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In order to improve the anti-interference ability and control accuracy of wind power brake, the improved active disturbance rejection control and Kalman filter algorithm are proposed based on the traditional three-closed-loop system. The traditional "large bandwidth, small error" is improved to "small bandwidth, small error" by introducing the speed tracking condition in the extended state observer to improve the observation efficiency of the observer. A new exponential reaching law sliding mode control is introduced into the error feedback control rate to enhance the control effect. The Kalman filter algorithm is introduced into the current loop to reduce the current noise. The mathematical model of the electric braking system is established by MATLAB/Simulink, and compared with the traditional active disturbance rejection control strategy and PID control strategy. The results showed that the improved control strategy can improve the response speed and reduce the starting torque and current noise. The brake showed good control effect and anti-interference ability under different operating conditions, which enhanced the system's stability and dynamic performance. [ABSTRACT FROM AUTHOR]

The development of modern society requires not only attention to development methods and efficiency, but also correct evaluation of various environmental issues that arise during development, to achieve optimal social development without damaging the ecological environment. Intelligent power systems utilize new energy technologies, energy storage technologies, and smart grid technologies. Against this development background, how to achieve sustainable, environmentally friendly, and Low Carbon (LC) development in the power system operation, which has been at the center of China's national economic construction and development system, has become a common concern for all participants. Therefore, this paper proposed a study on supporting role of intelligent power systems based on LC and environmental protection. In the new era, the target functions to be achieved by smart grids can provide the necessary support for the construction and development of LC power systems, and its importance is self-evident. Based on this reality, this article took LC power systems as the research object and combined the analysis of the functions of smart grids to discuss clean energy power systems. The experimental results showed that the Power Generation (PG) capacity of thermal PG increased from 280.4 billion kWh to 289.7 billion kWh. The increase in wind PG from 2.5 billion kWh to 5.5 billion kWh showed that although clean energy accounted for a small proportion, the power system was still dominated by traditional PG. However, clean energy PG is increasingly being valued. At the end of this article, corresponding rectification suggestions were also proposed, with the purpose of helping to optimize the development and improvement of LC power systems. [ABSTRACT FROM AUTHOR]

Wind power is an important energy source that can be used to supply clean energy and meet current energy needs. Despite its advantages in terms of zero emissions, its main drawback is its intermittency. Deterministic approaches to forecast wind power generation based on the annual average wind speed are usually used; however, statistical treatments are more appropriate. In this paper, an intelligent statistical methodology to forecast annual wind power is proposed. The seasonality of wind is determined via a clustering analysis of monthly wind speed probabilistic distribution functions (PDFs) throughout n years. Subsequently, a methodology to build the wind resource typical year (WRTY) for the n + 1 year is introduced to characterize the resource into the so-called statistical seasons (SSs). Then, the wind energy produced at each SS is calculated using its PDFs. Finally, the forecasted annual energy for the n + 1 year is given as the sum of the produced energies in the SSs. A wind farm in Mexico is chosen as a case study. The SSs, WRTY, and seasonal and annual generated energies are estimated and validated. Additionally, the forecasted annual wind energy for the n + 1 year is calculated deterministically from the n year. The results are compared with the measured data, and the former are more accurate. [ABSTRACT FROM AUTHOR]

In this paper, based on the lift-type wind turbine, an adaptive double-drive lift–drag composite vertical-axis wind turbine is designed to improve the wind energy utilization rate. A drag blade was employed to rapidly accelerate the wind turbine, and the width of the blade was adaptively adjusted with the speed of the wind turbine to realize lift–drag conversion. The aerodynamic performance analysis using Fluent showed that the best performance is achieved with a blade curvature of 30° and a drag-type blade width ratio of 2/3. Physical experiments proved that a lift–drag composite vertical-axis wind turbine driven by dual blades can start when the incoming wind speed is 1.6 m/s, which is 23.8% lower than the existing lift-type wind turbine's starting wind speed of 2.1 m/s. At the same time, when the wind speed reaches 8.8 m/s, the guide rail adaptive drag-type blades all contract and transform into lift-type wind turbine blades. The results show that the comprehensive wind energy utilization rate of the adaptive dual-drive lift–drag composite vertical-axis wind turbine was 5.98% higher than that of ordinary lift-type wind turbines and can be applied to wind power generation in high-wind-speed wind farms. [ABSTRACT FROM AUTHOR]

This study introduces a novel hybrid forecasting model for wind power generation. It integrates Artificial Neural Networks, data clustering, and Particle Swarm Optimization algorithms. The methodology employs a systematic framework: initial clustering of weather data via the k-means algorithm, followed by Pearson's analysis to pinpoint pivotal elements in each cluster. Subsequently, a Multi-Layer Perceptron Artificial Neural Network undergoes training with a Particle Swarm Optimization algorithm, enhancing convergence and minimizing prediction discrepancies. An important focus of this study is to streamline wind forecasting. By judiciously utilizing only sixteen observation points near a wind farm plant, in contrast to the complex global numerical weather prediction systems employed by the European Center Medium Weather Forecast, which rely on thousands of data points, this approach not only enhances forecast accuracy but also significantly simplifies the modeling process. Validation is performed using data from the Italian National Meteorological Centre. Comparative assessments against both a persistence model and actual wind farm data from Southern Italy substantiate the superior performance of the proposed hybrid model. Specifically, the clustered Particle Swarm Optimization-Artificial Neural Network-Wind Forecasting Method demonstrates a noteworthy improvement, with a reduction in mean absolute percentage error of up to 59.47% and a decrease in root mean square error of up to 52.27% when compared to the persistence model. [ABSTRACT FROM AUTHOR]

Summary: Numerous wind turbines form large‐scale wind farms, which are complex nonlinear systems with uncertain parameters. The issue of maximum wind energy capture and coordinated control has always been a research hotspot. In this article, under the condition of limited input and uncertain parameters, the preset controller and the adaptive cooperation control are designed to realize the maximum wind energy capture for every wind turbine and the adaptive cooperation control of multiple wind turbines. The research gap lies in that the Hamilton model of wind power generation system is established with uncertain parameters, and the preset controller (method) is designed to capture the maximum wind energy. Under the hypothesis that the uncertain part can be expressed as a linear form about unknown parameter, and using the saturation function processing method in the diagonal matrix, an adaptive feedback controller with limited input is designed to realize the adaptive cooperation control of multiple wind turbines. The simulation results show that under the conditions such as variable wind speed, limited input and uncertain parameters, the wind turbine remain normal operation at the desired angular velocity. It can be concluded that not only the maximum wind energy capture is realized under the condition of variable wind speed, in which the wind turbine can operate on the optimal power curve to improve the utilization of wind energy, but also the adaptive cooperation control of multiple wind turbines can be achieved with limited input and parameter perturbation. [ABSTRACT FROM AUTHOR]

