Wind-driven turbines utilizing the doubly-fed induction generators aligned with the progressed IEC 61400 series standards have engrossed specific consideration as of their benefits, such as adjustable speed, consistent frequency mode of operation, self-governing competencies for voltage and frequency control, active and reactive power controls, and maximum power point tracking approach at the place of shared connection. Such resource combinations into the existing smart grid system cause open-ended problems regarding the security and reliability of power system dynamics, which needs attention. There is a prospect of advancing the art of wind turbine-operated doubly-fed induction generator control systems. This section assesses the smart grid-integrated power system dynamics, characteristics, and causes of instabilities. These instabilities are unclear in the wind and nonlinear load predictions, leading to a provisional load-rejection response. Here, machine learning computations and transfer functions measure physical inertia and control system design’s association with power, voltage, and frequency response. The finding of the review in the paper indicates that artificial intelligence-based machine and deep learning predictive diagnosis fields have gained prominence because of their low cost, less infrastructure, reduced diagnostic time, and high level of accuracy. The machine and deep learning methodologies studied in this paper can be utilized and extended to the smart grid-integrated power context to create a framework for developing practical and accurate diagnostic tools to enhance the power system’s accuracy and stability, software requirements, and deployment strategies.
This research explores a distinctive control methodology based on using an artificial neural predictive control network to augment the electrical power quality of the injection from a wind-driven turbine energy system, engaging a Doubly Fed Induction Generator (DFIG) into the grid. Because of this, the article focuses primarily on the grid-integrated wind turbine generation’s dependability and capacity to withstand disruptions brought on by three-phase circuit grid failures without disconnecting from the grid. The loading of the grid-integrated power inverter causes torque and power ripples in the DFIG, which feeds poor power quality into the power system. Additionally, the DC bus connection of the DFIG’s back-to-back converters transmits these ripples, which causes heat loss and distortion of the DFIG’s phase current. The authors developed a torque and power content ripple suppression mechanism based on an NNPC to improve the performance of a wind-driven turbine system under uncertainty. Through the DC bus linkage, it prevented ripples from being transmitted. The collected results are evaluated and compared to the existing control system to show the advancement made by the suggested control approach. The efficacy of the recommended control methodology for the under-investigation DFIG system is demonstrated through modelling and simulation using the MATLAB Simulink tool. The most effective control technique employed in this study’s simulations to check the accuracy of the suggested control methodology was the NNPC.
The reliability assessment of smart grid-integrated distributed power-generating coordination is an operational measure to ensure appropriate system operational set-ups in the appearance of numerous issues, such as equipment catastrophes and variations of generation capacity and the connected load. The incorporation of seasonable time-varying renewable energy sources such as doubly fed generator-based wind turbines into the existing power grid system makes the reliability assessment procedure challenging to a significant extent. Due to the enormous number of associated states involved in a power-generating system, it is unusual to compute all possible failure conditions to determine the system’s reliability indicators. Therefore, nearly all of the artificial intelligence methodology-based search algorithms, along with their intrinsic conjunction mechanisms, encourage establishing the most significant states of the system within a reasonable time frame. This review’s finding indicates that machine learning and deep learning-based predictive analysis fields have achieved fame because of their low budget, simple setup, shorter problem-solving time, and high level of precision. The systems analyzed in this review paper can be applied and extended to the incorporated power grid framework for improving functional and accurate analytical tools to enrich the power system’s reliability and accuracy, overcome software constraints, and improve implementation strategies. An adapted IEEE Reliability Test System (IEEE-RTS) will be applied to authenticate the relevance and rationality of the proposed approach.
Recently, scientists and academics are discovering progressive improvements in the arena of wind power technology economically and reliably, allowing them to produce electricity focusing on renewable energy resources. Wind turbines (WT) using the Doubly Fed Induction Generators (DFIGs) have attracted particular attention because of their advantages such as variable speed constant frequency (VSCF) operation, independent control capabilities for maximum power point tracking (MPPT), active and reactive power controls, and voltage control strategy at the point of common coupling (PCC). When such resources have to be integrated into the existing power system, the operation becomes more challenging, particularly in terms of stability, security, and reliability. A DFIG system with its control strategies is simulated on MATLAB software. This entails the rapid control prototype testing of grid-connected, variable speed DFIG wind turbines to investigate the WT’s steady-state and dynamic behavior under normal and disturbed wind conditions. To augment the transient stability of DFIG, the simulation results for the active and reactive power of conventional controllers are compared with the adaptive tracking, self-tuned feed-forward PI controller model for optimum performance. Conclusive outcomes manifest the superior robustness of the feed-forward PI controller in terms of rising time, settling time, and overshoot value.
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