含风力发电的配电网动态无功优化研究
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摘要
风力发电是一种新型可再生能源发电技术,因其分布广与无污染等特点,成为了最具商业开发前景的分布式发电技术之一。但随着大量风电场接入配电网,因其输出功率的不稳定,会对电力系统的无功造成影响。而电力系统无功优化是提高电网运行经济性、安全性和电能质量的重要手段。所以对含有风电场的配电网,研究更有效的无功优化技术成为了迫切需要解决的问题。
本文首先分析了风力发电的原理,对变频恒速风力发电机组中最具发展潜力的双馈感应风电机组进行了全面的分析。建立了双馈感应风电机的稳态模型,并研究其功率特性。因其具有调节无功功率的能力,我们可以利用其无功功率调节能力,把它作为连续无功电源参与到配电网的动态无功优化中去。
然后,本文对负荷和风电场的输出功率做出预测,因为在含有风电场的配电网中影响无功优化结果的不确定因素主要有负荷和风电场输出功率。最小二乘支持向量机(以下简称LS-SVM)具有结构简单、全局最优和推广能力强等优点,被应用于模式识别和回归处理问题等领域。本文采用聚类分析与SVM相结合的方法来预测负荷和风电场的输出功率,对样本进行模糊C均值聚类,选取与预测样本特征相似的样本作为训练样本,分别构建负荷预测和风电场输出功率预测的模型。
最后,本文建立了以有功功率损耗最小为目标函数的动态无功优化模型,提出用自适应粒子群优化算法对其进行求解。基于每个时段用自适应粒子群优化算法进行静态无功优化,获得各个时段静态优化的控制设备值。通过相邻时段之间同一控制设备变化差值形成预动作表,然后动态调整动作表,最终形成完整的动态无功优化策略。仿真结果表明,本文能够有效地得到控制设备一天之内的控制方案,同时将双馈电机风电场作为无功电源可以节省风电场并网后额外安装的大容量无功补偿装置产生的配置费用,并可避免传统无功调节手段离散化和调节速度慢等问题。
Wind power generation ,one of the renewable energy generation technologies, is becoming one of the most commercial development distributed generation technology ,because of its wide distribution and no pollution. But because of its unstable power output, it will affect reactive power of the power system. While reactive power optimization is the important means to improve the economic, safety and power quality of running grid. The research of more effective reactive power optimization with distribution network contained wind farms becomes an urgent problem needed to solve.
Firstly, this paper introduces the principle of wind power and makes comprehensive analysis to the doubly fed induction generator (hereinafter referred to as DFIG) wind which is the most potential in the variable-speed constant-frequency wind turbine. The steady state model of doubly-fed induction wind generator is established and its power characteristics is also studied, and its ability of adjusting reactive power is applied for the participation in the dynamic reactive power optimization of distribution network as a continuous power source.
Furthermore, this paper makes the forecast to the load and output of wind farm, since the main uncertainties which affected reactive power optimization in the distribution network contained wind farms are the load and output power of wind farm. Least squares support vector machine (hereinafter referred to as LS-SVM) method has advantage in simple structure, global optimal and generalization ability and is applied for pattern recognition, regression problem and other fields. This paper uses the LS-SVM method combining cluster analysis to forecast the load and output power of wind farms. The samples are analyzed using Fuzzy C-means method, and the forecasting samples with similar characteristics are selected as training samples to make forecasting model for load and output power of wind farms.
Finally, this paper establishes the dynamic reactive power optimization model with the objective function as active power loss minimum and uses adaptive particle swarm optimization to solve the model. Based on the static reactive power optimization with each time using particle swarm optimization, the control equipment values are obtained. The pre-action table is formatted through the different between the same control equipment at the adjacent time, and is adjusted dynamically. Then the dynamic reactive power optimization strategy is completely formed. The experiment results show that the method employed in this paper can obtain the effective control programs of the equipments in one day. In the meanwhile the DFIG win farms used for reactive power source can save the cost of additional reactive compensation installation, and the method can overcome the difficulties of traditional reactive power optimization.
