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This study aims to comprehensively analyze five weather forecasting models obtained from the Open-Meteo historical data repository, with a specific emphasis on evaluating their impact in predicting wind power generation. Given the increasing focus on renewable energy, namely, wind power, accurate weather forecasting plays a crucial role in optimizing energy generation and ensuring the stability of the power system. The analysis conducted in this study incorporates a range of models, namely, ICOsahedral Nonhydrostatic (ICON), the Global Environmental Multiscale Model (GEM Global), Meteo France, the Global Forecast System (GSF Global), and the Best Match technique. The Best Match approach is a distinctive solution available from the weather forecast provider that combines the data from all available models to generate the most precise forecast for a particular area. The performance of these models was evaluated using various important metrics, including the mean squared error, the root mean squared error, the mean absolute error, the mean absolute percentage error, the coefficient of determination, and the normalized mean absolute error. The weather forecast model output was used as an essential input for the power generation prediction models during the evaluation process. This method was confirmed by comparing the predictions of these models with actual data on wind power generation. The ICON model, for example, outscored others with a root mean squared error of 1.7565, which is a tiny but essential improvement over Best Match, which had a root mean squared error of 1.7604. GEM Global and Gsf Global showed more dramatic changes, with root mean squared errors (RMSEs) of 2.0086 and 2.0242, respectively, indicating a loss in prediction accuracy of around 24% to 31% compared to ICON. Our findings reveal significant disparities in the precision of the various models used, and certain models exhibited significantly higher predictive precision. [ABSTRACT FROM AUTHOR]

Compared with the standard finite-set model-based predictive current control (FS-MPCC), the finite-set ultra-local model-based predictive current control (FS-ULMPCC) removes the use of actual system parameters and thus has some advantages like good robustness and easy implementation. However, the steady-state performance of FS-ULMPCC is relatively weak. In this paper, a novel FS-ULMPCC method is proposed and applied to the AC/DC converter of a direct-driven wind power generation system. The proposed method is designed based on a linear-extended state observer (LESO). In particular, a new control set reconstruction strategy is proposed to improve the steady-state performance. Only three options are included in the reconstructed control set, and each one is associated with two independent, active voltage vectors and their durations. The proposed FS-ULMPCC method is compared with the traditional one through experiments. The proposed method includes enhanced steady-state performance and reduced computational burden. [ABSTRACT FROM AUTHOR]

Recently, there has been a push by countries to diversify their energy mix considering various factors. In this regard, there have been several studies conducted to assess the potential for using sources such as wind and solar to generate supplemental energy to the already present energy generation setup. In this regard, this study explores the potential of wind for the Commonwealth of Kentucky. To perform this study, wind data were sourced for eight locations across Kentucky from the publicly accessible wind speed information present at Weather Underground for the years 2020–2021 (two years). An analysis was performed concerning the seasonal, monthly, and hourly variation in the wind speed so as to identify the expected times of sufficient wind energy generation. Moreover, a comparison of the collected data was performed with data from a home-based weather station as well as a deployed wind turbine to validate the variation pattern of the publicly sourced data. Finally, in order to investigate the variation patterns of wind and solar energy sources, a comparative analysis was also performed using data from a solar power generation plant in Kentucky. A seasonal and monthly complementarity was observed between the wind and solar energy. However, when considering daily patterns, the wind was found to follow solar generation with an offset. While further research is required, this analysis indicates that it is possible to deploy wind energy power generation projects in the Commonwealth of Kentucky. The seasonal complementary behavior of wind and solar energy can be used along with battery storage in conjunction with natural gas to provide a diversified electricity generation portfolio. [ABSTRACT FROM AUTHOR]

A new type of generator, a transgenerator, is introduced, which integrates the wind turbine and flywheel into one system, aiming to make flywheel-distributed energy storage (FDES) more modular and scalable than the conventional FDES. The transgenerator is a three-member dual-mechanical-port (DMP) machine with two rotating members (inner and outer rotors) and one stationary member (stator). The transgenerator–flywheel system is introduced with its configuration, transgenerator overview, flywheel operation principle and power management strategies, and control system. Simulations are performed in MATLAB 2023b/Simulink to verify the system viability, including control system verification and flywheel storage performance evaluation. The results show that the inner and outer rotors can be controlled independently with an accurate and fast control response, and the grid-side control works properly. The flywheel performs well, with considerable charging power and storage capacity. [ABSTRACT FROM AUTHOR]

