Reduction Of Overshoot Research Articles

A novel artificial intelligence based multistage controller for load frequency control in power systems

The imbalance between generated power and load demand often causes unwanted fluctuations in the frequency and tie-line power changes within a power system. To address this issue, a control process known as load frequency control (LFC) is essential. This study aims to optimize the parameters of the LFC controller for a two-area power system that includes a reheat thermal generator and a photovoltaic (PV) power plant. An innovative multi-stage TDn(1 + PI) controller is introduced to reduce the oscillations in frequency and tie-line power changes. This controller combines a tilt-derivative with an N filter (TDn) with a proportional-integral (PI) controller, which improves the system’s response by correcting both steady-state errors and the rate of change. This design enhances the stability and speed of dynamic control systems. A new meta-heuristic optimization technique called bio-dynamic grasshopper optimization algorithm (BDGOA) is used for the first time to fine-tune the parameters of the proposed controller and improve its performance. The effectiveness of the controller is evaluated under various load demands, parameter variations, and nonlinearities. Comparisons with other controllers and optimization algorithms show that the BDGOA-TDn(1 + PI) controller significantly reduces overshoot in system frequency and tie-line power changes and achieves faster settling times for these oscillations. Simulation results demonstrate that the BDGOA-TDn(1 + PI) controller significantly outperforms conventional controllers, achieving a reduction in overshoot by 75%, faster settling times by 60%, and a lower integral of time-weighted absolute error by 50% under diverse operating conditions, including parameter variations and nonlinearities such as time delays and governor deadband effects.

Advanced hybrid control for induction motor combining ANN-PID speed controller with neural predictive current regulator based on particle swarm optimization

This paper presents a hybrid control strategy for an induction motor (IM) organized into two nested loops. In the outer loop, an adaptive artificial neural network-based proportional-integral-derivative (ANN-PID) controller is used for speed control. The ANN-PID is divided into two stages: the first stage includes an adaptive ANN speed estimator, and the second stage includes an adaptation algorithm that fine-tunes both the weights and biases of the ANN estimator and the parameters of the PID controller. In the internal loop, a predictive current controller is used to control IM currents using neural networks. Specifically, the neural predictive controller (NPC) approaches the control problem by presenting it as an optimization problem using a neural predictor for IM currents. The considered objective function includes two elements: the current tracking error and the electromagnetic torque ripple. This function is computed over a given time horizon informed by predictions of IM currents. The particle swarm optimization (PSO) algorithm is applied to determine the optimal solution for this optimization task. The effectiveness of the hybrid control scheme is demonstrated by simulation results obtained using Matlab/Simulink, highlighting its superiority over conventional control methods. The proposed hybrid control is characterized by a reduction in torque ripple and overshoot, while also showing robustness to variations in load, rotor resistance, and rotor inductance. Comparisons with an ANN controller and a fuzzy PI controller further demonstrate the superior performance of the hybrid controller in handling nonlinear dynamics and maintaining control accuracy under various operating scenarios.

Data-centric predictive control with tuna swarm optimization-backpropagation neural networks for enhanced wind turbine performance

Wind energy is a significant renewable resource, but its efficient harnessing requires advanced control systems. This study presents a Data-Centric Predictive Control (DPC) system, enhanced by a Tuna Swarm Optimization-Backpropagation Neural Network (TSO-BPNN) for predictive wind turbine control. It's like a smart tool that uses innovative fusion of deep learning, predictive Control, and reinforcement learning. Unlike traditional control methods, the proposed approach uses real-time data to optimize turbine performance in response to fluctuating wind conditions.The system is validated using simulations on the FAST platform, which demonstrate its superior performance in two critical operational regions. Specifically, in Region II, where the objective is to maximize power extraction from the wind, the DPC achieves a 1.07 % reduction in overshoot and an improvement of 36.14 units in steady-state error compared to traditional methods. The response time remains comparable to existing Model Predictive Control (MPC) strategies, ensuring real-time applicability without sacrificing efficiency. In Region III, where maintaining constant power output is crucial, the DPC outperforms both the baseline and MPC methods, reducing overshoot by 0.58 % and improving accuracy by 17.27 units compared to the baseline method. These results highlight the effectiveness of the proposed DPC system in optimizing turbine performance under variable wind conditions, offering a significant improvement over traditional methods in both accuracy and control precision.

