A decentralized multivariable controller for hydrostatic wind turbine drivetrain

Improving the reliability and energy production of large wind turbine with a digital hydrostatic drivetrain

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.

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.

Abstract. The priority flow divider valve (PFDV) is used in power hydraulic system to split the supply flow in dual paths, i.e., primary path and secondary path. The flow ratio in each path is either predetermined or can be adjustable as per the loading conditions. The stated properties of the valve can be applied in a steering mechanism in automobile and a hydrostatic power transmission unit in a wind turbine. In a steering mechanism, it helps the system to perform two different functions against two different loads simultaneously, whereas, in a wind turbine, it can be used in hydrostatic power transmission unit to reduce the power fluctuation obtained from it. The hydrostatic power transmission system with PFDV (SMHPTSPFDV) used in a steering mechanism and the hydrostatic power transmission system with PFDV (WTHPTSPFDV) used in a wind turbine are modeled using the bond graph technique, and simulated in SYMBOLS Shakti software to analyze the steady and dynamic performance of the PFDV. The user-defined rectangular and sinusoidal variable pump speed has been used as inputs for system performance analysis. It is found that the PFDV may handle dual loads simultaneously, and it eliminates the use of multiple control valves to operate the same dual loads. Hence, it not only improves the system stability but also reduces the maintenance cost of the system. Moreover, the power and energy loss through the PFDV have been analyzed for both applications. It has been found that the power loss through the PFDV is higher when it is connected with SMHPTSPFDV. Also, the influences on the power and energy loss through PFDV are analyzed under various loading conditions.

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.

Small wind turbines placed at city locations are affected by variable-speed winds that frequently change direction. Architectural constructions, buildings of different heights and abrupt orography of Cities make the winds that occur at City locations more variable than in flat lands or at sea. However, the performance of Small-wind turbines under this type of variable wind has not been deeply studied in the specialised literature. Therefore, this article analyses the behaviour of small wind turbines under variable and gusty winds of cities, also considering three types of power electronics conversion configurations: the generally used Maximum Power Point Tracking (MPPT) configuration, the simple only-rectifier configuration and an intermediate configuration in terms of complexity called pseudo-MPPT. This general-purpose analysis is applied to a specific type of wind turbine, i.e., the Ayanz wind turbine with screw blades, which presents adequate characteristics for city locations such as; safety, reduced visual and acoustic impacts and bird casualties avoidance. Thus, a wide simulation and experimental tests-based analysis are carried out, identifying the main factors affecting the maximisation of energy production of small wind turbines in general and the Ayanz turbine in particular. It is concluded that the mechanical inertia of the wind turbine, often not even considered in the energy production analysis, is a key factor that can produce decrements of up to 25% in energy production. Then, it was also found that electric factors related to the power electronics conversion system can strongly influence energy production. Thus, it is found that an adequate design of a simple pseudo-MPPT power conversion system could extract even 5% more energy than more complex MPPT configurations, especially in quickly varying winds of cities.

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.

The increasing penetration of wind power impacts the frequency stability of power systems. A doubly fed induction generator (DFIG)-based wind power plant naturally does not provide frequency response because of the decoupling between the output power and the grid frequency. DFIGs also lack power reserve margin because of the maximum power point tracking (MPPT) operation. Therefore this study presents a novel frequency regulation by DFIG-based wind turbines to coordinate inertial control, rotor speed control and pitch angle control, under low, medium or high wind speed mode. Inertial control emulates the inertia of wind generators and supports frequency control during transient. The gain of inertial control is calculated from a creative viewpoint of protecting the wind turbine from stalling. Rotor speed control and pitch angle control enable DFIGs to reserve sufficient active power for a steady-state frequency adjustment. The numerical simulations demonstrate that the coordinated control enhances the frequency regulation capability and damps the frequency oscillations effectively.

