Development Of Wind Power Generation Research Articles

Stochastic operation model for readiness assessment of small wind turbines based on Markov theory

AbstractWind energy is now one of the most important alternative sources of renewable energy. The development of wind power generation is a prerequisite to meet environmental and sustainable development requirements while achieving energy security goals. The condition for the effectiveness of the proper operation of a wind power plant is its ability to perform the intended functions, which can be expressed by the coefficient of technical readiness. This article presents an original analysis of the readiness of small wind turbines. As part of the research, an original stochastic operation model was developed based on the application of the Markov process theory. Based on the collected data, a 3‐state phase space of the studied process was identified. Various analyses were carried out to calculate the operating coefficients of wind power plants. The ergodic (boundary) probabilities were calculated using three methods. The conditional probabilities were then determined based on the initial distribution. The resulting readiness factors, not exceeding 30%, indicate the low operational efficiency of the small wind turbines in question. Furthermore, an analysis of the sensitivity of the performance factors was carried out for the analysed objects.

Multidimensional clustering based fault diagnosis method for wind turbine transmission chain

Abstract With the rapid development of wind power generation, ensuring the reliability and fault diagnosis of wind turbine transmission chains has become a focal point. Traditional clustering methods that address the fault diagnosis problem in wind turbine transmission chains suffer from low fault discrimination and accuracy, as they rely on a single indicator. This paper proposes a two-dimensional clustering model that utilizes multiple indicators to address the issue of distinguishing similar faults within the wind turbine transmission chain from various perspectives and dimensions. The proposed approach involves collecting vibration signals from the transmission chain of wind turbines using sensors and using the root mean square and kurtosis of the fault signal as clustering features. A multidimensional polar coordinate clustering model is established, with the signal kurtosis representing the polar angle and the root mean square representing the polar radius. The clustering of fault signals within wind turbine transmission chains is achieved by optimizing the clustering boundaries by establishing a clustering boundary optimization equation. The results of the developed clustering model are evaluated using external and internal evaluation methods. Experimental results demonstrate that this method exhibits high accuracy and low computational complexity in diagnosing faults within wind turbine transmission chains. In comparison with other clustering methods, the proposed method outperforms them according to the experimental results. Hence, this study presents a novel approach for health monitoring and fault diagnosis of the transmission chain of wind turbines, which has significant implications for improving the reliability of wind turbine operation and reducing maintenance costs.

Analysis of wind power generation technology and its application in energy saving

Demand for electricity is on the rise as the economy is constantly on the move. Traditional thermal power generation is inefficient and harmful to environment. Wind power generation has gradually become one of the most important energy sources developed by various countries because it is a clean energy source. Wind power is widely distributed in China and the geographic advantages accelerate the construction of large wind farms. In order to develop wind power generation more efficiently, it is necessary to analyse the technology and environmental conditions in detail. This article has been a review of the current state of wind power generation in China. The principles of wind power generation and the types of wind turbines are introduced. The impact of wind farms on the climate is also discussed. It also examines the role of wind energy as a means of saving energy and reducing pollution. This paper may offer a reference for the development of wind power generation.

Short‐term wind power prediction based on the combination of firefly optimization and LSTM

AbstractWith the development of social resources, people's consumption of energy is huge, so renewable energy, such as wind energy, has been widely concerned and developed. Although there has been sufficient development of wind power generation, its output has some problems such as uncertainty, which leads to insufficient utilization of wind energy resources and uneven power output quality level, which brings great challenges to the grid connection. To solve this problem, a short‐term wind power prediction model combining firefly algorithm and long term memory network is proposed. The main motivation of the research is to improve the accuracy of wind power prediction and thus improve the utilization of wind energy resources. Compared with the existing methods, the innovation of FA‐LSTM model lies in the integration of the two algorithms, making full use of the advantages of FA in global search optimization and LSTM in time series data processing, and improving the accuracy and stability of prediction. During the experiment, we used different wind farm data to train and test the model. The results show that the FA‐LSTM model can improve the optimal fitness by more than 50% compared with other algorithms, and the iterative prediction error is smaller. Standard root mean square error (RMSE) and mean absolute error (MAE) were used to evaluate the model. The accuracy of RMSE and MAE reached over 97% and 98% respectively. When the test data is highly volatile, the data accuracy of FA‐LSTM model reaches 92% and 94%, and the FA‐LSTM model drops to the stable value faster. FA‐LSTM model has the best fitting degree with the true value curve, and the fitting degree reaches more than 90%. Comparing the actual power and predicted power of different units, the actual power of Unit 1 is 34.875, and the predicted power obtained by FA‐LSTM model is 34.935, with an error of only 0.06. The key finding of this study is that the prediction model combining FA and LSTM has high accuracy and stability in wind power prediction, and can effectively deal with the uncertainty and volatility of wind energy resource utilization. FA‐LSTM model provides an effective solution for wind power prediction, which is helpful to improve the utilization rate of wind energy resources.