A novelty dual-stator brushless doubly-fed generator (DSBDFG) with magneticbarrier rotor structure is put forward for application in wind power. Compared with a doublyfed induction generator, the DSBDFG has virtues of high reliability and low maintenance costs because of elimination of brush and sliprings components. Therefore, the proposed structure has tremendous potential as a wind power generator to apply in wind power. According to the operating principle of electric machine, the DSBDFG is studied in wind power application. At first, the topology, the winding connecting, the rotor structure, the power flow chart of different operating models and the variable speed capability of electric machine are discussed and analyzed. Then, a 50 kW DSBDFG is designed. Based on the principal dimension of the design electric machine, the electromagnetic characteristics of the DSBDFG with different running modes are analyzed and calculated to adopt the numerical method. From the result, it meets the requests of electromagnetic consistency and winding connecting in the design electric machine. Meanwhile, it confirms the proposed DSBDFG has the strong ability of speed regulation. [ABSTRACT FROM AUTHOR]

Taking small and medium-sized switched reluctance wind turbines as the research object, aiming at the requirements of the rapidity and accuracy of the maximum power point tracking control under different wind speed conditions during the generation process, a maximum power point tracking control strategy based on adaptive weighted grey Wolf optimization PID algorithm was proposed. When the external wind speed changes, the optimal speed of the wind turbine is calculated according to the real-time wind speed. The difference between the actual speed and the weighted adaptive gray wolf PID control algorithm is used as the input of the closed-loop PID control parameter of the speed, so as to output the optimal duty ratio of the voltage pulse width modulation and realize the maximum power point tracking control under the variable wind speed. The simulation results show that compared with the traditional PID control, the adaptive weighted grey wolf optimization PID algorithm can quickly and accurately achieve the maximum power point tracking control under the condition of wind speed change. [ABSTRACT FROM AUTHOR]

The design, operational planning, and integration of wind power plants with other renewables and the grid face challenges attributed to the intermittent nature of wind power generation. Addressing this issue necessitates the development of a smart wind power (and in particular wind speed) forecasting approach. This is a complex task due to substantial fluctuations in wind speed. To overcome the inherent stochastic nature of wind speed and mitigate related challenges, traditionally, numerical weather prediction (NWP) models are employed for wind speed forecasting. However, the applicability of NWP models is limited to short-term forecasting due to their computational constraints. In this study, a hybrid AI-based approach is proposed to improve forecast accuracy over a 48 h horizon for the city of Montreal. The results demonstrate that by integrating the probability distribution of wind speed with a deep learning model, the forecasted values align closely with the observed values in terms of seasonality and trend, exhibiting enhanced accuracy. Evaluation metrics reveal a substantial reduction in the root mean squared error (13–31%) across three prediction horizons (summer, fall, and winter) compared to a single long, short-term memory model. Furthermore, integrating the improved model with the numerical weather prediction model yields increased accuracy and decreased error compared to the LSTM–Weibull model. [ABSTRACT FROM AUTHOR]

To alleviate the environmental pollution caused by waste wind turbine blades and provide new ideas for recycling, the thermochemical characteristics of four basic components of composite materials commonly used in wind turbine blades were studied in this paper. Thermogravimetric experiments in different atmospheres and heating rates, and thermogravimetric‐infrared combined experiments were carried out. The results show that the reaction of epoxy resin and thermoplastic polyurethane was easy and deep in N2 and CO2. Except for glass fiber, the other three components showed different combustion characteristics in air. The maximum reaction rates and activation energy of resin‐based components in N2 increased with increasing heating rate. The pyrolysis products of the four basic components all contained CO2 and CO, and some aromatic substances could be generated during the thermal transformation of epoxy resin and thermoplastic polyurethane. It can be concluded that the thermal transformation properties of the basic components of the composite material of wind turbine blades can be used to treat the waste blades, making the resin as the matrix material undergo a thermal transformation reaction and recover the fiber. This is to effectively guarantee the green and sustainable development of wind power generation. [ABSTRACT FROM AUTHOR]

The accuracy of wind power generation predicted by computational fluid dynamics (CFD) simulations combined with meteorological wind data was validated based on comparisons with directly measured data for a small vertical axis wind turbine system installed on a university campus. The CFD simulations were performed in accordance with established guidelines and frameworks for the prediction of urban wind environments. At the rooftop location, where small wind turbines are typically installed, the deviations in wind velocity from the measurements are quite large. However, in the present study, the prediction accuracy for the wind turbine site, which was 4 m above the ground, was acceptable. The total power generation estimated using the assumed power curve based on the rated output of the turbine was 56% larger than that directly measured by the power generator. However, using the power curves obtained from the measurements, the total power generation could be predicted with a high degree of accuracy and with an error of approximately 3%. It is suggested that not only the accuracy of the wind velocity but also that of the power curve is very important because they are directly related to that of the predicted power generation. [ABSTRACT FROM AUTHOR]

In response to the problem of reduced system inertia and insufficient frequency regulation capacity due to large-scale wind power connection to the grid, an integrated control strategy scheme for wind turbine inertia support and primary frequency regulation is proposed. In this scheme, the load reduction control of wind turbine is adjusted based on the characteristics of wind turbine slurry distance angle, and the reserve capacity required for wind turbine frequency modulation is reserved, which further solves the combination problem between the frequency change of the power grid and the virtual moment of inertia in the load reduction mode. In addition, the wind turbine speed protection is considered to set the static adjustment coefficient, and combined with the inertia support control, to achieve the wind turbine integrated frequency control strategy. The co-simulation model including wind farm is built in MATLAB/Simulink platform. The results show that the wind turbine can quickly provide virtual rotational inertia support to the grid, reduce the rate of change of the initial grid frequency perturbation, and regulate the output power according to the adjusted static regulation differential coefficients to improve the frequency stability of the wind power connected to the grid, which verifies the rationality of the proposed control strategy. [ABSTRACT FROM AUTHOR]

