Wind Power Applications Research Articles

Overview of Wind and Photovoltaic Data Stream Classification and Data Drift Issues

The development in the fields of clean energy, particularly wind and photovoltaic power, generates a large amount of data streams, and how to mine valuable information from these data to improve the efficiency of power generation has become a hot spot of current research. Traditional classification algorithms cannot cope with dynamically changing data streams, so data stream classification techniques are particularly important. The current data stream classification techniques mainly include decision trees, neural networks, Bayesian networks, and other methods, which have been applied to wind power and photovoltaic power data processing in existing research. However, the data drift problem is gradually highlighted due to the dynamic change in data, which significantly impacts the performance of classification algorithms. This paper reviews the latest research on data stream classification technology in wind power and photovoltaic applications. It provides a detailed introduction to the data drift problem in machine learning, which significantly affects algorithm performance. The discussion covers covariate drift, prior probability drift, and concept drift, analyzing their potential impact on the practical deployment of data stream classification methods in wind and photovoltaic power sectors. Finally, by analyzing examples for addressing data drift in energy-system data stream classification, the article highlights the future prospects of data drift research in this field and suggests areas for improvement. Combined with the systematic knowledge of data stream classification techniques and data drift handling presented, it offers valuable insights for future research.

Multilevel Middle Point Clamped (MMPC) Converter for DC Wind Power Applications

This manuscript introduces a novel multilevel middle point clamped (MMPC) DC-DC converter and its associated switching scheme aimed at maintaining the desired medium-voltage DC (MVDC) collector grid within offshore all-DC wind farms. Building upon previous work by the authors, which proposed an all-DC structure serving as a benchmark system, this study explores the application of the MMPC DC-DC converter within this framework. Within the all-DC wind generation system, a 9-phase hybrid generator (HG) integrated into the wind turbine is linked to the MVDC collector grid through an AC-DC stage, which is a passive rectifier. This passive rectifier offers elevated voltage ratings and protection against back power flow. The conventional neutral point clamped (NPC) converter concept has been thoroughly investigated and expanded upon to develop the proposed MMPC DC-DC converter. The proposed MMPC DC-DC converter integrates boosting capabilities, facilitating the connection of the generator’s rectified voltage to the MVDC collector grid while regulating variable rectified voltage to a fixed MVDC collector grid voltage. The MVDC collector grid is further interconnected with high-voltage DC (HVDC) through a DC-DC converter situated in an offshore substation. This paper further provides a comprehensive overview of the proposed MMPC DC-DC converter, detailing its operational modes and corresponding switching schemes. Through an in-depth examination of operational modes, duty cycles for each switch and mode are defined, subsequently establishing the relationship between rectified input voltage and MVDC output voltage for the MMPC DC-DC converter. Utilizing the middle point clamped architecture, this innovative converter offers several advantages, including low ripple voltage, a modular structure, and reduced switching stress because of the multilevel voltage and the incorporation of a hard point, which also facilitates the capacitor voltage balancing. Finally, the effectiveness of the proposed converter is evaluated via simulation studies of a wind turbine conversion system utilizing two cascaded MMPC DC-DC converters operating under variable input voltage conditions. The simulations confirm its efficacy, supported by promising results, and validating its performance.

FEM-Circuit co-simulation of superconducting synchronous wind generators connected to a DC network using the homogenized J–A formulation of the Maxwell equations