Study on the thermal transformation of basic components of wind turbine blade

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

Current Harmonic Control Method of Wind Turbine Based on PIR Controller

Wind power equipment technology is the basis for the development of wind power generation. Aiming at the problem that the traditional PI controller is difficult to control the AC component of the current in the DQ axis, resulting in a large number of current harmonics in the process of power generation, which seriously affects the efficiency of power generation. This paper proposes a current harmonic control method for wind turbines based on a PIR controller. The mathematical model of wind power generation is constructed based on the synchronous rotating coordinate system. The current harmonic components are calculated according to the DQ rotor speed, shaft component, and other parameters. The PIR current harmonic controller is established by improving the ordinary PI controller to track and control the AC current signal. And finally, a current harmonic control target value is obtained according to different main control targets, and a specific harmonic component is eliminated. The experimental results show that the harmonic content is 3.12%, 2.06%, 1.17%, and 1. 02%, respectively, when the harmonic current order is 5, 7, 11, and 13. It is better than the comparison method. Therefore, this method can greatly reduce the harmonic content of the current in the process of different current orders, reduce the harmonic loss, and improve the generating efficiency of the unit.

The Most Promising New Energy Source—Wind Power

Energy is an important part of the development of human society, but due to the large consumption of oil, coal and other resources, it also brings environmental pollution. Therefore, people have higher and higher requirements for environmental protection and sustainable development. Renewable energy plays a key role in meeting climate change agreement targets, increasing energy security, improving access to electricity and reducing fossil energy consumption. Wind power is a sustainable energy source. Because it is environmentally friendly and rich in resources, scientists have spent thousands of years looking for an effective method. This paper mainly introduces the development of wind power utilization and generation, wind navigation aid, wind water diversion and wind heat. In addition, the present situation, advantages and disadvantages of these technologies are described in detail, and the future development of wind power generation is briefly described. Hopefully this article will give a general idea of what wind power can be used for.

Improvement of Grid-connected Reactive Power Compensation for Wind Farms Based on STATCOM

Due to the recent rapid development of wind power generation, people have higher requirements for the stability of wind power systems. The randomness and intermittency of wind farms are strong. When the system is disturbed, or the line fails, it is easy to cause voltage instability. In order to ensure the stable operation of the power system, it is necessary to set up a static reactive power compensation device (STATCOM) to provide reactive power in the power system. This paper simulates and analyzes the wind power generation system with a STATCOM device. The results show that the operation process of the wind power system with STATCOM is more stable.

Research on short-term wind speed prediction based on deep learning model in multi-fan scenario of distributed generation

The climate and environmental pollution problems caused by carbon dioxide and other harmful gases emitted from traditional fossil fuel thermal power plants are increasingly threatening the living environment of mankind. In September, 2020, the Chinese government clearly put forward the national strategic goal of “Carbon Peak and Carbon Neutrality”. Distributed generation is the main means to effectively reduce carbon emissions, especially the rapid development of wind power generation. Accurate and stable wind speed prediction can reasonably formulate power generation plans and optimize power dispatching, which is an effective means to reduce the overall carbon emissions of the power system.This paper designs and proposes a hybrid wind speed prediction model based on convolutional neural network and long short-term memory network deep learning model. Based on the historical wind speed data set collected at the location of multi-fan in the same wind farm, high-precision and stable short-term wind speed prediction is realized. Firstly, singular spectrum analysis aims to remove the noise components in the wind speed series and reduce the impact of noise on the prediction performance of the model. Secondly, convolutional neural network (CNN) is introduced to extract the features of the de-noised wind speed sequence, which provides more effective information for the training of the prediction network. Then, a prediction network based on sparrow search algorithm and long short-term memory network is constructed, and the modified sparrow search algorithm is used to optimize the selection of long short-term memory (LSTM) Hyper-parameters. Eventually, to verify the superiority of the proposed model, an evaluation system based on accuracy, stability and complexity indicators is constructed. The experimental results show that the index values of the wind speed prediction model proposed in this paper based on dataset 1 in the one-step prediction simulation experiment are the smallest among all comparison models, and the same is true for dataset 2.

