Proposing and evaluation of MPPT algorithms for high-performance stabilized WIND turbine driven DFIG

Assessment of proposed maximum power point tracking–unity power factor algorithms for optimum operation permanent magnet synchronous generator–based wind turbine

This paper discusses modeling of a micro-grid with PV-Wind-Diesel generator hybrid system and its operations. The PV system is modeled with a DC/AC inverter with a pre-defined Maximum Power Point Tracking (MPPT) control. Fixed speed induction generator based wind turbines are used to model the wind farm. The diesel generator controls are modeled with droop governor and automatic voltage regulator (AVR). The PV inverter controls, including MPPT and voltage droop, are modeled in the DC/AC inverter itself. The proposed MPPT control is more stable with additional input of PV cell temperature. The complete system is modeled in PSCAD/EMTDC software. Performance of this system is studied on (i) fault tripping of the main grid and (ii) islanded operation of micro-grid with intermittent power from the wind and PV plants. The simulation results confirm the smooth operations of the proposed concept for disturbances from main grid as well as due to the intermittent wind and PV renewable power plants.

The accuracy of doubly fed induction generator (DFIG) models and parameters plays an important role in power system operation. This paper proposes a parameter identification method based on the hybrid genetic algorithm for the control system of DFIG converters. In the improved genetic algorithm, the generation gap value and immune strategy are adopted, and a strategy of “individual identification, elite retention, and overall identification” is proposed. The DFIG operation data information used for parameter identification considers the loss of rotor current, stator current, grid-side voltage, stator voltage, and rotor voltage. The operating data of a wind farm in Zhangjiakou, North China, were used as a test case to verify the effectiveness of the proposed parameter identification method for the Maximum Power Point Tracking (MPPT), constant speed, and constant power operation conditions of the wind turbine.

The maximum power point tracking technique is applied in wind power generation system to improve the utilization of wind energy. A review of existing maximum power point tracking methods is presented in this paper, based on which an intelligent maximum power point tracking is developed by the authors to improve the system performance and to facilitate the control implementation. The developed maximum power point tracking algorithm adopts variable duty cycle step size to maximize the output power of the wind generator, without the need for either knowledge of wind turbine characteristics or the measurements of mechanical quantities such as wind speed and turbine rotor speed. System simulation results have confirmed the functionality and performance of this method.

This paper presents an optimal Direct Torque Control (DTC) strategy for Doubly Fed Induction Generator based wind turbine. The proposed strategy is considered based on Particle Swarm Optimization (PSO) Algorithm. The PSO is found to be robust and fast in solving nonlinear problems. Motivation for application of PSO approach is to overcome the limitation of the conventional controllers design, which cannot guarantee satisfactory control performance when designed by trial and error. In this work PSO algorithm was used to adjust the hysteresis torque and flux comparators bandwidths and to tune the parameters of Variable Gain PI (VGPI) controller designed for Maximum Power Point Tracking (MPPT) speed control of wind turbine to ensure high performance torque control. The optimal control strategy is considered in detail and it is shown that the use of the optimized controllers reduces rotor flux and electromagnetic torque ripples and improves the system dynamic performances. The effectiveness of the proposed direct torque control based on PSO algorithm (optimal DTC) method is illustrated through simulations on 1.5 MW DFIG based wind turbine. Simulation results illustrate the improved performances of optimal DTC compared with the conventional DTC.

Wind power as a renewable energy has the potential to become a clean energy source in almost all countries of the world but, there are lots of technical challenges that need to be addressed in advance. Wind speed variation (WSV) is one of the most important issues. Separate from its mechanical effects on wind turbines (WTs), it causes electrical power variations on WT as well. Doubly Fed Induction Generators (DFIGs) and Maximum Power Point Tracking (MPPT) system have been helpful so far but some problems such as inertia still needed to be solved. Inertia in the WTs causes a delay on MPPT. This paper proposes a novel method to increase WTs output power in which a DFIG equipped with an energy storage system such as Ultra-capacitor helps MPPT system to track the wind variations rapidly by absorption or injection energy during the wind changes. The ultra-capacitor and control system are modeled and simulated in Matlab/Simulink® environment. The simulation results proofs that the proposed system can improve electrical power and increase electrical energy during WSVs. The simulation model will lead to more penetration of wind power and also enables engineers to optimize the system.