Wind power generation has increased in China to achieve the target of decreasing CO2 emissions by 2050, but there are high levels of wind curtailment due to the mismatch between electricity supply and demand. This paper proposes a single-stage air source heat pump coupled with thermal storage for building heating purposes. The main objective is to find the proper system designs and operating strategy, which can help to avoid peak demand periods while obtaining minimized running costs and reduced wind energy curtailment. Dynamic simulations were performed using TRNSYS to investigate its use in a typical office building based on an actual electricity tariff, wind power, and meteorological data. The proper system designs, including the tank size and thermal storage temperature, were determined to maximize the system's performance. It was found that a proper combination of the two parameters exists for a specific application. Further, results showed that the use of auxiliary electric heating is necessary for single-stage air source heat pumps to participate in a wind curtailment reduction. The operating strategy of the system was also studied. Results indicate that by implementing a proper operating strategy, non-renewable power consumption can be reduced by 11% for the studied building, with a total wind power utilization of 3348 kWh during the heating season while still satisfying the heating demands of users. These findings can contribute to the green and low-carbon development of the building industry and further enhance the grid's accommodation capacity for renewable energy sources. [ABSTRACT FROM AUTHOR]

A major challenge in distribution systems is the issue of voltage drop along the distribution line resulting from an increased load capacity connected to the utility. A significant voltage drop can affect the performance of a distribution system and cause quality issues for end users, impacting the system's long-term sustainability and reliability. Therefore, regulations have been set stating that the voltage level should not be more that 5% higher or lower than the rated voltage. Thus, in this study, we aimed to evaluate the voltage level characteristics of a 22 kV distribution system that replicates the actual distribution system in the Provincial Electricity Authority. A voltage improvement technique based on distributed generation placement was proposed. In addition, the distribution system characteristics with and without distributed generation placement were evaluated under fault conditions. The results indicate that distributed generation placement in the distribution system can improve the voltage level along the distribution line. However, the level of increase in voltage depends on the size of the load, the capacity of the distributed generation, and the location of the distributed generation system on the distribution line. Furthermore, placing a distributed generation system with a minimum capacity at the proposed location can improve the voltage within the utility's standard level. Thus, the installation of a distributed generation system in the distribution system is beneficial in terms of voltage improvement in the distribution system and provides the power system with a sustainable method to address the issue of voltage drop. [ABSTRACT FROM AUTHOR]

The performance of wind turbines directly determines the profitability of wind farms. However, the complex environmental conditions and influences of various uncertain factors make it difficult to accurately assess and monitor the actual power generation performance of wind turbines. A data-driven approach is proposed to intelligently monitor the power generation performance evolution of wind turbines based on operational data. Considering the inherent nonlinearity and structural complexity of wind turbine systems, a data-derived characteristic construction and dimensionality reduction method based on KPCA is adopted as a prerequisite. Additionally, an AdaBoost-enhanced regressor is applied to wind power prediction with adequate inputs, and day-oriented deviation indicators are further constructed for quantifying performance fluctuations. The final validation phase includes two application cases: In the first case, the results show that the proposed method is sensitive enough to capture the early characteristics of blade damage faults. In the second case, an uncertainty error within ±0.5% demonstrates that the proposed method has high-level accuracy in the quantitative assessment of the power performance and good practical effectiveness in real engineering applications. [ABSTRACT FROM AUTHOR]

Wind power generation has been introduced to reduce carbon emissions; however, recycling or recovering the waste of wind blades, which contain fibre-reinforced plastic, is difficult. Converting the recovered materials for secondary use is also difficult owing to the decreased strength and low material value. Many countries, including Japan, have not considered the future energy and CO2 emission scenarios, particularly CO2 emissions from wind blade waste. Based on these scenarios, Japan has planned to introduce large amounts of onshore/offshore wind power generation through 2050. Therefore, we aimed to evaluate quantitatively the total amount of waste and the global warming potential (GWP) from multiple blade waste treatment processes. Based on the average lifetime of blades (20–25 years), we found that the GWP of wind blade waste treatment in Japan may reach a maximum of 197.3–232.4 MtCO2eq by 2060–2065. Based on this lifetime, the wind blade treatment in 2050 accounted for 63.9–80.1% of the total greenhouse gas emissions in 2050. We also showed that the rise in CO2 emissions from the wind blade wastes would make up 82.5–93.6% of the potential reduction in the GWP, which is achievable by shifting from thermal to wind power generation. [ABSTRACT FROM AUTHOR]

The large size of wind turbines and wind farm clustering aggravate the effect of wake on output power, resulting in a reduction in the economic benefits of wind farms. This paper took the actual operating turbines of an onshore wind farm in China as the research object and analyzed the influence of wake on energy efficiency loss by combining SCADA data. The research established a complete loss assessment method and proposed the corresponding evaluation criteria. The results showed that typical wind turbines seriously affected by wake accounted for 32.8% of the wind farm. The actual output power was only 84.2% of the theoretical output power at the lowest month, and the wake loss of the wind farm is serious. The economic efficiency of the wind farm is lower in the summer months (June–August). The study can provide a theoretical basis for the arrangement of wind farms and the development of an operation control strategy. [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]