High-temperature superconductors (HTS) are greatly appealing for the development of high efficient, and high energy density power devices. They are particularly relevant for applications requiring light and compact machines such as wind power generation. In this context, to ensure the proper design of the superconducting machines and their reliable operation in power systems, it is then important to develop models that can accurately include their physics but also can describe properly their interaction with the system. To achieve such a goal, one approach is the co-simulation. This numerical technique can bring fine geometrical and physical details of the machines through a finite element model (FEM) meanwhile dealing with the operation of the whole system that incorporates the machine and a subset of the power grid represented by an external electrical circuit. The goal of the present work is to put to use this numerical technique when superconducting components are involved. Here, a case study is proposed involving a 15 MW hybrid superconducting synchronous generator (HTS rotor and conventional stator) coupled to a direct current network via a rectifier and its associated filter. The case study related to wind power application allows grasping the technical issues when employing co-simulation dealing with HTS machines. The FEM of the generator is done in the commercial software COMSOL Multiphysics, which interacts with the circuit simulator Simulink through the built-in Functional Mock-up Unit. For the present study, a new version of the latest J–A formulation combined with homogenization technique is introduced allowing an even faster computation time compared to the T–A formulation. Distributed variables and global variables such as current density, magnetic flux density, and local losses for the former and voltage, current, electromagnetic torque, and power quality for the latter are estimated and compared for both formulations. The idea is to find the best-suited combination FEM-circuit under criteria of computational speed, accuracy, and numerical stability. Thus, it is shown that all formulations generate an error of less than 5% on the machine parameters and that the J–A formulation with first order elements stands out with a significant 4-fold reduction in computational costs.

Propeller Optimization in Marine Power Systems: Exploring Its Contribution and Correlation with Renewable Energy Solutions

The goal of increasing fuel efficiency and decreasing greenhouse gas (GHG) emissions has increased interest in the application of renewable energy sources and the usage of new technologies in the maritime industry. In order to implement the most suitable source, factors such as voyage duration, storage availability, and the condition of existing vessels as well as those that are still under construction should be taken into account. Propeller optimization is proposed as a long-term solution. This paper investigates the environmental aspects of propeller optimization, focusing on its potential to reduce ship vibrations fuel consumption, and, therefore, the ship’s carbon footprint. The case study presents propeller optimization on a Ro-Ro passenger ship. The data collected during sea trials before and after propeller optimization will be compared. Expected fuel oil consumption will be correlated to the CO2 emission reduction. Besides propeller optimization, the paper performs a SWOT (strengths, weaknesses, opportunities, threats) analysis comparing it with solar and wind power applications on ships.

Current and flux correlation analysis for detection, location, and phase identification of stator inter-turn short-circuit fault in doubly-fed induction generator

This study proposes an innovative method for identifying and locating inter-turn short-circuit faults in doubly-fed induction generators (DFIGs), commonly used in wind-energy production. Our approach uniquely integrates data from both current and magnetic-flux monitoring, offering a complete framework for fault diagnostics. We effectively distinguish between normal and abnormal conditions in the generators by observing the patterns in current and flux waveforms. Normal conditions are marked by uniform waveforms, whereas faults create noticeable imbalances. A key feature of our research is the development of a technique to accurately determine which phase of the generator is affected by the fault, facilitating quicker and more effective repairs. To pinpoint the exact location of the fault, we use a set of four magnetic-flux sensors strategically placed around the generator. These sensors detect variations in magnetic flux, enabling the precise location of the fault. By focusing on these three essential elements—fault detection, phase identification, and exact fault localisation—our proposed algorithm significantly improves the dependability of DFIGs in wind-power applications.

Optimization of Flanged Diffuser for Small-Scale Wind Power Applications

The development of renewable and clean energy has become more crucial to societies due to the increasing energy demand and fast depletion of fossil fuels. A state-of-the-art design for an augmented wind turbine has been introduced in the past years to increase the efficiency of compact horizontal axis wind turbines, exceeding the ideal Betz’s limit of the maximum energy captured from the wind. The optimization of the flanged diffuser - so-called diffuser augmented wind turbine DAWT - is investigated numerically using the multi-objective genetic algorithm “MOGA”. A 2D computational model is developed using ICEM CFD and solved by ANSYS Fluent. The Turbulence model selected is shear stress transport K-omega, with a pressure-based solver and a coupled algorithm scheme. The optimization objectives are to maximize the velocity ratio at the shroud throat and minimize shroud form dimensions. 517 design points were solved, and the design dimensions were categorized into four types: compact, small, medium, and large design. The results showed that the diffuser dimensions are the main parameters to increase velocity inside the shroud throat, where a long diffuser with a low converging angle drags more air inside the shroud, reaching in some cases more than double the upwind velocity. While the nozzle and flange are also effective in the different design types. It was found that a super long diffuser with a length ratio of 2.9 LD to throat diameter D is optimal with a diverging angle of 7.6˚, accompanied by a nozzle of ratio 1.2 LN/D and 12.6˚ converging angle and a flange length ratio of 0.6 LF/D. This optimal design increased the velocity ratio by almost 2.5 times.

