2015
DOI: 10.3233/ifs-151552
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Z-Source Inverter Fed Induction Motor Drive control using Particle Swarm Optimization Recurrent Neural Network

Abstract: The suggestion is prepared for Particle Swarm Optimization (PSO) Recurrent Neural Network (RNN) based Z-Source Inverter Fed Induction Motor Drive in this document. The proposed method is employed to develop the presentation of the induction motor while decreasing the Total Harmonic Distortion (THD), eliminating the oscillation period of the stator current, torque and speed. Currently, as the input parameters, the PSO technique uses the induction motor speed and reference speed. It optimizes the raise of the PI… Show more

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Cited by 19 publications
(8 citation statements)
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“…The input parameters, for example, rotor speed and three phases current required for the proposed system, are accumulated from the induction motor. In this section, the adequacy of the proposed strategy is dissected by contrasting the implementation results and the distinctive existing strategies; for example, salp swarm optimization algorithm (Faris et al, 2018; Mirjalili et al, 2017b) and grasshopper optimization algorithm (Aljarah et al, 2018; Mirjalili et al, 2017a), binary salp swarm optimization algorithm (Faris et al, 2018), support vector machine (Aljarah et al, 2018), particle swarm optimization with recurrent neural network (Selva Santhose Kumar et al, 2015). The setpoint speed w r * and the actual speed w r are given to the input of the grasshopper optimization algorithm.…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…The input parameters, for example, rotor speed and three phases current required for the proposed system, are accumulated from the induction motor. In this section, the adequacy of the proposed strategy is dissected by contrasting the implementation results and the distinctive existing strategies; for example, salp swarm optimization algorithm (Faris et al, 2018; Mirjalili et al, 2017b) and grasshopper optimization algorithm (Aljarah et al, 2018; Mirjalili et al, 2017a), binary salp swarm optimization algorithm (Faris et al, 2018), support vector machine (Aljarah et al, 2018), particle swarm optimization with recurrent neural network (Selva Santhose Kumar et al, 2015). The setpoint speed w r * and the actual speed w r are given to the input of the grasshopper optimization algorithm.…”
Section: Resultsmentioning
confidence: 99%
“…The recurrent neural network is worked based on the human brain and includes some artificial neurons that are artificial training and testing algorithms. Normally, the recurrent neural network structure involves three layers named as the input layer, hidden layer, and output layer (Selva Santhose Kumar and Girirajkumar, 2015). The actual quadrature axis current I Q and the setpoint quadrature axis current I Q * exist in the input layer.…”
Section: Flywheel Replacement Of Induction Motor With Convenient Powementioning
confidence: 99%
“…Utilizing distinctive forms of WECS, the fault protection schemes were examined and the upgrading of LVRT ability within the line during fault condition was pointed out by Howlader and Senjyu. 21 To upgrade the LVRT ability of doubly fed induction generator (DFIG), a functioning crowbar assurance (ACB_P) framework was depicted by Swain and Ray, 22 and it additionally enhances the PQ of the system. Not at all like the conventional crowbar (CB) was the protection scheme outlined with the resistor in series of capacitor and it having resistors as it were.…”
Section: Recent Research Work: a Brief Reviewmentioning
confidence: 99%
“…In general, input layer, output layer, hidden layer are three essential layers of the RNN. For demonstrating complex time‐dependent phenomena, a few kinds of RNN structures have been proposed 39 . Much advancement has been made with feed‐forward systems and consideration has as of late swung to creating algorithms for systems with recurrent connectionist which have critical capacities not found in feedforward systems including attractor elements and the capacity to store data for later utilization specifically noteworthy is their capacity to manage time‐shifting information or yield through their own natural temporal operation 40 .…”
Section: Modeling Of Mg Architecture With Proposed Methodsmentioning
confidence: 99%