Intelligent pitch angle control scheme for variable speed wind generator systems

The increasing penetration of installed the wind turbines is leading the power system towards instability and requires robust controllers to overcome the instability issues. The conventional synchronous generator based wind turbines comprise of an uncontrolled rectifier, a dc/dc boost converter, a controlled inverter and a pitch blade controller in grid-tied mode. The dc/dc boost converter controls the dc-link current according to the generator's rating and optimizes the rotor speeds to a preset value by extracting maximum energy using the MPPT algorithm, during lulls and gusts of wind. The inverter controls the AC power fed into the grid, regulates the dc link voltage and maintains the unity power factor by using d-q transformation. The pitch blade controller reduces the sudden mechanical stresses on the wind turbine during high speed periods. However, the PI or fuzzy controllers for the above-mentioned system are mostly manually tuned by trial and error methods for specific set-points. This approach lacks the dynamic behavior. Therefore in this paper, the authors have developed an optimal pitch blade controller to regulate the output wind power under variable wind conditions. A novel practical swarm (PSO) based search algorithm is employed to tune the fuzzy membership functions (MFs) dynamically according to the wind speed variations. The complete system is simulated in the Matlab2014b/Simulink environment. The results show that the proposed controller performs excellently.

The operation at maximum power in condition of rapid wind velocity modifications poses a challenge for wind power plants. Based on measured data, the authors determine for generator and turbine, the mathematical models. Also, with current measured values for mechanical angular speed, ω, and wind speed, v, the authors determine the mathematical model in a timely manner and, on its basis, compute the optimal mechanical angular speed ωOPTIM, at which the value of the wind turbine power is maximum. We determine the dependence of the optimal / ideal mechanical angular speed, ωOPTIM, to the wind speed, v, and bring the wind turbine into optimal operating area by modification of the generators load. By measuring the wind velocity and monitoring the electric energy flowing from the permanent magnets synchronous generator (PMSG), we compute the wind turbine power. Based on those remarks, to get maximum power out from a wind turbine, it is necessary that between wind velocity and mechanical angular speed should always exist a correlation, according to the wind velocity changes and the inertia moment, J.

Aerodynamics performance of continuously variable speed horizontal axis wind turbine with optimal blades

Control of Variable Speed Wind Turbines with Doubly-Fed Induction Generators

This paper presents an application of fuzzy controllers for improving wind energy capture for variable speed wind turbines. In recent works, it has been shown that variable speed wind turbines with torque and blade-pitch linear control provide an excellent performance of the closed loop system. However, this performance can be enhanced by using fuzzy control. In the present work, a computational fuzzy model is presented for a variable speed, variable pitch wind turbine. Simulation results show an excellent performance improving transition between power optimization and power limitation of the wind turbine, specially in the rated wind speed working conditions.

The main converter of mechanical wind energy into electricity in wind turbines is an electric generator. Typically, such systems use synchronous generators with permanent magnets on the rotor. The main disadvantage of this design is the complexity or the impossibility of adjusting the output parameters of the generator: voltage, power, etc. Known methods and tools aimed at solving this problem relate to cases where the wind speed is constant, ie constant. In real conditions, the nature of the wind is changeable. The average annual wind speed for Ukraine varies between ≈ 5-6 m / s. The current value of wind speed depends on weather conditions, time of day and season. Accordingly, the nature of the output power of the generator will be variable. In this paper, the authors evaluate the effectiveness of the correction of the output power of the generator of the multiplier wind turbine at discrete and random values of wind speed. The main power unit of this study is a magnetoelectric synchronous generator with two-sided arrangement of magnets on the rotor and with axial magnetic flux. To solve this goal, a numerical simulation mathematical model of the system was developed, consisting of a multiplier-free wind turbine and a magnetoelectric synchronous generator with two-way arrangement of magnets on the rotor and with axial magnetic flux in the MATLAB-Simulink software package. The developed simulation model takes into account the change of the output parameters of the generator when the wind speed changes and vice versa, the system in which the change of the initial state of the generator leads to a change in the parameters of the rotor of the wind turbine. The variability and discreteness of wind speed is realized in the MATLAB-Simulink system by generating signals, the values of which at certain points in time are a random variable distributed according to the normal (Gaussian) law with predetermined parameters. Using the developed mathematical model, numerous simulation experiments were performed, which investigated the efficiency of correction of the output power of the studied system when connecting static capacitors to the armature winding of the generator and when applying current to the additional winding of the magnetoelectric generator. When connecting an additional magnetizing capacitance ≈30 μF to the generator terminals, there is an increase in output power by ≈5-10%. When the voltage is applied to the excitation winding Uf = 8 V, there is an increase in the output power of the generator ≈30-40% than without regulation. Therefore, it is a more efficient way to correct the output power of the magnetoelectric generator. The developed mathematical model can be used in further research to synthesize the control law of the additional winding of the magnetoelectric generator for the most efficient conversion of mechanical wind energy into electrical energy.

