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.

Role of Filters in electronic devices is evident in this modern era. Filtering is a term that is used in most DSP applications to pass specific frequencies and reject other ones. There is a wide range of application areas of digital filters including image processing, voice, and video and data communication. A large number of techniques are used to design IIR filters. This paper's idea is to design, analyze and compare performance of Chebyshev I, Chebyshev II and Butterworth IIR filters using MATLAB FDA tool. Noise free signal will be rendered at the output of filter. Further effect of different parameters such as order, type of filter and cut off frequency are filter.

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.

A classical Van der Pol based forced oscillator is used to model the cardiac pacemakers to give results similar to those achieved with modified relaxation oscillators. Cardiac pacemakers can be modeled accurately with relaxation oscillator with some complex changes that make it difficult to tune the model matching with the exact behavior of the cardiac potential generators because of different varying parameters. The proposed model is simple and can easily be tuned with two parameters which make it attractive and useful. The model is based on external forcing function and it is not modeled as self-excited oscillator. The sinusoidal forcing function is applied and its behavior is controlled using two different damping parameters. The results achieved in this way are exactly the same as obtained with the relaxation oscillators. Matlab is used to simulate the model.

Vehicular ad hoc networks (VANETs) using reliable protocols of routing have become crucial in identifying the changes to topology on a continuous basis for a large collection of vehicles. For this purpose, it becomes important to identify an optimal configuration of these protocols. There are several possible configurations that have been preventing the configuration of efficient protocols that do not make use of automatic and intelligent design tools. It can further motivate using the techniques of metaheuristics like the tools, which are well‐suited to be able to solve these problems. The glowworm swarm optimization (GSO), simulated annealing (SA), and slow heat‐based SA‐GSO algorithms have been proposed in this work. The SA is a method of optimization, which imitates the manner in which the thermal system has been frozen down to its lowest state of energy. In the GSO, there is guidance to the rules of feasibility, where the swarm converges to its feasible regions very fast. Additionally, for overcoming any premature convergence, there is a local search strategy that is based on the SA and is used for making a search that is near to its true optimum solutions. Finally, this sluggish temperature‐based SA‐GSO algorithm will be employed to solve routing problems and problems of heat transfer. There is a hybrid slow heat SA‐GSO algorithm with a faster speed of convergence and higher precision of computation that is more effective in solving problems of constrained engineering.