改进QPSO算法在风电并网系统无功优化中的应用
发布时间：2018-07-19 19:57
【摘要】：在能源短缺和环境恶化的双重压力下,可再生新能源的开发利用受到了广泛的关注,其中风能以其清洁无污染、分布广的优点逐渐成为颇具发展前景的新能源。但在实际应用中,由于风能的波动性和不确定性,风电场的接入将改变传统配电网的潮流分布,影响系统的网络损耗和节点电压水平。因此,研究考虑风电不确定性的无功优化对系统安全稳定运行以及风电可靠并网都具有重要意义。论文首先阐述了风电的发展现状以及风电并网对系统无功优化的影响,详细综述了风电并网系统无功优化的研究现状：在介绍了三种类型风电机组结构的基础上,分析了异步风电机组的稳态模型及其在潮流计算中的节点处理方法；针对异步风电机组并网给配网无功优化带来的不确定性问题,提出了基于风速预测的场景分析法,将不确定性模型转换成多个典型的确定性场景问题；在应用中分析了多风电机组同时并网的系统场景划分方法,进而建立了全场景下的兼顾系统经济性和安全稳定性的多目标无功优化模型；接着采用最大模糊满意度准则将多目标模型转换成单目标模型；并提出了一种改进的量子行为粒子群算法来对模型进行求解,该算法引入自适应权重系数、柯西变异算子以及收缩-扩张系数自适应策略对量子行为粒子群算法进行改进,从而改善了其在求解复杂多峰函数时存在搜索速度慢以及迭代后期容易发生早熟收敛等缺陷,并采用一个标准特征函数来验证改进算法的有效性。以改进的69节点辐射状配电系统来进行算例分析,结果表明：所构建的无功优化模型对一天中风电出力的随机变化具有更好的适应性,且能很好的协调系统经济性和安全稳定性之间的关系；通过对三种算法的结果进行详细比较分析,验证了所提出的改进量子行为粒子群算法在模型求解中的收敛性和优越性,这也为其他形式的新能源接入电网提供了一定的理论依据。
[Abstract]:Under the double pressure of energy shortage and environmental deterioration, the development and utilization of renewable new energy have been paid more and more attention, among which wind energy has become a promising new energy with its advantages of clean and pollution-free and wide distribution. However, in practical applications, because of the volatility and uncertainty of wind energy, the access of wind farm will change the distribution of power flow of traditional distribution network, and affect the network loss and voltage level of the system. Therefore, the study of reactive power optimization considering the uncertainty of wind power is of great significance to the safe and stable operation of the system and reliable grid connection of wind power. Firstly, the paper describes the development of wind power and the influence of wind power grid connection on reactive power optimization of wind power system, and summarizes the research status of reactive power optimization of wind power grid connection system in detail: on the basis of introducing the structure of three types of wind turbine units, The steady-state model of asynchronous wind turbine and its node processing method in power flow calculation are analyzed, and the scene analysis method based on wind speed prediction is proposed to solve the uncertainty of reactive power optimization caused by asynchronous wind turbine grid connection. The uncertainty model is transformed into several typical deterministic scene problems, and the system scenario partition method of multi-wind turbine is analyzed in the paper. Then, the multi-objective reactive power optimization model with both system economy and safety and stability is established, and then the multi-objective model is transformed into a single-objective model by using the maximum fuzzy satisfaction degree criterion. An improved quantum behavior particle swarm optimization algorithm is proposed to solve the model. The algorithm introduces adaptive weight coefficient, Cauchy mutation operator and contract-expansion coefficient adaptive strategy to improve the quantum behavior particle swarm optimization algorithm. In order to solve the complex multimodal function, it has some defects such as slow searching speed and precocious convergence in the late iteration, and a standard characteristic function is used to verify the effectiveness of the improved algorithm. An example of an improved 69 node radiative distribution system is presented. The results show that the proposed reactive power optimization model is more adaptable to the random variation of wind power output in a day. The results of the three algorithms are compared and analyzed in detail to verify the convergence and superiority of the improved quantum behavior particle swarm optimization algorithm in the model solving. This also provides a certain theoretical basis for other forms of new energy access to the grid.
【学位授予单位】：长沙理工大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM614
，
本文编号：2132910
[Abstract]:Under the double pressure of energy shortage and environmental deterioration, the development and utilization of renewable new energy have been paid more and more attention, among which wind energy has become a promising new energy with its advantages of clean and pollution-free and wide distribution. However, in practical applications, because of the volatility and uncertainty of wind energy, the access of wind farm will change the distribution of power flow of traditional distribution network, and affect the network loss and voltage level of the system. Therefore, the study of reactive power optimization considering the uncertainty of wind power is of great significance to the safe and stable operation of the system and reliable grid connection of wind power. Firstly, the paper describes the development of wind power and the influence of wind power grid connection on reactive power optimization of wind power system, and summarizes the research status of reactive power optimization of wind power grid connection system in detail: on the basis of introducing the structure of three types of wind turbine units, The steady-state model of asynchronous wind turbine and its node processing method in power flow calculation are analyzed, and the scene analysis method based on wind speed prediction is proposed to solve the uncertainty of reactive power optimization caused by asynchronous wind turbine grid connection. The uncertainty model is transformed into several typical deterministic scene problems, and the system scenario partition method of multi-wind turbine is analyzed in the paper. Then, the multi-objective reactive power optimization model with both system economy and safety and stability is established, and then the multi-objective model is transformed into a single-objective model by using the maximum fuzzy satisfaction degree criterion. An improved quantum behavior particle swarm optimization algorithm is proposed to solve the model. The algorithm introduces adaptive weight coefficient, Cauchy mutation operator and contract-expansion coefficient adaptive strategy to improve the quantum behavior particle swarm optimization algorithm. In order to solve the complex multimodal function, it has some defects such as slow searching speed and precocious convergence in the late iteration, and a standard characteristic function is used to verify the effectiveness of the improved algorithm. An example of an improved 69 node radiative distribution system is presented. The results show that the proposed reactive power optimization model is more adaptable to the random variation of wind power output in a day. The results of the three algorithms are compared and analyzed in detail to verify the convergence and superiority of the improved quantum behavior particle swarm optimization algorithm in the model solving. This also provides a certain theoretical basis for other forms of new energy access to the grid.
【学位授予单位】：长沙理工大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM614
，
本文编号：2132910
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