Study on the topology of a system for delivery of electricity from deep sea wind through voltage source converter

It is a difficult problem to maintain high reliability while designing the topology of the voltage source converter (VSC) for deep-sea wind power in a compact manner. In this paper, the topology of the converter, the main wiring type and grounding method, and the DC system wiring scheme are studied. The advantages and disadvantages of each scheme are compared in offshore wind power application scenarios. Finally, the half-bridge converter topology and the true bipolar with metal return line topology are recommended for the topology design of the VSC system for deep and remote offshore wind power. The power operating range is also presented. The scheme proposed in this paper paves the way for promoting the lightweight and highly reliable system for the delivery of electricity from deep sea wind through VSC.

A novel approach to generate bias-corrected regional wind infeed timeseries based on reanalysis data

Weather data, particularly from reanalysis models, are often applied in simulations of infeed patterns for renewable energy systems. The reanalysis datasets provide spatially differentiated weather timeseries for historical years. However, their exactness in wind power applications deserves detailed scrutiny. Notably, the physical model abstracts from boundary layer friction. Abstaining from physical flow models, scientific scholars proposed ex-post bias correction methods to better depict local wind speeds. Yet, such bias correction often is performed on national aggregated figures, as public data is scarce. In this work, a dataset of approx. 23,000 wind turbines for Germany is used to assess deviations between simulated and measured energy infeed for four different years. In line with other studies, we identify in detail that in flat terrain, simulations based on reanalysis data overestimate measured results. In topographically complex regions, a minor overestimation and occasionally an underestimation can be observed. Multilinear regression at turbine level shows that these deviations can be explained by local factors. Reanalysis data in combination with bias-correction based on local factors from 2016 enhance energy output simulations on turbine level on average by 71% for 2020, 93% for 2021 and 97% for 2022.

Current stress optimization of nonagonal MMC for FFTS‐based offshore wind power application

SummaryNonagonal Modular Multilevel Converter (MMC) is a new type of three‐port AC‐AC converter suited for applications in FFTS‐based offshore wind power. It can accommodate three AC ports with only nine branches, greatly reducing the cost and size of energy conversion. However, the multiplexing structure of nonagonal MMC inevitably increases the peak branch current resulting in high semiconductor ratings, which weakens its advantage in cost and size. Since the control degrees of freedom are reduced, traditional current attenuation methods are not applicable. To address this issue, in this paper, a parameter adjustment method is proposed for nonagonal MMC to reduce the current stress. By establishing the mathematical model of nonagonal MMC, phase angle difference between ports of nonagonal MMC is proved to be a key influencing factor of peak branch current. By adjusting phase angle difference, the average current stress of nonagonal MMC can be reduced by as great as 66.31%. Finally, the correctness of the conclusions of this paper is verified by RT‐LAB results.

Control of a variable speed and permanent magnet wind turbine: GARBI 150

This paper describes the revolutionary new wind turbine GARBI 150. The 150 kW permanent magnet synchronous generator and its full power converter with control in four quadrants make it interesting in medium scale wind power applications. The generator allows direct connection to the electrical distribution grid. The torque and pitch control have been designed to maximize energy capture and minimize fatigue loading of the turbine structure. These controllers are being validated with a first wind turbine prototype. A supervision system has also been implemented.

A dual-stator brushless doubly-fed generator for wind power application

A dual-stator brushless doubly-fed generator for wind power application

User interface of an expert system for the application of wind power in agriculture

The paper defines the relevance of the use of electricity generated by wind energy conversion by farms. A digital platform has been developed to monitor the condition of the wind turbine system, to ensure timely maintenance and repair, and to promote the efficient and rational use of the resources obtained with their help. The primary findings are presented in the paper. The document also includes a description of the functionality of the user categories and a selection of the user interface prototype. The methodology of object-oriented design was employed in the creation of this system, with the results of structural analysis methods serving as the foundation for this approach.

