多时间尺度下考虑机组变桨动作优化的风电场有功控制系统研究

发布时间：2018-01-12 20:16

本文关键词：多时间尺度下考虑机组变桨动作优化的风电场有功控制系统研究　出处：《南京理工大学》2017年硕士论文　论文类型：学位论文

【摘要】：随着电网中风电渗透率的不断提高,为了应对大规模风电接入给电网有功调度和安全稳定运行带来的巨大压力和新的挑战,减小大规模风电并网对电力系统调频、调度的不利影响,提高电网对风电的消纳能力,有必要深入研究风电场的有功功率控制策略,充分发掘风电机组功率调节及参与电网辅助服务的能力。本文主要对风电场有功控制系统中的机组有功控制系统和风电场有功分配系统开展相关研究,具体工作如下:针对现有风电机组有功控制策略在高风速段会引起变桨系统频繁动作的问题,本文提出了基于转速与桨矩协调控制的改进有功控制策略,兼顾风电机组功率调节效果与运行工况。该方法充分利用风机具备的高转速调节裕度,优先通过调节风轮动能平衡风力机输入/输出的不平衡功率,从而能够有效降低风机在全风速段的变桨动作频率与幅度,减轻变桨机构疲劳,延长风机运行寿命,最后在Matlab/Simulink仿真平台中验证了所提新型风电机组有功功率控制策略的有效性。针对由风速波动和预测误差所引起的风电场发电误差的问题,本文在风电场有功控制系统中提出了基于多时间尺度的双层有功分配控制框架,包括长时间尺度上的上层全局优化分配策略和短时间尺度上的下层实时修正控制策略。其中,全局优化分配策略综合考虑场内各机组的预测信息,运行状态与控制特性等多方面影响因素,以风电场发电误差最小、风机启停次数最少和机组控制系统动作次数最小构建目标函数,制定各机组的优化调度指令,实现风电场经济运行;实时修正控制策略在全局优化分配策略的基础上,针对由风速波动及调度指令过高所引起的机组有功出力不足,利用具有功率调节裕度的机组来缓解风电场有功波动,实时调整机组的有功调度指令,进而提高风电场输出功率的稳定性与可靠性。最后,通过仿真算例对比对所提风电场有功分配策略的有效性进行了验证。
[Abstract]:With the increasing permeability of wind power in the power grid, in order to cope with the huge pressure and new challenges brought by large-scale wind power access to the grid active power dispatching and safe and stable operation. It is necessary to study the active power control strategy of wind farm in order to reduce the adverse effect of large-scale wind power grid connection on power system frequency modulation and dispatch, and to improve the absorption ability of wind power grid. Fully explore the power regulation of wind turbines and the ability to participate in the auxiliary service of power grid. This paper mainly studies the active power control system of wind farm and the distribution system of active power in wind farm. The specific work is as follows: in view of the problem that the active power control strategy of the existing wind turbine will cause the variable propeller system to move frequently in the high wind speed, this paper proposes an improved active power control strategy based on the coordinated control of rotational speed and propeller moment. The method makes full use of the high speed adjustment margin of the fan and balances the unbalanced power of the wind turbine input / output by regulating the kinetic energy of the wind turbine. Thus it can effectively reduce the frequency and amplitude of the variable propeller movement of the fan in the whole wind speed section, reduce the fatigue of the propeller mechanism, and prolong the running life of the fan. Finally, the effectiveness of the proposed active power control strategy for wind turbine is verified on the Matlab/Simulink simulation platform. The wind farm power generation error caused by wind speed fluctuation and prediction error is analyzed. The question. In this paper, a two-layer active power distribution control framework based on multi-time scale is proposed in the active power control system of wind farm. It includes global optimal allocation strategy for long time scale and real-time modified control strategy for lower layer on short time scale, in which global optimal allocation strategy synthetically takes into account the prediction information of each unit in the field. The operation state and control characteristics are affected by the minimum power generation error of wind farm, the minimum number of fan start and stop and the minimum number of actions of the unit control system to construct the objective function, and to formulate the optimal dispatching instructions of each unit. To realize the economic operation of wind farm; Real-time modified control strategy on the basis of global optimal allocation strategy, due to the fluctuation of wind speed and the excessive dispatch command, the generating unit has insufficient active power output. The active power fluctuation of wind farm can be alleviated by using the unit with power regulation margin, and the active power dispatching instruction of the unit can be adjusted in real time, thus improving the stability and reliability of the output power of the wind farm. Finally. The effectiveness of the proposed active power allocation strategy for wind farms is verified by a simulation example.
【学位授予单位】：南京理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM614

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