In this study, the focus is on the magnetic path formation and its effects on the performance of a counter-rotating dual-rotor permanent-magnet flux-switching machine (CR-DRPMFSM) for direct-drive counter-rotating wind power generation, based on different stator slot and rotor pole combinations. To fully exploit rotor-shaft bore and improve fault-tolerant design, as well as increase torque density, dual-rotor topologies with the capability for dual electrical and dual mechanical ports are investigated. Moreover, the direct-drive counter-rotating wind power generation technique offers a brushless topology, thus reducing maintenance cost and improving energy conversion efficiency compared to single-blade wind turbine systems. Using finite element analysis (FEA), the inherent magnetic coupling of the series and parallel paths shows varied impacts on the electromagnetic performance of four different CR-DRPMFSMs based on the slot/pole combinations (MI to MIV) considered in this study. The key electromagnetic performance indices, such as torque, cogging torque, torque ripple, power factor, and efficiency, show proportionate variation to the coupling level. A comparative analysis shows that MI exhibits higher average torque, lower torque ripples, and high efficiency, reaching 90% with a power factor of 0.6. As an optimal design, an MI test prototype is developed. The experimental test prototype validates the FEA results under no-load and on-load conditions. [ABSTRACT FROM AUTHOR]

The blades of some airborne equipment are prone to icing under supercooled cloud conditions. In this paper, we propose an anti-deicing spray method to prevent blades from icing at low temperatures. Using computational fluid dynamics modeling and orthogonal experimental methods, we investigated the effects of the blade angle of attack, inlet wind speed, and nozzle mass flow rate on the thickness and coverage of the liquid layer of spray material and examined the use of deflectors in this study. We found the magnitude and change rule of the influence of the previously mentioned parameters on the liquid film thickness and coverage of sprayed material to be the nozzle mass flow rate is greater than the blade angle of attack and greater than the inlet wind speed. Under the optimal combination of conditions of α = 30°, u0 = 6 m/s, and Q = 0.003 kg/s, the liquid film thickness was maximized, and the liquid film thickness was 0.037 mm; under the optimal combination of conditions of α = 60°, u0 = 6 m/s, and Q = 0.003 kg/s, the liquid film coverage was maximized, and the liquid film coverage was 99.81%. The anti-deicer spraying method proposed herein for use on blades is effective when considered from a number of perspectives. It provides an innovative and feasible solution to the wind turbine blade freezing problem. However, the method must be explored and modified to maximize its chances of general application, and other factors must also be considered to fully optimize the sprinkler de-icing technique to improve the performance and reliability of blades. [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]

The prediction of wind power output is part of the basic work of power grid dispatching and energy distribution. At present, the output power prediction is mainly obtained by fitting and regressing the historical data. The medium- and long-term power prediction results exhibit large deviations due to the uncertainty of wind power generation. In order to meet the demand for accessing large-scale wind power into the electricity grid and to further improve the accuracy of short-term wind power prediction, it is necessary to develop models for accurate and precise short-term wind power prediction based on advanced algorithms for studying the output power of a wind power generation system. This paper summarizes the contribution of the current advanced wind power forecasting technology and delineates the key advantages and disadvantages of various wind power forecasting models. These models have different forecasting capabilities, update the weights of each model in real time, improve the comprehensive forecasting capability of the model, and have good application prospects in wind power generation forecasting. Furthermore, the case studies and examples in the literature for accurately predicting ultra-short-term and short-term wind power generation with uncertainty and randomness are reviewed and analyzed. Finally, we present prospects for future studies that can serve as useful directions for other researchers planning to conduct similar experiments and investigations. [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]

Flux-switching permanent magnet (FSPM) machines have attracted significant research attention in the field of wind power generation. In this study, the utilization of a magnetic flux barrier to improve the performance of the nine-phase FSPM generator designed for low-speed wind power applications is conducted. The proposed approach involves introducing magnetic flux barriers of different topologies to the conventional FSPM generator and analyzing their performance using 2D finite element simulations. Results suggested that |-shaped magnetic flux barriers exhibited the highest performance among other topologies, making them the appropriate choice for this generator. The geometry of the |-shaped flux barriers was further optimized using response surface methodology to maximize the generator's performance. The proposed generator exhibits a significant decrease in cogging torque, achieving a remarkable reduction of up to 23.7%, while maintaining electromotive force. Moreover, it shows a significant decrease in permanent magnet eddy-current loss, with a noteworthy reduction of up to 51%. Additionally, significant improvements were demonstrated in terms of electromagnetic torque, torque ripple, output power, and efficiency. Details on the physical reasoning behind these improvements have been provided. Overall, the proposed FSPM generator with inserted flux barriers has the potential to meet the demands of low-speed wind power generation effectively. [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]