The increasing use of onshore wind energy is leading to an increased deployment of wind turbines in structurally rich habitats such as forests. Forest-affiliated bats, in turn, are at risk of colliding with the rotor blades. Due to the legal protection of bats in Europe, it is imperative to restrict the operation of wind turbines to periods of low bat activity to avoid collisions. However, bats have also been observed to avoid wind turbines over several hundred meters distance, indicating a displacement that cannot solely be explained by modifications to the habitat. This avoidance suggests a displacement of bats by indirect factors related to wind turbine operation, e.g., wake turbulences and noise emissions. Therefore, we investigated whether the activity of forest-affiliated bats is influenced by operation mode (on/off) under variable wind conditions along transects from 80 to 450 m distance to wind turbines. We divided recordings by foraging guild, i.e., either narrow-space (Myotis, Plecotus), edge-space (Pipistrellus, Barbastella), or open-space foraging bats (Nyctalus, Eptesicus, Vespertilio), and analyzed the effects of wind turbine operation and wind speed on the recorded bat guild activity with mixed effects models. The acoustic activity of narrow-space foraging bats decreased by 77% with increasing wind speed when wind turbines were operating, while bat activity remained unaffected by wind speed when turbines were not operating. This was neither observed for open-space foraging bats nor for edge-space foraging bats, and neither wind turbine operation nor wind speed (ranging between 0 – 4 m/s at 10 m height above ground) were found to affect bat activity when considered alone. Wind turbine noise emissions are known to increase with rotor speed and consequently, wind speed, thus presenting a likely explanation for the interactive negative effect of turbine operation and wind speed specifically on noise-sensitive narrow-space foraging bats. To understand potential ecological long-term consequences for bat populations in forest areas with wind turbines and to design effective conservation measures, future research should focus on disentangling the effects of different disturbances related to turbine operation.

This project presents a new approach for simulating wind turbines by using a Vector Controlled Induction Motor Drive (VCIMD) system enhanced with a Fuzzy Logic Controller (FLC). The goal of this system is to optimize the performance and efficiency of wind energy conversion. By accurately controlling the induction motor drive through vector control, the dynamic characteristics of wind turbines can be realistically emulated, allowing for a more true-to-life representation of real-world conditions. Integrating a Fuzzy Logic Controller provides intelligent adaptive control capabilities to improve stability and response. This synergistic combination of VCIMD and FLC not only enables efficient power generation but also ensures robust operation across varying wind conditions. The project investigates the dynamic interaction between these technologies, providing a comprehensive solution for wind turbine emulation that shows great potential for advancing renewable energy research and development. The findings from this study contribute to ongoing efforts to optimize wind energy systems for greater reliability and sustainability.

In this paper an intelligent controller is designed to obtain the maximum power of a large floating offshore wind turbine. The control of these turbines is more complex due to the strong loads they are subjected to and the uncertainty that comes from the environment, mainly wind and waves, and from its non-linear dynamics. In this case, the control goal is to maximize the output power of the wind turbine by controlling the rotor speed. An incremental PD-type fuzzy controller has been implemented; it generates the pitch angle reference. The performance of this control scheme on the NREL 5 MW floating offshore wind turbine has been compared with the internal control that is provided within the FAST software. Results are encouraging, showing that the intelligent control strategy is able to produce more energy.KeywordsIntelligent controlFuzzy logicPitch angleFloating offshore wind turbineRenewable energy

Ensemble of long short-term memory prediction and variable universe fuzzy control for seismic response reduction using electromagnetic dynamic vibration absorber

The control strategy of AMT (Automated Mechanical Transmission) is one of the most popular and heated-discussed problems in the research of automobile transmission. To realize the reasonable control on a vehicle, it is significant to meet the need of dynamics and economy, but it is more important to work out intelligence gearshift and control strategy accord with driver's operating intentions and realistic driving conditions. This paper builds up a tracing model of driver's intentions in the guidance of fuzzy control theory. The model has self-adaptation fuzzy features of tracing throttle and other parameters according to a vehicle's driving parameter and specific road status, and improves the function of fuzzy controller greatly. Furthermore, establish the shift decision-making based on ANFIS (Adaptive Network based Fuzzy Inference System) self-adaptation neural-fuzzy inference system. Design intelligent control shift strategies system of AMT according to summary driver experience, which can coordinate driver's behavior and wish. Thus, the vehicle's performances are improved greatly.