Optimization Design of 90kW High-power LLC Converter for Wind Power Applications

Optimization Design of 90kW High-power LLC Converter for Wind Power Applications

Wind Speed Downscaling of the WRF Model at Subkilometer Scale in Complex Terrain for Wind Power Applications

Wind Speed Downscaling of the WRF Model at Subkilometer Scale in Complex Terrain for Wind Power Applications

Determination of Parasitic Capacitance of High-Power Medium Frequency Transformers: Case Study of a High Voltage DC Biased Transformer for Wind Power Application

Determination of Parasitic Capacitance of High-Power Medium Frequency Transformers: Case Study of a High Voltage DC Biased Transformer for Wind Power Application

Nonlinear control algorithm for a self-excited induction generator for wind power applications

A non-linear state-feedback input-output linearizing control with tracking controllers for an isolated Induction Generator (IG) driven by a wind turbine and supplying an AC load is presented. For this purpose, an appropriate mathematical model of the IG based on the so-called stator-flux field oriented is detailed. Simulation results show that the input-output linearizing control provides an excellent control performances and a perfect tracking for both IG stator voltage magnitude and DC bus voltage. The proposed system can be directly explored for wind power generation purposes. Simulation results are curried out to demonstrate the feasibility of the proposed approach.

Statistical analysis of wind speed distribution based on sixWeibull Methods for wind power evaluation in Garoua, Cameroon

Wind data analysis and accurate wind energy potential assessment are critical factors for suitable development of wind power application at a given location. This paper explores wind speed distribution to select the two-parameter Weibull methods that provide accurate and efficient estimation of energy output for Wind Energy Conversion Systems (WECS).The dimensionless shape parameter k and the scale parameter C are determined based on measured hourly mean wind speed data in times-series from 2007 to 2012, collected at the Garoua International Airport, main meteorological station, in Garoua, Cameroon. Six numerical methods, namely Empirical Method (EM), Energy Pattern Factor method (EPF), Graphical Method (GM), Maximum Likelihood Method (MLM), Moment Method (MM) and Modified Maximum Likelihood Method (MMLM) are examined to estimate the Weibull parameters. To analyze the efficiency of the methods and to ascertain how closely the measured data follow the Weibull methods, goodness of fit tests were performed using the chi-square test (X2), correlation coefficient (R2), root mean square error (RMSE) and Kolmogorov-Smirnov test (KOL). The results revealed that the EPF followed by the MM were the most accurate and efficient methods for determining the value of C and k to approximate wind speed distribution. The statistical tests rejected the GM as an adequate method and revealed as well that the EM, MLM and MMLM ranked respectively third, fourth and fifth. Furthermore, the potential for wind energy development in Garoua is not fitted for generating electricity and a very fruitful result would be achieved if windmills were installed for producing community water supply, livestock watering, and farm irrigation.

Norwegian offshore wind power—Spatial planning using multi‐criteria decision analysis

AbstractThe Norwegian government recently agreed on the goal 30by40, which involves opening Norwegian offshore areas to host 30 GW of installed wind power by 2040. We address this goal by presenting a first mapping of wind power suitability scores (WPSS) for the entire Norwegian economic zone (NEZ) using a multi‐criteria decision analysis framework (MCDA), namely, the analytical hierarchical process (AHP) approach. We obtain WPSS considering relevant criteria like wind resources, techno‐economic aspects, social acceptance, environmental considerations, and met‐ocean constraints such as wind and wave conditions. The results starts with a baseline scenario, where the criterion importance is pairwise compared in the context of balancing economic incentives and conflicting interests. Additionally, to reveal regions that are robust to changes in criterion importance, we carry out a sensitivity analysis by introducing three additional scenarios. These scenarios represent stereotypical actors with distinct preferences for siting of wind farms: the investor, the environmentalist, and the fisherman. The results show that the southern part of the NEZ is the most suitable and robust region for offshore wind power deployment. This region receives the highest suitability category (“very high” suitability for wind power application) throughout all the scenarios. Areas in the Norwegian part of the Barents Sea and the near‐coastal areas outside mid‐Norway are also well suited regions, but these are more sensitive to the choice of criterion importance. The use of AHP within the framework of MCDA is shown to be a promising tool for pinpointing the best Norwegian offshore areas for wind power application.

