基于模糊神经网络的风电场无功补偿容量研究
Research on reactive power compensation capacity based on fuzzy neural network for wind power station
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摘要: 以某一风电场为研究对象,提出基于模糊神经网络的风电场无功补偿容量计算方法.以该风电场的有功功率为输入,通过潮流计算得到使风电场母线电压稳定所需的无功补偿容量.计算结果表明,该方法能够准确计算风电场所需无功补偿容量,简化了风电场无功补偿容量计算过程.Abstract: With a wind power station as the research object,a computing method of reactive power compensation capacity was presented based on fuzzy neural network for wind power,which takes the active power of the wind power station as input, calculates the reactive power compensation capacity through power flow calculation to stabilize the bus voltage for wind power station. The calculation results showed that this method could accurately calculate the reactive power compensation capacity for wind power station, simplified calculation process of wind power reactive power compensation capacity.
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[1]
张红光. 大容量风电并网对电力系统安全稳定的影响研究[D].北京:华北电力大学,2008.
-
[2]
马昕霞,宋明中,李永光,等. 风力发电并网技术及若干问题的研究[J].吉林电力,2006(8):1.
-
[3]
高赐,威何叶,胡荣. 考虑大规模风电接入的电力规划研究[J].电网与清洁能源,2011(10):53.
-
[4]
方军,王大光,林因. 风电机组模型及风电场接入系统研究[J].国际电力,2004(5):23.
-
[5]
张伟,卫志农,刘玉娟. 基于混沌优化的含风电场的最优潮流计算[J].中国电力,2011(10):25.
-
[6]
王海超,周双喜,鲁宗相,等. 含风电场的电力系统潮流计算的联合迭代方法及应用[J].电网技术,2005,29(18):59.
-
[7]
陈树勇,申洪,张洋,等. 基于遗传算法的风电场无功补偿及控制方法的研究[J].中国电机工程学报,2005,25(8):1.
-
[8]
江岳文,陈冲,温步瀛. 随机模拟粒子群算法在风电场无功补偿中的应用[J].中国电机工程学报,2008,28(13):47.
-
[9]
王海超,鲁宗相,周双喜. 风电场发电容量可信度研究[J].中国电机工程学报,2005,25(10):103.
-
[10]
丁明,吴义纯. 风力发电系统运行和规划问题综述[J].电网技术,2003,27(3):36.
-
[11]
吴义纯,丁明,张立军. 含风电场的电力系统潮流计算[J].中国电机工程学报,2005(4):36.
-
[12]
陈珩. 电力系统稳态分析[M].北京:中国电力出版社,2007.
-
[13]
候媛彬,杜京义,汪梅. 神经网络[M].西安:西安电子科技大学出版社,2007.
-
[14]
Syedali M,Balasubramaniam P. Exponential stability of uncertain stochastic fuzzy BAM neural networks with time-varying delays[J].Neurocomputing,2009,72(4):1347.
-
[15]
Castro J R,Castillo O,Melin P,et al. A hybrid learning algorithm for a class of interval type-2 fuzzy neural networks[J].Information Sciences,2009,179(13):2175.
-
[16]
Park J H,Huh S H,Kim S H,et al. Direct adaptive contrailer for nonaffine nonlinear systems using self-structuring neural networks[J].IEEE Transactions on Neural Networks,2005,16(2):414.
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