Wind Power Technology Research Articles

Off-Grid Hybrid Electrical Generation Systems in Remote Communities: Trends and Characteristics in Sustainability Solutions

The objective of this review is to present the characteristics and trends of hybrid renewable energy systems for remote off-grid communities. Traditionally, remote off-grid communities have used diesel oil-based systems to generate electricity. Increased technological options and lower costs have resulted in the adoption of hybrid renewable energy-based systems. The evaluated 168 studies from the period 2002–2019 considered energy developments in Asia, northern Europe, Africa and South America, with the great majority in the northern hemisphere (n = 152, 90.5%). Many of the studied systems were located in tropical (44.1%) and subtropical areas (31.0%). Our review shows that most of the studied approaches combined photovoltaic (PV) and wind energy and that diesel generators are the preferred backup system (61.3%), while batteries are the preferred method of energy storage (80.4%). Communities far from coasts have more options for renewable energy sources, such as biogas. Although half the studies were related to communities with access to marine-based renewable energy resources, their use was only referred to in fifteen studies. In terms of trends, the studies show a mature development of PV and wind-power technology for off-grid hybrid systems independent of the latitude, which is preferred as they are proven and accessible methods. The preferred storage method is batteries, and diesel is the preferred backup system given the low efficiency of PV and the intermittent character of wind power.

The influencing factors and hierarchical relationships of offshore wind power industry in China.

As a new and cost-effective renewable energy power generation technology, offshore wind power is getting more and more attention. The development of offshore wind power industry is affected by policy-making, technology management, resources and environment, market supply and demand, and the relationship among the influencing factors is complex. This paper analyzes the factors that affect offshore wind power industry from a unique and comprehensive perspective. Fourteen factors are selected and interpretative structural model (ISM) is established to study the relationship between the influencing factors of offshore wind power industry. The results show that 14 influencing factors can be divided into five levels: the first level is the surface factors, including the economic incentive policy, operation mechanism, industrial chain, energy market mechanism, investment, and financing mechanism; the second and third levels are the intermediate factors, including generation cost, operation management, and offshore wind power technology; the fourth and fifth levels are deep-seated factors, including development planning and grid price, site selection, R&D investment, environmental protection policy, and offshore wind power supply. Deep-seated factors have a direct impact on the intermediate factors, the intermediate factors have an important impact on the surface factors, and the surface factors directly affect the development of offshore wind power industry. The influence of the 14 factors selected in this paper on offshore wind power industry is from bottom to top, from deep to shallow.

Differential Pulse Voltammetry Study for Quantitative Determination of Dysprosium (III) in Acetonitrile Solution

Dysprosium has gained global interest due to its key application in renewable technology, such as wind power technology. The presence of this rare earth element (REE) can be determined by several spectroscopic methods. Recently, a voltammetry method has provided an alternative method for the simple and fast detection of REEs. However, to the best of our knowledge, this experiment is usually carried out in an aqueous solvent, and the response of the REE in an organic solvent by the voltammetry method has rarely been investigated. In this research, the quantitative detection of dysprosium and dysprosium mixtures with samarium, europium and gadolinium in acetonitrile is reported by differential pulse voltammetry. A Box-Behnken design was applied to predict the optimum condition of the measurements. Three factors, namely potential deposition, deposition time and amplitude modulation, were found to significantly influence the signal under optimal conditions, which are -1.0 V, 83.64 s and 0.0929 V, respectively. The surface characterization of dysprosium deposited on a Pt surface shows better deposition under 100% acetonitrile compared to a lower concentration of acetonitrile. The evaluation in this study shows a detection limit of 0.6462 mg•L-1 and a quantitation limit of 2.1419 mg•L-1, with a precision value and recovery value of 99.97% and 93.62%, respectively.

Wind Energy in Denmark: A Short History [History

This history of wind energy in Denmark describes how top-down policy support and bottom-up initiatives shaped the Danish wind power sector, ultimately facilitating the integration of wind energy in the grid. From the early days of wind power, innovative entrepreneurs tinkered with and improved upon the emergent wind power technologies, but coal and oil remained the prioritized fuels for the almost entirely energy importdependent country. After the economic shock of the global energy crisis in the 1970s, the Danish government focused on energy efficiency, independence, and diversification and sought alternatives to oil. Proactive antinuclear movements called for alternatives to nuclear power.

