基于SVR数据驱动的发电机进相极限最优化求解方法

SVR Data-Driven Optimization of Generator Leading Phase Operation Limit

针对发电机进相限制条件中多变量间复杂非线性强耦合关系导致的机理建模难题,提出一种基于支持向量回归(SVR)数据驱动的发电机进相极限最优化求解方法。该方法将发电机进相极限求解问题转化为计及多个进相限制因素约束下的无功功率最小值问题。基于发电机功角方程推导建立无功功率的目标函数方程;基于SVR驱动模型建立约束变量与目标函数自变量的非线性映射关系,形成约束方程模型;采用改进的二阶振荡粒子群算法对优化模型进行求解。算例分析表明,所提方法建模简单,具有较高的精度和较强的泛化能力,可实现对任意已知有功出力工况下的发电机进相极限的快速计算,适用于发电机进相裕度在线建模和监测。

In view of the difficulty in modeling the mechanism caused by the complex and strong coupling nonlinearities between the multiple variables in the limiting conditions of leading phase operation,a novel method is proposed in this paper to optimize the leading phase operation limit of generator based on data-driven support vector machine regression(SVR).The limit calculation of generator leading phase operation is converted to the minimization of reactive power subject to the multiple constraints of leading phase.Based on the generator power angle equation,the objective function equation of reactive power is established.In order to formulate the constraint equation model,the nonlinear mapping relationship between constraint variables and independent variables in the objective function is constructed based on SVR data-driven model.The improved second-order oscillation particle swarm optimization algorithm is then applied to solve the optimization model.The case studies show that in addition to its modelling simplicity,the proposed method has exhibited high accuracy and strong adaptability,for the purpose of fast calculation of the generator leading phase limit under the conditions of any given active power output.Therefore it can be used for the online modeling and monitoring of the margin of generator leading phase operation.