Real time implementation of an ANFIS-based induction motor drive via feedback linearization for performance enhancement

Mishra, Rabi Narayan; Mohanty, Kanungo Barada

doi:10.1016/j.jestch.2016.09.014

Cited by 36 publications

(23 citation statements)

References 27 publications

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“…Five layers are displayed in the ANFIS. Each layer contains few nodes in the node function: (a) fuzzification process, (b) fuzzy of the past piece of fuzzy rules, (c) membership of normalization functions, (d) fuzzy rules of the consequent piece, and (e) output of network. To decide the fuzzy inference system parameters in the ANFIS structure of a hybrid Sugeno, a training algorithm is utilized.…”

Section: Implementation Of Hybrid Methods For Emsmentioning

confidence: 99%

Application of ANFASO for optimal power flow management of MG‐connected system with energy storage

Roy

Mandal

2020

Int J Energy Res

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The power flow management scheme for a microgrid (MG)-connected system utilizing a hybrid technique is suggested in this dissertation. An MG-connected system includes photovoltaic, wind turbine, micro turbine and battery storage.Due to the use of this resource, power production is intermittent and unpredictable, as well as unstable, which causes fluctuation of power in hybrid renewable energy system. To ensure the fluctuation of power, an optimal hybrid technique is suggested. The suggested hybrid technique is joint execution on ANFIS and ASOA. ANFIS stands for adaptive neuro fuzzy interference system, and ASOA stands for advanced salp swarm optimization algorithm, thus it is commonly known as the ANFASO method. In the established method, ANFIS is applied to continuously track the MG-connected system's required load. ASOA optimizes the perfect combination of MG in terms of predicted required load. The suggested methodology is used for optimal cost and to increase renewable energy sources (RESs). Constraints are RES accessibility, power demand and the storage elements. Using the MATLAB/Simulink work site, the ANFASO approach is executed and implemented compared with existing methods. The suggested method is compared with genetic algorithm (GA), BFA and the artificial bee colony algorithm (ABC), and the observed elapsed time of ABC is 37.11 seconds, BFA is 36.96 seconds and GA is 38.08 seconds. The elapsed time of the proposed technique was found to be lower (36.47 seconds) compared to existing techniques. Significant improvements regarding utilization of RES and total generation cost accuracy are attainable by utilizing the proposed approach. K E Y W O R D Sbattery storage, constraints, micro turbine, photovoltaic, power demand, power generations, wind turbine

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Section: Implementation Of Hybrid Methods For Emsmentioning

confidence: 99%

Application of ANFASO for optimal power flow management of MG‐connected system with energy storage

Roy

Mandal

2020

Int J Energy Res

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“…Keunggulan ANN ini bisa digunakan untuk membentuk rules dan MF dari FLC melalui adaptasi dan algoritma training yang dikenal dengan adaptive neiuro fuzzy inference system (ANFIS). ANFIS mampu menawarkan sistem yang cerdas dan efektif dengan fitur peningkatan konfigurasi dan performa [13]. Beberapa aplikasi ANFIS dalam vector control diantaranya mulai dari aplikasi estimator untuk kecepatan seperti pada [14], pengendalian kecepatan seperti pada [15] dan [16], kemudian untuk aplikasi pengendalian torsi seperti pada [13], hingga untuk optimasi efisiensi seperti pada [17].…”

Section: Pendahuluanunclassified

Performa Pengendali Arus Stator Dengan Menggunakan Anfis Pada Pengemudian Motor Induksi Berbasis Vector Control

Rusli

2019

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Vector control terdiri atas dua pengendali arus stator dq-axis. Performa pengendalian motor induksi secara keseluruhan bergantung pada salah satu atau kedua pengendali arus stator tersebut. Umumnya pengendali arus stator menggunakan pengendali PI, namun pengendali ini memiliki beberapa kelemahan utama yaitu susahnya menentukan gain dari proportional maupun integral. ANFIS yang menggabungkan fuzzy logic controller dan artificial neural network menawarkan kemampuan training, adaptif, cepat, dan handal. Pada paper ini pengendali ANFIS diterapkan untuk pengendali arus stator d-axis pada pengemudian motor induksi berdaya 10 HP dengan metode pengemudian vector control. Keseluruhan sistemnya divalidasi melalui MATLAB/Simulink. Pengendali ANFIS dibandingkan dengan pengendali PI untuk mengevaluasi performa dari motor. Evaluasi performa yang diamati yaitu performa kecepatan dinamik dan performa arus dengan skema pengujian berbeban konstan dan bervariasi. Dari kedua pengujian, pengendali arus stator d-axis PI dan ANFIS menghasilkan trend respon kecepatan dinamik yang sama, namun pengendali arus stator d-axis ANFIS mampu mereduksi konsumsi arus fasa, ripple arus stator d-axis, dan THD arus fasa.

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“…It is crucial to consider the powerful and flexible MG, which can perform reliable operation under all environmental conditions. Not to the slightest extent such as joint and master‐slave controllers that require a high‐bandwidth communication system . The conventional droop‐based controllers are used for the need of high‐bandwidth communication framework.…”

Section: Introductionmentioning

confidence: 99%

Analysis of power management and cost minimization in MG—A hybrid GOAPSNN technique

Roy

2019

Int J Numerical Modelling

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This paper presents a hybrid technique for optimal power flow management and production cost minimization of microgrid (MG)-connected system with renewable energy sources (RES). The hybrid technique is the joint execution of both the particle swarm optimization (PSO)-aided artificial neural network (ANN) and grasshopper optimization algorithm (GOA) named as GOAPSNN technique. The proposed technique is concerned with the mathematical optimization problems that involve more than one objective function to be optimized. At first, the PSO-ANN technique predicts the load demand in the MG-connected system by using the inputs of MG-connected system like photovoltaic (PV) system, wind turbine (WT), microturbine (MT), diesel engine (DE), and battery. According to the load demand, GOA is utilized to choose an optimal configuration of MG, ie, fuel cost minimization, emission factors, operating, and maintenance cost. The proposed technique is executed in MATLAB/Simulink platform and compared with the existing strategies, such as squirrel optimization with gravitational search-aided neural network (SOGSNN), adaptive neuro-fuzzy interference system and advanced salp swam optimization algorithm (ANFASO). In this paper, the maximum generated power of PV, WT, MT, and battery is 7. 9, 9.8, 15.7, and 4.56 kW, respectively. The state of charge (SoC) of the proposed technique is investigated, which is around 83%. In light of the load demand, the proposed approach has less production cost.

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Real time implementation of an ANFIS-based induction motor drive via feedback linearization for performance enhancement

Cited by 36 publications

References 27 publications

Application of ANFASO for optimal power flow management of MG‐connected system with energy storage

Application of ANFASO for optimal power flow management of MG‐connected system with energy storage

Performa Pengendali Arus Stator Dengan Menggunakan Anfis Pada Pengemudian Motor Induksi Berbasis Vector Control

Analysis of power management and cost minimization in MG—A hybrid GOAPSNN technique

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