Gravitational-Search Algorithm for Optimal Controllers Design of Doubly-fed Induction Generator

The primary function of microgrid (MG) is to share the power accurately among different distributed generation (DG) units according to the load demands. The droop control methods are normally used for appropriate power sharing. However, these control techniques suffer badly due to the feeder impedances and uncontrollable power coupling issues. Many techniques have been proposed to share power accurately, most of them rely on communication link. In this work a localized/communication free signal generating mechanism is proposed. The mechanism injects the transient reactive power in frequency droop characteristics by observing the changes in microgrid. This injection disturbs the actual active power sharing which is helpful for the computation of reactive power sharing errors. These errors are minimized by utilizing the proportional integral (PI) controller. The PI controller modifies the y-intercept and the slop of Q-E (Reactive Power –Voltage) droop characteristic for the correction of reactive power sharing error. Moreover, the parameters of droop and PI controller of inner control loop are optimized by Particle Swarm Optimization (PSO) to obtain high precision and reliability. The proposed technique has the following salient features:1. Accurately shares the reactive power without the information of feeder line impedances2. This accurate reactive power sharing scheme has been started locally without the need of communication signal from the central controller. Thus, there is no delay in monitoring the notable changes in microgrid caused by DGs. 3. The careful selection of PI & droop controller parameters through PSO makes the system more stable as compared to the conventional methods in terms of transients and steady state responses.

In this paper, we present a comprehensive and in-depth investigation on the optimization of Proportional-Integral (PI) controller tuning for achieving stability and desired overshoot in the step response. The main objective of this study is to compare the effectiveness of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) techniques in finding the optimal parameters for the PI controller. The PI controller is a widely used control algorithm that plays a crucial role in many industrial processes. Its tuning greatly affects the system's performance, particularly in terms of stability and overshoot. Therefore, finding the optimal tuning parameters is of utmost importance. To address this optimization problem, we propose the utilization of two popular metaheuristic algorithms, GA and PSO. These algorithms are known for their ability to efficiently search through large solution spaces and find near-optimal solutions. By applying these algorithms to the PI controller tuning problem, we aim to determine which technique yields better results in terms of stability and overshoot tuning. In our comparative study, we provide a detailed explanation of both GA and PSO algorithms, focusing on their working principles and mathematical formulations. We also describe how these algorithms can be applied to the PI controller tuning problem. Furthermore, we highlight the key differences between GA and PSO, shedding light on their strengths and weaknesses. To assess the performance of GA and PSO, we conduct several experiments using different benchmark functions and step response models. We measure the stability and overshoot metrics for various parameter settings obtained through GA and PSO. By thoroughly analyzing the obtained results, we draw meaningful conclusions regarding the effectiveness of each technique. Our findings demonstrate that both GA and PSO exhibit promising results in optimizing PI controller tuning. These observations provide valuable insights and guidelines for choosing the appropriate algorithm based on specific control requirements. In conclusion, this comparative study is thought to contribute to the field of control systems engineering by offering a comprehensive analysis of GA and PSO techniques in the context of PI controller tuning. By highlighting their strengths and weaknesses, it is aimed to provide researchers and practitioners with valuable information for making informed decisions when optimizing control parameters for stability and overshoot reduction purposes.

This paper presents a load frequency control (LFC) of five areas connected to same tie-line and it consist of non-conventional main power grids (Thermal-Hydro) and micro-grids (Wind-Diesel). Performance of conventional PI controllers is compared with PSO (Particle Swarm Optimization) tuned PI controller in a regulated and deregulated environment. A five area power system model has been made using Matlab/simulink to analyse load frequency control measures under different conditions based on participation factors, load disturbances and variations in local load demands. Simulation results shows that PSO(Particle Swarm Optimization) tuned PI(Proportional & Integral) controller's gains give much better performance than the conventional PI controller parameters which are more difficult to determine and time-consuming. The effect of DFIG (Doubly fed Induction Generator) in the power system which helps to smooth the tie line power is also been described.

