C. A. Mehmood

Performance of adaptive PI based on fuzzy logic for Indirect Vector Control Induction Motor drive

A Novel Adaptive PI based on Fuzzy Logic Reasoning (FLR) is presented in this paper for Indirect Vector Control (IVC) three phase Induction Motor (IM). The main objective is to achieve fast dynamic response and robustness for speed variation, parameter uncertainties, load disturbances, electrical faults perturbations, and to acquire maximum torque and efficiency. The d-q modeling of the IM in synchronously rotating reference frame and Space Vector Pulse Width Modulation (SVPWM) employed in power inverter are carried out in Matlab/Simulink. Both PI and Adaptive PI based on FLR are analyzed, designed, and simulated for IVC IM drive system. Furthermore, the critical and analytical assessment of the aforesaid designed control methodology provides robust and faster response with low overshoot, rise, and settling time for the load disturbances, parameter uncertainties, speed variation, and electrical faults perturbation of IVC IM drive system, compared to prior works.

Control of DC link voltage for grid interfaced DFIG using Adaptive Sliding Mode & Fuzzy based on Levenberg-Marquardt algorithm during symmetrical fault

Enhancement of fault ride through capability of Doubly Fed Induction Generator (DFIG) is demanding issue in developed countries. In this paper crowbar circuit is implemented for limitation of fault current while the Adaptive Sliding Mode Controller (ASMC) & Adaptive Fuzzy controller (AFC) based on Levenberg-Marquardt algorithm is proposed to enhance fault ride through capability. The Adaptive controller is used to control DC link voltage during normal and faulty condition. The result of controlled DC link voltage with ASMC & AFC is critically and analytically compared with the conventional tuned PI controller. The test bench model is IEEE 5 bus system composed of 9MW DFIG interfaced with 120kv grid.

Fault tolerant speed regulation of Induction Motor using Artifical Neural Network

This paper successfully develops and investigates the implementation of Artificial Neural Network (ANN) based robust speed control strategy for Indirect Vector Control (IVC) three phase Induction Motor (IM) drive. The IM is modeled in terms of dq in synchronously rotating reference frame for IVC in Matlab/Simulink. The main purpose of the proposed design is to accomplish robustness for load disturbances, speed variation, parameter uncertainties, and electrical faults. The performance of aforesaid control technique is compared with that of conventional tuned PI control scheme. Simulation results of the ANN guarantee effectiveness and robustness regarding overshoot, undershoot, rise time, fall time and chattering for different operating condition in comparison to traditional PI control scheme.

Indirect Vector Control of Induction Motor using Adaptive Sliding Mode Controller

This paper presents an implementation of Adaptive Sliding Mode Controller (ASMC) based robust speed control strategy for Indirect Vector Control (IVC) three phase Induction Motor (IM) drive. The software platform for modelling of IM in the synchronous rotating frame of reference for IVC is Matlab/Simulink. The primary focus of the proposed controller is to accomplish robustness for electrical faults perturbations, load disturbances, speed variation, and parameter uncertainties. The performance of the proposed control technique is compared with that of the conventionally tuned PI control scheme. The Simulation results of the ASMC guarantee effectiveness and robustness regarding overshoot, undershoot, rise time, fall time, and chattering for different operating conditions compared to the traditional PI control scheme.

Stochastic and Statistical Analysis of Utility Revenues and Weather Data Analysis for Consumer Demand Estimation in Smart Grids.

In smart grid paradigm, the consumer demands are random and time-dependent, owning towards stochastic probabilities. The stochastically varying consumer demands have put the policy makers and supplying agencies in a demanding position for optimal generation management. The utility revenue functions are highly dependent on the consumer deterministic stochastic demand models. The sudden drifts in weather parameters effects the living standards of the consumers that in turn influence the power demands. Considering above, we analyzed stochastically and statistically the effect of random consumer demands on the fixed and variable revenues of the electrical utilities. Our work presented the Multi-Variate Gaussian Distribution Function (MVGDF) probabilistic model of the utility revenues with time-dependent consumer random demands. Moreover, the Gaussian probabilities outcome of the utility revenues is based on the varying consumer n demands data-pattern. Furthermore, Standard Monte Carlo (SMC) simulations are performed that validated the factor of accuracy in the aforesaid probabilistic demand-revenue model. We critically analyzed the effect of weather data parameters on consumer demands using correlation and multi-linear regression schemes. The statistical analysis of consumer demands provided a relationship between dependent (demand) and independent variables (weather data) for utility load management, generation control, and network expansion.