Energy consumption and environmental issues have become major drivers of increasing renewable energy penetration levels. The electricity generated from renewable energy sources is decentralized throughout distributed generation (DG), which is located at the distribution level. However, the presence of DG can change distribution system characteristics and affect protection systems. Thus, this study aims to investigate the impact of DG in term of its sizing and placement on distribution systems under both normal and fault conditions. In addition, the effects on voltage improvement under normal conditions and current under fault conditions are also considered. The case study system in this study was modelled after an actual section of a 22 kV distribution line from the Provincial Electricity Authority of Thailand using PSCAD software. For DG, wind turbine generation was selected as a renewable energy source. The simulation results demonstrated that the presence of DG has a significant impact on both voltage and current characteristics under both normal and fault conditions. These impacts on the distribution system caused by DG can affect the operation of conventional distribution systems, which require further analysis and preventive measures in order to ensure good system reliability. [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]

The intermittence and randomness of wind speed leads to the fluctuation of wind turbine output power. In order to study the applicability of battery, super capacitor and flywheel energy storage technology in suppressing wind power fluctuation, this paper takes a 3 MW direct drive wind turbine as an example, and, through the establishment of a wind storage system model, the dynamic response characteristics and application effects of the three typical energy storage technologies to suppress the power fluctuation of the wind turbine under two wind speed fluctuation scenarios are simulated and studied, and the stability of output power is quantitatively analyzed. Results show that all the three energy storage systems respond well to power command curves, but when the wind power fluctuation is large, the flywheel energy storage has a better effect on suppressing the wind power fluctuation, which can suppress about 70% of the power fluctuation. [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]

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]

Wind power generation has been developed rapidly due to rising global interest in renewable clean energy sources. Accurate prediction of the potential amount of such energy is of great significance to energy development. As wind changes greatly by season, time series analysis is considered as a natural approach to characterize the seasonal fluctuation and exponential growth. In this paper, a dual integrated hybrid model is presented by using random forest (RF) to incorporate the extreme gradient boosting (XGB) with empirical mode decomposition (EMD) and a fractional order accumulation seasonal grey model (FSGM). For seasonal fluctuation in vertical dimension processing, the time series is decomposed into high and low frequency components. Then, high and low frequency components are predicted by XGB and extreme learning machine (ELM), respectively. For the exponential growth in horizontal dimension processing, the FSGM is applied in the same month in different years. Consequently, the proposed model can not only be used to capture the exponential growth trend but also investigate the complex high-frequency variation. To validate the model, it is applied to analyze the characteristics of wind power time series for China from 2010 to 2020, and the analysis results from the model are compared with popularly known models; the results illustrate that the proposed model is superior to other models in examining the characteristics of the wind power time series. [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]

In the context of global ecological degradation and the gradual depletion of fossil energy sources, the importance of new energy power in production and development is rising year by year. In order to reduce the impact of fluctuations in wind power systems on the grid due to various reasons during grid connection, this paper proposes an improved Linear Active Disturbance Rejection Control (LADRC) combined with a neural network based grid-side inverter control method, which, based on the analysis of the characteristics of permanent magnet motors and the current situation of their use, enables the converter to show better control characteristics in terms of DC-side voltage fluctuation amplitude and recovery time during operation. Simulation results show that the controller is more robust and adaptable to the uncertainty and external disturbance changes of the converter under different operating conditions than the traditional control method. The system has good dynamic response performance. [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]

Based on the energy storage type of hydraulic wind turbines (HWTs) and in view of the unit frequency drop problem under high wind power proportion conditions, this paper proposes a method of primary frequency control under maximum power point tracking (MPPT). HWT power output is affected by wind speed randomness and volatility. In addition, traditional wind turbines do not have inertial adjustment ability, leading to a decrease in the frequency stability of the power system caused by the increase in wind power permeability. In the paper, a hydraulic energy storage system and synchronous generator are combined to carry out primary frequency modulation, and a mathematical model of the hydraulic energy storage system, the hydraulic main transmission system, and the generator active power regulation system after grid connection is established. By analyzing the load changing rules of power systems and frequency fluctuation caused by the power system load after the wind turbine is grid-connected, the variable parameter frequency modulation compensation control strategy of combined turbine-energy storage systems is established, and simulation verification under different load fluctuations is carried out, verifying the effectiveness of the frequency modulation control strategy, which achieves a good control effect for improving the frequency modulation ability of hydraulic wind turbines. [ABSTRACT FROM AUTHOR]