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

Photovoltaic (PV) systems are broadly utilized, especially for remote electrification. It is easier for installation and is free of greenhouse gases, so the impacts on the environment are reduced. The temperature and irradiance change during the day, and the circumstances are more dynamic on cloudy days. Hence, attaining maximum power under some environmental conditions is complex and also requires individual techniques for tracking the maximum available power. These approaches are named maximum power point tracking (MPPT) methods. These are designed through power electronic converters, which supply the PV power to the load. In recent years, meta-heuristic-based algorithms have emerged because of their local optima avoidance ability and flexibility. However, the maximum power point (MPP) will not be tracked efficiently due to the constraint factors between some circuit parameters. This limitation has to be solved in this study. Here, MPPT constraint conditions of the PV system with an inverter are found by analyzing its integrated mathematical model. The MPP of a PV system is tracked by diverse techniques. Though these approaches differ in complexity, effectiveness, and convergence speed, cost and sensors are required. With the effect of these challenges, the main intention of this paper is to design and develop a new modified MPPT algorithm for enhancing the efficiency of the power grid connected with a DC-AC single-phase full-bridge inverter and a proportional integral derivative (PID) controller. Here, the parameters of the PID controller are tuned or optimized by the newly improved meta-heuristic algorithm termed modified tunicate swarm algorithm with new condition (MTSA-NC) thus maximizing the energy extraction of the PV system. The search process for newly improved MTSA-NC works well to adapt the MPPT to cope with a "grid-connected" inverter by attaining a faster convergence rate. Here, MTSA-NC is adopted by introducing the fitness-based solutions for updating the swarm behavior of tunicates to maximize the energy efficiency of "grid-connected" inverter systems for PV arrays. In MTSA-NC, the optimal fitness value is known as a food source, which is used for determining the solution updating. Finally, the experimental tests validate the success of the designed scheme in a PV system during uniform irradiance and partial shading conditions.

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)

Aggregated electrical vehicles (EVs) can flexibly serve grid frequency regulation (FR) with adjustable FR capacity (FRC) via vehicle-to-grid (V2G) technology. Therefore, current research applies integral reinforcement learning (IRL) to FR as it can easily solve difficult modeling and optimization problems. However, communication between EVs is vulnerable to denial of service (DoS) attacks, which can significantly degrade FR performance and even destabilize V2G FR systems. This paper proposes an adaptive V2G FRC-based FR scheme with IRL to improve FR resilience and mitigate FR performance degradation against DoS attacks. The proposed scheme optimizes V2G control by IRL without analytical models, integrating an event-triggered mechanism to reduce communication burden. It adapts V2G FRC to attack intensity to minimize frequency deviation and mitigate the impact of DoS attacks. The analytical estimation of convergence rate establishes the quantitative relationships among control performance, V2G FRC, and attack intensity, thereby deriving an adaptive mechanism. The theoretical analysis of the proposed scheme yields stability conditions that guarantee the FR performance. The simulations were performed on the IEEE 39-bus test system to verify the effectiveness and advantages of the proposed scheme. The results indicate that V2G FRC should be scaled down to mitigate performance degradation when intensive attacks occur.

In this work, a control operation of a 1.5 MW offshore wind turbine (WT) for maximum power point tracking (MPPT) when wind speed is below rated, is studied. The implemented controller is designed using the general Direct Speed Control (DSC) scheme in which artificial neural networks (ANN) are incorporated to close the control loop. The neural controller acts in an unsupervised mode updating its weights with the incorporation of a learning algorithm. The optimal configuration parameters of the controller are determined by genetic algorithms. With this intelligent control strategy, the generator speed is regulated by varying the electromagnetic torque while adapting to the external phenomena in real time. Then, the output power, through the power coefficient ( $${C}_{p}$$ ), reaches the maximum wind power generation in that region. The offshore WT model is subjected to external loads due to wind and waves, which increase the system complexity and produce tower vibrations, negatively impacting the control efficiency. Despite that, it is shown that the proposed controller is able to operate with satisfactory results in terms of power generation and even reducing vibration, and it has been compared to the OpenFAST embedded torque control for the same WT providing better results.

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.

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