The volatility and intermittency of wind energy result in highly unpredictable wind power output, which poses challenges to the stability of the intact power system when integrating large-scale wind power. The accuracy of wind power prediction is critical for maximizing the utilization of wind energy, improving the quality of power supply, and maintaining the stable operation of the power grid. To address this challenge, this paper proposes a novel hybrid forecasting model, referred to as Hybrid WT–PSO–NARMAX, which combines wavelet transform, randomness operator-based particle swarm optimization (ROPSO), and non-linear autoregressive moving average with external inputs (NARMAX). The model is specifically designed for power generation forecasting in wind energy systems, and it incorporates the interactions between the wind system's supervisory control and data acquisition's (SCADA) actual power record and numerical weather prediction (NWP) meteorological data for one year. In the proposed model, wavelet transform is utilized to significantly improve the quality of the chaotic meteorological and SCADA data. The NARMAX techniques are used to map the non-linear relationship between the NWP meteorological variables and SCADA wind power. ROPSO is then employed to optimize the parameters of NARMAX to achieve higher forecasting accuracy. The performance of the proposed model is compared with other forecasting strategies, and it outperforms in terms of forecasting accuracy improvement. Additionally, the proposed Prediction Error-Based Power Forecasting (PEBF) approach is introduced, which retrains the model to update the results whenever the difference between forecasted and actual wind powers exceeds a certain limit. The efficiency of the developed scheme is evaluated through a real case study involving a 180 MW grid-connected wind energy system located in Shenyang, China. The proposed model's forecasting accuracy is evaluated using various assessment metrics, including mean absolute error (MAE) and root mean square error (RMSE), with the average values of MAE and RMSE being 0.27% and 0.30%, respectively. The simulation and numerical results demonstrated that the proposed model accurately predicts wind output power. [ABSTRACT FROM AUTHOR]

This research study proposes a methodology based on data science to estimate the technical feasibility of implementing hybrid solar-wind power systems by calculating the plant factor, also known as capacity factor (CF). Data is gathered from the NOVALYNX 110-WS-18 weather station, between the years 2018 and 2020, to determine the energetic potential available for the Candelaria campus at the La Salle University (Colombia). A k-means data clustering algorithm, developed in Matlab, is applied over the following descriptors: time, solar radiation, wind speed, and temperature. The results show that the photovoltaic and wind power systems have a CF of 36.69% and 2.15%, respectively. It is concluded that it is technically feasible to implement a photovoltaic system at the La Salle University, but not a wind power system as it does not meet the minimum CF required. [ABSTRACT FROM AUTHOR]

Considering the escalating rates of exhaustion of non-renewable energy resources, coupled with the harmful environmental side effects of harnessing them (e.g. damage to public health via air pollution), the need for a near-complete transition to renewable energy production seems inevitable. In recent times, renewable energy production has seen a strong support from investors, governmental initiatives, and industries across the world. Globally installed wind power capacity has seen an increase of 345.24% over the past decade. This increase brings along a need for robust power production management systems having a potential for predicting wind turbine power outputs primarily based on real-time input wind velocities. We propose and compare five optimized robust regression models for forecasting the wind power generated through turbines based on wind velocity vector components, out of which the Extreme Gradient Boosting regression model provided the best results. The forecasted output of our model can be compared with a city's daily average threshold power requirement in order to make informed decisions about either shutting down an appropriate number of turbines to avoid excessive power production and wastage, or to compensate forecasted shortcomings in production on less windy days via alternative energy generation methodologies. [ABSTRACT FROM AUTHOR]

Wind power generation does not emit greenhouse gases or pollutants, but there are some carbon emissions from the manufacturing, transportation, operation, and waste disposal of wind turbines. Directly driven permanent magnet and doubly fed asynchronous wind turbines currently have the largest market share in China, but few Chinese studies have compared their differences in carbon reduction potential. This paper uses life cycle assessment (LCA) to quantitatively analyze the full life cycle carbon emissions of the two wind turbines to determine which type of wind turbine has greater carbon reduction potential, obtaining the following results. (1) The full life cycle greenhouse gas emissions of 2.5 MW directly driven permanent magnet and doubly fed asynchronous wind turbines are 8.48 and 10.43 g CO2/kWh, respectively. The direct‐driven permanent magnet wind turbine is superior in terms of carbon reduction. (2) The stage with the greatest impact and the greatest difference between the two wind turbines in the full life cycle is the production stage, during which the carbon emissions of the directly driven permanent magnet and doubly fed asynchronous wind turbines are 1.045 × 106 and 1.210 × 106 kg, respectively. (3) According to sensitivity analysis, proper waste disposal and transportation can reduce carbon emissions from wind turbines. These research findings can be used to help achieve carbon peaking and neutrality goals, as well as the technological development of wind power enterprises. [ABSTRACT FROM AUTHOR]

To solve the problem of large output power fluctuations in wind turbines and improve grid adaptability, a hydraulic energy storage system is introduced in traditional hydraulic wind turbines. Based on the working principle of energy storage hydraulic wind turbines, an energy storage hydraulic wind turbine state space model is established, and the feedback linearization method is introduced to solve the multiplication nonlinear problem in the modeling process. The output power is taken as the control output, and the torque compensation controller is established with the feedback linearization method. The displacement of the variable displacement pump motor is controlled to realize hydraulic energy storage system energy charging and discharging, and the wind turbine output power smoothing control is realized with the fluctuating wind speed. The power smoothing control strategy is verified with the 24 kW energy storage hydraulic wind turbines semi‐physical simulation experimental platform. The proposed control strategy lays the groundwork for the wide application of the energy storage hydraulic wind turbines. [ABSTRACT FROM AUTHOR]