Multi-objective design of fractional frequency-load control for hydro-thermal system considering nonlinear models and uncertainty

One of the crucial quantities in the power system problem is frequency, which should never exceed its nominal value. In normal conditions of load changes, the frequency is adjusted by automatic generation control. Still, in the face of special conditions, such as logging of the downstream network from the main network or a significant amount of generated power leaving the circuit, the power imbalance between generation and consumption causes a rapid frequency drop. In this case, the automatic generation control cannot stop the frequency drop, so the frequency tends toward instability. In this paper, to automatically produce a water-thermal system in two zones of discrete and continuous modes, the design of a fractional controller is discussed and investigated in different functional conditions and uncertainties. The proposed NSGA-II multi-objective method is used in this article to optimize each area’s load frequency control parameters for various goals. The ideal response for the entire network will be obtained by modifying these responses using a multi-factor method. This paper defines a multi-zone frequency load control based on time objective functions and eigenvalues. Based on the speed changes for the proposed algorithm and the novel approachfunction method employed in the control design. The simulation performed and analyzed in different working conditions with different comparative criteria has been discussed and investigated. Compared to more contemporary techniques, the simulation results show that the system’s performance is guaranteed. The main successes of the suggested method in comparison to earlier methods have been the reduction of overshoot, undershoot, and settling time.

Dynamic load frequency control in Power systems using a hybrid simulated annealing based Quadratic Interpolation Optimizer

Ensuring the stability and reliability of modern power systems is increasingly challenging due to the growing integration of renewable energy sources and the dynamic nature of load demands. Traditional proportional-integral-derivative (PID) controllers, while widely used, often fall short in effectively managing these complexities. This paper introduces a novel approach to load frequency control (LFC) by proposing a filtered PID (PID-F) controller optimized through a hybrid simulated annealing based quadratic interpolation optimizer (hSA-QIO). The hSA-QIO uniquely combines the local search capabilities of simulated annealing (SA) with the global optimization strengths of the quadratic interpolation optimizer (QIO), providing a robust and efficient solution for LFC challenges. The key contributions of this study include the development and application of the hSA-QIO, which significantly enhances the performance of the PID-F controller. The proposed hSA-QIO was evaluated on unimodal, multimodal, and low-dimensional benchmark functions, to demonstrate its robustness and effectiveness across diverse optimization scenarios. The results showed significant improvements in solution quality compared to the original QIO, with lower objective function values and faster convergence. Applied to a two-area interconnected power system with hybrid photovoltaic-thermal power generation, the hSA-QIO-tuned controller achieved a substantial reduction in the integral of time-weighted absolute error by 23.4%, from 1.1396 to 0.87412. Additionally, the controller reduced the settling time for frequency deviations in Area 1 by 9.9%, from 1.0574 s to 0.96191 s, and decreased the overshoot by 8.8%. In Area 2, the settling time was improved to 0.89209 s, with a reduction in overshoot by 4.8%. The controller also demonstrated superior tie-line power regulation, achieving immediate response with minimal overshoot.

ADRC Control of Ultra-High-Speed Electric Air Compressor Considering Excitation Observation

With the increasing power of fuel cells, ultra-high-speed electric air compressors (UHSEACs) have been widely used. However, due to the ultra-high speeds involved, UHSEACs face problems such as long speed adjustment times and large speed fluctuations. Compared to other control methods, Active Disturbance Rejection Control (ADRC) is well-suited for highly nonlinear systems like UHSEACs. The Extended State Observer (ESO), a key component of the ADRC, struggles to accurately observe high-frequency excitations. To address this, the first step is to add a cascaded structure to the ESO and design a Current State Extended State Observer (CS-ESO) to better observe the electromagnetic and load excitations in the UHSEAC. The second step involves designing the ADRC based on the CS-ESO and performing speed adjustment simulations. The third step is to build a UHSEAC experimental platform and a conduct speed adjustment experiment. The findings indicate that, compared to the Proportional Integral Derivative (PID) control, the ADRC with the ESO, and the Sliding Mode Control (SMC), the use of the ADRC with the CS-ESO results in a significant reduction in overshoot—by at least 760 RPM under load-increasing conditions and 140 RPM under load-reducing conditions. Furthermore, the speed regulation time is notably decreased by at least 0.2 s and 0.1 s under these respective conditions.

