Search Results (83)

This work proposes an effective control technique for enhancing the stability of Doubly Fed Induction Generator-Based Wind Turbines (DFIG-WTs) connected to the grid during voltage sag and swell events, ensuring the reliable and efficient operation of wind energy systems integrated with the grid. The proposed approach integrates a Dynamic Voltage Restorer (DVR) in series with a Wind Turbine Generator (WTG) output terminal to enhance the Fault Ride-Through (FRT) capability during grid disturbances. To develop a flexible control strategy for both unbalanced and balanced fault conditions, a combination of feedforward and feedback control based on a sliding mode control (SMC) for DVR converters is used. This hybrid strategy allows for precise voltage regulation, enabling the series compensator to inject the required voltage into the grid, thereby ensuring constant generator terminal voltages even during faults. The SMC enhances the system’s robustness by providing fast, reliable regulation of the injected voltage, effectively mitigating the impact of grid disturbances. To further enhance system performance, Model Predictive Control (MPC) is implemented for the Rotor-Side Converter (RSC) within the back-to-back converter (BTBC) configuration. The main advantages of the predictive control method include eliminating the need for linear controllers, coordinate transformations, or modulators for the converter. Additionally, it ensures the stable operation of the generator even under severe operating conditions, enhancing system robustness and dynamic response. To validate the proposed control strategy, a comprehensive simulation is conducted using a 2 MW DFIG-WT connected to a 120 kV grid. The simulation results demonstrate that the proposed control approach successfully limits overcurrent in the RSC, maintains electromagnetic torque and DC-link voltage within their rated values, and dynamically regulates reactive power to mitigate voltage sags and swells. This allows the WTG to continue operating at its nominal capacity, fully complying with the strict requirements of modern grid codes and ensuring reliable grid integration. Full article

In a smart microgrid (SMG) system that deals with unpredictable loads and incorporates fluctuating solar and wind energy, it is crucial to have an efficient method for controlling frequency in order to balance the power between generation and load. In the last decade, cyberattacks have become a growing menace, and SMG systems are commonly targeted by such attacks. This study proposes a framework for the frequency management of an SMG system using an innovative combination of a smart controller (i.e., the Fuzzy Logic Controller (FLC)) with three conventional cascaded controllers, including Fractional-Order PI (FOPI), Tilt Integral Fractional Derivative (TID^μ), and Proportional Integral Derivative Acceleration (PIDA). The recently released Eel and Grouper Optimization (EGO) algorithm is used to fine-tune the parameters of the proposed controller. This algorithm was inspired by how eels and groupers work together and find food in marine ecosystems. The Integral Time Squared Error (ITSE) of the frequency fluctuation (ΔF) around the nominal value is used as an objective function for the optimization process. A diesel engine generator (DEG), renewable sources such as wind turbine generators (WTGs), solar photovoltaics (PVs), and storage components such as flywheel energy storage systems (FESSs) and battery energy storage systems (BESSs) are all included in the SMG system. Additionally, electric vehicles (EVs) are also installed. In the beginning, the supremacy of the adopted EGO over the Gradient-Based Optimizer (GBO) and the Smell Agent Optimizer (SAO) can be witnessed by taking into consideration the optimization process of the recommended regulator’s parameters, in addition to the optimum design of the membership functions of the fuzzy logic controller by each of these distinct algorithms. The subsequent phase showcases the superiority of the proposed EGO-based FFOPI-TID^μ-PIDA structure compared to EGO-based conventional structures like PID and EGO-based intelligent structures such as Fuzzy PID (FPID) and Fuzzy PD-(1 + PI) (FPD-(1 + PI)); this is across diverse symmetry operating conditions and in the presence of various cyberattacks that result in a denial of service (DoS) and signal transmission delays. Based on the simulation results from the MATLAB/Simulink R2024b environment, the presented control methodology improves the dynamics of the SMG system by about 99.6% when compared to the other three control methodologies. The fitness function dropped to 0.00069 for the FFOPI-TID^μ-PIDA controller, which is about 200 times lower than the other controllers that were compared. Full article

Recent results indicate that the characteristic locus method provides a convenient approach for analyzing power system low-frequency stability. In this study, an enhanced version of the method, referred to as the inverse characteristic locus method, is introduced. By inverting the similarity matrix of the loop transfer function matrix of the system, a more reliable and accurate stability metric is obtained. The proposed method is applied to assess the impact of changes in wind turbine generator (WTG) dynamics and system operating conditions on stability. Simulation results demonstrate that variations in system operating conditions exert a greater influence on stability compared to changes in WTG dynamics. Full article

