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Industrial applications of induction motor were increased these days due to robust construction. When a three-phase diode clamped inverter is driving this induction motor, the knowledge of inverter performance is very much necessary for the proper operation of induction motor drive. Conventional three-level inverter can drive the induction motor for its speed control. Out of various configurations of MLI, Diode clamped MLI is popular. It has the advantages of simple configuration and requires less number of individual DC sources. But the basic knowledge of faults in the inverter circuit can be very handy for the effective design of induction motor drive system. For the fault mitigation basically the fault detection is to be known. This paper introduces a simple algorithm for the fault identification. This algorithm is very much useful in finding the faulty phase. Out of different faults in the inverter circuit, gate driver faults are common and this paper discusses the gate open fault which is very often fault in the inverter circuit. The simple algorithm also can detect the switch in a leg where this fault occurs. This algorithm is very handy for the fault mitigation. The analysis was carried out using Matlab/Simulink software.

Improvements in the sensorless direct torque control of an induction motor by employing fuzzy logic switching controller (FLSC) plus neural network-based duty ratio controller (NNDRC) are explained in this paper. The conventional direct torque control (CDTC) of an induction motor suffers from major drawbacks such as high ripples in motor torque and flux response, poor performance during low speed and starting, and switching frequency variations due to hysteresis bands. Duty ratio controller evaluates the time for which active switching state is applied (δ), and for the remaining time period, zero switching vector is applied. The simulation results show that by using FLSC with NNDRC, considerable reduction in torque and flux ripples and improvement in dynamic response of drive compared to CDTC are achieved.

The main objective of this paper is to ameliorate the performance of direct torque control of induction motor for a wide range of speed control using fuzzy logic. The major drawbacks of conventional direct torque control are high torque, flux ripples and poor dynamic response at low speed. In this paper the CDTC is improved by fuzzy logic switching controller and fuzzy logic duty cycle controller. In FDTC with FDCC the selected voltage vector is applied only for the duty cycle time period (δ) determined. Simulation results validate that the proposed scheme shows improved dynamic performance of the drive for a wide range of speed (0–100 rad/s) and the torque and flux ripples are reduced by 90% compared to CDTC. Keywords: Direct torque control (DTC), Fuzzy Direct Torque control (FDTC), Fuzzy duty cycle controller (FDCC), Induction motor (IM), Membership function (MF)

The aim of this paper is to develop an optimal duty ratio controller to improve dynamic performance of induction motor with reduction in Torque and Flux ripple using Fuzzy logic. The major disadvantage of conventional DTFC is High torque and flux ripples and variable switching frequency. In DTFC with duty ratio controller concept of duty cycle is introduced, instead of applying a selected voltage vector for the entire switching period if applied for a portion of the switching period determined by optimal duty cycle [20]. A PWM based duty ratio controller is introduced in conventional DTFC. The duty cycle can be optimized using fuzzy logic. The improved performance and effectiveness of DTFC with Fuzzy DRC compared to SDRC is evaluated using Matlab-Simulink environment.

DC motors are simple choice for drive systems having very easy speed control techniques. DC motors are used where DC supply is available. Commutation in the conventional DC motors is carried out by commutator which is rotating part placed on the rotor and brushes. Due to these mechanical parts, conventional DC motor consist high amount of losses. Brushless DC (BLDC) Motors are very extensively used motors these days because of its advantages over conventional DC motors. Commutation is carried out with the help of solid-state switches in BLDC motor instead of mechanical commutator as in conventional DC motor. This improves the performance of the motor. BLDC motor shows good speed control characteristics especially in low power applications like hard-disk drives, laser printers and many more. BLDC with current control scheme was discussed in this paper. BLDC drive with fixed speed and variable speeds are developed using Matlab/Simulink and results were shown for both fixed and variable speed applications. Basic operation of BLDC motor was explained with open loop and closed loop speed control. Matlab results were shown for BLDC motor with open loop speed control, BLDC motor for fixed speed closed loop control and BLDC motor with variable speed closed loop control.

In this paper a novel sensorless speed control scheme of Induction Motor (IM) by means of Direct Torque Fuzzy Control (DTFC), PI-type fuzzy speed regulator and fuzzy based Model Reference Adaptive Systems (MRAS) speed estimator strategies has been proposed, which seems to be a boom in sensorless speed control schemes of AC drives. Normally, the conventional sensorless speed control performance of IM drive deteriorates at low speed. Hence the attention has been focused to improve the performance of the IM drive at low speed range as well, by implementing fuzzy control strategies. So, this research work describes a novel adaptive fuzzy based speed estimation mechanism which replaces the conventional PI controller used in MRAS adaptation mechanism. The proposed scheme is validated through extensive numerical simulations on MATLAB. The simulated results signify that the proposed control scheme provides satisfactory high dynamic performance and robustness during low speed operations of IM drive compare to conventional sensorless speed estimator of DTFC scheme.