This paper proposes optimal tuning of Proportional Integral (PI) controller using Grey Wolf Optimizer (GWO) to overcome the slow convergence into local optimum associated with the Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) tuning methods. The optimal gains of the PI controller obtained from tuning using GWO, PSO and GA were implemented in the pitch angle control of the fixed speed Wind Turbine integrated into the distribution system. The objectives are to enhance the efficiency and reduced the fluctuation in the Wind Turbine output. Similarly, the Ziegler Nichols (ZN) tuned PI controller, the PI-fuzzy logic and Fuzzy Logic controllers were implemented in the same distribution system. The implementation of the tuned PI controllers has enhanced the output power of the Wind Turbine compared to the value obtained using the Hybrid or Fuzzy controllers. Furthermore, the GWO tuned PI controller recorded the high average output power of the Wind Turbine with low mean power error compared to other controllers. Also, it recorded the second-position in smoothing the Wind Turbine output power. The significance of the research is it has enhanced the efficiency and smoothing the output power of the fixed speed Wind Turbine.

This paper proposes an advanced control strategy for DFIG based Wind Turbine. The designed method is based on a combination of Particle Swarm Optimization (PSO) and Artificial Neural Network (ANN). PSO combined ANN is suggested to track the available maximum power point (MPPT) at varying wind speeds. Thus, PSO is used to improve the dynamic performance of Doubly Fed Induction Generator (DFIG) by optimizing its Proportional Integral (PI) controller gains. This work highlights the performance of the PSO optimized PI controller against classical one. The proposed control strategy is verified via 2 MW DFIG-WT using the MATLAB/Simulink environment. The obtained results validate the proposed PSO-PI as an effective tool for improving the dynamic behavior of DFIGWT, it reveals that the overshoots are reduced by 50 % compared to the classical PI. In addition, a faster transient response is achieved using the proposed control strategy.

Greenhouse system (GHS) is the worldwide fastest growing phenomenon in agricultural sector. Greenhouse models are essential for improving control efficiencies. The Relative Gain Analysis (RGA) reveals that the GHS control is complex due to 1) high nonlinear interactions between the biological subsystem and the physical subsystem and 2) strong coupling between the process variables such as temperature and humidity. In this paper, a decoupled linear cooling model has been developed using a feedback-feed forward linearization technique. Further, based on the model developed Internal Model Control (IMC) based Proportional Integrator (PI) controller parameters are optimized using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to achieve minimum Integral Square Error (ISE). The closed loop control is carried out using the above control schemes for set-point change and disturbance rejection. Finally, closed loop servo and servo-regulatory responses of GHS are compared quantitatively as well as qualitatively. The results implicate that IMC based PI controller using PSO provides better performance than the IMC based PI controller using GA. Also, it is observed that the disturbance introduced in one loop will not affect the other loop due to feedback-feed forward linearization and decoupling. Such a control scheme used for GHS would result in better yield in production of crops such as tomato, lettuce and broccoli.

The main problem of using a Proportional Integral (PI) Controller in Brushless Direct Current (BLDC) motor speed control is tuning the PI’s parameter and its performance cannot adapt to the system behavior changes. Particle Swarm Optimization (PSO) has been chosen to optimize the tuning. Fuzzy Logic Controller (FLC) is used to online tuning PI’s parameters to adapt to system conditions. Optimal adaptive PI, which combines the PSO method and FLC method to tune PI, is proposed. It was successfully implemented in the simulation environment. The test was carried out in three conditions: step responses, set-point changes, and disturbance rejection. The proposed algorithm is superior with no overshoot/undershoot. Whereas in terms of settling time is in between PI and PI-PSO. PI controller has the smallest control effort. However, the other parameter is the worst. PI-PSO is superior in terms of settling time and Integral of Absolute Error (IAE) except for the step response test. The proposed method has lower IAE and higher control effort by 78.73 % and 60 % compared to PI control. On the other hand, it has a higher IAE dan lower control effort by 11.82 % and 33.88 % compared to PI-PSO. Therefore, the optimal adaptive PI control can reduce energy consumption compared to optimal PI with better performance than PI control.

Application of optimal fuzzy and PI hybrid controllers for permanent magnet synchronous generator–based wind energy conversion systems

A Static Synchronous Compensator (STATCOM) is one of the Flexible AC Transmission System (FACTS) device which plays an vital role in power system network in alleviation of small and large transient disturbances and better voltage regulation. This paper proposed a self tuning Proportional-Integral (PI) controller for STATCOM which provided a voltage support at point of common coupling (PCC). Self tuning of PI controller was achieved by adapting PI controller gains with system parameters by using Bats Echolocation controller which mimics the Bats Echolocation behavior. In the proposed controller, the system voltage (parameter) at point of common coupling compared with reference voltage and then Bats Echolocation Controller updates the PI controller gains according to the variation obtained between them. This paper also proposed a Cascaded Multilevel Inverter (CMLI) instead of conventional Voltage Source Inverter (VSI) for STATCOM to obtain better voltage regulation at point of common coupling. The voltage imbalance between H-bridge cells in CMLI was rectified by prosing Multicarrier Pulse Width Modulation (PWM) techniques, The proposed controller was validated by comparing the simulation results with fixed gain PI controller and Self tuning PI controller using Neural controller and Particle Swarm Optimization.