Wind and solar energy systems are among the most promising renewable energy technologies for electric power generations. Hybrid renewable energy systems (HRES) enable the incorporation of more than one renewable technology, allowing increased reliability and efficiency. Nevertheless, the introduction of variable generation sources in concurrence with the existing system load demand necessitates maintaining the power balance between the components of the HRES. Additionally, the efficiency of the hybrid power supply system is drastically affected by the number of converters interfacing its components. Therefore, to improve the performance of the HRES, this paper proposes a robust sliding mode control strategy for both standalone and grid-connected operation. The control strategy achieves maximum power point tracking for both the renewable energy sources and stabilizes the DC-bus and load voltages irrespective of the disturbances, change in load demand, variations of irradiance level, temperature, and wind speed ensuring an efficient energy management. Furthermore, the solar PV system is directly linked to the DC-bus obviating the need for redundant interfacing boost converters with decreased costs and reduced system losses. Lyapunov candidate function is used to prove the asymptotic stability and the convergence of the entire system. The robustness of the proposed control strategy is tested and validated under various conditions of HRES, demonstrating its efficacy and performance under various conditions of the HRES. [ABSTRACT FROM AUTHOR]

The energy industry, primarily based on the use of fossil fuels (e.g., coal and oil) is rapidly shifting toward renewable energy for securing sustainable resources. Thus, preparing for large wind power ramp events is essential to retain reliable and secure power systems. This study proposed a new statistical approach to predict wind power ramp events, and evaluated the performance of prediction. The empirical data, which is the observed wind power output data and wind speed data from Taebaek (South Korea) were used for analyzing ramp events and for evaluation. Based on the data analysis, a practical metric for evaluating the performance of wind power ramp events forecasting was developed and presented in detail. Notably, the accuracy of forecasting was evaluated through various metrics, whereas the normalized mean absolute error (NMAE) analysis demonstrated ≤ 10% values for all the analyzed months. In addition, a system review was conducted to check if the methodology suggested in this study has helped enhance the security of power systems. The results show that evaluating and considering the ramp events can improve the accuracy of wind power output forecasting which can secure the smart energy systems. [ABSTRACT FROM AUTHOR]

A microgrid can effectively improve the system reliability of a distribution network. When a fault occurs, the microgrid only has a determined division scheme under a fixed boundary method, and it is difficult to adapt to the random load and distributed power. In this paper, a novel renewable energy planning method considering the flexible region of the microgrid is proposed. Based on the randomness of the load and the output of distributed generations (DG) in the microgrid, the dynamic division method of the microgrid is proposed and the optimal allocation model of the distributed energy in the microgrid is established. Further, the model and method proposed are verified by the IEEE-33 bus test system. The simulation results show that the allocation of renewable energy in the microgrid considering the flexible region of the microgrid can effectively increase the utilization of renewable energy and improve the reliability of microgrid operation. [ABSTRACT FROM AUTHOR]

Wind power generation provides an attractive method for tackling global environmental issues. However, the power grid cannot accommodate large amount of wind farms (WFs) because the fluctuation of WF output degrades the power quality (frequency and voltage) in the power grid. Technical requirements that are related to WF power fluctuation are issued in many countries in order to introduce the WF without degrading power quality. Therefore, it is essential to smooth the WF output in order to satisfy the technical requirements. This paper proposes an operation methodology for a system that is composed of energy storage systems (ESSs) and WF by kinetic energy (KE) control. Moreover, an optimal KE control is presented. The economical aspect and the advantage of the proposed system are verified through scenario simulations. [ABSTRACT FROM AUTHOR]

Bringing electricity to areas of difficult terrain is a complicated task, so it is convenient to generate power using local natural resources, such as wind, through a small horizontal-axis wind turbine (S-HAWT). However, at the rotor height of these wind turbines, the wind is often turbulent due to obstacles such as trees and buildings. For a turbine to function properly in these conditions, the action of the wind force on the rotor must be smoothed out by controlling the pitch angle. A commercial derivative-integral-proportional (PID)-type pitch controller works well when system dynamics are stable, but not when there are disturbances in the system. This paper proposes a hierarchical fuzzy logic controller (HFLC) to solve the nonlinear system effects produced by atypical winds. The methodology includes a statistical analysis of wind variability at the installation site, which determines the functions of belonging and its hierarchy. In addition, installing an anemometer in front of the turbine allows an advanced positioning of the blades in the presence of wind gusts. The algorithm was implemented in an S-HAWT, and a comparison was made to quantify the performance difference between the proposed control strategy and a conventional PID controller. [ABSTRACT FROM AUTHOR]

