B. Khan

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.

Fast Performance Computing Model for Smart Distributed Power Systems

Plug-in Electric Vehicles (PEVs) are becoming the more prominent solution compared to fossil fuels cars technology due to its significant role in Greenhouse Gas (GHG) reduction, flexible storage, and ancillary service provision as a Distributed Generation (DG) resource in Vehicle to Grid (V2G) regulation mode. However, large-scale penetration of PEVs and growing demand of energy intensive Data Centers (DCs) brings undesirable higher load peaks in electricity demand hence, impose supply-demand imbalance and threaten the reliability of wholesale and retail power market. In order to overcome the aforementioned challenges, the proposed research considers smart Distributed Power System (DPS) comprising conventional sources, renewable energy, V2G regulation, and flexible storage energy resources. Moreover, price and incentive based Demand Response (DR) programs are implemented to sustain the balance between net demand and available generating resources in the DPS. In addition, we adapted a novel strategy to implement the computational intensive jobs of the proposed DPS model including incoming load profiles, V2G regulation, battery State of Charge (SOC) indication, and fast computation in decision based automated DR algorithm using Fast Performance Computing resources of DCs. In response, DPS provide economical and stable power to DCs under strict power quality constraints. Finally, the improved results are verified using case study of ISO California integrated with hybrid generation.

An adaptive controller scheme implemented on multiple links VSC-HVDC for effective damping of low frequency power oscillations

Inter-area power oscillation damping is of fundamental importance in todays era of sophisticated and highly complex power system. The present paper demonstrates a practical solution obtaining satisfactory performance in damping power system oscillations utilizing the flexible Voltage Source Converter-High Voltage Direct Current (HVDC-VSC) transmission technology in an optimal combination with a Power Oscillation Damping (POD) controller such as Nonlinear Autoregressive Moving Average (NARMA-L2), utilizing active power modulation technique. The primary focus of the control scheme is to improve the damping of lightly damped or even unstable modes of the interconnected AC/DC power system through modal identification and estimation techniques. The performance of aforesaid control technique is compared with that of conventionally tuned PI control scheme. The designed control scheme guarantees stability margins through eigenvalue analysis and non-linear time domain simulations during symmetrical and asymmetrical faults. The software platform under which the various simulation scenarios were implemented is MATLAB/Simulink.

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.