A deep learning-based evolutionary model for short-term wind speed forecasting: A case study of the Lillgrund offshore wind farm

Due to expanding global environmental issues and growing energy demand, wind power technologies have been studied extensively. Accurate and robust short-term wind speed forecasting is crucial for large-scale integration of wind power generation into the power grid. However, the seasonal and stochastic characteristics of wind speed make forecasting a challenging task. This study adopts a novel hybrid deep learning-based evolutionary approach in an attempt to improve the accuracy of wind speed prediction. This hybrid model consists of a bidirectional long short-term memory neural network, an effective hierarchical evolutionary decomposition technique and an improved generalised normal distribution optimisation algorithm for hyper-parameter tuning. The proposed hybrid approach was trained and tested on data gathered from an offshore wind turbine installed in a Swedish wind farm located in the Baltic Sea with two forecasting horizons: ten-minutes ahead and one-hour ahead. The experimental results indicated that the new approach is superior to six other applied machine learning models and a further seven hybrid models, as measured by seven performance criteria.

Blowing in the wind: A brief history of wind energy and wind power technologies in Denmark

This paper explores the history of wind power technologies and the integration of wind energy in the Danish energy system. It does so focusing particularly on historical events, socio-cultural and socio-political changes and priorities that were important on this technological journey. From the first primitive wind turbines in the late 1800s, to the world wars, through the energy- crisis in the 70s, and into the decades of growing environmental awareness and concern, this historical account describes how policy priorities in Denmark gradually translated from the focus on energy diversification, energy efficiency and energy independence to the focus on sustainability and renewable energy resources, with wind energy and wind power technologies at the top of the priority list. Historically, wind power has been - and it still is - popular in Denmark. However, as the modern wind power turbines gradually grew to industrial scale heights, unchallenged support for specific local wind farms could no longer be taken for granted. This paper also touches upon the frequently tacit dilemmas of renewable energy technology planning and deployment, for example issues of environmental justice, wind farm ownership structures, and the role of social psychology for low-carbon energy transition processes. Notably, attention is drawn to the representation biases that may result in the emphasis of wind farm opposition / grievances in the wider wind farm related debates, where resistance to local wind farm projects might derive from minority opposition groups. Insights and lessons learnt from this Danish history of wind power may prove valuable and inspirational for other countries engaging in low-carbon energy transitions.

Can China’s offshore wind power achieve grid parity in time?

ABSTRACT Facing an increasing financial burden and declining costs, China plans to phase out supporting policies for renewable energy before 2030. In this context, whether offshore wind power can achieve grid parity by the time the subsidies are eliminated is a great concern for policy makers as well as potential investors. To address this issue, we employ the learning curve framework to analyze the critical conditions under which the key factors of offshore wind power technology should evolve to achieve grid parity in time. Ceteris paribus, to achieve grid parity in 2030, the learning rate needs to be as high as 12.0%, the annual deployment needs to be 5.9 GW, and the capacity factor needs to be improved to 40.7%. The feasibility of achieving these key conditions is discussed, and we conclude that the enhancement of innovative technologies is crucial for offshore wind power to improve its cost competitiveness in a finite time without policy support.

An empirical analysis of applications of artificial intelligence algorithms in wind power technology innovation during 1980–2017

We investigated the applications of artificial intelligence (AI) algorithms in wind power technology changes over time and found that AI accelerates the automation of wind power systems. This study shows evidence of the evolution of wind technology innovation following the advancement in AI algorithms using the patents data issued in four intellectual property (IP) offices from 1980 through 2017. Artificial intelligence and more advanced data analytics can be effectively applied to increase the efficiency of wind power systems and to optimize wind farm operations. This study empirically analyzes the evolution of applications of AI algorithms in wind power technology by employing machine learning-based text mining and network analysis, demonstrating the dynamic changing pattern of applications of AI algorithms in wind power technology innovation.

Selection of Wind Turbine Based on Fuzzy Analytic Network Process: A Case Study in China

Wind turbine selection is an evaluation problem involving many factors, such as technology, economy, society, etc., and there exist internal dependencies and circular relationships among these factors. This increases the complexity of the selection problem. At the same time, with the development of wind power technology, the types of wind turbines on the market are increasing. Therefore, it is necessary to establish a scientific and comprehensive evaluation system to guide the selection work. This paper extends the traditional indicator system, selecting a total of twelve evaluation indicators from three aspects: operation reliability, economy, and supplier cooperation. The selected indicators are defined in detail to clarify the relationship between them. Then the triangular fuzzy number is introduced to accurately reflect the preference information obtained from experts, and a fuzzy analytical network process (FANP) model for wind turbine unit selection is constructed by combining fuzzy preference programming (FPP) with analytic network process (ANP). In the end, a case study in China is carried out. Results show that the 2.5 W unit produced by Goldwind obtains the best comprehensive evaluation value, which is consistent with the expanding market share permanent magnet direct-drive wind turbines. This paper could provide references for future wind turbine selection questions.

