In this paper, nature inspired search algorithms, namely particle swarm optimization (PSO) and genetic algorithm (GA) are used to design fractional order proportional and integral (FOPI) and artificial neural network (ANN) controller based distribution static compensator (DSTATCOM) and electronic load controller (ELC) for power quality improvement. Improvement in power quality is achieved using DSTATCOM and an ELC. DSTATCOM is designed using FOPI and ANN based controllers, as opposed to conventional PI controllers which are comparatively less efficient. PSO and GA techniques are employed to determine the optimal parameters for the controllers. The improvement in the performance of the ANN and FOPI as compared to PI controller for the DSTATCOM and ELC is validated using MATLAB based modeling and simulations. Linear consumer loads were used to perform a comparative study in terms of maximum percentage error. Further, we analyzed the system for a nonlinear load and demonstrated decrease in the harmonic distortion.

中文翻译：

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基于进化计算的 DSTATCOM PI、FOPI 和 NN 控制器的比较研究

在本文中，自然启发的搜索算法，即粒子群优化（PSO）和遗传算法（GA）用于设计分数阶比例积分（FOPI）和基于人工神经网络（ANN）控制器的分布静态补偿器（DSTATCOM）和用于改善电能质量的电子负载控制器 (ELC)。使用 DSTATCOM 和 ELC 可以改善电能质量。DSTATCOM 是使用基于 FOPI 和 ANN 的控制器设计的，与效率相对较低的传统 PI 控制器相反。采用 PSO 和 GA 技术来确定控制器的最佳参数。与 DSTATCOM 和 ELC 的 PI 控制器相比，ANN 和 FOPI 的性能改进通过基于 MATLAB 的建模和仿真得到验证。线性消费者负载用于在最大百分比误差方面进行比较研究。此外，我们分析了系统的非线性负载，并证明了谐波失真的降低。