| 摘要: |
| 为了提高飞轮储能系统无位置传感器控制性能，飞轮电机必须具有参数辨识和参数自整定的功能。因此，提出一种基于递推最小二乘离散法在线辨识未知参数的转子位置扩张观测器，以使位置观测环节具有较强的鲁棒性能。首先，建立飞轮电机数学模型并进行离散化分析，在此基础上，采用通过将反电动势估算值反馈引入到非线性扩张观测器计算中的转子位置估算方法。随后，将基于递推最小二乘辨识算法应用于飞轮电机离散模型并可同时辨识定子电阻、交直轴电感等关键电磁参数，以便消除因飞轮电机模型误差引起的转子位置估算误差，提高系统无位置检测性能。最后，搭建基于DSP2812的硬件实验平台。仿真和实验结果表明所提算法对未知参数的辨识具有一定的准确性和实时性，可实现对飞轮储能系统的无位置传感器运行控制。 |
| 关键词: 储能系统 飞轮电机 递推最小二乘法 离散模型 非线性扩张观测器 无位置传感器 |
| DOI：10.7667/PSPC180715 |
| 投稿时间：2018-06-12修订日期：2018-07-22 |
| 基金项目:国家自然科学基金项目资助(51777197) |
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| Research on FESS sensorless control based on recursive least-squares discrete identification |
| ZHANG Wenyuan |
| (Shanxi International Electricity Group Limited Company, Taiyuan 030022, China) |
| Abstract: |
| In order to improve the control performance of a position-sensorless in a flywheel energy storage system, a flywheel motor necessarily has functions of parameter identification and parameter self-tuning. This paper provides a rotor position extension observer on the basis of online identifying an unknown parameter by means of a recursive least squares discrete identification method, such that a position observation link has relatively high robust performance. Firstly, the mathematical model of flywheel motor is established and discretized. On this basis, rotor position estimation method is adopted by introducing back EMF estimation value into the calculation of nonlinear expansion observer. Then, recursive least square identification algorithm is applied to flywheel motor discrete model and key electromagnetic parameters such as stator resistance and AC-DC axis inductance can be identified at the same time, so as to eliminate rotor position estimation error caused by flywheel motor model error and improve system position-free detection performance. Finally, a hardware experimental platform based on DSP2812 is established. Simulation and experimental results show that the mentioned algorithm has certain accuracy and a certain real-time feature for the identification of the unknown parameter, and can achieve the operation control of the position-sensorless in the flywheel energy storage system. This work is supported by National Natural Science Foundation of China (No. 51777197). |
| Key words: energy storage system flywheel motor recursive least-squares identification discrete model nonlinear extended state observer sensorless control |