Augmenting effectiveness of control loops of a PMSG (permanent magnet synchronous generator) based wind energy conversion system by a virtually adaptive PI (proportional integral) controller

Wind energy is a promising source of electricity in the world and fastest-growing. Doubly-Fed Induction Generator (DFIG) systems dominate and widely used in wind power system because of their advantages over other types of generators, such as working at different speeds and not needing continuous maintenance. In this paper used the PI controller and Flexible AC Transmission System (FACTS) device specifically static compensator (STATCOM) to investigate the effect of the controller and FACTS device on the system. PI controller tuning by Particle Swarm Optimization technique (PSO) to limit or reduced the fault current in (DFIG) system. The responses of different kinds of faults have been presented like; two lines to ground faults and three lines to ground faults at different operating conditions. Faults are applied to three proposed controllers; the first controller is the Proportional-Integral (PI), the second controller is PI-controller based on Particle Swarm Optimization (PI-PSO) technique and STATCOM. A reactive power static synchronous compensator (STATCOM) is used, the main aim for the use of STATCOM is to improve the stability of a wind turbine system in addition to this is improving voltages profile, reduce power losses, treatment of power flow in overloaded transmission lines. The simulation programming is implemented using MATLAB program.

This paper presents a fuzzy proportional-integral (PI) controller, which is a discrete-time version of a conventional PI controller. The data base as well as the constant PI control gains are optimized by using a genetic algorithm according to the following design specifications: minimizing the overshoot, the settling time and smoothing of the output curve. Thus, the optimization problem close to a multiobjective optimization one, resulting in an optimal fuzzy PI controller. Simulation results are shown to demonstrate the improvement over a conventional one.

This paper proposes a design of modified Guo Tao based optimized Proportional Integral (PI) controller for the level control system. The performance of a designed system is compared with Fuzzy PI and conventional PI controller. The parameters of PI controller are optimized using Genetic Algorithm (GA) with Elite Preservation Strategy (EPS). Guo Tao proposed a subspace searching GA (GTA) in his research. Limitations of Guo Tao's algorithm (GTA) motivate us to propose a modified GTA using elite preservation strategy. Multi-parent crossover is used with EPS to enhance the performance of controller. A transfer function of a level loop of fabricated experimental setup is obtained by using the step response analysis. The performance of proposed controller is checked and verified by transfer function analysis using Ziegler Nicholas (Z-N) method in MATLAB environment. The proposed controller algorithm possesses an excellent transient response as compare to Fuzzy PI and conventional PI controller.

This paper presents a modern approach for speed control of an induction motor (IM) using the particle swarm optimization (PSO) method for determining the optimal parameters, Kp and Ki, of the integral proportional (IP) controller. The use of PSO as an optimization algorithm makes the drive robust, with faster dynamic response, higher accuracy and insensitive to load variation. Comparison between different controllers is achieved, using an IP controller which is tuned by two methods, firstly manually and secondly using the PSO technique. Integral time absolute error, integral absolute error and integral square error performance indices are considered to satisfy the required criteria in output speed of an IM. From the simulation results it is observed that the IP controller designed with PSO yields better results when compared to the traditional method in terms of performance index.

The documented study in this paper is the comparison between Model Predictive Control (MPC) and fine-tuned conventional proportional Integral (PI) controllers. The algorithms used for fine tuning of PI controllers are Particle Swarm Optimization, Genetic Algorithm, Z-N (Ziegler Nichols), and Grey Wolf Optimization. The MATLAB/Simulink environment is used for comparison of results. The MPC based steam condenser presented excellent and superior dynamic performance in response to different step changes. Also, the detailed tabular comparison of various control metrics showed the better performance of MPC. The overall simulation results demonstrated that MPC can be an efficient alternative to PI controller for the steam condenser.