In order to achieve maximum power point tracking (MPPT) of wind energy systems, the rotating speed of wind turbines (WTs) ought to be adjusted in the constant as indicated by wind speeds. However, fast wind speed varieties and heavy inertia bargain the MPPT control of WTs. This chapter proposes a fuzzy logic controller (FLC)-based MPPT strategy for Wind Energy Conversion Systems (WECS). The performance of the proposed MPPT strategy is analyzed mathematically and verified by simulation using MATLAB/PSIM/Simulink software. The proposed method improves the speed and accuracy of MPPT. Furthermore, the simulation results have been conducted to approve the performance of the proposed MPPT strategy, and all results have confirmed the adequacy of the proposed MPPT strategy.KeywordsMaximum power point tracking (MPPT)Wind energy conversion systems (WECS)Fuzzy logic controller (FLC)Wind turbine (WT)

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In this work, a torque controller for a variable rotational speed wind turbine has been modelled using Reinforcement Learning and considering the Optimal Torque - Maximum Power Point Tracking problem as one of optimization. The reward optimization function is designed as a non-linear function depending mainly on the rotor power variation. Based on this, an optimal action (electromagnetic torque variation) regulates the turbine rotational speed. A simulated 1.5 MW three bladed wind turbine operation is managed by the torque controller. It keeps the turbine working at optimal operational conditions after a successful training process, which is carried out using the Proximal Policy Optimization algorithm. For the controller training, the turbine confronts constant and then randomly staggered wind speed behaviour. Time series of rotor angular speed, torque and power are presented. Our results show that the modelled controller is able to reach and maintain the wind turbine operation at its optimal power generation conditions. This methodology avoids using some empirical parameter characteristic of the Optimal Torque - Maximum Power Point Tracking algorithm widely used in wind turbine control systems.

This paper presents a stator winding faults detection in induction generator based wind turbines by using artificial neural network (ANN). Stator winding faults of induction generators are the most common fault found in wind turbines. This fault may lead to wind turbine failure. Therefore, fault detection in induction generator based wind turbines is vital to increase the reliability of wind turbines. In this project, the mathematical model of induction generator based wind turbine was developed in MATLAB Simulink. The value of impedance in the induction generators was changed to simulate the inter-turn short circuit and open circuit faults. The simulated responses of the induction generators were used as inputs in the ANN model for fault detection procedures. A set of data was taken under different conditions, i.e. normal condition, inter-turn short circuit and open circuit faults as inputs for the ANN model. The target outputs of the ANN model were set as ‘0’ or ‘1’, based on the fault conditions. Results obtained showed that the ANN model can detect different types of faults based on the output values of the ANN model. In conclusion, the stator winding faults detection procedure for induction generator based wind turbines by using ANN was successfully developed.

This paper focuses on an intelligent MPPT (maximum power point tracking) algorithm for permanent magnet vertical-axis direct-drive wind turbine systems. The relationship between generator speed and buck convert’s duty ratio is analyzed. then, the output and the speed of wind turbine are controlled by convert’s duty ratio. In this way an intelligent MPPT algorithm, the different step length is given in different directions, is used to this system. At last, the system is simulated by software, and the simulation results indicates that compared with the conventional MPPT strategy, the rapidity and the stability are increased by intelligent MPPT algorithm.

This paper presents a neotype control strategy for maximum power point tracking (MPPT) of doubly-fed induction generator based wind turbine (DFIG-WT). The strategy adopts proportional-integral (PI) based regulators and feedback linearization control (FLC) based regulators in the power and current control loops of DFIG, respectively. The FLC based regulator can fully decouple and linearize the nonlinear system and provide optimal control performance in a wide range operating conditions. However, since the FLC strategy might require some unaccessible state variables of the mechanical system, the PI based regulators are able to simplify the control implementation in the power loop. Simulation studies are undertaken in a single-machine system operating under ramp-change and random wind profiles, respectively. Simulation results reveal that the proposed strategy performs better than the conventional vector control (VC) scheme under varying operation conditions.

