Моделирование системы управления рысканием ветротурбины

Electrical Parts, Control Systems and Power Electronics of Wind Turbines

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 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.

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.

The demand for electricity is continuously growing around the world and thus the need for renewable and long-lasting sources of energy has become an essential challenge. Wind turbines are considered one of the major sources of renewable electricity generation. Therefore, there is a crucial demand for wind turbine model and control systems that are capable of precisely simulating the actual wind power systems. In this paper, an advanced fuzzy logic controller is proposed to control the speed of a wind turbine system. Initially, aero dynamical, mechanical and electrical models of two mass wind turbines models are derived. Analytical calculation of the power coefficient is adopted through a nonlinear function of six coefficients that mainly depends on pitch angle and tip speed ratio. The ultimate power output from the turbine can reach up to 50 % which is achieved at zero pitch angle with an approximately tip speed ratio of eight. This is then followed by designing a hybrid fuzzy-plus I pitch controller to regulate the speed of the wind turbine shaft. In general, fuzzy logic control strategy have the advantages over traditional control techniques especially when the system is highly non-linear and has to deal with strong disturbances such as wind turbulence. To evaluate the reliability and robustness of the controller, the response of the wind turbine system is tested under several types of disturbances including wind fluctuation, sudden disturbances on high and low speed shafts. Simulation findings reveals that the performance of fuzzy-integral control technique outweighs that of conventional fuzzy approach in terms of multiple performance evaluation indexes such as zero overshoot and steady state error, rise time and a settling time of (32.9 s) (44.7 s) respectively. The reliability and robustness of the controller is tested by applying speed and torque disturbances of 25% of their maximum ranges. Results have revealed that the controller was able to reject all disturbances efficiently with a change in pitch angle up to a maximum of 10 degrees in order to retain a constant rotor speed at 1000 rpm.

This paper presents an efficient technique for monitoring and control of wind turbines. Fault tolerant control (FTC) is designed using Parity-Space approach to diagnose the sensor faults in the wind turbine. Based on the fault detection system an FTC scheme is proposed to accommodate and reconfigurable the control signals. The main contribution of this study is to develop a monitoring and control system of wind turbine, which reduces the system components as well as to increase the reliability and safety of wind turbines. The sensors which considered in this study are pitch position, generator speed and rotor speed sensors. Real data of wind measurement and real parameters of wind turbine is used to illustrate the proposed scheme.

Wind energy is one of the most relevant renewable energy. A proper wind turbine maintenance management is required to ensure continuous operation and optimized maintenance costs. Larger wind turbines are being installed and they require new monitoring systems to ensure optimization, reliability and availability. Advanced analytics are employed to analyze the data and reduce false alarms, avoiding unplanned downtimes and increasing costs. Supervisory control and data acquisition system determines the condition of the wind turbine providing large dataset with different signals and alarms. This paper presents a new approach combining statistical analysis and advanced algorithm for signal processing, fault detection and diagnosis. Principal component analysis and artificial neural networks are employed to evaluate the signals and detect the alarm activation pattern. The dataset has been reduced by 93% and the performance of the neural network is incremented by 1000% in comparison with the performance of original dataset without filtering process.

A yaw dynamics analysis was developed for a two-bladed horizontal axis wind turbine with passive cyclic pitch variation achieved by letting the blade pair freely oscillate about a common axis with which the blades formed a small prelag angle. This type of rotor was found capable of high yaw rates without imposing vibratory hub moments and without producing noticeable flapping amplitudes. Experiments were conducted with a tail vane stabilized 7.6-m-diam wind rotor driving a three-phase alternator tuned and loaded to produce a rotor torque proportional to the square of the rotor speed. Two yaw control systems which replaced the usual blade feathering controls were investigated: an active yaw control system using a hydraulic rotor speed governor, and a passive system responding to a combination of rotor thrust and torque. During strong gusts both systems limited rotor speed quite accurately. The passive system appeared to be more promising because of its greater reliability and because of the greater ease of adapting it to larger size wind turbines.