A novel design of an adaptive neuro fuzzy inference strategy (ANFIS) for controlling some of the parameters, such as speed, torque, flux, voltage, current, etc. of the induction motor is presented in this paper. Induction motors are characterized by highly non-linear, complex and time-varying dynamics and inaccessibility of some of the states and outputs for measurements. Hence it can be considered as a challenging engineering problem in the industrial sector. Various advanced control techniques has been devised by various researchers across the world. Some of them are based on the fuzzy techniques. Fuzzy logic based controllers are considered as potential candidates for such an application. Fuzzy based controllers develop a control signal which yields on the firing of the rule base, which is written on the previous experiences & these rules are fired which is random in nature. As a result of which, the outcome of the controller is also random & optimal results may not be obtained. Selection of the proper rule base depending upon the situation can be achieved by the use of an ANFIS controller, which becomes an integrated method of approach for the control purposes & yields excellent results, which is the highlight of this paper. In the designed ANFIS scheme, neural network techniques are used to select a proper rule base, which is achieved using the back propagation algorithm. This integrated approach improves the system performance, cost-effectiveness, efficiency, dynamism, reliability of the designed controller. The simulation results presented in this paper show the effectiveness of the method developed & has got faster response time or settling times. Further, the method developed has got a wide number of advantages in the industrial sector & can be converted into a real time application using some interfacing cards.

A high-sensitivity capacitive pressure transducer with active processing circuit on the chip has been demonstrated and evaluated. The transducer configuration has been optimized by computer-aided simulation and design to achieve highest sensitivity for a given maximum dimension. The measured sensitivity of the devices is in the range of 50-150 MV/V mmHg, which is approximately one order of magnitude higher than the sensitivity of the piezoresistive pressure transducer of comparable size. Theoretical analysis also shows that sensitivity of the order of 1000 pV/V mmHg is also possible using the capacitive approach if the dimension of the device can be enlarged and the full-scale pressure range is lowered.

This paper presents implementation of Direct Torque and Flux control of induction machine using modified switching table and Fuzzy Logic Controller. The major concern in DTFC is selection of the switching state vector in order to meet Torque and flux demand of the drive. Major disadvantage of Conventional DTFC is high flux and Torque ripples due to hysteresis controllers and switching table cannot produce accurate voltage vector. Fuzzy logic controller can divide the torque error, flux error and stator flux angle into smaller subsections which results in optimal selection of switching state there by reduction in torque and flux ripples. The proposed methods are evaluated using simulation by Matlab/Simulink.

This paper proposes a simple and cost effective fault diagnosis technique in Vector controlled Pulse width Modulated Voltage Source Inverter. Current errors generated by comparing reference currents and actual currents are used to diagnose the fault behavior. Distribution of three phase currents is divided into six stages. During fault occurrence some of these six stages are healthy and some are faulty. Using distortions in current errors these healthy and faulty stages are determined then fault can be diagnosed. Simulations by MATLAB/ Simulink are carried out for single switch and double switch fault to check effectiveness of proposes fault detection.

This paper presents improvements in Direct Torque control of an induction motor using Fuzzy logic with Fuzzy logic and neural network based duty ratio controller. The conventional DTC (CDTC) of induction motor suffers from major drawbacks like high torque and flux ripples and poor transient response. Torque and flux ripples are reduced by replacing hysteresis controller and switching table with Fuzzy logic switching controller (FDTC). In FDTC the selected switching vector is applied for the complete switching time period. The FDTC steady state performance can be improved by using duty ratio controller, the selected switching vector is applied only for the time determined by the duty ratio (δ) and for the remaining time period zero switching vector is applied. The selection of duty ratio using Fuzzy logic and neural networks is projected in this paper. The effectiveness proposed methods are evaluated using simulation by Matlab/Simulink.

Brushless DC motor is a synchronous machine that makes use of electronic commutation instead of mechanical commutator. Brushless DC motors makes use of inverter encompassing static switches for its operation. A simple bridge converter when used for BLDC drive as front end converter makes input source power factor to get reduced which is unacceptable in the power system. To avoid the distortions in the source voltage and source currents, Buck converter which was used as power factor correction (PFC) converter in this paper to improve the power factor. Presence of power electronic converters deteriorates system power factor effecting overall system performance. This paper presents buck converter for power factor correction in brushless DC motor drive system. Buck converter is operated with current control strategy rather to conventional voltage follower control. Simulation model was obtained using MATLAB/SIMULINK software and the brushless DC motor performance characteristics were shown for conditions with different DC link voltages and step variation in DC link voltage. Total harmonic distortion in source current was also presented.