This work develops an underfrequency preventive control strategy for an islanded power system with a high penetration of wind power generation. First, the preventive control strategy uses the frequency nadir forecasting module to analyze the frequency stability under largest diesel generator tripping (N-1) contingency events. If predicted frequency nadir is too low, four frequency support methods are then analyzed and used for preventing potential frequency stability problem. They include generator rescheduling (GR), the use of battery energy storage system (BESS), direct load control (DLC) and emergency demand response program (EDRP). In terms of the GR method, the optimal diesel generator dispatch is obtained, with sufficient frequency stability and minimal fuel cost and start-up cost. In the BESS method, the optimal instantaneous power output from BESS is obtained based on its frequency support capability. With the DLC or EDRP method, the optimal contract-based load-shedding or the load-reduction to provide frequency support is obtained, respectively. Then, the operating costs of each method to support frequency are analyzed. The research methods and simulation results are very useful to the low-frequency protection of actual power systems with high renewable power generation. This work proposed a complete defense strategy in a microgrid system. It combines GR, BESS, DLC and EDRP. Therefore, the system operators have many options to implement their defense strategies, based on the operating costs of various methods. In other words, the proposed defense strategy provides a more flexible solution for the protection of micro grids with a high renewable power penetration. Therefore, the solution considers the system safety and economical aspects. [ABSTRACT FROM AUTHOR]

Featured Application: The article highlights a trend in the use of Maximum Power Point Tracking controllers with Expert Systems for wind turbines, due to the complexity of the mathematical models and the non-linearity of the system. Wind power is a renewable energy source that has been developed in recent years. Large turbines are increasingly seen. The advantage of generating electrical power in this way is that it can be connected to the grid, making it an economical and easily available source of energy. The fundamental problem of a wind turbine is the randomness in a wide range of wind speeds that determine the electrical energy generated, as well as abrupt changes in wind speed that make the system unstable and unsafe. A conventional control system based on a mathematical model is effective with moderate disturbances, but slow with very large oscillations such as those produced by turbulence. To solve this problem, expert control systems (ECS) are proposed, which are based on human experience and an adequate management of stored information of each of its variables, providing a way to determine solutions. This revision of recent years, mentions the expert systems developed to obtain the point of maximum power generation in a wind turbine with permanent magnet synchronous generator (PMSG) and, therefore, offers a control solution that adapts to the specifications of any wind turbine. [ABSTRACT FROM AUTHOR]

As more clean energy sources contribute to the electrical grid, the stress on generation scheduling for peak-shaving increases. This is a concern in several provinces of China that have many nuclear power plants, such as Guangdong and Fujian. Studies on the unit commitment (UC) problem involving the characteristics of both wind and nuclear generation are urgently needed. This paper first describes a model of nuclear power and wind power for the UC problem, and then establishes an objective function for the total cost of nuclear and thermal power units, including the cost of fuel, start-stop and peak-shaving. The operating constraints of multiple generation unit types, the security constraints of the transmission line, and the influence of non-gauss wind power uncertainty on the spinning reserve capacity of the system are considered. Meanwhile, a model of an energy storage system (ESS) is introduced to smooth the wind power uncertainty. Due to the prediction error of wind power, the spinning reserve capacity of the system will be affected by the uncertainty. Over-provisioning of spinning reserve capacity is avoided by introducing chance constraints. This is followed by the design of a UC model applied to different power sources, such as nuclear power, thermal power, uncertain wind power, and ESS. Finally, the feasibility of the UC model in the scheduling of a multi-type generation unit is verified by the modified IEEE RTS 24-bus system accommodating large scale green generation units. [ABSTRACT FROM AUTHOR]

Many countries have the ambition to increase the share of renewable sources in electricity generation. However, continuously varying renewable sources, such as wind power or solar energy, require that the power system can manage the variability and uncertainty of the power generation. One solution to increase flexibility of the system is to use various forms of energy storage, which can provide flexibility to the system at different time ranges and smooth the effect of variability of the renewable generation. In this paper, we investigate three questions connected to investment planning of energy storage systems. First, how the existing flexibility in the system will affect the need for energy storage investments. Second, how presence of energy storage will affect renewable generation expansion and affect electricity prices. Third, who should be responsible for energy storage investments planning. This paper proposes to assess these questions through two different mathematical models. The first model is designed for centralized investment planning and the second model deals with a decentralized investment approach where a single independent profit maximizing utility is responsible for energy storage investments. The models have been applied in various case studies with different generation mixes and flexibility levels. The results show that energy storage system is beneficial for power system operation. However, additional regulation should be considered to achieve optimal investment and allocation of energy storage. [ABSTRACT FROM AUTHOR]