The fundamental principles governing wind energy conversion, including the power equation and the Betz limit, are explained to establish a theoretical foundation. The chapter further delineates the key components of modern horizontal- and vertical-axis wind turbines, such as rotor blades, nacelles, and drivetrains, and classifies systems by scale and location. A significant focus is placed on the operations and control of wind turbines and wind farms, covering methods like pitch and yaw control, Maximum Power Point Tracking (MPPT), and wake management. Finally, the chapter explores the transformative role of Artificial Intelligence (AI) in condition monitoring, contrasting traditional methods with advanced AI techniques like machine learning and deep learning to highlight their benefits in predictive maintenance, fault diagnosis, and overall system reliability. Through this structured examination, the authors aim to present wind energy as a cornerstone of the global transition to sustainable and renewable power sources.

This paper analyzes the operating characteristics of wind turbine and introduces the principle of maximum power point tracking control system of variable speed wind turbine. A improved maximum power tracking control strategy is proposed for large inertia wind power systems in order to achieve maximum wind power capture and increased utilization of wind energy when wind turbines is below the rated wind speed. A variable speed wind power generation system is modeled and simulated in the Simulink environment of the Matlab .The simulation results proves the correctness and feasibility of the tracking control strategy suggested in this paper.

Development of a PMSG-based Wind Turbine Modeling by ADAMS, FAST and MATLAB

This paper proposes a dual-loop back-to-back converter coordination control scheme with a DC-side voltage as the primary control target, along with a CROW unloading control strategy for low voltage ride-through (LVRT) capability enhancement. The feasibility and effectiveness of the proposed system topology and control strategy are verified through MATLAB/Simulink simulations. Furthermore, a hybrid short-term wind power prediction model based on data-driven and deep learning techniques (CEEMDAN-CNN-Transformer-XGBoost) is introduced in the wind turbine control system. The coordination control strategy seamlessly integrates wind power prediction, pitch angle adjustment, and the control system, embodying a predictive-driven intelligent optimization control approach. This method significantly improves prediction accuracy and stability, theoretically reduces unnecessary pitch angle adjustments, lowers mechanical stress, and enhances system adaptability in complex operating conditions. The research findings provide a valuable theoretical foundation and technical reference for the intelligent and efficient operation of wind power generation systems.

Wind energy is currently the fastest-growing energy source in the world, with a concurrent growth in demand for the expertise of engineers and researchers in the wind energy field. There are still many unsolved challenges in expanding wind power, and there are numerous problems of interest to systems and control researchers. In this paper, we first review the basic structure of wind turbines and then describe wind turbine control systems and control loops. Of great interest are the generator torque and blade pitch control systems, where significant performance improvements are achievable with more advanced systems and control research. We describe recent developments in advanced controllers for wind turbines and wind farms, and we also outline many open problems in the areas of modeling and control of wind turbines.

This article presents a general up‐to‐date review of the aerodynamics of wind turbine technology. The article kicks off with the history of wind energy applications, then the current status of worldwide wind energy capacity is reviewed. The principles of rapid techniques used in aerodynamic and acoustic wind turbine design, such as blade element/momentum (BEM) and dynamic wake effect, are provided. Insight into more advanced methods such as vortex wake theory and computational fluid dynamics (CFD) is also given. The article then moves on to analysis of the structure of a wind turbine, with focus on the more common horizontal axis wind turbine (HAWT). This is followed by discussion of some hot research topics such as fluid–structure interaction (FSI) and unsteady aerodynamic factors. Recent studies on wind turbine control systems and wind farms are briefly reviewed. To summarize in one sentence, wind power has a bright future, but in order to fulfill its potential as a substantial source of renewable energy, it still has some way to go.

Control systems for wind turbines have become an active area of research over the past decade as the wind industry has grown and more turbines are installed. Properly sited turbines experience many transitions between below rated operation, where the goal is to extract maximum energy from the wind, and rated speed operation, where the goal is to regulate the power capture to the rated power of the turbine. Many of the largest structural loads are induced during the transition between these operating regions. This paper focuses on using preview wind speed measurements to schedule, optimize, and track a desired trajectory of the wind turbine states and inputs during region transitions between below-rated and above-rated operation. The goal of this control system is to reduce the structural loading on the turbine components through smoother region transitions. The wind speed preview measurements are used to generate an initial desired trajectory of the turbine. This trajectory is optimized by finding a regulation trajectory that lies on the turbine trajectory manifold which is close to the desired trajectory in a weighted L2 sense. The regulation trajectory is then used as a reference for a time-varying linear quadratic optimal controller.