本文首先分析了风力发电的原理,对变频恒速风力发电机组中最具发展潜力的双馈感应风电机组进行了全面的分析。建立了双馈感应风电机的稳态模型,并研究其功率特性。因其具有调节无功功率的能力,我们可以利用其无功功率调节能力,把它作为连续无功电源参与到配电网的动态无功优化中去。
然后,本文对负荷和风电场的输出功率做出预测,因为在含有风电场的配电网中影响无功优化结果的不确定因素主要有负荷和风电场输出功率。最小二乘支持向量机(以下简称LS-SVM)具有结构简单、全局最优和推广能力强等优点,被应用于模式识别和回归处理问题等领域。本文采用聚类分析与SVM相结合的方法来预测负荷和风电场的输出功率,对样本进行模糊C均值聚类,选取与预测样本特征相似的样本作为训练样本,分别构建负荷预测和风电场输出功率预测的模型。
最后,本文建立了以有功功率损耗最小为目标函数的动态无功优化模型,提出用自适应粒子群优化算法对其进行求解。基于每个时段用自适应粒子群优化算法进行静态无功优化,获得各个时段静态优化的控制设备值。通过相邻时段之间同一控制设备变化差值形成预动作表,然后动态调整动作表,最终形成完整的动态无功优化策略。仿真结果表明,本文能够有效地得到控制设备一天之内的控制方案,同时将双馈电机风电场作为无功电源可以节省风电场并网后额外安装的大容量无功补偿装置产生的配置费用,并可避免传统无功调节手段离散化和调节速度慢等问题。
Wind power generation ,one of the renewable energy generation technologies, is becoming one of the most commercial development distributed generation technology ,because of its wide distribution and no pollution. But because of its unstable power output, it will affect reactive power of the power system. While reactive power optimization is the important means to improve the economic, safety and power quality of running grid. The research of more effective reactive power optimization with distribution network contained wind farms becomes an urgent problem needed to solve.
Firstly, this paper introduces the principle of wind power and makes comprehensive analysis to the doubly fed induction generator (hereinafter referred to as DFIG) wind which is the most potential in the variable-speed constant-frequency wind turbine. The steady state model of doubly-fed induction wind generator is established and its power characteristics is also studied, and its ability of adjusting reactive power is applied for the participation in the dynamic reactive power optimization of distribution network as a continuous power source.
Furthermore, this paper makes the forecast to the load and output of wind farm, since the main uncertainties which affected reactive power optimization in the distribution network contained wind farms are the load and output power of wind farm. Least squares support vector machine (hereinafter referred to as LS-SVM) method has advantage in simple structure, global optimal and generalization ability and is applied for pattern recognition, regression problem and other fields. This paper uses the LS-SVM method combining cluster analysis to forecast the load and output power of wind farms. The samples are analyzed using Fuzzy C-means method, and the forecasting samples with similar characteristics are selected as training samples to make forecasting model for load and output power of wind farms.
Finally, this paper establishes the dynamic reactive power optimization model with the objective function as active power loss minimum and uses adaptive particle swarm optimization to solve the model. Based on the static reactive power optimization with each time using particle swarm optimization, the control equipment values are obtained. The pre-action table is formatted through the different between the same control equipment at the adjacent time, and is adjusted dynamically. Then the dynamic reactive power optimization strategy is completely formed. The experiment results show that the method employed in this paper can obtain the effective control programs of the equipments in one day. In the meanwhile the DFIG win farms used for reactive power source can save the cost of additional reactive compensation installation, and the method can overcome the difficulties of traditional reactive power optimization.
