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首页> 外文期刊>Renewable Power Generation, IET >Variable learning adaptive gradient based control algorithm for voltage source converter in distributed generation
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Variable learning adaptive gradient based control algorithm for voltage source converter in distributed generation

机译：分布式发电中基于变学习自适应梯度的电压源变换器控制算法

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摘要

This study presents an adaptive control algorithm known as variable learning and adaptive gradient based least mean square for improving the power quality features in standalone distributed generation. Further, frequency and voltage are regulated to set reference value at the terminal of the self-excited single-phase induction generator running in isolated mode. The variable learning and gradient-based least mean square (VLGLMS) algorithm is insensitive to its gradient, step size and sensors noise unlike least mean square algorithm whose convergence performance is influenced by the step-size parameter. In this study, VLGLMS is utilised to compute the active and reactive weights of fundamental load current for estimation of the reference source current. The sinusoidal reference current estimation is followed by generation of gate pulses for operating the DSTATCOM for improving the power quality features of single-phase induction generator based distributed power generation system. A prototype model is developed using MATLAB SIMULINK and tested in the laboratory under linear and non-linear loads. Based on implementation, the performance of control algorithm is validated and obtained results are found satisfactory.
机译：这项研究提出了一种称为可变学习和基于自适应梯度的最小均方的自适应控制算法，用于改善独立分布式发电的电能质量特征。此外，在隔离模式下运行的自励单相感应发电机的端子上，调节频率和电压以设置参考值。可变学习和基于梯度的最小均方（VLGLMS）算法对其梯度，步长和传感器噪声不敏感，这与最小均方算法不同，后者的收敛性能受步长参数的影响。在这项研究中，VLGLMS用于计算基本负载电流的有功和无功权重，以估算参考源电流。在正弦参考电流估计之后，将产生用于操作DSTATCOM的选通脉冲，以改善基于单相感应发电机的分布式发电系统的电能质量特征。使用MATLAB SIMULINK开发了原型模型，并在线性和非线性负载下在实验室进行了测试。在此基础上，对控制算法的性能进行了验证，得出的结果令人满意。

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