The large-scale integration of wind turbines (WTs) in renewable power generation induces power oscillations, leading to frequency aberration due to power unbalance. Hence, in this paper, a secondary frequency control strategy called load frequency control (LFC) for power systems with wind turbine participation is proposed. Specifically, a backpropagation (BP)-trained neural network-based PI control approach is adopted to optimize the conventional PI controller to achieve better adaptiveness. The proposed controller was developed to realize the timely adjustment of PI parameters during unforeseen changes in system operation, to ensure the mutual coordination among wind turbine control circuits. In the meantime, the improved particle swarm optimization (IPSO) algorithm is utilized to adjust the initial neuron weights of the neural network, which can effectively improve the convergence of optimization. The simulation results demonstrate that the proposed IPSO-BP-PI controller performed evidently better than the conventional PI controller in the case of random load disturbance, with a significant reduction to near 10 s in regulation time and a final stable error of less than 10 − 3 for load frequency. Additionally, compared with the conventional PI controller counterpart, the frequency adjustment rate of the IPSO-BP-PI controller is significantly improved. Furthermore, it achieves higher control accuracy and robustness, demonstrating better integration of wind energy into traditional power systems. [ABSTRACT FROM AUTHOR]

Faulted phase identification is one of the segments of conventional system protection that is severely vulnerable in the presence of inverter-based resources (IBR) such as Type IV wind and solar PV power plants. The work presented in this paper investigates the effect of IBRs on the conventional phasor-based faulted phase identification methods widely implemented in contemporary commercial protection relays using theoretical analysis and simulation results. Moreover, this premise is further validated by testing commercial line protection relays using hardware-in-the-loop simulations. This paper also evaluates the applicability of recently proposed transients/incremental quantities-based techniques to overcome the deficiencies of conventional methods to correctly identify the faulted phase in systems with IBRs through real-time and control hardware-in-the-loop simulations. Comparisons with commercial relays show that transient/incremental quantities-based methods are more suitable for systems with a high penetration of IBRs. [ABSTRACT FROM AUTHOR]

Flux-switching permanent magnet (FSPM) machines have attracted wide attention in many rotating applications that require high-power density. In this research, we propose for the first time a novel six-phase FSPM generator with a stator featuring a V-shaped flux-focusing magnet arrangement. The design is targeted for low-speed wind power generation. To achieve the design objectives as a wind generator, the highly comprehensive structural parameters, including the number of rotor poles, split ratio, and rotor pole width, are designed and optimized using 2D finite-element analysis. From findings, the optimal stator/rotor pole combination is discovered to be 12/19 for the considered power and speed requirements. When compared to the initial structure, the optimized structure of the V-shaped FSPM generator is found to produce a significant improvement in EMF, cogging torque, electromagnetic torque, power, and efficiency. The power-generating performance of the proposed FSPM generator is found to be outstanding when compared to the radial-flux PM generators described in the literature. Therefore, the proposed V-shaped FSPM generator is capable of being used for low-speed wind power generation. The machine configuration adjustment approach presented in this work can also be utilized for the design of permanent magnet wind generators. [ABSTRACT FROM AUTHOR]

As theincrease of wind power penetration, the fluctuations of wind power results in serious power system frequency turbulences. This article proposes a low-pass virtual filter (VF) for wind energy conversion systems (WECSs) to smoothen output power. First, a linearized model is established for a WECS. Then, a low-pass VF is derived for the active power control loop in WECS to modulate the kinetic energy stored in rotating mass for output smoothing. The principle is to modify the traditional wind power controller to emulate a physical energy storage system controlled as a low-pass filter to smooth the wind power output. The proposed low-pass VF does not need the physical deployment of energy storage system but still can filter the high-frequency fluctuations in the output wind power. Furthermore, in order to ensure operation stability of the wind turbine (WT), a stability-constrained coefficient is determined according to the proposed stability criteria to ensure the stability of WTs. Simulation and experiment results verify the effectiveness of the proposed VF in WECS for output wind power fluctuation reduction and the stability-constrained coefficient for stability ensuring. [ABSTRACT FROM AUTHOR]

This article analyzes the power transfer limit (Pmax) of wind power plants (WPPs) under the weak grid condition. It is pointed out that the impact of different grid parameters and grid configurations on Pmax cannot be fully reflected by the short-circuit ratio (SCR), but can be readily captured by voltage sensitivity (∂Q/∂V). Hence, ∂Q/∂V turns out to be a more appropriate metric for the assessment of Pmax compared with the SCR. Based on this insight, the voltage control of the WPP is suggested to be implemented at the bus with the highest ∂Q/∂V to maximize the power transfer capability. Moreover, a dynamic voltage control is further added at the turbine level to improve the disturbance rejection capability of the WPP as well as its dynamic robustness against grid strength variations. Finally, simulation and field test results are given to demonstrate the theoretical analysis and the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

The wind–thermal-bundled transmission system is a feasible way to transmit wind power generation; however, the stability of the system should be paid more attention under high wind power penetration. In this article, the impact of wind energy penetration on torsional vibration and electrical characteristics is investigated for wind–thermal-bundled transmitted by high-voltage direct current system. The torsional vibration responses of generator shaft with different wind–thermal-bundled ratios are analyzed by time and frequency methods. The electrical characteristics of wind–thermal-bundled transmission system are disclosed under the influence of the wind farm penetration. The analysis illustrates that the penetration of doubly fed induction generation makes the torsional vibration of turbo generator and wind turbine shaft present different mode characteristics, and the risks of different modes are different. The current and voltage of wind–thermal-bundled transmission system show regular evolution with the change of wind–thermal-bundled proportion. Analytical results provide the value for the design and control of the wind–thermal-bundled transmitted system, which contributes to the improvement of power system stability. [ABSTRACT FROM AUTHOR]

Although the global wind energy industry has made considerable progress in recent years, wind turbines suffer from frequent failures since the systems are complicated and the working conditions are far from being satisfactory. For the wind turbines to function well, it is imperative to study the overall status of the wind turbine unit, evaluate the performance of the wind farm, apply intelligent operation and maintenance technology, and improve operation and maintenance strategies on an on-going basis, all of which are based on the operation data of the unit. This paper focuses on the evaluation and extension of the lifetime of wind turbines. Based on relevant knowledge and theories from previous studies, an evaluation method based on big data was designed to do the evaluation, and the results of which were verified by real cases. With the duration of catastrophic failures taken into account, the proposed life prediction algorithm was proved to be effective. If the bearing runs for 34 days, the actual remaining life of wind turbines is 0.2 days. The number predicted for LRM is 0.8 days and that predicted for ILRM is 0.31 days. Compared with LRM, the prediction for ILRM is much more accurate. [ABSTRACT FROM AUTHOR]