With the increasing integration of new energy into the grid, the level of system inertia has been significantly reduced, posing a severe challenge to frequency stability. Consequently, there is an urgent need for wind turbine generators (WTGs) to actively provide inertia support through virtual inertia control. Assessing the inertia support capability of WTGs reasonably and setting appropriate controller parameters based on this assessment is a topic worthy of discussion. As WTGs’ characteristics are mostly ignored in the evaluation of inertia support capability for WTGs, an evaluation method based on symmetrical operation is proposed. The proposed method considers the impact of real inertia and aerodynamic characteristics, thereby helping to determine reasonable virtual inertia coefficients and de-loading reserve capacity for WTGs. With the proposed method, it can be determined that large WTGs can provide inertia support capabilities close to those of synchronous generators to the grid without exceeding a 0.1% reduction in reserve capacity during de-loading operation. Full article

Virtual synchronous generator (VSG) control, which can provide inertia output, damp power oscillations, and offer frequency and voltage support to power grids, has become a growing trend in the control field of wind power generation. As a new technology, there are still challenges that VSG control has not solved well, such as transient overvoltage suppression. A kind of transient overvoltage, which often occurs during the commutation failures of HVDC transmission systems, will trigger a mass of wind turbine generators (WTGs) disconnecting from grids. To reduce the grid-disconnection risk of the virtual synchronous generator control-based doubly fed induction generators (VSG-DFIGs), this paper first analyzes the mechanism of the automatic voltage regulation (AVR) control usually employed by VSG-DFIGs, then proposes measures to suppress the transient overvoltage. To solve the problem of the reactive power response lag issued by VSG-DFIGs, which will further aggravate the transient overvoltage in continuous low and high voltage faults, the time constant of the AVR control is switched. To fully exploit the potential of the DFIGs’ reactive power support, the droop coefficient of the AVR control is switched during the abnormal voltage stages. The switched droop coefficient will change the rotor excitation current magnitude, thus adjusting the internal potential of a DFIG, finally better supporting or suppressing the terminal voltage during the low or high voltage periods. Simulation results based on the DIgSILENT/PowerFactory platform demonstrate that the proposed method can effectively suppress the transient overvoltage that occurs in continuous low and high voltage events caused by the commutation failures of HVDC transmission systems, reducing the number of WTGs disconnecting from the grids. Full article

The widespread adoption of the power grid has led to increased attention to load frequency control (LFC) in power systems. The LFC strategy of multi-source hybrid power systems, including hydroelectric generators, Wind Turbine Generators (WTGs), and Photovoltaic Generators (PVGs), with thermal generators is more challenging. Existing methods for LFC tasks pose challenges in achieving satisfactory outcomes in hybrid power systems. In this paper, a novel method for the multi-source hybrid power system LFC task by using an optimal active disturbance rejection control (ADRC) strategy is proposed, which is based on the combination of the improved linear quadratic regulator (LQR) and the ADRC controller. Firstly, an established model of a hybrid power system is presented, which incorporates multiple regions and multiple sources. Secondly, utilizing the state space representation, a novel control strategy is developed by integrating improved LQR and ARDC. Finally, a series of comparative simulation experiments has been conducted using the Simulink model. Compared with the LQR with ESO, the maximum relative error of the maximum peaks of frequency deviation and tie-line exchanged power of the hybrid power system is reduced by 96% and 83%, respectively, by using the proposed strategy. The experimental results demonstrate that the strategy proposed in this paper exhibits a substantial enhancement in control performance. Full article

This paper proposes a variable-power-tracking-operation-based frequency regulation scheme for a wind turbine generator (WTG) that employs a frequency-excursion-based control parameter to suppress frequency excursion under various wind speeds and disturbances. To this end, an improved system frequency response model is built, and then the frequency regulation characteristics of the WTG are investigated. The frequency-excursion-based control parameter is determined. Simulations based on the Matlab/Simulink simulator verify the effectiveness of the proposed variable-power-tracking-operation-based frequency regulation strategy. The simulation results clearly indicate that the suggested scheme is beneficial for reducing the maximum frequency deviation. The proposed frequency regulation scheme could enhance the networking capacity of wind farms and improve the capacity of the power grid to absorb renewable energy. Full article