This paper presents some improvements in direct torque control of an induction motor using fuzzy logic switching controller along with fuzzy logic and neural network based duty ratio controller. The conventional direct torque control of induction motor suffers from major drawbacks like high torque and flux ripples, current and torque distortion when sector changes and poor transient response. High torque and flux ripples are reduced to some extent by replacing hysteresis controller and switching table with fuzzy logic switching controller (FDTC). However, in FDTC the selected switching vector is applied for the complete switching time period which results in ripples under steady state. The FDTC steady state performance is improved by using duty ratio controller. Using duty ratio controller, the selected switching vector is applied to voltage source inverter only for the time determined by the duty ratio (δ) and for the remaining time, a zero switching vector is applied. As the switching vector is applied for only the time period till torque reaches its reference value, it results in the reduction of torque and flux ripples of induction motor. The selection of duty ratio is a nonlinear function of flux error, flux error and stator flux angle which is effectively implemented in this paper using the following artificial intelligence techniques: fuzzy logic and neural networks. The effectiveness of the proposed methods are evaluated by the simulation with Matlab/Simulink.

Recent developments in Soft computing techniques, power electronic switches and low-cost computational hardware have made it possible to design and implement sophisticated control strategies for sensorless speed control of AC motor drives. Such an attempt has been made in this work, for Sensorless Speed Control of Induction Motor (IM) by means of Direct Torque Fuzzy Control (DTFC), PI-type fuzzy speed regulator and MRAS speed estimator strategy, which is absolutely nonlinear in its nature. Direct torque control is known to produce quick and robust response in AC drive system. However, during steady state, torque, flux and current ripple occurs. So, the performance of conventional DTC with PI speed regulator can be improved by implementing fuzzy logic techniques. Certain important issues in design including the space vector modulated (SVM) 3-Ф voltage source inverter, DTFC design, generation of reference torque using PI-type fuzzy speed regulator and sensor less speed estimator have been resolved. The proposed scheme is validated through extensive numerical simulations on MATLAB. The simulated results indicate the sensor less speed control of IM with DTFC and PI-type fuzzy speed regulator provides satisfactory high dynamic and static performance compare to conventional DTC with PI speed regulator., {"references":["S. Benaicha, R. Nait-Said, F. Zidani, M-S. Nait-Said, B. Abdelhadi, \"A\ndirect torque fuzzy control of SVM inverter-fed Induction Motor drive\",\nProc. of the International Journal of Artificial Intelligence and Soft\nComputing Vol. 1, Nos. 2-4, pp. 259 - 270, 2009.","Mei C. G., Panda S. K., Xu J. X., Lim K. W., Direct Torque Control of\nInduction Motor-Variable Switching Sectors. IEEE Int. Conf. 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In this paper, an application of the fuzzy logic scheme for direct torque fuzzy control (DTFC) of an Induction Motor (IM) AC drive is proposed. The proposed DTFC is based on fuzzy logic technique switching table is described and compared with conventional direct torque control (DTC). The comparison with DTC shows that the use of the DTFC reduces the torque, stator flux, and current ripples. The proposed fuzzy control strategy is simulated using MATLAB SIMULINK.

The growth of industries is very essential for the growth of any nation. Industries are mainly depending on electrical energy, but unfortunately the sources for electrical energy are depleting and hence the gap between the supplier and the load is continuously increasing. The work presented in this paper gives the results of application of a few DSM techniques along with batteries applied to an industrial customer. The work presented in this paper is lowering the maximum demand during peak hours and savings in the energy bill has been achieved by avoiding the penalty charges on MD. Application of few DSM techniques and batteries results in clipping down the peaks and filling the valleys that flattens the load curve of a milk industry, hence a great improvement in the load factor and savings in the energy bill for the consumers.

The resources for electrical energy are depleting and hence the gap between the supply and the demand is continuously increasing. Under such circumstances, the option left is optimal utilization of available energy resources. To overcome this problem recently, a concept of demand side management (DSM) has emerged in power system planning and management. The main idea of DSM is to discuss the mutual benefits between supplier and consumer for maximum benefits and minimum inconvenience. The work presented in this paper gives the results of application of neural network and DSM techniques applied to an industrial consumer. The study indicates the improvement in energy efficiency of the system in terms of load factor, in addition the consumer also gets saving or reduction in the energy bill due to lowering of maximum demand (MD).