Assist in GHG Abatement of Offshore Ships: Design and Economic Analysis of an Integrated Utilization Model of hydrogen-powered Ship and Offshore Wind Power

As a hopeful solution to help the shipping sector achieve the greenhouse gas (GHG) abatement goal of the IMO by 2050, the application of hydrogen-powered ships has attracted more and more attention. To solve the problem of hydrogen supplement for offshore polymer electrolyte membrane fuel cell (PEMFC) ships, this paper presents a scenario design for the integrated application of coastal hydrogen-powered ships and offshore wind power, and analyzes its techno-economic feasibility. This model concept considers the problems in offshore hydrogen transportation and offshore wind power development together. It provides a feasible integrated scheme and operation mode for the combination of offshore wind power and hydrogen energy storage. A transformation scheme is also provided for the marine ranch ships, and these PEMFC-powered ships are combined with the hydrogen production of offshore wind farms. The analysis results show that the integrated utilization model is economically feasible and with a significant effect on decarbonization. It also shows great potential for further expansion to other coastal areas.

Wind power estimation on local scale—A case study of representativeness of reanalysis data and data-driven analysis

IntroductionWith hydropower being the dominant source of renewable energy in Austria and recent years being disproportionally dry, alternative renewable energy sources need to be tapped to compensate for the reduction of fossil fuels and account for dry conditions. This becomes even more important given the current geopolitical situation. Wind power plays an essential role in decarbonizing Austria's electricity system. For local assessments of historic, recent, and future wind conditions, adequate climate data are essential. Reanalysis data, often used for such assessments, have a coarse spatial resolution and could be unable to capture local wind features relevant for wind power modeling. Thus, raw reanalysis data need post-processing, and the results need to be interpreted with care. The purpose of this study is to assess the quality of three reanalysis data sets, such as MERRA-2, ERA5, and COSMO-REA6, for both surface level and hub height wind speed and wind power production at meteorological observation sites and wind farms in flat and mountainous terrain. Furthermore, the study aims at providing a first knowledge baseline toward generating a novel wind speed and wind power atlas at different hub heights for Austria with a spatial resolution of 1 × 1 km and for an experimental region with sub-km resolution. Thus, the study tries to answer (i) the questions if the reanalysis and analysis data can reproduce surface-level wind speed and (ii) if wind power calculations based on these data can be trusted, providing a knowledge base for future wind speed and wind power applications in complex terrain.MethodsFor that purpose, a generalized additive model (GAM) is applied to enable a data-driven gridded surface wind speed analysis as well as extrapolation to hub heights as a first step toward generating a novel wind speed atlas. In addition, to account for errors due to the coarse grid of the re-analysis, the New European Wind Atlas (NEWA) and the Global Wind Atlas (GWA) are used for correction using an hourly correction factor accounting for diurnal variations. For the analysis of wind power, an empirical turbine power curve approach was facilitated and applied to five different wind sites in Austria.Results and discussionThe results showed that for surface-level wind speed, the GAM outperforms the reanalysis data sets across all altitude levels with a mean average error (MAE) of 1.65 m/s for the meteorological sites. It even outperforms the NEWA wind atlas, which has an MAE of 3.78 m/s. For flat regions, the raw reanalysis matches the production data better than NEWA, also for hub height wind speeds, following wind power. For the mountainous areas, a correction of the reanalysis data based on the NEWA climatology, or even the NEWA climatology itself, significantly improved wind power evaluations. Comparisons between modeled wind power time series and real data show mean absolute errors of 8% of the nominal power in flat terrain and 14 or 17% in mountainous terrain.