Implementation of renewable energy sources (RESs) in power systems can reduce the dependence on fossil-fuel-based thermal power generation systems. At the same time, however, the system inertia decreases as synchronous generators decrease; this is crucial for maintaining the stability of the power system. Virtual inertia control (VIC) can regulate the output of an inverter-based resource (IBR) by increasing the inertia. For a wind turbine generator (WTG), output control factors such as pitch angle control and maximum power point tracking (MPPT) significantly affect the performance of the VIC. This paper theoretically clarifies that the pitch angle control contributes to improvements in the performance of the VIC and helps avoid a loss of operation of the WTG in frequency drop events by analyzing the movement of the operational point of the WTG based on the physical characteristic of the WTG and control characteristics of the pitch angle control and MPPT; an electromagnetic transient (EMT) simulation, performed to verify the analysis, is also presented. Full article

An effective way to perform maintenance on the wind turbine generator (WTG) blades installed in grid-connected wind farms is to inspect them using Unmanned Aerial Vehicles (UAV). The ability to detect wind turbine blade defects from these laser and RGB images captured by drones has been the subject of numerous studies. The issue that most applied techniques battle with is being able to locate different wind turbine blade defects with high confidence scores and precision. The accuracy of these models’ defect detection decreases due to varying testing image scales. This article proposes the Res-CNN3 technique for detecting wind turbine blade defects. In Res-CNN3, defect region detection is achieved through a bipartite process that processes the laser delta and RGB delta structure of a wind turbine blade image with an integration of residual networks and concatenated CNNs to determine the presence of typical defect regions in the image. The loss function is logistic regression, and a Selective Search (SS) algorithm is used to predict the regions of interest (RoI) of the input images for defects detection. Several experiments are conducted, and the outcomes prove that the proposed model has a high prospect for accuracy in solving the problem of defect detection in a manner similar to the advanced benchmark methods. Full article

In a smart grid (SG) system with load uncertainties and the integration of variable solar and wind energies, an effective frequency control strategy is necessary for generation and load balancing. Cyberattacks are emerging threats, and SG systems are typical cyber-attack targets. This work suggests an improved gorilla troops optimizer (iGTO)-based fuzzy PD-(1+PI) (FPD-(1+PI)) structure for the frequency control of an SG system. The SG contains a diesel engine generator (DEG), renewable sources like wind turbine generators(WTGs), solar photovoltaic (PV), and storage elements such as flywheel energy storage systems (FESSs) and battery energy storage systems (BESSs) in conjunction with electric vehicles (EVs). Initially, the dominance of the projected iGTO over the gorilla troops optimizer (GTO) and some recently suggested optimization algorithms are demonstrated by considering benchmark test functions. In the next step, a traditional PID controller is used, and the efficacy of the GTO method is compared with that of the GTO, particle swarm optimization (PSO), and genetic algorithm (GA) methods. In the next stage, the superiority of the proposed FPD-(1+PI) structure over fuzzy PID (FPID) and PID structures is demonstrated under various symmetry operating conditions as well as under different cyberattacks, leading to a denial of service (DoS) and delay in signal transmission. Full article

In recent years, the penetration of wind power generation has been growing steadily to adapt to the modern trend of boosting renewable energy (RE)-based power generation. However, the dynamic power flow of wind turbine generators (WTGs) is unpredictable and can have a negative impact on existing power grids. To solve this problem efficiently, this paper presents a multifunctional WTG intelligent compensator (WTGIC) for the advanced power management and compensation of power systems embedded with WTGs. The proposed WTGIC consists of a power semiconductor device (PSD)-based bidirectional three-phase inverter module and an energy storage unit (ESU). In order to reduce system costs and improve reliability, efficiency, and flexibility, various control functions and algorithms are integrated via a modularized all-digital control scheme. In this paper, the configuration of the proposed WTGIC is first introduced, and then the operating modes and related compensation and control functions are addressed. An online efficiency optimization algorithm is proposed, and the required controllers are designed and implemented. The designed functions of the proposed WTGIC include high-efficiency charging/discharging of the ESU, real-time power quality (PQ) compensation, and high-efficiency power smoothing of the WTGs. The feasibility and effectiveness of the proposed WTGIC are verified using case studies with simulations in the Powersim (PSIM) environment and the implementation of a small-scale hardware experimental system with TI’s digital signal processor (DSP) TI28335 as the main controller. Full article

