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风电变流技术 储能系统 ★ 5.0

基于数据驱动升维线性潮流的风电场无模型最优电压-无功控制

Model-free Optimal Volt-VAR Control of Wind Farm Based on Data-driven Lift-dimension Linear Power Flow

语言:

中文摘要

提出一种基于数据驱动潮流的风电场无功与电压优化控制方法。该方法无需风电场先验参数信息,具有无模型特性。基于Koopman算子方法,通过状态空间映射与升维线性化构建并网风电场的潮流模型。考虑风电机组及静止无功发生器(SVG)等无功设备,建立基于全局灵敏度的无功电压线性优化控制模型。以最小化无功调节量为目标,结合节点电压与无功注入的灵敏度关系,实现无功功率的最优分配,有效降低有功损耗,并满足风电场快速电压响应需求。基于宁夏某风电场历史数据验证了方法在参数不准确情况下的可行性。相比基于模型的方法,所提方法在参数依赖性和计算效率方面更具优势。

English Abstract

This paper proposes a reactive power and voltage optimal control method for wind farms based on data-driven power flow.Because no prior knowledge of wind farm parameters is necessary,the proposed method is model-free.Based on Koopman operator-based method,this paper constructs a power flow model of wind farms connected to the grid by using state space mapping and lift-dimension linearization.Considering reactive power devices such as wind turbines and static var generator(SVG)in wind farms,a global sensitivity-based reactive power and voltage linear optimization control model is proposed.Taking minimum reactive power adjustment of wind turbines and SVG as the objective function,combined with the sensitivity relationship between node voltage and reactive power injection,the proposed model-free voltage control method can realize optimal reactive power distribution,effectively reduce active power loss,and satisfy the requirement of rapid voltage control response of wind farms.Based on historical data of a wind farm in Ningxia,feasibility of the proposed voltage optimal control method under inaccurate parameters is verified.Compared with model-based methods,the proposed method exhibits advantages on parameter dependency and efficiency.
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SunView 深度解读

该数据驱动的无模型电压-无功控制技术对阳光电源的储能和光伏产品线具有重要应用价值。特别适用于ST系列储能变流器和SG系列光伏逆变器的电网支撑功能优化。通过Koopman算子方法实现的无模型控制,可提升PowerTitan大型储能系统的电压调节性能,减少对系统参数依赖。该方法的全局灵敏度优化思路可用于完善储能变流器的GFM控制策略,提高电压响应速度。对于大规模新能源电站,这种基于数据的优化方法有助于提升iSolarCloud平台的智能调控能力,实现更精准的无功功率分配。