In general, unexpected failures in sensorless brushless DC (BLDC) motors can result in production downtime, costly repairs, and safety concerns. BLDC motors are commonly used in home appliances, the medical sector, aerospace, small-scale, and large-scale industries under uncertain operating conditions. Therefore, the fault detection and diagnosis (FDD) of BLDC motor drives can play a very important role in increasing their performance, reliability, robustness control, and operational safety under uncertain operating conditions in critical real-time applications. To satisfy these issues of hall effect sensor, misplacement of a hall-effect sensor, inverter IGBT open-switch fault diagnosis, failure of hall effect sensor, lack of robustness speed control of BLDC motor, which has received substantial interest in academic and industry sectors to establish the proposed work optimization techniques approach FDD strategy for speed control of sensorless BLDC motor under uncertain operating conditions. The proposed optimization techniques such as Bat Algorithm (BA), Grey Wolf Optimization (GWO), and Whale Optimization Algorithm (WOA) approach FDD strategies for BLDC motor drives. These FDD strategies simulated by the above optimization techniques on a sensorless BLDC motor with numerical Matlab/Simulink 2020a simulation results are verified. From the simulation results, out of three optimization techniques, the WOA-based FDD strategies are very effective for both bearing and stator winding faults detection and diagnosis in sensorless BLDC motor drives.

This article presents a modified genetic algorithm (MGA) to determine the five degrees of freedom parameters, namely K p , K i , K d , λ, and μ of a fractional order proportional integral derivative (FOPID) controller to achieve the speed control of a brushless direct current (BLDC) motor by sensorless technique. The MGA is a meta-heuristic inspired algorithm for solving nonlinearity problems such as sudden load disturbances, power fluctuations, and misalignment of the motor. The conventional genetic algorithm (CGA) is not very effective in solving the abovementioned problems. Hence, MGA-optimized FOPID (MGA-FOPID) controller is recommended for sensorless speed control of BLDC motor under varying load (T L ) conditions, and varying set speed (N s ) conditions. The proposed design of the MGA-FOPID controller has been implemented in MATLAB 2019a with Simulink models for optimal speed control of the BLDC motor. Also, the hardware experimental set-up and the results of the proposed controller are presented. The performance of the MGA-FOPID controller is observed and evaluated for time-domain characteristics. It is important to note that the proposed MGA-FOPID controller is more effective than the MGA optimized integer order proportional integral derivative controller in terms of minimizing time integral performance indexes.

This paper presents an 11-level symmetrical inverter with reduced number of semiconductor switches for the solar photovoltaic (PV) applications. The genetic algorithm (GA) and queen bee assisted genetic algorithm (QBAGA) are performing a major task for the proposed Multilevel Inverter (MLI). The proposed symmetrical MLI topology has reduced number of semiconductor devices compared to the conventional MLI. However, the modulation index threshold related to the drop in the number of inverter output voltage levels is higher than that of the MLI. The objective of this paper is to find the optimal value of modulation index ([Formula: see text]) using optimization algorithms in order to attain the maximum power point (MPP) from the solar PV array. An 11-level symmetrical inverter is taken into consideration whose optimal modulation index ([Formula: see text]) is calculated in order to eliminate the harmonics. The total harmonic distortion (THD) measurement is used to estimate the quality of inverter the output voltage which implies the improvement of power quality. The simulations are carried out in MATLAB/Simulink and the experimental prototype is implemented with field programmable gate array (FPGA)-based processor. The simulation and experimental results show lesser THD with the absence of filter components which expose the effectiveness of the proposed system.

The paper presents the design of Modular Multilevel Inverter (MMI) to control the Induction Motor (IM) drive using intelligent techniques for marine water pumping applications. The proposed inverter is of eleven level and has the ability to control the speed of an IM drive which is fed by solar photovoltaic's. It is estimated that the energy consumed by pumping schemes onboard ship is nearly 50% of the total energy. Considering this fact, the paper investigate and validates the proposed control design with reduced complexity intended for marine water pumping system employing an induction motor (IM) drive and MMI. The analysis of inverter is carried out with Proportional-Integral (PI) and Fuzzy Logic (FL) based controllers for improving the performance. A comparative analysis has been made with respect to better robustness in terms of peak overshoot, settling time of the controller and Total Harmonic Distortion (THD) of the inverter to show the effectiveness of the proposed scheme. Simulations are undertaken in MATLAB/Simulink and the detailed experimental implementation in real time with Field Programmable Gate Array (FPGA). The results thus obtained are utilized to analyze the controller performance, improved inverter output voltage, reliable induction motor speed control and power quality improvement by reduction of harmonics. The novelty of the proposed control scheme is the design and integration of MMI, IM drive and intelligent controller exclusively for marine water pumping applications.

Purpose The puspose of this paper, a novel systematic design of fractional order proportional integral derivative (FOPID) controller-based speed control of sensorless brushless DC (BLDC) motor using multi-objective enhanced genetic algorithm (EGA). This scheme provides an excellent dynamic and static response, low computational burden, the robust speed control. Design/methodology/approach The EGA is a meta-heuristic-inspired algorithm for solving non-linearity problems such as sudden load disturbances, modeling errors, power fluctuations, poor stability, the maximum time of transient processes, static and dynamic errors. The conventional genetic algorithm (CGA) and modified genetic algorithm (MGA) are not very effective in solving the above-mentioned problems. Hence, a multi-objective EGA optimized FOPID (EGA-FOPID) controller is proposed for speed control of sensorless BLDC motor under various conditions such as constant load conditions, varying load conditions, varying set speed (Ns) conditions, integrated conditions and controller parameters uncertainty. Findings This systematic design of the multi-objective EGA-FOPID controller is implemented in MATLAB 2020a with Simulink models for optimal speed control of the BLDC motor. The overall performance of the EGA-FOPID controller is observed and evaluated for computational burden, time integral performance indexes, transient and steady-state characteristics. The hardware experiment results confirm that the proposed EGA-FOPID controller can precisely change the BLDC motor speed is desired range with minimal effort. Research limitations/implications The conventional real time issues such as nonlinearity characteristics, poor controllability and stability. Practical implications It is clearly evident that out of these three intelligent controllers, the EGA optimized FOPID controller gives enhanced performance by minimizing the time domain parameters, performance Indices error and convergence time. Also, the hardware experimental setup and the results of the proposed EGA-FOPID controller are presented. Originality/value It shows the effectiveness of the proposed controllers is completely verified by comparing the above three intelligent optimization algorithms. It is clearly evident that out of these three intelligent controllers, the EGA optimized FOPID controller gives enhanced performance by minimizing the time domain parameters, performance Indices error and convergence time. Also, the hardware experimental setup and the results of the proposed EGA-FOPID controller are presented.