采用策略的是对电机诱导的3阶段矢量-直接矢量法的控制

ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Pub Date : 2021-10-10 DOI:10.26760/elkomika.v9i4.786

H. Fakhruddin, Handri Toar, Era Purwanto, Hary Oktavianto, Gamar Basuki, R. Apriyanto, Abdillah Aziz Muntashir

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引用次数: 1

摘要

向量控制是感应电动势控制提高其动态和效率的最佳解决方案。在这项研究中，一种PID的速度控制与一种多肽神经模糊的仿生系统结合，以提高各种基准速度的可靠性。智能分区优化方法(PSO)用于数据集优化。可靠性测试是通过比较传统的PID与引导电动机的3相2马力的pi - anfis进行的。研究验证是通过平台平台的模拟进行的。通过各种参照速度，传统的PID控制的结果要好得多。最快的上升时间作为健身功能产生的控制是关闭时间和上升时间的1.5倍。PID-ANFIS在高速测试中成功消除了低射和稳态振荡。关键词:矢量控制，有一个神经模糊的系统，参与Swarm优化，LabView ABSTRACTVector控制是最适合它的动态角色塑造和effiency的运动。在这项研究中，一个PID的速度控制器与一种多肽神经模糊的推理系统结合，以适应各种参考文献的可靠性。智能方法分区优化过去是为了优化我们的非现有数据。已完成的试验是由一个2马力3相位运动的皮与皮的共进完成的。研究证实了实验室平台的模拟。战场上的人们普遍认为，在一次广泛的引用中，超过开会的PID控制更可取。作为一种健身功能的替代，它有一个死亡的时间和醒来的时间1.5倍快。在高速测试期间，皮与成功的负面影响。keyword:向量控制，有一个神经功能模糊的子宫系统，子化粒子优化，LabView

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Strategi Implementasi Adaptive Neuro Fuzzy Inference System (ANFIS) pada Kendali Motor Induksi 3 Fase Metode Vektor-Tidak Langsung

ABSTRAKKendali vektor merupakan solusi terbaik dalam kendali motor induksi untuk meningkatkan karakter dinamis dan efisiensinya. Pada penelitian ini, sebuah kendali kecepatan PID dipadukan dengan Adaptive Neuro Fuzzy Inference System (ANFIS) untuk meningkatkan keandalan pada berbagai kecepatan acuan. Metode cerdas Particle Swarm Optimization (PSO) digunakan untuk optimasi dataset ANFIS. Pengujian keandalan dilakukan dengan membandingkan PID konvensional dengan PID-ANFIS pada motor induksi 3 fase berdaya 2HP. Validasi penelitian dilakukan melalui simulasi di platform LabView. PID-ANFIS membuktikan hasil yang jauh lebih baik dari kendali PID konvensional pada berbagai kecepatan acuan. Pemilihan rise time tercepat sebagai fungsi fitness menghasilkan kendali yang memiliki dead time dan rise time 1.5x lebih cepat. PID-ANFIS berhasil menghilangkan undershoot dan osilasi steady state ketika uji kecepatan tinggi.Kata kunci: Kendali Vektor, Adaptive Neuro Fuzzy Inference System, Particle Swarm Optimization, LabView ABSTRACTVector control is the best solution in induction motor control to enhance its dynamic character and efficiency. In this research, a PID speed controller is combined with the Adaptive Neuro-Fuzzy Inference System (ANFIS) to enhance reliability at various reference speeds. The intelligent method Particle Swarm Optimization (PSO) is used to optimize the ANFIS dataset. Reliability testing is done by comparing conventional PID with PID-ANFIS on a 2HP 3-phase induction motor. The research validation was carried out through a simulation on the LabView platform. The PID-ANFIS proved significantly better results than conventional PID control at a wide range of reference speeds. Selection of the fastest rise time as a fitness function results in a control that has a dead time and a rise time of 1.5x faster. PID-ANFIS successfully negates undershoot and steadystate oscillations during high-speed tests.Keywords: Vector Control, Adaptive Neuro Fuzzy Inference System, Particle Swarm Optimization, LabView

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ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika

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