A transfer method to estimate hub-height wind speed from 10 meters wind speed based on machine learning

The estimation of hub-height wind speed is critical to a comprehensive wind resource assessment, particularly for the evaluation of future energy mix scenarios. However, gridded datasets of wind speeds are often limited to near-surface winds, especially when it comes to climate model projections, which is a real limitation for using climate models. This study develops a transfer method to calculate 100 m wind speed using three machine learning methods, including the Least Absolute Shrinkage Selector Operator, Random Forest (RF) and extreme Gradient Boost (XGBoost). Compared with the traditional algorithm, based on empirical formulae, the tested machine learning-based algorithms allow much more accurate estimates of 100 m wind speeds. RF and XGBoost have good performance on the hourly scale, and correct the major biases of the classical, simplified algorithms, especially in the diurnal cycle of hub-height wind speeds. RF appears to be the best algorithm when compared with the reanalysis data. In addition, the machine learning transfer model is applied to 19 regional climate projections. Results show that the 100 m wind speed has decreased in most of Europe during 1979–2019, which is consistent with the observed stilling of surface winds in recent years. This trend is projected to increase in the future, under an uncurbed greenhouse gas emission scenario, which indicates adverse effects for the development of wind power generation in Europe. The approach established in this study can be applied to obtain numerical climate model outputs accurately, which is critical to the estimation of the long-term changes of global renewable energy resources.

An Evaluation of Wind Turbine-Induced Topographic Change in the Offshore Intertidal Sandbank Using Remote Sensing-Constructed Digital Elevation Model Data

With the rapid development of wind power generation, many marine wind farms have been developed on the offshore intertidal sandbank (OIS) along the coastal regions of Jiangsu Province, China during the last decade. In order to quantitatively assess the stability of offshore wind turbines and their induced topographic changes on the OIS, a digital elevation model (DEM)-based analysis supported by satellite remote sensing is adopted in the present study. Taking the Liangsha OIS at the middle of Jiangsu coast, China as the research area, we first used an enhanced waterline method (EWM) to construct the 30 m resolution DEMs for the years 2014 and 2018 with the embedment of tidal creeks to effectively express the detailed characteristics of the micro-terrain. Then, a hypothetical sandbank surface discrimination method (HSSDM) was proposed. By comparing the height difference between the hypothetical and the real terrain surface during the operation period, the wind turbine-induced topographic change rate (TCR) was estimated from the DEM of 2018. The results show that 73.47% of the 49 wind turbines in the Liangsha OIS have an erosional/depositional balanced influence on the intertidal sand body, 8.16% show a weak depositional influence, and 18.36% lead to weak erosion. The average erosional depth, 58.6 cm, reached nearly 6% to 10% of the maximum possible erosion estimated by the hydrodynamic model. Furtherly, using two DEMs for the years 2014 and 2018, the topographic change depths at the location of wind turbines were calculated. By comparing the wind turbine-induced terrain change with the naturally erosional/depositional depths of the OIS, the average contribution rate caused by the wind turbines achieved 42.17%, which meant that the impact of wind turbines on terrain changes could not be ignored. This work shows the potential of utilizing satellite-based remote sensing to monitor topographic changes in the OIS and to assess the influence of morphological variations caused by wind turbines, which will be helpful for offshore wind farm planning and intertidal environment protection.

Оцінка ефективності вітрогенераторів в реальних умовах з додатковими експлуатаційними витратами

The development of world energy is constantly accompanied by the growth of electricity consumption. Traditional sources of electricity in this process contribute to the growth of greenhouse emissions and the aggravation of climate problems, which are becoming more urgent every year. Against this background, alternative sources of electricity, in particular wind power plants, reduce the impact of traditional sources and contribute to solving environmental problems. The development of wind power generation is accompanied by an increase in the unit capacities of wind generators. The article analyzes the efficiency of the operation of a wind power plant with modern wind generators for certain location conditions, taking into account additional operating costs based on operating experience. Calculations of the technical and economic operational indicators of the designed four-unit wind power plant with various types of wind generators were carried out. During these calculations the wind conditions of a specific industrial territory for the construction of wind power plant were taken into account. This industrial territory is located in the Carpathian region of Ukraine. At the same time, the modern typical power capacities of wind generators and additional costs during the operation of wind power plants were taken into account. In order to make the optimal technical design decision for comparative evaluation, two types of wind generators — Enercon E-175 EP5 and Vestas V136-4.5 MW — were selected for installation at the wind power plant. For the determined location of the wind power plant at the upper reaches of the Carpathian mountain ranges in the Lviv region of Ukraine (coordinates of the area are (lat., lon.) 49.2470; 22.8763), the annual wind probability characteristic was calculated according to the Weibull analytical characteristic. According to these data, in the article the characteristics of an annual electrical productivity of the designed 4-unit wind power plant for the selected two types of wind generators were given. The obtained technical and economic indicators: the capital investment, operating costs, performance characteristics and calculated gross profits during the year of operation of the considered wind power plant with two types of wind generators — made it possible to estimate the payback period of its equipment. So, for a 4-unit station with wind generators of the Enercon E-175 EP5 type, the payback period is 3.7 years, and with wind generators of the Vestas V136-4.5 MW type — 3.6 years.