Wind is a clean, abundant, and inexhaustible source of energy. However, wind power is not constant, as windmill output is proportional to the cube of wind speed. As a result, the generated power of wind turbine generators (WTGs) fluctuates significantly. Power fluctuation leads to frequency deviation and voltage flicker inside the system. This paper presents a new methodology for controlling system frequency and power. Two decentralized fuzzy logic-based control schemes with a high-penetration non-storage wind-diesel system are studied. First, one is implemented in the governor of conventional generators to damp frequency oscillation, while the other is applied to control the pitch angle system of wind turbines to smooth wind output power fluctuations and enhance the power system performance. A genetic algorithm (GA) is employed to tune and optimize the membership function parameters of the fuzzy logic controllers to obtain optimal performance. The effectiveness of the suggested controllers is validated by time domain simulation for the standard IEEE nine-bus three-generator test system, including three wind farms. The robustness of the power system is checked under normal and faulty operating conditions. [ABSTRACT FROM AUTHOR]

This paper proposes the inrush current suppressor using an AC chopper in a large-capacity wind power generation system (WPGS) with two squirrel-cage induction machines (SCIMs), which are switched over depending on the wind speed. The input side of the AC chopper is connected to the source in parallel. The output side of the AC chopper is connected in series with the SCIM through matching transformers. In the proposed inrush current suppressor, the output voltage of the AC chopper is the same as the receiving-end voltage before connecting the SCIM. By gradually decreasing the output voltage of the AC chopper, the applied voltage of the SCIM is gradually increased without the inrush current. The basic principle of the proposed inrush current suppressor is discussed in detail. A computer simulation is implemented to confirm the validity and practicability of the proposed inrush current suppressor using a power system computer-aided design/electromagnetic transients including DC (PSCAD/EMTDC). Simulation results demonstrate that the proposed inrush current suppressor can suppress the inrush current. [ABSTRACT FROM AUTHOR]

In a small-to-medium-scale wind energy conversion system (WECS), diode rectifiers rather than pulse-width modulated (PWM) rectifiers are widely adopted due to the features of high reliability and low cost. However, large current harmonics are induced in the generator phase current by commutations of diode rectifier, eventually causing large torque ripple on the drive-train of the WECS and making it more likely to be fatigue breakdown. In this paper, a 12-pulse autotransformer rectifier unit (ATRU) with reduced volume and weight is proposed for WECS application. By analyzing the characteristics of the input current and output voltage of the ATRU within the entire operation range of WECS, the method to properly design its parameters is proposed. A 1.2 kW direct-drive WECS demonstration platform using ATRU is built in the lab. A modified sensorless overall power control strategy is adopted to control the WECS. Experimental tests are carried out and the results not only validate the feasibility of implementing ATRU in WECS applications, but also prove its high torque ripple reduction ability. [ABSTRACT FROM AUTHOR]

Due to the rapid economic development in China, the conflict between the increasing traditional energy consumption and the severe environmental threats is more and more serious. To ease the situation, greater use of wind energy in China could be the solution for energy conservation and sustainable environment in the long run. This paper describes the presentation of wind power in China, which covers distribution, bases, installed capacity, power generation from the spatial perspective and the environmental benefit. In addition, grey model (GM(1,1)) and scenario analysis are employed to forecast the installed capacity in China from 2017 to 2025, then the evaluation of two methods is presented. By this research, the results are shown as the following: (1) the North region has great wind energy with 2500-3000 giga watt (GW) and the offshore wind energy in the Southeast is abundant; (2) the Inner Mongolia base located in North China makes a great contribution to wind power as well as having great potential for wind power development with the potential of 1300 GW; (3) the growth rate of installed capacity and wind power generation in China is declining with 100% in 2006 to 30% in 2015, 107% in 2009 to 17% in 2015, respectively; (4) the "three North" region has made a great contribution to current installed capacity and wind power generation with 74% and 71%, respectively; (5) wind power has significant environmental benefits with coal reduction of 23,887 x 104 tce, CO2 reduction of 66,854 x 104 tons and SO2 reduction of 173 x 104 tons in total from 2008 to 2015; (6) the installed capacity in China from 2017 to 2025 is predicted utilizing a GM(1,1) model with 38,311.1810 x 10³ GW in 2025, while, with a scenario analysis, the installed capacity will reach up to 40,000 x 10³ GW in 2025 under the high GDP growth rate and 29,000 x 10³ GW in 2025 under the low GDP growth rate, respectively. Finally, it can be concluded that China has a solid foundation for the wind power development due to its abundant wind resources and great potential for wind power. Furthermore, the sustainable development can be guaranteed, and reduction in energy usage as well as emissions can be achieved by promoting wind power widely and effectively. [ABSTRACT FROM AUTHOR]