The actively commutated current source converter (CSC) does not need large energy storage capacitors and can realize the black start of wind farms, which provides a practicable scheme for the lightweight offshore wind power DC transmission system. However, resulted from utilizing pulse width modulation, the existing CSC has the disadvantages of high DC harmonics and high switching loss. To solve this problem, an offshore wind power transmission system based on the current source converter with fundamental frequency modulation employed (FFM-CSC) is investigated. Firstly, the topology and mathematical model are analyzed. Then, a steady-state control strategy coordinated by the offshore CSC and the onshore CSC is put forward, and the power operating range is researched. A black start control strategy for wind farms is also proposed. Finally, the offshore wind power transmission system is tested by simulation in PSCAD/EMTDC. The results demonstrate that the FFM-CSC-based offshore wind power transmission system can smoothly realize the black start, stably control the frequency and amplitude of AC voltage, adapt to output changes of wind turbines, and realize the fault ride-through. [ABSTRACT FROM AUTHOR]

Rising wind energy integration, accompanied by a decreasing level of system inertia, requires additional sources of ancillary services. Wind turbines based on doubly fed induction generators (DFIGs) can provide inertial and primary frequency support when equipped with specific controls. This paper investigates the effect of frequency support provision by DFIGs on the small-signal stability of power systems. To this end, a modified version of the Kundur two-area test system is employed to analyze different scenarios. Wind energy generation is either added to the existing system or displaces part of the synchronous generation. Simulations show that primary frequency support tends to improve the damping of electromechanical oscillations and deteriorate it for converter control-based ones. On the other hand, the inertial response depends on the control parameters. [ABSTRACT FROM AUTHOR]

In this paper, the outside cabin heat exchanger based on the porous media approach was established. The effects of altitude, viscous resistance coefficient, inertial resistance coefficient, and core thickness on the naturally captured air volume of the heat exchanger were investigated by numerical simulation. Results showed that the naturally captured air volume of the heat exchanger tends to be larger on both sides and smaller in the middle, and there is a quasi-linear increase proportional to the incoming wind velocity. With the increment of altitude, viscous resistance coefficient, and inertial resistance coefficient, the average naturally captured air volume of the heat exchangers shows a downward trend. The trend would be clear with the increment of the incoming wind velocity, nevertheless, the effect of core thickness is weak. In addition, the design values of the viscous resistance coefficient and the inertial resistance coefficient should be restricted in the order of 106 and below 500, respectively. Based on the weak effect of the naturally captured air volume of the heat exchanger, the thickness of the core can be appropriately increased to ensure the heat transfer area of the heat exchanger. [ABSTRACT FROM AUTHOR]

As the wind energy is progressively pursued to supplement conventional power generation, the stochastic wind behavior considerably deteriorates power quality (PQ) and stability of the connected grid. This article is attributed to the comprehensive control design of the grid-integrated doubly fed induction generator (DFIG) wind energy system that aims to address encountered wind variability and PQ intricacies. Battery energy storage with bidirectional dc–dc converter control is assimilated at the dc-link of the DFIG that regulates the dc-link voltage to mitigate the wind power fluctuations in specified grid power modes. A higher order adaptive control for grid-interfaced converter accurately computes desired weights of various components to exercise accurate power balance and PQ control. Moreover, a novel Jaya particle swarm optimization algorithm is adopted for the optimal tuning of wind maximum power point tracking and the dc-link voltage PI controller parameters that significantly improves steady-state and dynamic responses of both the controllers. The rotor speed converges faster with precise reference tracking that leads to almost zero steady-state error using optimized PI controller gains. Along with the attenuated initial overshoot and fast settling time during sudden large step wind speed change, the dc-link voltage also characterizes superior response with optimal tuning during load unbalance and abnormal grid voltage conditions. The presented control strategy analyzes the capability of DFIG system under variable wind-speed changes, specified grid power modes, load changes, and other grid disturbances. The satisfactory system response is endorsed through experimental results conducted on a laboratory prototype. [ABSTRACT FROM AUTHOR]

Probabilistic power flow (PPF) is pivotal to quantifying the state uncertainties of distribution power systems. However, it is very challenging due to underlying complex correlations among renewable outputs. To address this problem, a graph attention enabled convolutional network (GAECN) is proposed to approximate PPF in this article. Specifically, the graph convolutional layer of GAECN is used to aggregate the correlations among the nodal power injections during the training process. Within this layer, a full self-adaptive graph convolutional operation is proposed to automatically capture and learn the implicit correlation for achieving significantly enhanced accuracy of PPF. This layer is then followed by the convolutional neural network to capture the uncertain generation of renewable energy to achieve the robust computation of system state variable distributions. The simulation results demonstrate the accuracy and efficiency of the proposed method in IEEE 33, PG&E 69-node, 118-node, and practical 76-node distribution systems. [ABSTRACT FROM AUTHOR]

In this article, a sliding mode control (SMC) is applied to regulate the delivered power of a symmetrical six-phase induction generator. For this, the SMC strategy associated to the field-oriented control strategy is used in order to adjust the inner current loops of a squirrel cage six-phase induction generator (SC6PIG). In this way, the control algorithm switches between positive and negative feedback according to the position of the operating point in the phase plane. This technique guarantees the robustness of the SC6PIG in case of the loss of one to three stator phases or machine converter legs. The switching line slope and the initial operating point have been set so that the system starts with a negative feedback, then it continuously switches back and forth to positive feedback in order to maintain the desired trajectory of the system. The simulation and experimental results demonstrate the effectiveness and the robustness of this technique since the delivered power of the SC6PIG presents the same dynamics in healthy or faulty modes with one to two phase losses. [ABSTRACT FROM AUTHOR]

This paper presents a dynamic event data-based stability risk assessment method for power grids with high penetrations of inverter-based resources (IBRs). This method relies on obtaining the IBRs’ DQ admittance through dynamic event data and computing the system’s eigenvalues based on the admittance models. Two critical technologies are employed in this research, including time-domain and frequency-domain data fitting and $dq$ -frame voltage and current signal derivation. The first technology is key to obtaining the $s$ -domain expressions from the transient response data, and the $s$ -domain DQ admittance model from the frequency-domain measurements. The second technology is key to obtaining the $dq$ -frame voltage and current signals from either the three-phase instantaneous measurements or the phasor measurement unit (PMU) data. The method is illustrated using data generated from a Type-4 wind power plant modeled in PSCAD. This paper demonstrates the technical feasibility of the proposed approach. [ABSTRACT FROM AUTHOR]