Control of a Micro-Electro-Mechanical System Fast Steering Mirror with an Input Shaping Algorithm

Fast steering mirrors (FSMs) designed by the micro-electro-mechanical system (MEMS) technology are significantly smaller in volume and mass, offering distinct advantages. To improve their performance in the open-loop control mode, this study introduces a control algorithm and evaluates its performance on an electromagnetic-driven MEMS-FSM. The algorithm employs a method to shape the input signal by fitting the system’s transfer function and modifying the step response. This shaped signal is then applied to the system to minimize overshoot, reduce settling time, and improve working bandwidth, thereby enabling faster angular adjustments and improving the stability of the FSM. The experimental results demonstrate an 85.65% reduction in overshoot and a decrease in settling time from 84 ms to 0.4 ms. Consequently, the working bandwidth of the FSM system increases to 2500 Hz, demonstrating the effectiveness of the algorithm in enhancing MEMS-FSM’s performance.

Practical fixed-time non-singular sliding mode control of second order nonlinear dynamic systems with chattering and overshooting avoidance

This paper introduces a Practical Fixed-Time Stabilization technique (PFxT) designed for a specific class of nonlinear second-order systems. The closed-loop systems, when appropriately parameterized, exhibit convergence within a predetermined time frame to a confined region near the origin. This outcome is achieved by amalgamating a nonlinear sliding mode approach with a practical fixed-time tracking virtual trajectory. The control algorithmś design incorporates considerations for overshoot reduction and chattering cancellation. Furthermore, the Lyapunov method is employed to establish the practical fixed-time stability of the proposed solution, providing insights into the limits within which the algorithm can be effectively deployed. To complete the algorithm specification, a parameter selection approach is introduced, enabling customization of the desired settling time. Comprehensive simulations are conducted to validate the effectiveness and viability of the proposed PFxT technique.

Integer PI, fractional PI and fractional PI data trained ANFIS speed controllers for indirect field oriented control of induction motor

Induction motor drives with variable speed applications that employ vector control are quite popular nowadays because they provide strong dynamic performance and flexible speed control. By decoupling the torque-producing current components of stator current from the rotor flux, Indirect Field Oriented Control is recognized for generating excellent performance in induction motor drives. This investigation is being done to show the effectiveness of the novel FPI input-output data-trained ANFIS controller and compare the three controllers' performance in terms of load variation capabilities, motor parameter variation, and speed tracking. Consequently, a comparison of the three controllers is important to select which controller performs high in induction motor drive. Indirect Field Oriented Control of induction motor with Fractional Proportional Integral (FPI), Integer Proportional Integral (IPI), and Adaptive Neuro-Fuzzy Inference System (ANFIS) controllers are all discussed in this work along with their designs and comparative analysis. The square of error was used as a fitness function to genetically optimize the FPI and IPI controller parameters. The suggested Adaptive Neuro-Fuzzy Inference System (ANFIS) controller uses a hybrid learning approach. It is trained by the FPI controller's input-output data. Using the results of MATLAB simulations under various operating situations, the performance of the ANFIS controller was compared with FPI and IPI controllers. Because of FPI controller includes an extra parameter for adjustment, namely integration order, it performed better than IPI controller for speed control of the induction motor. According to the simulation findings, the percentage peak overshoots while employing ANFIS, FPI, and IPI controllers were 0.495 %, 12.062 %, and 14.699 % respectively. As a result, ANFIS exhibits a drastic reduction in overshoot. Additionally, with the ANFIS controlled induction motor drive, the speed achieves the required set value at 0.14 s. For no load, constant, and changing loads, the induction motor drive's performance has been examined.

A Proportional–Integral–Derivative type regulation scheme with non-Lipschitz control actions for mechanical systems with constrained inputs

Motivated by the benefits that recent finite-time continuous control approaches have proven to give rise, this work aims to design a proportional–integral–derivative (PID) type control scheme for the global regulation of constrained-input mechanical systems that incorporates design features characteristic of such finite-time continuous algorithms. This is proven to be achieved through a more general PID type control structure that incorporates exponential weights on the P and D type terms, through which such control actions are permitted to loose Lipschitz-continuity at the desired equilibrium values. This entails an important challenge consisting on the introduction of an appropriate analytical framework and the development of a suitable closed-loop analysis through which the resulting design is properly supported. The study is complemented by experimental tests which show that appropriate (less-than-unity) values on the incorporated exponential weights indeed give rise to closed-loop improvements characteristic of finite-time continuous control approaches, such as reduction of overshoot on the position error responses and of the control effort, alleviating such a performance adjustment task.

