Diagnosis of Air-Gap Eccentricity Fault for Inverter Driven Induction Motor Drives in the Transient Condition
Modelling and Simulation Study of a Helicopter with an External Slung Load System
Comparative Study of Single Phase Power Inverters Based on Efficiency and Harmonic Analysis
Trichotomous Exploratory Data Analysis [Tri–EDA]: A Post Hoc Visual Statistical Cumulative Data Analysis Instrument Designed to Present the Outcomes of Trichotomous Investigative Models
LabVIEW Based Design and Analysis of Fuzzy Logic, Sliding Mode and PID Controllers for Level Control in Split Range Plant
Nowadays, the inverter driven induction motor drives are being popular in the industries for variable speed applications and replacing existing techniques such as DC motors and thyristor bridges. In the present paper, a proposed dynamic simulation model is used to diagnose squirrel cage induction motor faults by wavelet transform's low frequency band approximation signal on the transient condition. The obtained results clearly demonstrate that the developed diagnostic technique may reliably separate healthy and faulty conditions of the motor in the transient conditions. Therefore, averted the motor faults before reaching in the disastrous condition and may save large revenues and unexpected shutdown for industries.
In this paper, an Inter Connected Hybrid Power System with Wind Energy in presence of Classical Controllers Tuned by Particle Swarm Optimization is studied. Integration of renewable resources into existing plants will affect the system frequency and hence design of a suitable controller is needed to maintain frequency within limits. In this paper, PSO technique is used to design conventional controllers such as I-controller, and PI- controller. For classical thermal and diesel units, the PSO method will tune the controller parameters in the secondary loop, whereas in case of wind turbine model; the pitch angle is controlled by the same Particle Swarm Optimization algorithm. Results are carried out in Matlab/Simulink software.
In the industrial sector, especially in the field of electric drives & control, induction motors play a vital role. Without proper controlling of the speed, it is virtually impossible to achieve the desired task for a specific application. Vector control has been preferred over scalar controlled induction motor due to its superior dynamic performance. As the PI controller has bounded operating limits and poor transient response, a search for an alternative controller arises. Due to advancement in Artificial Intelligence Technique such Fuzzy Logic (FL), Artificial Neural Network, ANFIS has gained momentum as a controller for nonlinear systems. In this paper, artificial intelligent controller such as FLC and ANN have been designed in a vector controlled induction motor drive. The dynamic modeling and simulation of induction motor drive has been done in MATLAB/SIMULINK. The performance evaluation of vector controlled drive has been presented for no load, positive and negative load as well zero speed conditions. The simulation results are presented with the PI controller and Artificial Intelligent controller such as Fuzzy logic and Neural Network. It proves that ANN based vector controlled induction motor drive have better performance as compared to PI and FLC controllers.
In this paper the main aim is to stabilize the Triple Link Inverted Pendulum (TLIP) system on a cart. The mathematical model and state space dynamics of the TLIP system is discussed. The TLIP system is inherently unstable, so a Linear Quadratic l Regulator Design (with a degree of stability) is presented. The stability, controllability, and observability are investigated. The choice of weighting matrices in LQR is also discussed. Then an Observer-based controller is designed for the TLIP system. The simulation is done with MATLAB environment and a comparative study of the time domain characteristics are shown.
In the paper, an Artificial Intelligence is proposed for detecting and controlling the magnetization level in magnetic core of a Welding Transformer, which is a part of a Middle-Frequency Direct Current (MFDC) Resistance Spot Welding System (RSWS). It consists of an input rectifier, which produces a DC bus voltage, an inverter, a welding transformer, and a fullwave rectifier that is mounted on the output of a transformer. During normal RSWS operation, welding transformer's magnetic core can become saturated due to the unbalanced resistances of both transformer secondary windings and different characteristics of output rectifier diodes, which causes current spikes and over-current protection switch-off of the entire system. In order to prevent saturation of the transformer magnetic core, the RSWS control must detect that the magnetic core is approaching the saturated region and maintains the saturation within optimal limits. The aim of this paper is to present a reliable method for detection and control of magnetic core saturation. Here, an Artificial Neural Network (ANN) is proposed for detecting the current spikes in the primary current and PI controller is used to maintain the saturation within optimal limits. This is achieved by a combined closed-loop control of the welding current and iron core saturation level.