Hybrid Renewable Energy Systems (HRES), which integrate solar and wind power, offer an effective solution for addressing energy demands in rural, off-grid areas. Despite the abundant availability of solar energy during the day and continuous wind energy, the intermittent nature of these resources presents challenges to system efficiency. Maximum Power Point Tracking (MPPT) techniques are crucial for optimizing energy extraction from photovoltaic (PV) panels and wind turbines, but fluctuating environmental conditions complicate their performance. This study adopted a narrative review approach and 127 related articles on the integration of Artificial Intelligence (AI) in MPPT, focusing on AI-driven algorithms like Artificial Neural Networks (ANNs), Fuzzy Logic Control (FLC), and Reinforcement Learning (RL), which enhance system performance by providing adaptive, predictive, and self-learning capabilities were successfully reviewed. ANNs offer high accuracy by predicting optimal operating points based on historical data, but require extensive datasets and computational resources. FLC manages uncertainty and nonlinearity using fuzzy rules but demands significant computational power and expert knowledge. RL autonomously learns optimal strategies and adapts in real time, though it requires substantial training data and computational resources. The incorporation of these AI techniques into HRES facilitates real-time optimization, improving energy efficiency and ensuring a reliable power supply despite dynamic environmental conditions. Additionally, the practical fabrication of AI-enhanced hybrid systems involves careful selection of solar panels, wind turbines, energy storage solutions, and power electronics, along with the implementation of AI-based MPPT controllers on microcontrollers or embedded processors. Simulation and experimental validation confirm the efficacy of these approaches, showcasing their potential to optimize power extraction and enhance energy reliability in remote applications, paving the way for efficient renewable energy systems in rural and off-grid areas.

Wind energy has recently become the most cost-effective and environmentally friendly amongst other renewable energy resources. However, since wind resource is intermittent in nature, the wind power output is inconsistent. Therefore, a control system needs to be implemented to ensure that maximum power is extracted from the wind resource at a wide range of wind speeds to optimize the performance of the wind turbine. This paper deals with the control of the rotor speed of a 2MW direct-driven permanent magnet synchronous generator to perform maximum power point tracking (MPPT). Vector-oriented control technique is used to control the machine-side converter and the types of controllers used are the phase-lead, phase-lag and lead-lag compensators. The proposed wind energy conversion system was implemented in PSIM software and the control system modelling has been carried out in MATLAB using bode plots and step-response curves. The overall system's performance analysis and validation were carried out by simulation. The results obtained show that the MPPT controller enabled the power generated by the WT to closely track pre-determined optimal power curves. This shows that MPPT was ensured when the wind speed was less than the wind turbine's rated wind speed.

SummaryCurrently, a maximum power point tracking (MPPT) unit implemented in a wind energy conversion system (WECS) extracts the maximum mechanical power from the wind turbine used in the WECS. Therefore, the MPPT unit acts as a maximum mechanical power tracker (MMPT) that performs optimal control of the wind turbine to extract the maximum mechanical power from the wind turbine. In this paper, the basic concept of a maximum electrical power tracker (MEPT) is presented both theoretically and technically. It is demonstrated that an MEPT implemented in a WECS maximizes the output electrical power of the WECS. Thus, in contrast with an MMPT, the proposed MEPT optimally controls the whole of the WECS, rather than the wind turbine, to extract the maximum electrical power from the WECS. Since, in the WECS, the power efficiency of the whole of the WECS, not the wind turbine, should be maximized to extract the maximum output electrical power from the WECS, the conventional MPPT unit acting as an MMPT should be replaced with the proposed MEPT, and this is the superiority of the proposed MEPT to an MMPT or a conventional MPPT unit. To provide experimental verifications, 2 novel MEPT and MMPT with simple structures and better performance compared to the MPPT techniques commonly used in WECSs have been constructed, which are presented in detail.