Enhancing Load Frequency Control of Interconnected Power System Using Hybrid PSO-AHA Optimizer

The integration of nonconventional energy sources such as solar, wind, and fuel cells into electrical power networks introduces significant challenges in maintaining frequency stability and consistent tie-line power flows. These fluctuations can adversely affect the quality and reliability of power supplied to consumers. This paper addresses this issue by proposing a Proportional–Integral–Derivative (PID) controller optimized through a hybrid Particle Swarm Optimization–Artificial Hummingbird Algorithm (PSO-AHA) approach. The PID controller is tuned using the Integral Time Absolute Error (ITAE) as a fitness function to enhance control performance. The PSO-AHA-PID controller’s effectiveness is evaluated in two networks: a two-area thermal tie-line interconnected power system (IPS) and a one-area multi-source power network incorporating thermal, solar, wind, and fuel cell sources. Comparative analyses under various operational conditions, including parameter variations and load changes, demonstrate the superior performance of the PSO-AHA-PID controller over the conventional PSO-PID controller. Statistical results indicate that in the one-area multi-source network, the PSO-AHA-PID controller achieves a 76.6% reduction in overshoot, an 88.9% reduction in undershoot, and a 97.5% reduction in settling time compared to the PSO-PID controller. In the dual-area system, the PSO-AHA-PID controller reduces the overshoot by 75.2%, reduces the undershoot by 85.7%, and improves the fall time by 71.6%. These improvements provide a robust and reliable solution for enhancing the stability of interconnected power systems in the presence of diverse and variable energy sources.

On the optimal multi-objective design of fractional order PID controller with antlion optimization

This study introduces an optimal multi-objective design approach for a robust multimachine Fractional Order PID Controller (FOPID) using the Antlion algorithm. The research focuses on the need for effective stabilizers in multimachine power systems by employing traditional speed-based lead-lag FOPID controllers. The study formulates a multi-objective problem, optimizing the damping factor and damping ratio of lightly damped electromechanical modes to maximize a composite set of objective functions, tackled through the Antlion algorithm. Stability analysis of Single-Machine Infinite-Bus (SMIB) and multimachine power systems is conducted based on rotor speed and power deviation minimization in the time domain response, along with damping ratio and eigenvalue analysis. The proposed approach is implemented and tested on three IEEE test cases, showcasing significant improvements in stability through the reduction of maximum overshoot (Mp) and settling time (ts) of speed deviation. Comparative analysis with other optimization-based FOPID controllers underscores the superiority of the proposed approach in enhancing stability in multimachine power systems. The main impact of this research lies in its contribution to the advancement of stability enhancement techniques in multimachine power systems, offering a systematic framework for optimal FOPID controller design and empowering decision-making processes in power engineering.

An optimal feedforward circuit with null audio susceptibility in feedback‐controlled buck converter

AbstractThe ability to manage abrupt disturbances in input supply voltage and sudden fluctuations in load represents a fundamental requirement for DC‐DC buck converters. While feedback control systems typically address such transients, certain applications necessitate even faster transient responses. This paper introduces an input voltage feedforward scheme (IVFS) integrated with voltage mode feedback controllers to enhance closed‐loop performance. Relevant transfer functions resulting from the inclusion of the feedforward circuit are derived, and an optimal condition for audio susceptibility nullification is identified. This scheme ensures an additional attenuation in magnitude of audio susceptibility, in contrast to the buck converter without feedforward. In the time domain, the incorporation of IVFS results in a 20% reduction in percentage overshoot for a 12‐8‐12 step in supply voltage. Additionally, for a 12‐15‐12 step, the percentage overshoot and settling time are completely eliminated. Various active and passive parasitics are included in modeling of the buck converter to aid in designing the feedforward and feedback controller. A Type‐III controller is employed as a feedback controller to assess the closed‐loop performance. A hardware prototype is built to demonstrate the efficacy of the proposed feedforward scheme. The implementation of the entire circuit is straightforward due to its requirement of only a few discrete components, namely, analog multiplier, and error amplifier.

