基于PSOGSA全参数连分式的风速预测模型  

Wind speed prediction model based on PSOGSA full-parameter continuous fractions

作　　者：张旭 张宏立[1] 王聪[1] ZHANG Xu;ZHANG Hongli;WANG Cong(College of Electrical Engineering,Xinjiang University,Urumqi 830047,China)

机构地区：[1]新疆大学电气工程学院,新疆乌鲁木齐830047

出　　处：《电力系统保护与控制》2020年第23期100-107,共8页Power System Protection and Control

基　　金：国家自然科学基金项目资助(51767022、51967019,51575469);新疆维吾尔自治区自然科学基金项目资助(2019D01C082)。

摘　　要：风的间歇性和时变性限制了风电场的发电能力,准确的风速预测有助于减小风力发电入网对电力系统的运行方式安排带来的影响。针对风速时间序列存在的复杂变化和混沌特性,为了提高预测精度和简化预测数学模型结构,提出一种结合万有引力搜索算法(GSA)全局寻优能力和粒子群优化算法(PSO)的局部快速收敛优势的全参数连分式预测模型。将n项截断式连分式转化为PSOGSA优化参数问题,进行多维空间上函数的优化。以风电场风速采集两组数据为预测对象,通过对复杂风速时间序列建模仿真,并利用基于PSOGSA优化的全参数连分式对序列进行多时间尺度的预测。仿真结果分别与传统的BP神经网络、RBF神经网络、当前时间序列预测利用较多的长短期记忆网络算法(LSTM)和粒子群优化支持向量机(PSO-SVM)进行对比得出:基于PSOGSA的全参数连分式预测模型具有精度高、结构简单和建模速度快等特点,具有更强的非线性预测能力。Intermittent and time-varying wind limits the power generation capacity of a wind farm.Accurate wind speed prediction will help reduce the impact of wind power connecting into a network on the operation modes arrangement of the power system.In view of the complex changes and chaotic characteristics of wind speed time series,in order to improve prediction accuracy and simplify the prediction mathematical model structure,this paper proposes a combined universal Gravitation Search Algorithm(GSA)full parameter continuous fraction prediction model with local optimization ability and the local fast convergence advantage of Particle Swarm Optimization(PSO).The n-term truncated continued fraction is transformed into a PSOGSA optimization parameter problem,and the function optimization in multidimensional space is carried out.Taking two groups of wind speed data collected from a wind farm as the prediction object,the complex wind speed time series is modeled and simulated,and the multi-scale prediction of the series is carried out using the full parameter continued fraction based on PSOGSA optimization.The simulation results are compared with the traditional BP neural network,RBF neural network,Long-Term Memory Network Algorithm(LSTM)that current time series prediction often uses,and the Particle Swarm Optimization Support Vector Machine(PSO-SVM).It is concluded that the full parameter continuous fraction prediction model based on PSOGSA has characteristics of high accuracy,simple structure and fast modeling speed,and stronger nonlinear prediction ability.

关 键 词：风速预测 混沌特性 全参数连分式 PSOGSA 多时间尺度 

分 类 号：TM614[电气工程—电力系统及自动化]