基于实测数据的光伏电站故障穿越关键参数辨识
Identification of Key Parameters for Fault Crossing in Photovoltaic Power Plants Based on Measured Data

为了更好地避免新能源场站并网故障给电网造成解列，验证光伏电站高低电压穿越能力，得到其准确的模型控制参数等关键问题，本文提出了一种基于高低电压穿越试验实测数据、考虑故障穿越全过程特性的光伏电站关键参数辨识方法。首先基于实测数据，建立光伏电站BPA模型，其次通过对模型参数进行灵敏度分析确定关键辨识参数，最后提出一种多策略改进的樽海鞘群算法(Salp Swarm Algorithm, SSA)的光伏电站模型辨识方法，不断优化更新待辨识参数并进行暂稳计算，计算仿真模型输出与实测数据误差，得到了光伏电站准确的模型参数。将该方法运用到贵州某实际光伏电站的并网性能测试中，验证在不同工况下电站的高低电压穿越能力，得到其准确的控制参数。结果表明：所提的参数辨识方法能准确地辨识系统在不同工况下高低电压穿越关键参数，且仿真与实测数据误差满足相关标准的技术要求，可用于工程实际计算。
In order to better avoid the grid disconnection caused by the grid faults of new energy power plants, verify the high and low voltage ride-through capability of photovoltaic power plants, and obtain accurate model control parameters, this paper proposes a key parameter identification method for photovoltaic power plants based on high and low voltage ride-through test measurement data, considering the characteristics of the entire fault ride-through process. Based on the measured data, a BPA model of the photovoltaic power plant is established. By conducting sensitivity analysis on the model parameters, key identification parameters are determined. A photovoltaic power plant model identification method based on an improved Salp Swarm Algorithm (SSA) with multiple strategies is proposed. The method continuously optimizes and updates the parameters to be identified and performs transient stability calculations. The error between the output of the simulation model and the measured data is calculated to obtain accurate model parameters for the photovoltaic power plant. This method is applied to the grid performance testing of an actual photovoltaic power plant in Guizhou, verifying the high and low voltage ride-through capability of the power plant under different operating conditions and obtaining accurate control parameters. The results show that the proposed parameter identification method can accurately identify the key parameters of high and low voltage ride-through under different operating conditions, and the error between the simulation and measured data meets the technical requirements of relevant standards, making it suitable for practical engineering calculations.