Risk ranking of wind turbine systems through an improved FMEA based on probabilistic linguistic information and the TODIM method

The technology of wind power is being widely developed worldwide. Ensuring the reliable operation of wind turbine systems is of significance. A popular tool to identify the potential risk of an engineering system is the failure mode and effect analysis (FMEA) method. However, when conducting a FMEA in a complex and uncertain environment, experts may find it challenging to select an appropriate linguistic term for the evaluation. The probabilistic linguistic term sets (PLTSs) are a useful fuzzy set to help experts in describing their assessment. However, different experts may assign different semantic values to the same linguistic terms, and this aspect has not been extensively examined in the existing studies. Therefore, considering the psychological behaviour of experts and the semantics of linguistic terms in the risk ranking process, an improved FMEA based on the probabilistic linguistic information and TODIM (an acronym in Portuguese of interactive and multicriteria decision making) method was developed to identify the risks in wind turbine systems. Furthermore, to demonstrate the utility of the proposed model, it was applied in a floating offshore wind turbine system. The effectiveness and the validity of the model were verified by comparing with some other methods.

Unmanned Aerial Drones for Inspection of Offshore Wind Turbines: A Mission-Critical Failure Analysis

With increasing global investment in offshore wind energy and rapid deployment of wind power technologies in deep water hazardous environments, the in-service inspection of wind turbines and their related infrastructure plays an important role in the safe and efficient operation of wind farm fleets. The use of unmanned aerial vehicle (UAV) and remotely piloted aircraft (RPA)—commonly known as “drones”—for remote inspection of wind energy infrastructure has received a great deal of attention in recent years. Drones have significant potential to reduce not only the number of times that personnel will need to travel to and climb up the wind turbines, but also the amount of heavy lifting equipment required to carry out the dangerous inspection works. Drones can also shorten the duration of downtime needed to detect defects and collect diagnostic information from the entire wind farm. Despite all these potential benefits, the drone-based inspection technology in the offshore wind industry is still at an early stage of development and its reliability has yet to be proven. Any unforeseen failure of the drone system during its mission may cause an interruption in inspection operations, and thereby, significant reduction in the electricity generated by wind turbines. In this paper, we propose a semiquantitative reliability analysis framework to identify and evaluate the criticality of mission failures—at both system and component levels—in inspection drones, with the goal of lowering the operation and maintenance (O&M) costs as well as improving personnel safety in offshore wind farms. Our framework is built based upon two well-established failure analysis methodologies, namely, fault tree analysis (FTA) and failure mode and effects analysis (FMEA). It is then tested and verified on a drone prototype, which was developed in the laboratory for taking aerial photography and video of both onshore and offshore wind turbines. The most significant failure modes and underlying root causes within the drone system are identified, and the effects of the failures on the system’s operation are analysed. Finally, some innovative solutions are proposed on how to minimize the risks associated with mission failures in inspection drones.

Levelized cost of offshore wind power in China.

With the deepening implementation of the energy revolution and the advent of the era in which renewable energy will be grid parity, China's offshore wind power projects have gradually taking steps to shape a large-scale development. This paper reviews the relevant policies for offshore wind power, adopting the levelized cost of electricity (LCOE) model to conduct an economic evaluation of offshore wind power projects in six typical provinces in China. Among the six provinces studied in this paper, Hebei Province has the highest LCOE value of 0.87 yuan/kWh, and Fujian Province has the lowest LCOE value of 0.71 yuan/kWh; the LCOE results of offshore wind power projects in Hebei and Guangdong provinces are higher than the policy guidance price by 8.75% and 6.25% respectively; the LCOE value of the offshore wind power projects in the other four provinces are lower than the guidance price, and the average profit is considerable; the expected annual utilization hours and unit cost have varied impact on the LCOE value in different provinces. The results show that with the upcoming offshore wind power on-grid price bidding era, China's offshore wind power grid parity era is just around the corner. Finally, this paper puts forward relevant policy suggestions, hoping to provide practical guidance for the promotion of offshore wind power technology in China.

