An SSM-PSO Based MPPT Scheme for Wind Driven DFIG System

SSRG international journal of electrical and electronics engineering, 2023

With the increase in energy power requirement and reduction in the availability of conventional fuel resources, the practice of generating electrical energy using renewable energy resources has gained importance. Wind is considered a significant resource commercially used for electricity generation. Wind velocity varies continuously, and hence, the output of the wind generator varies. As a result, the electrical power a wind turbine develops is not at the corresponding maximum value. Hence, a Maximum Power Point Tracking (MPPT) controller is designed for a wind turbine, enabling it to derive the maximum possible power at all wind speeds. In this paper, a comparative analysis of Optimal Torque (OT) and improved Particle Swarm Optimization (PSO) MPPT techniques for a Doubly-Fed Induction Generator (DFIG) is obtained. The Simulink model for DFIG is first obtained, and the system's output power without MPPT is examined. Conventional OT is then implemented. Secondly, an improved PSO MPPT technique is proposed, extracting a better quality of output power that exhibits better dynamics and gives more output power. The results of both methods are then compared and tabulated.

International Journal of System Assurance Engineering and Management, 2013

In this paper, an artificial intelligence method particle swarm optimization (PSO) algorithm is presented for determining the optimal PI controller parameters for the indirect control active and reactive power of doubly fed induction generator (DFIG) to ensure a maximum power point tracking of a wind energy conversion system. A digital simulation is used in conjunction with the PSO algorithm to determine the optimum parameters of the PI controller. Integral time absolute error, integral absolute error and integral square error performance indices are considered to satisfy the required criteria in output active and reactive power of a DFIG. From the simulation results it is observed that the PI controller designed with PSO yields better results when compared to the traditional method in terms of performance index.

International Journal of Hydrogen Energy, 2015

in recent years, there has been an evolution of electricity production based on wind energy, because its production is environmentally friendly. In this paper, Particle Swarm Optimization (PSO) is proposed to generate an On-Off Controller. On-Off Controller based maximum power point tracking is proposed to control a squirrel-cage induction generator (SCIG) of wind energy conversion system .Simulation studies are made with Matlab / Simulink to verify the effectiveness of the purposed method.

The Wind Energy Conversion System (WECS) has become very popular and more attractive to study the possibility of replacing the conventional power source by renewable energy. This paper is focusing on the modeling and analysis of (DFIG) in Matlab/Smulink with constant and variable speed wind. Three test systems are considered and implemented. The first system is studied with constant wind speed using sinusoidal pulse width modulation (SPWM) to control the switching of two level three phase back to back converters. The second system is investigated also with constant wind speed but using space vector pulse width modulation (SVPWM). The two systems have been simulated and the results shows the effect of each type of pulse width modulation. Two fault conditions are subjected to the second system, single line to ground fault at phase A (in 33KV line), programmable fault (three phase voltage drop to 0.5pu) at the Grid bus (132KV bus). Then the system recovery at the steady-state under faults is shown. For the third system the input was the variable speed wind, the simulation results illustrate that when the input is variable wind speed the generated power will be reduced and the system behavior unstable, therefore, the control circuit is needed for the optimization to reduce the losses of the generated power; this optimization can be made by tuning the controllers gains with new suitable values, so the optimization is made by using Particle Swarm Optimization (PSO). The new optimal values improved the system behavior, and illustrated the possibility of operation with variable wind speed.

In this paper, the proposed maximum power point tracking (MPPT) method is designed by taking rotor speed as an optimization problem, which is solved by Artificial Bee Colony (ABC) algorithm to generate the maximum power output. The main advantage of this algorithm is that its optimal solution is independent of the initial positions and requirement of lesser number of control parameters, which leads to simple and robust MPPT algorithm than other algorithm. Furthermore, the Hill Climb Search and Particle Swarm Optimization based MPPT algorithm are also discussed and the results obtained by these are compared to verify the effectiveness of proposed algorithm. Simulations for MPPT control along with doubly fed induction generator based wind energy conversion system is carried out in MATLAB/Simulink environment. Three statistical methods are used to evaluate the accuracy of each MPPT algorithm. All results are analyzed and compared under randomly selected wind as well as real wind speed configuration. Comparison of both numerical and simulation results under two different varying wind speed condition strongly suggest that the proposed ABC based MPPT algorithm is superior than other two MPPT algorithms.