In a wind farm, the interactions between turbines caused by wakes can significantly reduce the power output of the wind farm. Cooperative control among the turbines has the potential to improve the power output. However, existing centralized power optimization methods are computationally expensive and does not scale well for large wind farms, limiting their practical use in real-time control for time-varying wind conditions and turbine configuration (with adding or maintaining of turbines). To address this problem, this paper proposes a fully distributed power optimization method for wind farms using alternating direction method of multipliers (ADMM). The proposed method allows the wind farm power output to be optimized in fully distributed manner with turbine-to-turbine message passing over a mesh network, guarantees the implemented control actions satisfy the control constraints of all turbines, and provably converges to a stationary point of the wind farm power optimization problem. Simulation results demonstrate that the proposed method can significantly reduce the computation time with hardly sacrificing the power gain compared with centralized method and thus is computationally efficient for real-time power optimization of large wind farms. [ABSTRACT FROM AUTHOR]

The loss of small signal stability under parameter variation (e.g., fluctuation of loads and wind powers) can be ascribed to local bifurcations, i.e., saddle-node, Hopf, singularity-induced, and limit-induced bifurcations. Classic bifurcation calculation methods like the direct method and continuation method can only provide a bifurcation point or two-parameter bifurcation curve. Based on Galerkin method and the implicit function theorem, this paper proposes accurate polynomial approximations of bifurcation hypersurfaces in the multi-dimensional parameter space of interest. The proposed method can ensure high accuracy in this parameter space, and thus is preferable to the existing Taylor expansion-based local approximation method, given the intrinsic large-variation characteristic of wind powers. Acquired bifurcation hypersurfaces are immediately used to construct the small signal stability region of the continuous system, and then compute the stability margin for the operating point subjected to uncertainty of wind generation. Besides, the validity scope of the proposed method is analyzed, and the handling of limit-triggered equation switching as well as its computational difficulty is discussed. Computational results on the two-parameter 11-bus two-area and six-parameter IEEE 145-bus test systems validate the high accuracy and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

One of the short-coming challenges of power systems operation and planning is the difficulty to quantify the variability of power systems Kinetic Energy (KE) to unveil online additional information for the system operators’ decisions support. KE monitoring requires innovative methods to analyse the continuous fluctuations in the KE power’s systems. In this paper, we propose the use of information theory, specifically the concept of Information Length (IL), as a way to provide useful insights into the power system KE variability and to demonstrate its utility as a starting point in decision making for power systems management. The proposed IL metric is applied to monthly collected data from the Nordic Power System during three consecutive years in order to investigate the KE evolution. Our results reveal that the proposed method provides an effective description of the seasonal statistical variability enabling the identification of the particular month and day that have the least and the most KE variability. Additionally, by applying a Long Short-Term Memory (LSTM) neural network model to estimate the value of the IL on-line, we also show the possibility of using the metric as data-driven support. [ABSTRACT FROM AUTHOR]

This paper proposes a novel stochastic robust real-time power dispatch model considering wind uncertainty. In this dispatch model that incorporates automatic power generation control, system constraints are constructed with affinely adjustable robust optimization to enhance the system security, while the objective concerning the expected value of generation cost is optimized to improve the cost efficiency. The optimized objective is consistent with stochastic optimization, while the formulation for the objective is different. A Nataf conversion-based three-point estimate (NC-TPE) is adopted, which uses an approximating function to formulate the expected generation cost with deterministic estimate points. Compared to traditional stochastic sampling methods, the deterministic assessment method NC-TPE achieves an accurate formulation with more stable and efficient computation performance. The dispatch model is derived as a problem with nonconvex objective and bilinear constraints using the dual theory. To address the highly nonconvex problem, the objective and bilinear constraints are first converted to difference-of-convexity functions, and then a tailored difference-of-convexity optimization is employed to convexify the nonconvex functions, such that the original problem can be solved with an iterative quadratically constrained program. Numerical simulations demonstrate the economic superiority and robustness of the proposed model, as well as the effectiveness of the convexified solution. [ABSTRACT FROM AUTHOR]

The dynamics of wind power generation cannot be neglected in the modern power system and could have a great impact on the system dynamics, even raising the risk of a blackout. Because of this, power system simulation has to include the model of wind power generation. However, due to the high order of the full model of the wind power generator, it is impossible to model them in detail in the use of the power system dynamic simulation considering the thousands of wind generators in the grid. In this context, a simplified model is normally used with the trade-off in lower accuracy. As a direct-drive permanent magnet synchronous wind power generation system (D-PMSG) would take up a certain occupation in the modern power system, a proper D-PMSG simplified model is needed in the power system simulation. For a different research purpose in a different timescale, a different complexity of the model can be used to maximize the accuracy, in the meantime speeding up the simulation. This paper proposes a set of simplified models of the direct-drive permanent magnet synchronous wind power generation system (D-PMSG) and classifies them according to the timescale of the dynamics and the use cases, i.e., faults (transient stability analysis), system contingencies (voltage and frequency stability analysis) and wind speed variations (energy transformation). The accuracy of the proposed simplified models is verified by comparing them with the detailed D-PMSG electromagnetic transient mode in Matlab/Simulink, and their use case of the power system simulation is validated based on the case study of the IEEE 39-bus system considering the above scenarios. [ABSTRACT FROM AUTHOR]

Turbine-induced velocity deficit is the main reason to reduce wind farm power generation and increase the fatigue loadings. It is meaningful to investigate turbine-induced wake structures by a simple and accurate method. In this study, a series of single turbine wake models are proposed by combining different spanwise distributions and wake boundary expansion models. It is found that several combined wake models with high hit rates are more accurate and universal. Subsequently, the wake models for multiple wind turbines are also investigated by considering the combined wake models for single turbine and proper superposition approaches. Several excellent plans are provided where the velocity, turbulence intensity, and wind power generation for multiple wind turbines can be accurately evaluated. Finally, effects of thrust coefficient and ambient turbulence intensity are studied. In summary, the combined wake models for both single and multiple wind turbines are proposed and validated, enhancing the precision of wind farm layout optimization will be helped by using these wake models. [ABSTRACT FROM AUTHOR]