Block‐based procurement model between retailers and wind farms in medium‐long term market

With the fast development of wind power generation, many countries actively encourage wind power to trade in the medium-long term market. However, the traditional forward contracts from medium-long term markets are only suitable to conventional power generation enterprises, instead of wind farms. To facilitate the accommodation of wind power in the medium-long term market, this paper focuses on two problems: The framework of the block-based forward contract trading method and the procurement strategy of retailers with block-based contracts. First, a novel block-based forward contract is proposed, where the time attributes, that is, starting and ending time, and power during each period are stipulated. Aggregating all the block-based forward contracts will naturally form the power supply curve, which can provide boundary information for wind farms when they take part in other markets. Secondly, a chance-constrained procurement strategy model is proposed for retailers, where the uncertainty of wind power generation, constraints of block-based contracts, and quota obligations of retailers are considered. Furthermore, a bilinear Benders decomposition algorithm with a variant of Jensen's inequalities is applied to solve the proposed model. Finally, experimental results demonstrate the effectiveness of the proposed model and method, which is computationally efficient in solving the chance-constrained procurement strategy problem.

An Ultra-short-term Wind Power Forecasting Method with Special Error Distribution

The randomness and uncontrollability of wind energy have brought many difficulties to the development of wind power generation. How to obtain accurate forecasting results of wind power has become an increasingly important topic these years. This paper proposes an ultra-short-term wind power forecasting method using Elman neural network with an enhanced gradient descent training algorithm, which is able to compute the probabilistic distribution function (PDF) of the forecasting error by utilizing two kinds of series expansion for the purpose of regarding it as the loss function of the forecasting model. To validate the accuracy and efficiency of the purposed method, the conventional least mean square error (LMSE) based model is served as a benchmark in the comparison of simulation results. At last, historical wind power statistics of one month collected from a wind farm in the northern Hebei Province of China are used to perform single-point and probabilistic predictions in order to verify the effectiveness of the purposed method.

Optimal Scheduling Model of Virtual Power Plant and Thermal Power Units Participating in Peak Regulation Ancillary Service in Northeast China

In recent years, the rapid development of wind power generation and the low load growth rate have brought challenges to the peak regulation of power grid in Northeast China. In order to solve the peak regulation problem, virtual power plant (VPP) is proposed to participate in the peak regulation auxiliary service market in Northeast china, especially for deep peak regulation. Firstly, two modes of peak regulation in Northeast China are introduced in this paper, which are thermal power unit and VPP. With the definition of the peak regulation capacity for VPP, the Optimal Scheduling Model of VPP participating in peak regulation ancillary service is established. The simulations are carried out in MATLAB. The results show that the economy of peak regulation is significantly improved in peak regulation auxiliary service based on VPP.

A novel wind speed prediction strategy based on Bi-LSTM, MOOFADA and transfer learning for centralized control centers

With the rapid development of wind power generation, a centralized monitoring center for wind farms has emerged to save investment and reduce operating costs. However, it is a daunting challenge for the intelligent wind speed prediction system of centralized control center to realize the wind speed prediction of wind farms in different environments. To this end, this paper proposes a multi-wind farm wind speed prediction strategy suitable for wind farm centralized control center. Firstly, the Bi-LSTM deep learning model is pre-trained with the historical data of four wind farms in typical geographical locations to obtain four intelligent wind speed prediction models with different characteristic parameters. Then, transfer learning is used to transfer the four pre-trained models to the wind farm centralized control center, and the wind speed of any wind farm can be predicted using these four Bi-LSTM models. Finally, the MOOFADA optimization algorithm is used to weight the four sets of prediction results to obtain the optimal wind speed prediction results. Experiments and comparisons with a variety of algorithms show that this algorithm is far higher in prediction accuracy than other algorithms, and has strong adaptability, which can be widely used in wind speed prediction for wind farms.