This paper presents a coordinated control strategy for a hybrid wind farm with doubly-fed induction generator (DFIG)- and direct-driven permanent-magnet synchronous generator (PMSG)-based wind turbines under symmetrical grid faults. The proposed low-voltage ride-through (LVRT) strategy is based on a novel current allocation principle and is implemented for individual DFIG- or PMSG-based wind turbines. No communication equipment between different wind power generators is required. By monitoring the local voltages and active power outputs of the corresponding wind generators, the proposed control strategy can control the hybrid wind farm to provide the maximum reactive power to support the grid voltage during a symmetrical grid fault. As a result, the reduction in the active power output from the hybrid wind farm can be decreased, which also helps avoid generator over-speed issues and supply active power support for the power grid. In addition, the reactive current upper limits of DFIG- and PMSG-based sub-wind farms are investigated by considering different active power outputs and different grid voltage dip depths, and the feasible regions of the two types of sub-wind farms for meeting the LVRT requirements are further studied. Finally, the effectiveness of the proposed coordinated LVRT control strategy for the hybrid wind farm is validated by simulation and experimental results. [ABSTRACT FROM AUTHOR]

In modern wind farms, maximum power point tracking (MPPT) is widely implemented. Using the MPPT method, each individual wind turbine is controlled by its pitch angle and tip speed ratio to generate the maximum active power. In a wind farm, the upstream wind turbine may cause power loss to its downstream wind turbines due to the wake effect. According to the wake model, downstream power loss is also determined by the pitch angle and tip speed ratio of the upstream wind turbine. By optimizing the pitch angle and tip speed ratio of each wind turbine, the total active power of the wind farm can be increased. In this paper, the optimal pitch angle and tip speed ratio are selected for each wind turbine by the exhausted search. Considering the estimation error of the wake model, a solution to implement the optimized pitch angle and tip speed ratio is proposed, which is to generate the optimal control curves for each individual wind turbine off-line. In typical wind farms with regular layout, based on the detailed analysis of the influence of pitch angle and tip speed ratio on the total active power of the wind farm by the exhausted search, the optimization is simplified with the reduced computation complexity. By using the optimized control curves, the annual energy production (AEP) is increased by 1.03% compared to using the MPPT method in a case-study of a typical eighty-turbine wind farm. [ABSTRACT FROM AUTHOR]

The use of appropriate hourly reserve margins can maintain power system security by balancing supply and demand in the presence of errors in the forecast demand, generation outages, or errors in the forecast of wind power generation. Because the cost of unit commitment increases with larger reserve margins, cost analysis to determine the most economical reserve margin is an important issue in power system operation. Here, we define the "short-term reliability of balance" and describe a method to determine the reserve margin based on the short-term reliability of balance. We describe a case study, in which we calculate the reserve margin using this method with various standards of short-term reliability of balance. A cost analysis is then performed to determine the most economic standard, and a comparison between our method and a conventional method is carried out. The results show that our method with an economic short-term reliability of balance enables more reliable and efficient operation of the power system. Moreover, with an hourly reserve margin, we show that an increase in wind power generation can result in a significant decrease in the operating cost, which makes wind power generation economically viable. [ABSTRACT FROM AUTHOR]

The generator stator current gets distorted with unacceptable levels of total harmonic distortion (THD) because impedance-source wind power generation systems use three-phase diode rectifiers. The stator current harmonics will cause increasing losses and torque ripple, which reduce the efficiency and stability of the system. This paper proposes a novel half quasi-Z-source inverter (H-qZSI) for grid-connected wind power generation systems, which can reduce the generator stator current harmonics a great deal. When H-qZSI operates in the shoot-through zero state, the derivative of the generator stator current is only determined by the instantaneous value of the generator stator voltage, so the nonlinear relationship between generator stator current and stator voltage is improved compared with the traditional impedance-source inverter. Theoretically, it is indicated that the stator current harmonics can be reduced effectively by means of the proposed H-qZSI. Finally, simulation and experimental results are given to verify the theoretical analysis. [ABSTRACT FROM AUTHOR]

The Weibull probability distribution has been widely applied to characterize wind speeds for wind energy resources. Wind power generation modeling is different, however, due in particular to power curve limitations, wind turbine control methods, and transmission system operation requirements. These differences are even greater for aggregated wind power generation in power systems with high wind penetration. Consequently, models based on one-Weibull component can provide poor characterizations for aggregated wind power generation. With this aim, the present paper focuses on discussing Weibull mixtures to characterize the probability density function (PDF) for aggregated wind power generation. PDFs of wind power data are firstly classified attending to hourly and seasonal patterns. The selection of the number of components in the mixture is analyzed through two well-known different criteria: the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Finally, the optimal number of Weibull components for maximum likelihood is explored for the defined patterns, including the estimated weight, scale, and shape parameters. Results show that multi-Weibull models are more suitable to characterize aggregated wind power data due to the impact of distributed generation, variety of wind speed values and wind power curtailment. [ABSTRACT FROM AUTHOR]