Synchronous Control of High-Speed Train Lift Wing Angle of Attack Drive System Based on Chaotic Particle Swarm Optimization and Linear Auto-Disturbance Resistant Controller

In this study, a control scheme is proposed based on Chaotic Particle Swarm Optimization (CPSO) to enhance the Linear Auto-Disturbance Rejection Controller (LADRC). The focus is on addressing the challenge of high-precision variations in angle-of-attack through dual-motor cooperative control within the lifting wing of a high-speed train. The scheme initiates with the design of a dual-loop structure for LADRC, integrating position and current control. The position loop is further refined. Subsequently, the CPSO algorithm is employed to optimize the parameters of the LADRC controller. Ultimately, the loop is closed by feeding back the position error in the cross-coupled structure to the current loop, thereby achieving high-precision control. The performance of the proposed structure is validated through both Matlab/Simulink simulations and an experimental platform. The experimental results demonstrate that CPSO-LADRC, in comparison to traditional LADRC and Proportion-Integration-Differentiation (PID) control, exhibits an increase in the maximum response time by 3.76 s and 3.3 s, respectively, a reduction in overshoot by 1.12% and 0.8%, as well as a decrease in the maximum synchronization error by 0.45 cm and 1 cm, respectively. These findings validate the effectiveness of the proposed synchronous loop controller method in simplifying computational complexity, enhancing system responsiveness, robustness, and synchronization performance. Additionally, our approach facilitates precise angle-of-attack transformation for the lifting wings of high-speed trains effectively.

Multi-index control strategy from cement calcination denitration system: a model predictive control method for combined control of nitrogen oxide and ammonia escape.

The cement industry is one of the main sources of NOx emissions, and automated denitration systems enable precise control of NOx emission concentration. With non-linearity, time delay and strong coupling data in cement production process, making it difficult to maintain stable control of the denitration system. However, excessive pursuit of denitration efficiency is often prone to large ammonia escape, causing environmental pollution. A multi-objective prediction model combining time series and a bi-directional long short-term memory network (MT-BiLSTM) is proposed to solve the data problem of the denitration system and achieve simultaneous prediction of NOx emission concentration and ammonia escape value. Based on this model, a model predictive control framework is proposed and a control strategy of denitration system with multi-index model predictive control (MI-MPC) is built based on neural networks. In addition, the differential evolution (DE) algorithm is used for rolling optimization to find the optimal solution and to obtain the best control variable parameters. The control method proposed has significant advantages over the traditional PID (proportional integral derivative) controller, with a 3.84% reduction in overshoot and a 3.04% reduction in regulation time. Experiments prove that the predictive control framework proposed in this paper has better stability and higher accuracy, with practical research significance.

Optimized Proportional Integral Derivative Based Power System Stabilizer Using Jaya Algorithm for Angular Stability Enhancement

This study presents an optimized Proportional Integral Derivative Based Power System Stabilizer (PIDPSS) using Jaya Algorithm for angular stability enhancement. Jaya algorithm introduced by Rao, is an optimization technique with few control parameters which is used to minimize the objective function F(K). The modeling and simulation were done using Matlab /Simulink software version R2021b on IEEE 14-Bus system and Single Machine Infinite Bus (SMIB). A three-phase fault was introduced into the network at system runtime of 5s with a fault clearing time of 0.1s. The result of the simulation of the IEEE 14 Bus system showed a 74% and 24% reduction in overshoot time of speed deviation for generators 1 and 2, with settling times of 2.5s and 4s, respectively, in the presence of PIDPSS. The load angle experienced a 14% and 19% reduction in overshoot with settling times of 2s and 2.5s, respectively in the presence of PIDPSS for generators 1 and 2, respectively. The Electrical Power result showed 27% and 6% reduction in overshoot time as well as settling times of 2.5s and 4s, respectively, for generator 1 and 2 in the presence of PIDPSS. The result of the simulation of SMIB system also showed a 25% reduction in overshoot time in relation to deviation speed at a settling time of 4s in the presence of PIDPSS. The Load Angle showed- a 13% decrease in overshoot time at 2s settling time in the presence of PIDPSS. Also, the Electrical Power result highlighted a 15% drop-in overshoot time and settles within 2s. These results affirms that the PIDPSS introduced improved overall system stability.

Improvements in Frequency Control of an AC Microgrid by Means of Micro-Hydropower Combined Flow-Reduced Dump Load Control Method

Load Frequency Control (LFC) is an issue of top importance to ensure microgrids (MGs) safe and reliable operation in AC MGs. Primary frequency control of each energy source can be guaranteed in AC MGs in order to integrate other energy sources. This paper proposes a micro-hydro frequency control scheme, combining the control of a reduced dump load and the nozzle flow control of Pelton turbines operating in isolated from electricity grid. Some researchs have reported the integration of dump load and flow control methods, but they did not reduce the dump load value and adjust the nozzle flow linearly to the power value demanded by users, causing the inefficient use of water. Simulation results were obtained in Matlab®/Simulink® using models obtained from previous research and proven by means of experimental studies. The simulation of the proposed scheme shows that instantaneous frequency variation is about 2 % even for perturbations magnitude up to 12 %. The validation result shows a 60 % reduction in overshoot and settling time of frequency temporal behaviour operating the micro-hydro autonomously.