Study on Hydrodynamic Characteristics of Wind Turbine Monopile under Nonlinear Wave

As wind power technologies become maturer, the monopile foundation of offshore wind turbine is widely used because of its simple structure, few occupied space and low cost. However, under severe sea conditions, the impact of nonlinear wave load applied against the monopile foundation on the system structure safety cannot be ignored. In this paper, the 5MW offshore wind turbine of the National Renewable Energy Laboratory (NREL) was taken as the research object, and the computational fluid analysis software ‘STARCCM +’ was used to study the hydrodynamic characteristics of the monopile foundation of the wind turbine under different wave parameters. This paper mainly analyzed the upper wave, pressure and wave forces around the monopile foundation of the wind turbine under the same period and different wave heights. And the wave force calculated by CFD was compared with the result based on potential flow theory. The research results showed that with the rise of wave height, the upper wave, pressure and wave force around the monopile foundation increase continuously, and the second-peak phenomenon appeared at some measuring points on the water surface of the monopile foundation. Because the CFD method considers the fluid viscosity and is more in line with the real sea conditions, it is more accurate to obtain wave forces based on this method.

Carbon emission reduction technologies in China

Environmental pollution is mainly caused by carbon emissions, so carbon emission reduction is our top priority now. Carbon-containing greenhouse gas emissions mainly come from the following aspects: (1) fossil fuel combustion; (2) leakage and volatilization in the process of fuel extraction, processing, transportation, and industrial utilization; (3) traditional biomass fuel combustion. The greenhouse effect will cause an increase in temperature, the rise of sea level, and the reduction of biodiversity. Due to little or no carbon emissions, new energy is a current research direction. It mainly includes wind energy, solar energy, hydropower, nuclear energy, and biological energy. Among them, wind power technology is quite mature, and the cost of wind power has become competitive in the market. Solar energy is an inexhaustible, nonpolluting, renewable, and clean energy source, which is gradually entering the stage of large-scale development. Hydropower is clean energy, renewable, pollution-free, and low operating costs. Nuclear energy is characterized by high efficiency and low carbon, coming from the fission energy released by the fission reaction of the fissionable material (nuclear fuel) in the nuclear reactor. Biomass resources can be divided into four categories: forest resources, crop straws, poultry manure, and household garbage, and its biggest feature is its renewability. Besides, carbon capture and carbon storage are other ways to reduce carbon emissions. Carbon capture uses chemical adsorption, physical adsorption, adsorption separation, and membrane separation to capture carbon dioxide. Carbon storage injects supercritical CO2 into a closed geological structure containing oil, gas, water, or non-commercial coal seams through pipeline technology to form long-term or permanent CO2 storage

Development status and prospect of new energy technology

In the current severe situation of continuous shortage of energy resources, the global energy structure is undergoing profound changes. New energy is the main direction of the energy transformation, and the iterative innovation of new energy technology is an important means to promote the transformation of the global energy industry. From the perspective of new energy technology, this paper mainly introduces wind power generation technology and photovoltaic power generation technology. Firstly, the basic principles of wind power and photovoltaic power generation technology are described; Secondly, it counts the current status of the global wind power and photovoltaic market in 2019 and the change trend of installed capacity during 2001-2019, further analyze the development status of these two new energy technologies; finally, it summarizes the development status of new energy technology and makes a prospect for its future development.

An Analysis on Wind Speed Forecasting Result with the Elman Recurrent Neural Network Method

Weather factors in the archipelago have an important role in sea transportation. Weather factors, especially wind speed and wave height, become the determinants of sailing permits besides transportation’s availability, routes, and fuel. Wind speed is also a potential source of renewable energy in the archipelago. Accurate wind speed forecasting is very useful for marine transportation and development of wind power technology. One of the methods in the artificial neural network field, Elman Recurrent Neural Network (ERNN), is used in this study to forecast wind speed. Wind speed data in 2019 from measurements at the Badan Meteorolog Klimatologi dan Geofisika (BMKG) at Hang Nadim Batam station were used in the training and testing process. The forecasting results showed an accuracy rate of 88.28% on training data and 71.38% on test data. The wide data range with the randomness and uncertainty of wind speed is the cause of low accuracy. The data set is divided into the training set and the testing set in several ratio schemas. The division of this data set considered to have contributed to the MAPE value. The observation data and data division carried out in different seasons, with varying types of wind cycles. Therefore, the forecasting results obtained in the training process are 17% better than the testing data.