Reliability and safety must be carefully considered in today's power systems, which are rapidly evolving toward ever higher penetration of renewable, inverter-based generation units. Power systems are constantly stressed by active power disturbances, which can be exacerbated by wind and solar systems that are subject to rapid fluctuations in primary energy. In this framework, a comparative technical analysis of solutions to improve transient stability, both rotor angle stability and frequency stability, is carried out. These solutions can be adopted by the transmission system operator (e.g., an additional parallel transmission line), by the generation companies (e.g., a fast excitation system), or by both, such as SVC (static VAR compensator) and STATCOM (static synchronous compensator). Sensitivity analyses were carried out to assess the impact of the location of the wind turbines in the buses of the grid on their rated power and level of production. On the basis of these analyses, the worst-case fault was considered, and the critical fault recovery time was determined as an engineering parameter to compare the different solutions. For the numerical analysis, a modified IEEE 9-bus system was considered, and the PowerWorld software tool was used. Rotor angle and frequency stability analyses were performed. [ABSTRACT FROM AUTHOR]

This paper presents the research into the design and performance analysis of a novel five-phase doubly-fed induction generator (DFIG). The designed DFIG is developed based on standard induction motor components and equipped with a five-phase rotor winding supplied from the five-phase inverter. This approach allows the machine to be both efficient and reliable due to the ability of the five-phase rotor winding to operate during single or dual-phase failure. The paper presents the newly designed DFIG validation and verification based on the finite element analysis (FEA) and laboratory tests. [ABSTRACT FROM AUTHOR]

The integration of large-scale renewables into the multienergy system has brought a series of problems and challenges to the optimal dispatch of system, especially how to effectively realize the dispatch and accommodation of wind power. In this article, a dynamic economic dispatch (DED) model of wind/combined heat and power (CHP) hybrid energy system considering uncertainty of multiple wind farms and different dispatch time scales is proposed, which aims to minimize expected total energy generation cost based on the typical scenarios of upper bound of wind farm output. The spatial dependence of hourly output of multiwind farm is modeled by copula-based model and improved scenario reduction technique, and the different adjustment time scales of heating system and power system are modeled as well to achieve more reasonable and accurate system dispatching. In addition, an improved adaptive cuckoo search algorithm (ACSA) is proposed for the solution of the DED model of wind/CHP hybrid energy system with highly nonlinear constraints, and a shared parallel computing platform is employed to realize the acceleration of the ACSA. Finally, the corresponding numerical simulation and comparative analysis are performed on two test systems to verify the validity of the proposed model and method. [ABSTRACT FROM AUTHOR]

Nowadays, with the rapid development of hydrogen powered vehicles, the demand for hydrogen refueling stations (HRSs) in the transportation field is growing. The renewable energy, e.g., wind energy, is used to produce and store hydrogen on site for HRSs, which may be a relatively cheap and clean solution. Considering the wind farm (WF) and HRSs belong to different entities, a new cooperative operation model of the WF-HRSs combined system is proposed. Specifically, the robust optimization methods are implemented to characterize the uncertainties of wind power and market electricity price, respectively, to alleviate risk. To ensure the fairness of profit allocation, the bargaining power is measured by various contribution levels of each stakeholder based on the Nash–Harsanyi bargaining game theory. Furthermore, the model is transformed into two sequential subproblems: energy trading problem (SP1) and the payment bargaining problem (SP2). Accordingly, a solving technique adopting column and constraint generation algorithm is provided to solve the SP1. In addition, for the sake of privacy protection, a distributed approach based on data-centric mode is developed to solve SP2. Finally, the numerical results can validate the effectiveness and scalability for the proposed scheme and algorithms. [ABSTRACT FROM AUTHOR]

One of the major challenges in evacuating power from the offshore renewable energy source is reducing the number of equipment involved in the energy conversion process. In the strategy presented in this article, the output of a permanent magnet synchronous generator coupled with a wind turbine is converted to a low voltage DC by an AC–DC converter and then directly connected to a Medium Voltage Direct Current (MVDC) corridor using a new fault tolerant soft switched high gain DC–DC converter. During the fault in the MVDC corridor, the DC–DC converter is blocked forcing the fault current to zero, the faulty MVDC corridor is bypassed using the simple DC disconnectors, and then the DC–DC converter is connected to the healthy MVDC corridor. In this article, the efficacy of the proposed scheme is discussed in detail and substantiated through extensive simulation studies done in PSCAD/EMTDC. The theoretical and simulation analysis is also verified by experimental results. [ABSTRACT FROM AUTHOR]

This article presents an investigation and evaluation of an integrated flux-modulated machine for wind power generation. The integrated flux-modulated machine has two rotors which function as two contrarotating rotors connected to two sets of turbine blades. Hence, compared to conventional wind generators, more wind energy could be captured by this wind power generation system. Moreover, the integrated machine consists of two sets of stator windings. By regulating the currents in these windings, dual maximum power point tracking control strategy could be achieved. As a result, the wind power conversion efficiency is further improved. Moreover, this wind power generation system exhibits the advantage of high torque/power density due to the enhanced magnetic-gearing effect involved in the integrated flux-modulated machine. Hence, this machine is more suitable for direct-drive wind power generation, where the reliability is improved without the maintenance issues related to mechanical gearboxes. The topology and operating principle of the investigated machine are demonstrated in detail. Decoupled design of the two sets of windings is investigated, and a general rule to achieve decoupled windings by appropriate slot-pole combination selection is illustrated. The advantages of the investigated machine are confirmed in comparison to a benchmark machine. Finally, the investigated flux-modulated machine is prototyped. The validity of the operating principle and the simulation results are verified by experimental results. [ABSTRACT FROM AUTHOR]

With the large-scale wind power integration into the power system, the inertia and damping characteristics of the system have been weakened, which is not conducive to fulfill the grid-friendly operation of wind power. Aiming at this, an adaptive control scheme combining virtual inertia control (VIC) and supplementary damping controller (SDC) is proposed in this paper. Firstly, the mathematical model of wind grid-connected power system with VIC is established. Secondly, the polytopic linear parameter varying (LPV) system is built to describe the uncertainty of system operating points, and the gap metric-based nonlinear evaluation method is used for polytope vertex configuration. Next, Youla-Kucera parametrization are extended to polytopic LPV system to design an adaptive damping control, in which VIC and SDC are coordinated to provide damping and virtual inertia simultaneously. Finally, the proposed adaptive control scheme is compared with several existing control techniques to validate its effectiveness and advantages in suppressing system oscillation in the modified IEEE 39-bus system integrated with 2 wind turbines. [ABSTRACT FROM AUTHOR]

