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ANFIS and MRAS-PI controllers based adaptive-UPQC for power quality enhancement application

机译：基于ANFIS和MRAS-PI控制器的自适应UPQC，用于电能质量增强应用

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摘要

This paper presents a novel heuristic based adaptive control technique (ACT) for improved compensation capability of the unified power quality conditioner (UPQC). The compensation capability of UPQC is enhanced by the optimal regulation of DC link voltage. Among all power quality (PQ) distortions, voltage sag is the severe PQ problem that significantly deteriorates the regulation of DC link voltage. The conventional approaches utilize a fixed gain PI controller that increases the error between the reference and actual DC link voltage under sag condition. This ultimately results in poor compensation of PQ distortions at the point of common coupling. To enhance compensation capability, the performance of ACT is examined with analytical and artificial intelligence techniques. The model reference adaptive system (MRAS) is used in the analytical method for online self tuning PI controller. For artificial intelligence technique, the off-line trained ANFIS is processed in the simulation studies. The performance of ACT based UPQC is validated in Matlab/Simulink and the obtained results are compared with conventional scheme. To substantiate theoretical results of the proposed approach, the algorithm is tested in real time Xilinx system generator (XSG). (C) 2015 Elsevier B.V. All rights reserved.
机译：本文提出了一种新颖的基于启发式的自适应控制技术（ACT），以提高统一电能质量调节器（UPQC）的补偿能力。 UPQC的补偿能力通过对直流母线电压的最佳调节来增强。在所有电能质量（PQ）失真中，电压骤降是严重的PQ问题，它严重恶化了直流母线电压的调节。传统方法利用固定增益PI控制器，该控制器会在下垂条件下增加参考电压与实际DC链路电压之间的误差。这最终导致在公共耦合点对PQ失真的补偿不佳。为了增强补偿能力，使用分析和人工智能技术检查了ACT的性能。在线自适应PI控制器的分析方法中使用模型参考自适应系统（MRAS）。对于人工智能技术，在模拟研究中对离线训练的ANFIS进行处理。在Matlab / Simulink中验证了基于ACT的UPQC的性能，并将所得结果与常规方案进行了比较。为了证实所提出方法的理论结果，该算法在实时Xilinx系统生成器（XSG）中进行了测试。 （C）2015 Elsevier B.V.保留所有权利。

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