is not mature.
In the studies of power system load modeling, model structure is one of the basal and important problem. This paper studied the two aspects of model structure. One is mechanism model, the author instruct a new“Synthesis induction-motor model”to describe power system composite load. The other is non-mechanism model, which is divided into two parts: one is neural network composite load model, and the other is parallel difference equations composite load model.
The mechanism model is the most extensive load model used in transient simulation of power system, so it’s necessary to study in-depth. This paper instruct a new“Synthesis induction-motor model”to describe power system composite load. In this model, the distribution network is equivalent to a set of lumping line and transformer, and the influence of OLTC and transient reactive power compensation are considered. Which is more accurate than traditional inductor model.Experiments were carried out to test this model ,some data samples from substation are used in modeling.The result shows that the model has strong desription of the power system composite load, and the model has good identifiability and generalization , whose composite capability is better than tradtional motor model.
ANN is suitable for any nonlinear cure fitting at any precision theroetically and occupies simple structure. So it is widely used as the model of power composite load in past load modeling research.Through the in-depth analysis, the author points out that BP ANN is not suitable for describing the dynamic characteristics of power system composite load.This paper constructed a Elman neural network to model the power system composite load, and based on the study of neural network, the author combined the advantages of traditional difference equation and neural network, and presented a new parallel difference equation, which has good ability of description as difference equation and good ability of together calculation as neural network. The modeling instance to field measured data of power transformer substation shows that the three models presented by author can effectively describe the power system composite load characteristics and the composite load model with excellent performance.
引文
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[2] 沈善德.电力系统辨识.北京:清华大学出版社,1993,105-213
[3] 张景超,鄢安河,张承学,等.电力系统负荷模型研究综述.继电器,2007,35(6):83-88
[4] 鞠平,潘学萍,韩敬东.3种感应电动机综合负荷模型的比较.电力系统自动化,1999,23(19):40-47
[5] 鞠平.电力系统非线性辨识.南京:河海大学出版社,1999,74-98
[6] 鞠平,马大强.电力负荷的机理式集结模型.中国电机工程学报,1990,10(3):34-41
[7] Ju P,Handschin E,Rehtanz C.A Comparative Study on the Nonlinear Dynamic Load Models.In:Proc 10th PSCC.Dresden,1996:1069-1074
[8] 飞思科技产品研发中心.神经网络理论与MATLAB7实现.北京:电子工业出版社,2006,99-217
[9] 沈善德,朱守真,罗骏,等.快速BP网络在负荷动态建模中的应用.电力系统自动化,1999,23(19):8-11
[10] Shen Shande,Zhu Shouzhen,Luo Jun,et al.Application of fast back propagation neural networks in power system dynamic load modeling.Automation of Electric Power Systems,1999,23(19):8-11
[11] 顾丹珍,艾芊,陈陈,等.适用于快速暂态稳定计算的新型负荷模型和参数辨识方法.中国电机工程学报,2004,24(12):78-85
[12] D.Mahinda, Wijekoon.Artificial neural network based dynamic load models for real time applications.In:IPEC 2003-6th International Power Engineering Conference,London,2003,95-99
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