Towards Enhancing the Performance of Grid-Tied VSWT via Adopting Sine Cosine Algorithm-Based Optimal Control Scheme

The expanding trend of wind power technology motivates scholars to pursue more investigation on optimising energy extraction from the wind, and integrating high-quality power into the utility grid. This paper is aimed at introducing a novel application of the sine cosine algorithm (SCA) which attempts to find the optimal gains of proportional-integral (PI) controllers used to control the power electronic converters (PEC) equipped with the variable speed wind turbine (VSWT) such that a maximum power extraction and performance enhancement can be realized. The PEC equipped with the VSWT combines a machine side converter (MSC) and a grid-side inverter (GSI). Both the MSC and GSI are controlled by the proposed SCA-based PI controllers through cascaded vector control schemes. The MSC is responsible for controlling the wind generator’s rotational speed, active power, and reactive power. The GSI is used to regulate the direct current (DC)-link voltage and to keep the terminal voltage at the desired frame set by the operator. To obtain the optimum PI gains, the SCA is applied to minimize the sum of the integral squared error (ISE) of twelve PI controllers error inputs in the control schemes simultaneously. Performances of the proposed SCA-PI control schemes are assessed under severe grid disturbance and random wind speed variation to mimic more realistic conditions. The effectiveness of the proposed SCA-PI is verified in the MATLAB/Simulink environment, and the results are compared to those obtained using a grey wolf optimizer and particle swarm algorithm-based optimal PI controller. The simulation findings confirm the SCA-PI can be regarded as an efficacious way to enhance the performance of the VSWT.

Static and Dynamic Reliability Analysis of Laterally Loaded Pile Using Probability Density Function Method

Pile foundation is one of the common foundation forms in marine geotechnical engineering, especially in wind power engineering. Its operation safety is seriously affected by many uncertainties, such as the randomness of ground motion in intensity and frequency. The stochastic reliability analysis method can better characterize these uncertainties in the evaluation of the safety performance of pile foundation. The probability density functions (PDFs) of stochastic systems are important prerequisites for reliability analysis. However, for geotechnical problems, the coupling between parametric and excitation randomness and the nonlinear mechanical properties of rock and soil make it very difficult to obtain the associated PDFs. Instead, the probability density evolution method (PDEM) is introduced and is used to investigate the static and dynamic reliability of laterally loaded piles as an example of a geotechnical problem. Compared with Monte Carlo stochastic simulations, PDEM-based computing is shown to be highly efficient when applied to the seismic design of pile in geotechnical engineering, and its calculation efficiency is 20 times of the former for the seismic dynamic reliability of pile foundation. This study provides a new reference for the efficient design and safety evaluation of offshore pile foundation engineering based on static and dynamic reliability of multiple random factors.

Wind power plants functioning model in the power supply system

The system of complex energy supply of various objects is considered, existing problems in the field of wind power engineering are identified, those that leave complex energy supply using renewable energy sources are analyzed, and the quality of supplied energy is assessed by the efficiency factor. The structural model of wind power plant (HPU) functioning as a subsystem in the autonomous power supply system was considered separately. Methodological bases of designing in the form of structural scheme of methodology of development and creation of rotor HPU have been developed. All stages of designing and development of a wind power plant are analyzed.

Grid Evolution and the Business Ecosystem

For at least the past decade, a popular topic of discussion in the electricity industry has been change. It has become a regular practice at industry conferences to feature one or more sessions focused on “grid modernization” at which the potential impact of technological innovations and new business or regulatory paradigms is discussed, and it is usually implied in these sessions that electricity service is on the cusp of some fundamental transformation. In recent years, conferences have begun to be held that are exclusively devoted to the subject of transformation in the electricity sector. Various potential causes for such a transformation have been identified, including the continued governmental push at both the state and federal levels in earlier years for cleaner energy, the declining cost of solar and wind power and storage technologies, the growing popularity of customer‐owned “distributed resources,” and increasing concerns about grid “resiliency.” Legislative and/or regulatory initiatives introduced in several states have explored new regulatory ­models that might support if not create the impetus for fundamental change. But while at least some of these trends are potentially transformational, it has been a continuing challenge to develop a coherent vision of what the “grid of the future” might look like.