Artificial Intelligence Control System Applied in Smart Grid Integrated Doubly Fed Induction Generator-Based Wind Turbine: A Review

Abstract

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.

Characterization of a low power wind turbine prototype for interconnection to the grid

“…Artificial Intelligence can be used to control the turbine. There are various machine learning methodologies that allow the development of diagnostic tools to improve accuracy and stability, according to [7].…”

Analysis of Wind Characteristics for Grid-Tied Wind Turbine Generator Using Incremental Generative Adversarial Network Model

“…These AI techniques, including fuzzy logic (FL), machine/deep learning, expert systems (ES), neural networks (NN), robotics, genetic algorithms (GA), and natural language processing, emulate human intellectual functions, enabling computers to achieve self-healing capabilities. Machine and deep learning methodologies, in particular, have the potential to provide affordable, accessible, and highly accurate diagnostic tools for addressing complex optimization issues such as variable wind speeds, non-linear load variations, power dispatch, and load control at the point of coupling [23], [26], [27], [28].…”

Internet of Things-Based Control of Induction Machines: Specifics of Electric Drives and Wind Energy Conversion Systems

“…There is a tendency to deepen knowledge of double-output induction generator DOIG control systems operating in WECS. Thus, WECS equipped with DOIG have the advantages of operating with variable rotational speed input and constant frequency output and applying the techniques of maximum power point tracker MPPT, control the generated voltage, frequency, active, and reactive power [19]. The dynamic states of power systems embedded with smart grids and instabilities due to unpredictable wind and load changes, which can cause temporary load rejection, can be addressed using artificial intelligence and forecasting to predict component failures, reduce diagnostic time, and increase accuracy and stability for DOIG [19].…”

“…Of the most studied topics of induction generators are the technologies that allow WECS to connect to IoE, such as the requirements for networking and remote control, and avoiding or solving load rejection from instabilities due to the wind and load demands Table 1 [18,19]. • Also, the integration and monitoring of the hybrid wind-photovoltaics with IoT and ICT technologies, gained interest in the field of power supply restoration with early diagnosis and fault recovery in Table 4 [80].…”