引文
[1]王承煦,张源.风力发电.北京:中国电力出版社,2003,40-50
[2]梁有伟,胡志坚,陈允平.分布式发电及其在电力系统中的应用研究综述.电网技术,2003,27(12):71-75
[3]张希圣.风能的开发利用.北京:化学工业出版社,2004,12-15
[4]胡学浩.分布式发电技术及其并网问题.电工技术杂志,2004,(10):1-5
[5]世界风能协会.World Wind Energy Report 2010[EB/OL],2010,[2010-10]
[6]丁明,王敏.分布式发电技术.电力自动化设备,2004,24(7):31-36
[7] Willis H L,Scott W G.Distributed Power Generation Planning and Evaluation.New York:Marcel Decker,Inc,2000
[8] Borbely A M,Kreider J F.Distributed Generation.Baca Raton London&New York Washington,D.C.CRC Press.American,Nov,2003
[9]陈海焱,陈金富,段献忠.含风电机组的配电网无功优化.中国电机工程学报,2008,28(7):40-42
[10]雷亚洲.与风电并网相关的研究课题.电力系统及其自动化,2003,27(8):84-89
[11] Gertmar L,Lendanmann H.Wind energy powers-that-be successor generation in globalization.IEEE Transactions on Energy Conversion,2007,22(1):13-28
[12]陈琳,钟金,倪以信,等.含分布式发电的配电网无功优化.电力系统自动化,2006,30(14):20-21
[13]周晓娟,蒋炜华,马丽丽.基于改进遗传算法的电力系统无功优化.电力系统保护与控制,2010,38(7):37-38
[14]蔡昌春,丁晓群,王宽,等.动态无功优化的简化方法与实现.电力系统自动化,2008,32(5):43-44
[15] LIANG R H,CHENG C K.Dispatch of Main Transformer ULTC and Capacitors in A Distribution System. IEEE Trans on Power Delivery,2001,16(4):625-630
[16] J.Wiik,J.O.Gjerde,T.Gjengedal,et al.Steady State Power System Issues When Planning Large Wind Farms. IEEE Power Engineering Society Winter Meeting.2002,1:366-371
[17]肖运启.双馈型风力发电机励磁控制与优化运行研究:[华北电力大学博士论文].北京:华北电力大学,2008,1-10
[18]孙春顺.风力发电系统运行与控制研究:[湖南大学博士论文].长沙:湖南大学,2008,20-25
[19] Brady F J.A Mathematieal Model for the Doubly-Fed Wound Rotor Generator.IEEE Transactions on Power Apparatus and Systems ,1984,PAS-103(4)
[20] Brady F J.A Mathematieal Model for the Doubly-Fed Wound Rotor Generator-PartⅡ.IEEE Transactions on Energy Conversion,1986,EC-1(2)
[21]申洪.变速恒频风电机组并网运行模型研究及其应用:[中国电力科学研究院博士论文].北京:中国电力科学研究院,2003,24-28
[22]赵晶晶.含分布式发电的配电网优化运行研究:[重庆大学博士学位论文].重庆:重庆大学,2009,47-51
[23]杨秀媛,肖洋,陈树勇.风电场风速和发电功率预测研究.中国电机工程学报,2005,25(11):1-2
[24]孙劲松.风电场输出功率预测的研究:[华北电力大学硕士论文].北京:华北电力大学,2009,18-30
[25]粟然,陈倩,徐宏锐.考虑相关因素的最小二乘支持向量机风速预测方法.电力系统保护与控制,2010,38(21):146-147
[26]曹磊.考虑风电并网的超短期负荷预测:[华北电力大学硕士学位论文].保定:华北电力大学,2007,34-45
[27] Hsu Y.Y.,Kuo K.C.Dispatch of capacitors on distribution system using dynamic programming. IEE Proceedings-Generation, Transmission and Distribution, 1993,140(6):433~438
[28] Lu F.C.,Hsu Y.Y.Fuzzy dynamic programming approach to reactive power/voltage control in a distribution substation. IEEE Trans on Power Systems,1997,12(2):681~688
[29]蔡昌春.电力系统动态无功优化方法研究及其实现: [河海大学硕士论文].南京:河海大学,2007,51-60
[30] Deng Youman,Ren Xiaojuan,Zhao Changcheng,et al.A heuristic and algorithm in combined approach for reactive power optimization with time-varying load demand in distribution system. IEEE Trans on Power Systems,202,17(4):1068~1072
[31]陈琳,钟金,倪以信.含分布式发电的配电网无功优化.电力系统自动化,2006,30(14):20-25
[32]陈海焱,陈金富,段献忠.含风电机组的配网无功优化.中国电机工程学报,2008,3(5):40-46
[33] Twidell J, Weir T, Renewable Energy Resources, E.&F.N. Spon Ltd. 1986
[34]刘其辉.变速恒频风力发电系统运与控制研究:[浙江大学博士论文].杭州:浙江大学,2005,21-30
[35]万航羽.风电场模型研究及应用:[北京交通大学硕士学位论文].北京:北京交通大学,2008,13-16
[36]张新燕,王维庆.风力发电机并网后的电网电压和功率分析.电网技术,2009,33(17):130-134
[37]范昕炜.支持向量机算法的研究及其应用:[浙江大学硕士论文].杭州:浙江大学,2003,46-60
[38]李伟,闫宁,张振刚.基于粗糙集的混合支持向量机长期电力负荷预测研究.电力系统保护与控制,2010,38(13):31-32
[39]陈灿.电力系统短期负荷预测与动态无功优化技术研究:[浙江大学硕士论文].杭州:浙江大学,2008,1-15
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[42]杨浩,张磊,何潜,等.基于自适应模糊C均值算法的电力负荷分类研究.电力系统保护与控制,2010,38(16):111-112
