M. Senthil Kumaran

Direct Torque Control of matrix converter fed BLDC motor

This paper proposes a two-phase conduction Direct Torque Control (DTC) scheme for three-phase Brushless DC (BLDC) Motor in the constant torque region. The use of Matrix Converter (MC) produces rectangular output currents, while maintaining sinusoidal input currents close to unity power factor. The implemented scheme reduces the torque dip in the BLDC motor. The merits of the matrix converter fed BLDC motor is shown by comparing the results with a conventional AC-DC-AC converter fed BLDC, both implementing two-phase conduction DTC. A model based on Matlab/Simulink is implemented to verify the above-proposed theory.

Nominal features-based class specific learning model for fault diagnosis in industrial applications

Abstract Fault Detection and Isolation (FDI) is an initial stage of the real-time fault diagnosis in industrial applications. The increase in a number of faults will increase the size of the features that limit the accuracy. This paper proposes the suitable technique to extract the relevant features and classify the faults accordingly. Initially, the preprocessing removes the unfilled entries in the fault dataset (after the data collected from the sensor). Then, the Minimal Relevant Feature extraction predicts the features that correspond to the six types of fault classes. The minimum and maximum ranges of voltage, current, vibrations and speed due to the above classes regarded as the features. The modified objective function for class-specific support vector machine (CS-SVM) classifies the fault classes which highly contribute to the early diagnosis. The relevant feature prior to the Classification increases the accuracy effectively. The variations of voltage, current, and motor speed according to the injection of vibration faults from the motor bearings determine the impacts effectively. The comparison between the proposed Minimal Relevant Features-based Classification (MRFC) with existing SVM regarding the accuracy, precision, recall, sensitivity, specificity and coefficient (Jaccard, Dice and Kappa) confirms the effectiveness of proposed MRFC in earlier fault diagnosis in industrial applications.

Investigation of mathematical modelling of brushless dc motor(BLDC) drives by using MATLAB-SIMULINK

The main purpose of this research paper is to describe a mathematical simulation modelling of Brushless DC (BLDC) motor drive with the help of MATLAB - SIMULINK environment. The application of these motor is to spread widely in various fields like aerospace, robotics, industrial process control, precision machine tool, automotive electronics and household application. In this paper, the mathematical modelling of BLDC drive is efficiently controlled by using pulse width modulation (PWM) current controller technique. This model is developed for observing speed and torque characteristics of the BLDC motor and also the current and voltage components of the inverter. An elaborate explanation about operating principle of BLDC motor drive and PWM current control technique is shown. Here a whole mathematical modelling of BLDC motor drive simulation is carried out by using MATLAB-SIMULINK software.

IoT-Based Condition Monitoring and Fault Detection for Induction Motor

The Internet of Things (IoT) has enormous development in recent trends of industrial, environmental, and medical applications. The availability of massive amount of processing power in the cloud, new opportunities have emerged for complete automation of industrial devices along with Industrial Wireless Sensor Networks (IWSNs). The continuous monitoring and earlier fault detection of the machines builds the efficient process control in the industrial automation. The detection and classification of faults in the machine-learning techniques depends on the number of features involved in it. Increasing the dimensionality of the features will affect the classification accuracy adversely. To overcome these issues, the proposed method of Minimal Relevant Feature Extraction-based Class-Specific Support Vector Machine (CS-SVM) algorithm introduces the suitable technique to extract the relevant features and classify the faults accordingly. The relevant feature prior to the classification increases the accuracy effectively, and also the less dimensionality of the proposed algorithm is more suitable for time-consuming nature in cloud. The implementation of the above-proposed system provides an online machine condition monitoring and accurate fault prediction in the cloud platform using IoT service along with IWSNs.

Pre-channel scheduling and Priority-based reservation in medium access control for industrial wireless sensor network applications

Numerous technical advancements and the pervasive controlling schemes in the Industrial Wireless Sensor Network (IWSN) with the capability of interoperability among the nodes facilitate the reliable communication. With the increase of participating sensors size, the memory and energy consumption reduce the lifetime adversely. To alleviate these issues, this paper creates an energy efficient data management and routing architecture based on the data fusion techniques in IEEE 802.15.4. The data classification through proposed Multi-Stage Classification (MSC) prior to priority assignment techniques makes the immediate decision regarding the controlling actions. The split up of data using Reservation-based Medium Access Control (RMAC) raises the resource allocation requests according to the data type. The built up of the relationship between the number of slots and the data arrival enhances the resource utilization performance for each slot. The guarantee of transfer of immediate transmission with the priority supports the critical conditions with high energy efficiency.

A new slope suppression technique to compensate for voltage unbalance in multilevel inverters

Voltage fluctuation is one of the major issues encountered in multilevel inverters as it requires many isolated DC sources. The effect of source voltage unbalance results in unbalanced load current with reduced modulation index. This paper proposes slope suppression techniques to compensate for the source unbalance and achieve balanced output current. It is operated at its maximum modulation index. In order to analyze these effects, two methods namely carrier slope suppression (CSS) and reference slope suppression (RSS) techniques have been identified. An even level multilevel inverter topology comprising of a base six switch inverter, cascaded with H bridge modules in each phase is considered for analysis. Conventional sinusoidal pulse width modulation (SPWM) is developed for a four level inverter and its inadequacy to produce a balanced output under unbalanced voltage condition is analyzed. A comparison is made among the SPWM, CSS and RSS techniques. To validate the algorithm, simulation and experimental verification was done using MATLAB/SIMULINK and NEXYS 2 SPARTAN 3E FPGA with 3ϕ Induction Motor load.

Modeling and direct power control of DFIG for wind energy conversion system with a back to back converter

The variable-speed wind turbines are gaining more prominence due to their ability to generate power for a wide range of wind speeds. This class of turbines requires a fraction of the power be drawn from the grid in order to deliver the promised power when there is a deficiency in wind speed. For this purpose, a Doubly-Fed Induction Generator (DFIG) with a bidirectional converter (AC-DC-AC) on the rotor side is employed. The back-to-back converter controls the flow of energy from the machine to the grid and vice-versa. The Direct Power Control algorithm is used to control the power delivered by the stator to the grid. In this paper, the Space Vector Modulation (SVM) technique using the PI-controllers is used for controlling active and reactive powers. The SVM algorithm triggers the power electronic devices of the converter resulting in improved input current waveform. The paper discusses the simulation results of 1.5 MW DFIG machine connected to the grid via back-to-back converter.

FPGA Triggered Space Vector Modulated Voltage Source Inverter Using MATLAB/System Generator®

This paper involves the digital implementation of Space Vector Modulation (SVM) for a 3-phase Voltage Source Inverter (VSI). The System Generator (SG), which links both Xilinx and MATLAB, is used for constructing the Xilinx modules for the procedural implementation of the SVM. The SG converts the Matlab simulation of SVM done using Xilinx Blockset, into the corresponding Very High-Speed Integrated circuits Hardware Descriptive Language (VHDL) code. This VHDL code is compiled in Xilinx and the subsequent bit file generated, is loaded into the PROM of SPARTAN XILINX FPGA XCS3500e FG320. On execution of the bit file, the firing pulses are generated, which are applied to the VSI with Induction Motor Load.