Globally,wind power plants (WPPs) installations are dramatically increasing, leading to a large-scale integration into the power grids. Huge endeavors are devoted to achieving an efficient operation of WPPs. This paper offers a novel chaotic-billiards optimizer (C-BO) algorithm to properly design the fuzzy logic control (FLC) strategy for stability enhancement of grid-integrated WPPs. The C-BO algorithm has distinct features such as simple construction, low numbers of parameters required to design, high convergence speed, and low computational burden. The permanent-magnet synchronous generator (PMSG)-based WPP is interlinked into the electric power grid through power converters. A cascaded FLC approach, which is optimally fine-tuned by the C-BO, is offered as the control methodology for these converters. The C-BO algorithm fine-tunes the gain factors of multiple FLCs at a rapid convergence speed. A simulation-based optimization approach is applied, in which the integrated square error criterion is chosen as an objective function. For the sake of preciseness, the PMSG-based WPP is connected to the IEEE 39-bus New England test system. The effectiveness of the optimal FLC using the C-BO algorithm is compared with that achieved using the water cycle algorithm-based optimal FLC, the genetic algorithm-based optimal FLC, and the marine predator algorithm-based optimal proportional-integral controller, taking into account severe grid disturbances. Moreover, real wind speed measured data captured from Zaafarana Station in Egypt are extensively performed in the system studies. The validity of the offered control approach is widely verified by the simulation analyses using the MATLAB/Simulink environment. [ABSTRACT FROM AUTHOR]

In this paper, an approximate aggregated impedance model (AAIM) of a grid-connected wind farm with multiple wind turbine generators (WTGs) for the small-signal stability study is derived. The derivation is conducted under the condition that the WTGs inside the wind farm are of the same type and from the same manufacturer. The AAIM derived can be seen as the impedance model (IM) of a grid-connected aggregated WTG. The IM of the aggregated WTG is the weighted average of the IMs of the individual WTGs in the wind farm and hence is simple to be built. The reactance of the line connecting the aggregated WTG to the external AC grid is the maximum eigenvalue of the network reactance matrix of the grid-connected wind farm. In addition, the derivation justifies the representation of a wind farm by an aggregated WTG in the IM-based small-signal stability analysis. Finally, two example grid-connected wind farms are used to evaluate and demonstrate the effectiveness of the AAIM proposed in the small-signal stability analysis. It is also shown that the computational burden of the stability analysis of a large-scale wind farm could be effectively reduced when the AAIM is used. [ABSTRACT FROM AUTHOR]

This paper presents a wind turbine (WT) control strategy to deliver frequency response (FR) that is similar to conventional synchronous generators, i.e., to deliver accurate and efficient FR. This is achieved by the coordination of WT’s rotor speed control (RSC) and pitch angle control (PAC), wherein the RSC is the summation of the proposed deloading power point tracking curves and FR signal; the PAC is kept as adjusting its control law according to the WT rotor speed. Through the above coordination, the strategy pre-reserves a portion of wind power and releasing it during underfrequency events. Compared with existing methods, the strategy has two significant advantages: 1) it does not need the switching of WT controls according to the real-time wind speed (WS), nor the identification of the boundaries among high, medium and low WSs according to the required reserved power, ultimately reducing the complexity of applying the strategy to practice; 2) its active power response during FR period is independent of the deviated rotor speed and is equal to the FR signal, ultimately enabling the WT to provide accurate and efficient FR. The simulations focusing on the grid frequency side demonstrated the advantages of the strategy over existing methods. [ABSTRACT FROM AUTHOR]

Direct-drive generators are an attractive candidate for wind power application since they do not need a gearbox, thus increasing operational reliability and reducing power losses. However, this is achieved at the cost of an increased generator size, larger inverter and decreased thermal performance. The associated cooling system is therefore crucial to keep the generator and inverter sizes down and to operate within the safe thermal limits. Various cooling techniques suitable for generators are therefore reviewed and analyzed in this paper. The performance and maintenance requirements are unavoidable compromises that need to be investigated together, especially for large generators. The location of the wind turbine is also important and dictates critical issues such as accessibility and maximum size. The key novelty in this paper is the assessment of the cooling methods based on generator size, reliability and maintenance requirements. [ABSTRACT FROM AUTHOR]

DC collection and transmission is one of the main developing directions of the large-scale offshore wind power system. This article proposes a multifunctional dc collector (MDC) to construct a low-cost, high reliability, and high flexibility all-dc offshore wind power system. By introducing the MDC to achieve energy collection and cascade boost, not only the system cost, size, and intermediate links of power conversion are reduced but also the coupling among wind turbines (WTs) is greatly weakened. Accordingly, the maximum power point tracking operation can be achieved independently for each WT unit. Furthermore, a constant voltage ratio control with first harmonic elimination iterative algorithm is proposed for dc collector to link the HVdc bus voltage and discretized dc voltage for triggering the energy consumption operation and suppressing the inductor current ripple, and the detailed control parameter design is provided. Meanwhile, the various operating modes are considered for MDC, including practical engineering configuration, fault isolation, dynamic cut-in/out, and energy consumption operation, which provides a practical solution for the construction of a long-distance and large-capacity offshore system. Finally, the comprehensive experimental results are given to illustrate the effectiveness of MDC. [ABSTRACT FROM AUTHOR]

A power decoupling control for wind power converter based on series-connected modular multilevel converter (SCMMC) and open-winding permanent magnetic synchronous generator (OW-PMSG) is proposed in this article. The power converter based on SCMMC and OW-PMSG achieves medium voltage direct conversion and transmission and has higher dc-bus voltage compared with conventional MMC, which makes it a promising solution for copper consumption reduction of cable, low power transmission loss, and the elimination of the step-up converter. The proposed power decoupling control realizes independent power control of each phase in the wind power converter by decoupling the negative-sequence and zero-sequence current and guarantees the phase dc-link voltages balance of the SCMMC and the robustness to restrain the imbalance power disturbance. Instantaneous three phase dc-link voltages balance of the SCMMC is achieved especially when the imbalance of phase dc-link voltages occurs. The feasibility of the wind power converter based on SCMMC and OW-PMSG and the proposed power decoupling control is verified through simulations and a scaled-down laboratory prototype. [ABSTRACT FROM AUTHOR]