Wind Resource Potential Assessment and Implication for Climate Change Mitigation: The Case of Bale Zone, South Eastern Ethiopia

Wind power assessments as well as forecast of wind energy production potential are key issues in the wind energy industry. One of the necessary conditions for the development of wind power generation is to choose the optimal site. Alternative energy plays a great role for climate change mitigation, environmental protection and sustainable development. The objective of this study was to assess the distribution of wind resource based on WRF (Weather Research and Forecasting) model and its implication on climate change mitigation in Bale zone, south eastern Ethiopia. In this study, one year wind speed and wind direction at 6- hour intervals at a height of 10, 50 and 100 m were used. The data source is National Centers for Environmental Prediction (NCEP). In addition observational wind speed and wind direction data from National Meteorological Agency (NMA) of Ethiopia from ground based stations were used. The analysis result of the NCEP and NMA data by downscaling the model to 20 km by 20 km spatial resolution enabled to map the wind resource potential sites of Bale Zone applicable for wind mill installation. This study showed that most of the Bale zone areas have significant wind power potential to augment its current power generation. The analysis result revealed that wind resource potential is high during summer than winter season. Have a potential of installed 10,329MW wind capacity in Bale zone. If this potential wind resource will installed, so far environment as an estimation of about more than 5 thousand hectare of forest land per year would be preserved, and subsequently, equivalent amount of about 66,294 of CO 2 would be stored per year. Keywords: Wind speed; Wind direction; Wind power; WRF model; Bale zone DOI: 10.7176/JETP/11-1-01 Publication date: January 31 st 2021

Rooted Tree Optimization for the Backstepping Power Control of a Doubly Fed Induction Generator Wind Turbine: dSPACE Implementation

With the development of wind power generation in recent years, several studies have dealt with the active and reactive power control of wind power systems, along with the quality of energy produced and the connection to distribution networks. In this context, this research proposes a new contribution to the field. The major objective of this work is the development of a nonlinear adaptive backstepping control technique applied to a DFIG based wind system and an optimization technique that uses the rooted tree optimization (RTO) algorithm. The backstepping control strategy is based on the Lyapunov nonlinear technique to guarantee the stability of the system. It is applied to the two converters (i.e., machine and network sides) and subsequently improved with estimators to make the proposed system robust to parametric variation. The RTO technique is based on monitoring the behavior of the underlying foundation of trees in search of underground water in accordance with the level of underground control. The solution proposed for the control is validated using two methods: (1) a simulation on MATLAB/Simulink to test the continuation of the reference (real wind speed) and the robustness of the system and (2) a real-time implementation on a dSPACE-DS1104 board connected to an experimental bench in a laboratory. Simulation and experimental results highlight the validation of the proposed model with better performance compared with other control techniques, such as sliding mode control, direct power control, and field-oriented control.

Development of Wind Power Generation Forecasting Technology Based on Numerical Weather Prediction

Development of Wind Power Generation Forecasting Technology Based on Numerical Weather Prediction

Assessment of wind energy potential ofthe Murmansk Regionand performance of Kola wind farm

The paper gives an analysis of perspectives for development of wind power generation, information on the share of wind energy in electricity generation in the world and in Russian Federation is provided as well. Data on gross, technical, economic potential of wind energy of Russia and the Murmansk Region has been presented. When calculating the gross wind energy potential of the region, data from the last 10 years of observations carried out at 37 meteorological stations have been used. The territory of the region has been divided into 6 distinctive zones, based on the wind activity. Gross energy potential has been calculated for each zone: for the heights of 10, 50, 100, and 150 m. Gross wind energy potential of the region at the height of 150 m has thus been estimated at 23,090 billion kWh. The Murmansk Region's 201 MW Kola wind farm, which consists of 57 Siemens Gamesa SG 3.4-132 wind turbines with a unit capacity of 3.465 MW, is to be constructed by 2021 under the direction of Enel Green Power. Wind energy potential and annual power generation of the Kola wind farm have been assessed. The difference between the obtained results and calculations of Enel Group's specialists amounts to less than 15 %. For the cases of relocation of Kola wind farm to different wind zones, the annual power generation of the wind farm has been estimated. It has been determined, that in case of Kola wind farm's relocation to the zone with the highest wind activity its annual electricity generation could be increased up to 1.5 times. A model of the Kola energy system has been developed in NEPLAN software, its validity has been proven. The calculations of the wind farm's operation modes show that voltage levels of the system nodes and powerflows are within the boundaries defined by normative documents. The effectiveness of reactive power regulation of the wind farm has been shown.