Wind energy is essential for promoting sustainability and renewable power solutions. However, ensuring stability and consistent performance in DFIG-based wind turbine systems (WTSs) remains challenging due to rapid wind speed variations, grid disturbances, and parameter uncertainties. These fluctuations result in power instability, increased overshoot, and prolonged settling times, negatively impacting grid compliance and system efficiency. Conventional proportional-integral (PI) controllers are simple and effective in steady-state conditions, but they lack adaptability in dynamic situations. Similarly, artificial intelligence (AI)-based controllers, such as fuzzy logic controllers (FLCs) and artificial neural networks (ANNs), improve adaptability but suffer from high computational demands and training complexity. To address these limitations, this paper presents a hybrid adaptive neuro-fuzzy inference system (ANFIS)-PI controller for DFIG-based WTS. The proposed controller integrates fuzzy logic adaptability with neural network-based learning, allowing real-time optimization of control parameters. Implemented within the rotor-side converter (RSC) and grid-side converter (GSC), ANFIS enhances reactive power management, grid compliance, and overall system stability. The system was tested under a step wind speed signal varying from 10 m/s to 12 m/s to evaluate its robustness. The simulation results confirmed that the ANFIS-PI controller significantly improved performance compared with the conventional PI controller. Specifically, it reduced rotor speed overshoot by 3%, torque overshoot by 12.5%, active power overshoot by 2%, and DC link voltage overshoot by 20%. Additionally, the ANFIS-PI controller shortened settling time by 50% for rotor speed, by 25% for torque, by 33% for active power, and by 16.7% for DC link voltage, ensuring faster stabilization, enhanced dynamic response, and greater efficiency. These improvements establish the ANFIS-PI controller as an advanced, computationally efficient, and scalable solution for enhancing the reliability of DFIG-based WTS, facilitating seamless integration of wind energy into modern power grids.

This paper presents hybrid model predictive control-based automatic generation regulator design for dominant wind energy penetrated multisource power system. The other power generation sources hydro and thermal are also considered in each area. The proposed AGC regulator is designed for each independent area considering only local power system states to ensure the stability of closed-loop system under small perturbation. The performance of the proposed AGC regulator has been validated for 2% step load perturbation. Furthermore, system parameter is varied to 20% of nominal value to assess the robust performance of the regulator. Performance evaluation between the hybrid MPC, conventional MPC, and traditional AGC regulators considering the dominant participation of the DFIG wind turbines is presented to demonstrate the superior performance of the hybrid MPC AGC regulator.

In this paper we worked on Two-Wheel Inverted Pendulum (TWIP). It is highly non-linear, unstable and strong coupling system. Two wheel self balancing robot is based on this concept. During simulation, transfer function is displayed on MATLAB-2016 software. This all work is based on the study of various controllers on Two Wheel Self Balancing Robot (TWSBR). The model is designed on the basis of state space the Mathematical Modeling of Controllers ie LQG & MPC, is well designed and show the comparison in between these two controller. The basic aim of designing this paper is to originate an efficient controller to operate robot in real time environment. The designed LQG controller has better performance and the designed MPC has the best results.

Achieving sustainable energy goals requires efficient integration of renewables like wind energy. Doubly fed induction generator (DFIG)-based wind turbine systems (WTSs) operate efficiently across a range of speeds, making them well-suited for modern renewable energy systems. However, sudden wind speed variations can cause power oscillations, rotor speed fluctuations, and voltage instability. Traditional proportional–integral (PI) controllers struggle with such nonlinear, rapidly changing scenarios. A control approach utilizing support vector regression (SVR) is proposed for the DFIG wind turbine system. The SVR controller manages both active and reactive power by simultaneously controlling the rotor- and grid-side converters (RSC and GSC). Simulations under a sudden wind speed variation from 10 to 12 m per second show the SVR approach reduces settling time significantly (up to 70.3%), suppresses oscillations in rotor speed, torque, and power output, and maintains over 97% DC-link voltage stability. These improvements enhance power quality, reliability, and system performance, demonstrating the SVR controller’s superiority over conventional PI methods for variable-speed wind energy systems.