International Journal of Engineering Research and Technology (IJERT), 2014

https://www.ijert.org/a-novel-maximum-power-point-tracking-system-for-wind-energy-conversion-system-using-particle-swarm-optimization https://www.ijert.org/research/a-novel-maximum-power-point-tracking-system-for-wind-energy-conversion-system-using-particle-swarm-optimization-IJERTV3IS20976.pdf In this paper, a novel maximum power point tracking (MPPT) controller using particle swarm optimization is proposed. Particle Swarm Optimization algorithm is used to optimize the value of power coefficient. By this method the total wind energy captured increases and therefore the overall efficiency. The design details on how to realize the improved MPPT method and the principle of choosing a proper system dynamics are both pointed out after analyzing the system dynamics. The system features higher reliability, lower complexity and cost, and less mechanical stress of the WG. The proposed algorithm shows enhanced stability and fast tracking capability under both high and low rate of change wind speed conditions. Experimental results of the proposed system indicate near optimal WG output power. The simulation results show that the proposed algorithm can achieve maximum power capture of wind power generation system, improve the dynamic response and efficiency.

2009

This paper proposes the method combining artificial neural network with particle swarm optimization (PSO) to implement the maximum power point tracking (MPPT) by controlling the rotor speed of the wind generator. With the measurements of wind speed, rotor speed of wind generator and output power, the artificial neural network can be trained and the wind speed can be estimated. The proposed control system in this paper provides a manner for searching the maximum output power of wind generator even under the conditions of varying wind speed and load impedance.

2016

In recent days, people migrated towards renewable energy sources to meet their power demand. Among all these renewable energy sources wind energy system is widely preferred because of its pollution free in nature, provides all time energy source and also occupies less space at ground level as compared to solar panels. Reliability and efficiency are two major factors in wind energy conversion system. A high gain Resonant Switched Capacitor (RSC) converter operates at high frequency will eliminate the switching losses and also reduces the size of the passive elements. PSO based MPPT controller can improve the accuracy of which the maximum power transmitted for the time varying wind speed. This method will reduce the time required for convergence and adaptive step size variation is achieved as compared to conventional Hill Climb Search based MPPT controller. The developed power will be applied to AC micro grid with the help of voltage source inverter. Micro grid is a local network supp...

International Journal of Electrical and Computer Engineering (IJECE), 2020

The paper demonstrates the feasibility of an optimal backstepping controller for doubly fed induction generator based wind turbine (DFIG). The main purpose is the extract of maximum energy and the control of active and reactive power exchanged between the generator and electrical grid in presence of uncertainty. The maximum energy is obtained by applying an algorithm based on artificial bee colony approach. Particle swarm optimization is used to select optimal value of backstepping's parameters. The simulation is carried out on 2.4 MW DFIG based wind turbine system. The optimized performance of the proposed control technique under uncertainty parameters is established by simulation results. 1. INTRODUCTION The use of energy plays a vital role in making industrial and manufacturing process much more efficient. However, due to this large use, the production of unwanted materials that pollute air and contaminate soil and water was spawned an increase. In this way, the maximum rate of petroleum extraction has been reached and that subsequent methods of extraction cannot increase the rate further. One optimal solution to this problem is to use renewable energy sources. Their interest is that they do not emit greenhouse gases and produce no toxic and radioactive waste. Wind energy is one of the purest and eficient energy in the world for the production of electricity. The kinetic energy of wind is harnessed by wind turbines and converted into mechanical energy and finally into electrical energy. Quite recently, a large variety of publications have been undertaken for doubly fed induction generator modeling and control, in which vector control combined with proportional-integral (PI) loops is widely used in industry, due to its simple architecture, big advantages of decoupling active and reactive power, in addition high efficiency [1]. The main purpose of DFIG control system is to efficiently extract the wind power whatever the weather conditions, this is usually named maximum power point tracking MPPT [2-3]. A substantial review of this control is given on [4]. Meanwhile, an approach to attenuate the impact of failures in DFIG generator based wind is often required, so that DFIG can withstand some typical disturbances wind system. However, the major deficient of vector control is that it cannot keep a high level performance when parameter's system vary as its PI parameters are fixed, while system nonlineaty on DFIG is strong resulted from the fact that is a typical time-varying dynamic system with parametric uncertainties. Many efficient parameters tunning methods have been proposed to enhance the PI controller,

2015 Australasian Universities Power Engineering Conference (AUPEC), 2015

This paper presents a direct power control (DPC) design of a grid connected doubly fed induction generator (DFIG) based wind turbine system in order to track maximum absorbable power in different wind speeds. A generalized regression neural network (GRNN) is used to estimate wind speed and thereby the maximum absorbable power is determined online as a function of wind speed. Finally the proposed DPC strategy employs a nonlinear robust sliding mode control (SMC) scheme to calculate the required rotor control voltage directly. The concept of sliding mode control is incorporated into particle swarm optimization (PSO) to determine inertial weights. The new DPC based on SMC-PSO scheme has acceptable harmonic spectra of stator current by using space vector modulation (SVM) block with constant switching frequency. Simulation results on 660-kw grid-connected DFIG are provided and show the effectiveness of the new technique, for tracking maximum power in presence machine parameters variation.