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[44]李辉.风电场风速和输出功率的多尺度预测研究:[兰州理工大学].兰州:兰州理工大学,2010,10-30
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[47]赵波,郭创新,张鹏翔,等.基于分布式协同粒子群优化算法的电力系统无功优化.中国电机工程学报,2005,25(21):1-7
[48] Baran M E,Wu F F.Network Reconfiguration in Distribution Systems for Loss Reduction and Load Balancing.IEEE Transactions on Power Delivery.1989,4(2):1401-1407
[2]梁有伟,胡志坚,陈允平.分布式发电及其在电力系统中的应用研究综述.电网技术,2003,27(12):71-75
[3]张希圣.风能的开发利用.北京:化学工业出版社,2004,12-15
[4]胡学浩.分布式发电技术及其并网问题.电工技术杂志,2004,(10):1-5
[5]世界风能协会.World Wind Energy Report 2010[EB/OL],2010,[2010-10]
[6]丁明,王敏.分布式发电技术.电力自动化设备,2004,24(7):31-36
[7] Willis H L,Scott W G.Distributed Power Generation Planning and Evaluation.New York:Marcel Decker,Inc,2000
[8] Borbely A M,Kreider J F.Distributed Generation.Baca Raton London&New York Washington,D.C.CRC Press.American,Nov,2003
[9]陈海焱,陈金富,段献忠.含风电机组的配电网无功优化.中国电机工程学报,2008,28(7):40-42
[10]雷亚洲.与风电并网相关的研究课题.电力系统及其自动化,2003,27(8):84-89
[11] Gertmar L,Lendanmann H.Wind energy powers-that-be successor generation in globalization.IEEE Transactions on Energy Conversion,2007,22(1):13-28
[12]陈琳,钟金,倪以信,等.含分布式发电的配电网无功优化.电力系统自动化,2006,30(14):20-21
[13]周晓娟,蒋炜华,马丽丽.基于改进遗传算法的电力系统无功优化.电力系统保护与控制,2010,38(7):37-38
[14]蔡昌春,丁晓群,王宽,等.动态无功优化的简化方法与实现.电力系统自动化,2008,32(5):43-44
[15] LIANG R H,CHENG C K.Dispatch of Main Transformer ULTC and Capacitors in A Distribution System. IEEE Trans on Power Delivery,2001,16(4):625-630
[16] J.Wiik,J.O.Gjerde,T.Gjengedal,et al.Steady State Power System Issues When Planning Large Wind Farms. IEEE Power Engineering Society Winter Meeting.2002,1:366-371
[17]肖运启.双馈型风力发电机励磁控制与优化运行研究:[华北电力大学博士论文].北京:华北电力大学,2008,1-10
[18]孙春顺.风力发电系统运行与控制研究:[湖南大学博士论文].长沙:湖南大学,2008,20-25
[19] Brady F J.A Mathematieal Model for the Doubly-Fed Wound Rotor Generator.IEEE Transactions on Power Apparatus and Systems ,1984,PAS-103(4)
[20] Brady F J.A Mathematieal Model for the Doubly-Fed Wound Rotor Generator-PartⅡ.IEEE Transactions on Energy Conversion,1986,EC-1(2)
[21]申洪.变速恒频风电机组并网运行模型研究及其应用:[中国电力科学研究院博士论文].北京:中国电力科学研究院,2003,24-28
[22]赵晶晶.含分布式发电的配电网优化运行研究:[重庆大学博士学位论文].重庆:重庆大学,2009,47-51
[23]杨秀媛,肖洋,陈树勇.风电场风速和发电功率预测研究.中国电机工程学报,2005,25(11):1-2
[24]孙劲松.风电场输出功率预测的研究:[华北电力大学硕士论文].北京:华北电力大学,2009,18-30
[25]粟然,陈倩,徐宏锐.考虑相关因素的最小二乘支持向量机风速预测方法.电力系统保护与控制,2010,38(21):146-147
[26]曹磊.考虑风电并网的超短期负荷预测:[华北电力大学硕士学位论文].保定:华北电力大学,2007,34-45
[27] Hsu Y.Y.,Kuo K.C.Dispatch of capacitors on distribution system using dynamic programming. IEE Proceedings-Generation, Transmission and Distribution, 1993,140(6):433~438
[28] Lu F.C.,Hsu Y.Y.Fuzzy dynamic programming approach to reactive power/voltage control in a distribution substation. IEEE Trans on Power Systems,1997,12(2):681~688
[29]蔡昌春.电力系统动态无功优化方法研究及其实现: [河海大学硕士论文].南京:河海大学,2007,51-60
[30] Deng Youman,Ren Xiaojuan,Zhao Changcheng,et al.A heuristic and algorithm in combined approach for reactive power optimization with time-varying load demand in distribution system. IEEE Trans on Power Systems,202,17(4):1068~1072
[31]陈琳,钟金,倪以信.含分布式发电的配电网无功优化.电力系统自动化,2006,30(14):20-25
[32]陈海焱,陈金富,段献忠.含风电机组的配网无功优化.中国电机工程学报,2008,3(5):40-46
[33] Twidell J, Weir T, Renewable Energy Resources, E.&F.N. Spon Ltd. 1986
[34]刘其辉.变速恒频风力发电系统运与控制研究:[浙江大学博士论文].杭州:浙江大学,2005,21-30
[35]万航羽.风电场模型研究及应用:[北京交通大学硕士学位论文].北京:北京交通大学,2008,13-16
[36]张新燕,王维庆.风力发电机并网后的电网电压和功率分析.电网技术,2009,33(17):130-134
[37]范昕炜.支持向量机算法的研究及其应用:[浙江大学硕士论文].杭州:浙江大学,2003,46-60
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