复杂地形风电场有功调节和机组部件疲劳分布的综合优化控制

Under the urgent demand of active power dispatching, how to avoid the shortening of fatigue life of unit components caused by the active output regulation and the wind condition of complex terrain is an important problem in the field of wind power generation. In this project, we will deeply study the relationship between the active power control strategy, the active power output and the fatigue of the unit components, establish the damage equivalent load (DEL) model of units using the probability and statistics method as the data mining means, design a comprehensive optimal control strategy based on the optimization and prediction theory for the active regulation of wind farms in complex terrain and the uniform distribution of fatigue load of the unit components. Regarding the condition that the comprehensive optimization problem of the complex terrain wind farm is non-convex, nonlinear, and with inequality constraints, and the DEL data model based on wind characteristics measurement has the limited dynamic performance, an intelligent control strategy employing the particle swarm optimization method based on the time series forecasting DEL is proposed. In view of the fact that the wind conditions of complex terrain wind farm units will induce the deviation of the active regulation deviation when the fatigue distribution is optimized, it is proposed a composite control strategy which arranges a PI active auxiliary loop inside the control loop of the active power distribution. The result of this project will provide a systematic solution for the comprehensive optimal control of complex terrain wind farms, and has important scientific significance and application value.

在电网有功调度迫切要求下，如何在完成电网有功调度指令的同时，避免因有功输出调节和复杂地形风电场机组风况差异而导致的机组部件疲劳寿命缩短，是风力发电领域重要难题。本项目将深入研究机组有功控制策略、有功输出和机组部件疲劳之间的关系，采用概率统计方法进行数据挖掘建立机组部件损坏等效载荷(DEL)模型，基于优化、预测等理论设计以复杂地形风电场有功调节和机组部件疲劳载荷均匀分布为综合优化目标的控制策略。考虑综合优化控制问题具有不等式约束下的非线性非凸特点及DEL数据模型动态性能受限情况，研究提出基于时间序列预测DEL的粒子群寻优有功分配的智能控制策略；进一步考虑复杂地形风电场复杂风况易导致疲劳分布优化时风电场有功调节偏差超限问题，提出一种在有功智能分配控制环内设置PI有功辅助调节内环的复合控制策略。本研究将为复杂地形风电场综合优化控制难题提供一种系统解决方案，具有重要科学意义和应用价值。

随着国内风电行业的快速发展，陆上风电开发开始转向高海拔地区和复杂山地区域。山地风电场的复杂风况加剧了风电机组之间的疲劳载荷分布不均匀问题，导致风电机组寿命大大缩短。与此同时，传统的风电场有功功率调度策略在复杂风况下存在适用性较差的问题。针对以上难题，本项目以复杂地形风电场风电机组作为研究对象，分析了风电机组各个部件的疲劳载荷特性，深入探索了机组有功控制策略、有功输出和机组部件疲劳之间的关系。通过采用概率统计和数据挖掘方法，建立了机组部件疲劳载荷模型和损坏等效载荷(DEL)模型；在上述基础上，提出数据驱动建模-风速时空预测-多目标优化的疲劳载荷优化技术，并研制了复杂地形风电场控制系统仿真实验平台。本项目的研究成果为复杂地形风电场综合优化控制难题提供了系统解决方案。所提出的面向复杂山地风电机组疲劳载荷优化策略，在维持功率输出稳定的基础上，有效降低了机组部件载荷，为降低复杂地形风电场运维成本提供了潜在技术手段。依托本项目的开展，项目团队在风电技术方向培养了博士研究生1名，硕士研究生4名；已发表SCI/EI论文24篇；获得授权发明专利2项。