zThe main motivation of this study is to adjust the blade angle (), which affects the mechanical output power of wind turbines (WTs), by particle swarm optimization (PSO).For this purpose, the standard PSO structure has been modified.Thus, the modified PSO algorithm has become capable of updating itself depending on the change of wind speed.In this study, the blade angles of wind turbines operating at variable wind speeds were controlled and adjusted using an optimization algorithm based on swarm intelligence.Within the scope of the study, a variable speed wind turbine was operated in two different simulation environments.In the first case, the values were obtained by keeping the wing angle constant (for =0).In the second case, the proposed modification was operated by adjusting the blade angle with the angle produced by PSO, and the results of both cases were discussed. Bu almann ana motivasyonu, rzgar trbinlerinin (RT) mekanik k gcne etki eden kanat asnn () parack sr optimizasyonu (PSO) ile ayarlanmasdr. Bu amaca ynelik olarak, standart yapdakiPSO modifiye edilmitir.Bylece modifiye edilmi PSO algoritmas rzgar hznn deiimine bal olarak kendini gncelleyebilen bir duruma getirilmitir.Yaplan alma da, deiken rzgar hzlarnda alan rzgar trbinlerinin kanat alarnn kontrol sr zekasna dayal bir optimizasyon algoritmas kullanlarak ayarlanmas salanmtr.alma kapsamnda deien hzl rzgar trbinini iki farkl ekilde benzetim ortamnda altrlmtr.Birinci durumda kanat as sabit tutularak ( = 0 iin) deerler elde edilmitir

Ensuring the stability of power systems is essential to promote energy sustainability. The integrated operation of these systems is critical in sustaining modern societies and economies, responding to the increasing demand for electricity and curbing environmental consequences. This study focuses on the optimization of energy system stability through the coordination of power system stabilizers (PSSs) and power oscillation dampers (PODs) in a single-machine infinite bus energy grid configuration that has flexible AC alternating current transmission system (FACTS) devices. Intelligent control strategies using PSS and POD techniques are suggested to increase power system stability and generate supplementary control signals for both the generator excitation system and FACTS device switching control. An intelligent optimal modal control framework equipped with deep learning methods is introduced to control the generator excitation system and thyristor-controlled series capacitor (TCSC). By optimally choosing the weighting matrix Q and implementing close-loop pole shifting, an optimal modal control approach is formulated. To harness its adaptive potential in fine-tuning controller parameters, an auxiliary deep learning-based optimization algorithm with actor–critic architecture is implemented. This comprehensive technique provides a promising path to effectively reduce electromechanical oscillations, thereby enhancing voltage regulation and transient stability in power systems.

Purpose: This study aims to enhance DC bus voltage regulation and battery operation reliability in a large-scale hybrid residential microgrid through an intelligent predictive control approach. Methodology: A hierarchical control framework is proposed in which a Nonlinear Autoregressive Moving Average with Exogenous Inputs (NARMA-L2) neural network is implemented as a secondary predictive controller for DC bus voltage regulation and battery management. The controller is designed to learn the inverse dynamics of the DC bus–battery system and anticipate voltage disturbances caused by renewable variability and load changes. The framework is applied to a redesigned hybrid residential microgrid supplying a high-consumption villa in Jeddah, Saudi Arabia, comprising a 40 kW photovoltaic array, a 15 kW wind turbine, and a 100 kWh lithium-ion Battery Energy Storage System (BESS), serving a daily energy demand of 177.5 kWh. Performance evaluation is conducted using MATLAB/Simulink under realistic environmental and load profiles representative of Jeddah conditions. Findings: Simulation results demonstrate that the proposed NARMA-L2-based control strategy significantly improves DC bus voltage stability compared to a conventional PI controller. The DC bus voltage Root Mean Square Error (RMSE) is reduced by approximately 68% (from 3.15 V to 1.01 V), and voltage recovery time is improved by over 7%. In addition, the enhanced generation capacity and predictive control framework increase renewable energy utilization by about 12%, while maintaining battery State-of-Charge (SOC) within safe operating limits and ensuring stable power balance. Unique Contribution to Theory, Practice, and Policy: This study provides practical evidence of the effectiveness of neural network-based predictive control for voltage stabilization in large-scale residential microgrids. The proposed framework bridges the gap between conventional rule-based controllers and intelligent data-driven control strategies, offering a scalable solution for high-demand residential applications. From a policy perspective, the results support the deployment of advanced control technologies as a key enabler for resilient residential microgrids aligned with Saudi Vision 2030 sustainability objectives.

Diagnosing bearing defects (BFs) in squirrel cage induction machines (SCIMs) is essential to ensure their proper functioning and avoid costly breakdowns. This paper presents an innovative approach that combines intelligent direct torque control (DTC) with the use of Hilbert transform (HT) to detect and classify these BFs. The intelligent DTC allows precise control of the electromagnetic torque of the asynchronous machine, thus providing a quick response to BFs. Using HT, stator current is analyzed to extract important features related to BFs. The HT provides the analytical signal of the current, thus facilitating the detection of anomalies associated with BFs. The approach presented incorporates an intelligent DTC that adapts to stator current variations and characteristics extracted via the HT. This intelligent control uses advanced algorithms such as neural networks (ANN-DTCs) and fuzzy logic (FL-DTCs). In this paper, a comparison between these two algorithms was performed in the MATLAB/Simulink environment for a three-phase asynchronous machine to evaluate their effectiveness under the proposed approach. The results obtained demonstrated a high ability to detect and classify BFs, confirming the effectiveness of each algorithm. In addition, this comparison highlighted the specific advantages and disadvantages of each approach. This information is valuable in choosing the most suitable algorithm according to the constraints and specific needs of the application.