Design of Delay-based Proportional Integral Controllers (δ-PI) for Second-order LTI Systems

This note focuses on the design of a delay-based proportional integral (PI) control scheme, proposed as an alternative to the classical PI controller. By adopting a geometric approach, we establish tuning rules to achieve closed-loop stability, ensure a specific decay rate, and assess its fragility. Specifically, through the appropriate design of the delay parameter, the proposed controller facilitates improvement in settling time and reduction of overshoot, while maintaining control efforts lower than its classical counterpart. We present several numerical examples to illustrate the effectiveness of the proposed scheme.

A Hybrid Controller Enhancing Transient Performance for an Aerial Manipulator Extracting a Wedged Object

Autonomous aerial manipulation requires the capability to handle inevitable dynamic changes during physical interaction. Previously, very few studies have addressed the stability and transient performance of the scenarios involving abrupt changes in dynamics. This paper proposes a hybrid controller enhancing transient performance for an aerial manipulator extracting an object wedged in a static structure. This task incurs a significant jump in the interaction force on the end-effector so that the analysis using the concept of hybrid dynamical systems is required. To demonstrate the dynamic characteristics of the object-extracting aerial manipulator, we derive the dynamic equations for two flight modes, i.e., free-flight and object-extracting, and the rule of state jumps. Also, we design control strategies which enhance the transient performance during flight mode transition. Then, the stability of the proposed control law is proven, and the overshoot reduction after the object extraction is analyzed. To show the improved performance, we conduct plug-pulling experiments with a quadrotor-based aerial manipulator using the proposed controller and two different existing controllers. The comparative results confirm that our controller enables the aerial manipulator to maintain its stability after the flight mode transition and shows the best transient performance in overshoot minimization among three controllers. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation for this article is the desire to prevent unexpected collisions between obstacles and an aerial manipulator after the vehicle extracts a wedged object from a static structure. To resolve this problem, we present a hybrid control method for such tasks while avoiding an excessive overshoot after the extraction. This method can be utilized in tasks involving abrupt changes in the dynamic model such as retrieving a device attached to a tall structure, reclaiming an object in disaster recovery, or pulling a plug out of a socket. Unlike existing methods, the proposed method simultaneously considers the stability <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">and</i> the initial overshoot right after pulling the object out of the structure, by employing a disturbance observer (DOB) in a hybrid control structure. It can be utilized for the aerial manipulator to avoid collision in a narrow space or to keep it in a safe operation envelope even though the task requires producing a relatively large pulling force.

Hybrid ANFIS‐PI‐Based Robust Control of Wind Turbine Power Generation System

This paper introduces a novel hybrid controller designed for a wind turbine power generation system (WTPGS) that utilizes a permanent magnet synchronous generator (PMSG). This hybrid controller combines the adaptability of an adaptive neuro‐fuzzy inference system (ANFIS) with the simplicity of a proportional‐integral (PI) controller. The PI controllers are traditionally used for stability and noise handling. ANFIS adds adaptability, making it more suitable to cope with the variable nature of wind energy. The primary objective of this hybrid strategy is to augment the overall control performance and reliability of PMSG‐based WTPGS when encountered with continuous variable wind conditions. However, implementing the PI controller alone with the WTPGS often suffers from high overshoot and sluggish response in nonlinear systems like WTPGS. In contrast, ANFIS controllers offer superior performance to PI and other artificial intelligence controllers but are still susceptible to noise issues. In this paper, the proposed WTPGS system is designed in MATLAB/Simulink software where a hybrid controller (ANFIS‐PI) is implemented in the machine‐side converter (MSC) and grid‐side converter (GSC) of a variable speed PMSG‐based wind turbine to enhance its performance subjected to wind variations. The hybrid controller is implemented in such a way that the ANFIS controller is implemented in the outer layers while the PI controller is applied in the inner layers of both MSC and GSC. The simulation results for this hybrid controller in the MSC outperform those of the conventional PI controller. They demonstrate minimal overshooting and settling time, maintaining consistent stability even when subjected to various test signals at different intervals. Similarly, the GSC also surpasses conventional PI controllers, achieving a significant 6.4% reduction in maximum overshoot and a decrease of 4.36 seconds in settling time. This highlights its strong suitability for wind turbine applications.