To assess wind energy potential in Northern Morocco, a validated approach based on the two-parameter Weibull distribution is employed, utilizing wind direction and speed data. Over a span of two years, from January 2019 to December 2020, measurements taken every 10 min are collected. This study is centered on a comprehensive and statistical analysis of electricity generated from a wind farm situated in the Tetouan region in Morocco. This wind farm boasts a total capacity of 120 MW, comprising 40 wind turbines, each with a 3 MW capacity, strategically positioned along the ridge. Among the available techniques for estimating Weibull distribution parameters, the maximum likelihood method (MLM) is chosen due to its statistical robustness and exceptional precision, especially for large sample sizes. Throughout the two-year period, monthly wind speed measurements fluctuated between 2.1 m/s and 9.1 m/s. To enhance accuracy, monthly and annual theoretical power densities were recalculated using the Weibull parameters and compared with actual measurements. This has enabled the detection of production disparities and the mitigation of forecast errors throughout the entire wind farm. In conclusion, over the two-year production period, turbines WTG 30 and WTG 33 displayed the most significant shortcomings, primarily attributed to orientation issues within the “Yaw system”. Full article

Digital twins (DTs) have been implemented in various applications, including wind turbine generators (WTGs). They are used to create virtual replicas of physical turbines, which can be used to monitor and optimize their performance. By simulating the behavior of physical turbines in real time, DTs enable operators to predict potential failures and optimize maintenance schedules, resulting in increased reliability, safety, and efficiency. WTGs rely on accurate wind speed measurements for safe and efficient operation. However, physical wind speed sensors are prone to inaccuracies and failures due to environmental factors or inherent issues, resulting in partial or missing measurements that can affect the turbine’s performance. This paper proposes a DT-based sensing methodology to overcome these limitations by augmenting the physical sensor platform with virtual sensor arrays. A test bench of a direct drive WTG based on a permanent magnet synchronous generator (PMSG) was prepared, and its mathematical model was derived. MATLAB/Simulink was used to develop the WTG virtual model based on its mathematical model. A data acquisition system (DAS) equipped with an ActiveX server was used to facilitate real-time data exchange between the virtual and physical models. The virtual sensor was then validated and tuned using real sensory data from the physical turbine model. The results from the developed DT model showed the power of the DT as a virtual sensor in estimating wind speed according to the generated power. Full article

The frequency stability of modern power systems is challenged due to widespread application of large-scale renewable energy systems, of which the offshore wind farm (OWF) is one of the dominant resources. The OWFs are usually integrated into the grid by multi-terminal direct current (MTDC) transmission systems, which makes the energy flow complicated and the frequency control design challenging. A frequency support control method of MTDC system integrated OWFs (referred to as the OWF-MTDC system) is proposed in this paper. First, the wind turbine generation system (WTGS) is controlled to reserve a certain amount of available power according to the real-time wind speed for more comprehensive frequency regulation. Then, the frequency support control of OWFs is designed, and they can release the rotor kinetic energy and reserved power to support the onshore grid frequency. In addition, the virtual inertia control of a modular multi-level converter (MMC) is designed, which can also provide frequency support in an emergency by use of the DC capacitor. To ensure that the frequency control of the OWF-MTDC system does not degrade the stability of the system, a detailed DC impedance model of the MMC-based MTDC systems is developed, considering the constant power control and DC voltage control. Based on the impedance model, the impact of the frequency control coefficients on the DC side stability of the MTDC system is analyzed. Simulation results validate the stability analysis and verify the proposed frequency control method, which can effectively provide frequency support to the onshore power grid. Full article

Fault alarm time lag is one of the difficulties in fault diagnosis of wind turbine generators (WTGs), and the existing methods are insufficient to achieve accurate and rapid fault diagnosis of WTGs, and the operation and maintenance costs of WTGs are too high. To invent a new method for fast and accurate fault diagnosis of WTGs, this study constructs a stacking integration model based on the machine learning algorithms light gradient boosting machine (LightGBM), extreme gradient boosting (XGBoost), and stochastic gradient descent regressor (SGDRegressor) using publicly available datasets from Energias De Portugal (EDP). This model is automatically tuned for hyperparameters during training using Bayesian tuning, and the coefficient of determination (R²) and root mean square error (RMSE) were used to evaluate the model to determine its applicability and accuracy. The fitted residuals of the test set were calculated, the Pauta criterion (3σ) and the temporal sliding window were applied, and a final adaptive threshold method for accurate fault diagnosis and alarming was created. The model validation results show that the adaptive threshold method proposed in this study is better than the fixed threshold for diagnosis, and the alarm times for the GENERATOR fault type, GENERATOR_BEARING fault type, and TRANSFORMER fault type are 1.5 h, 5.8 h, and 3 h earlier, respectively. Full article