Applied Sciences

Editorial

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3 pages, 171 KiB

Open AccessEditorial

Special Issue on “Wind Energy Conversion Systems”

by Zhe Chen

Appl. Sci. 2019, 9(16), 3258; https://doi.org/10.3390/app9163258 - 09 Aug 2019

Cited by 1 | Viewed by 1856

Abstract

A single paragraph of about 200 words maximum. For research articles, abstracts should give a pertinent overview of the work. We strongly encourage authors to use the following style of structured abstracts, but without headings: (1) Background: place the question addressed in a [...] Read more.

A single paragraph of about 200 words maximum. For research articles, abstracts should give a pertinent overview of the work. We strongly encourage authors to use the following style of structured abstracts, but without headings: (1) Background: place the question addressed in a broad context and highlight the purpose of the study; (2) Methods: describe briefly the main methods or treatments applied; (3) Results: summarize the article’s main findings; and (4) Conclusions: indicate the main conclusions or interpretations. The abstract should be an objective representation of the article; it must not contain results that are not presented and substantiated in the main text and should not exaggerate the main conclusions. Full article

(This article belongs to the Special Issue Wind Energy Conversion Systems)

Research

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17 pages, 5889 KiB

Open AccessArticle

Wind Power Short-Term Prediction Based on LSTM and Discrete Wavelet Transform

by Yao Liu, Lin Guan, Chen Hou, Hua Han, Zhangjie Liu, Yao Sun and Minghui Zheng

Appl. Sci. 2019, 9(6), 1108; https://doi.org/10.3390/app9061108 - 15 Mar 2019

Cited by 172 | Viewed by 6168

Abstract

A wind power short-term forecasting method based on discrete wavelet transform and long short-term memory networks (DWT_LSTM) is proposed. The LSTM network is designed to effectively exhibit the dynamic behavior of the wind power time series. The discrete wavelet transform is introduced to [...] Read more.

A wind power short-term forecasting method based on discrete wavelet transform and long short-term memory networks (DWT_LSTM) is proposed. The LSTM network is designed to effectively exhibit the dynamic behavior of the wind power time series. The discrete wavelet transform is introduced to decompose the non-stationary wind power time series into several components which have more stationarity and are easier to predict. Each component is dug by an independent LSTM. The forecasting results of the wind power are obtained by synthesizing the prediction values of all components. The prediction accuracy has been improved by the proposed method, which is validated by the MAE (mean absolute error), MAPE (mean absolute percentage error), and RMSE (root mean square error) of experimental results of three wind farms as the benchmarks. Wind power forecasting based on the proposed method provides an alternative way to improve the security and stability of the electric power network with the high penetration of wind power. Full article

(This article belongs to the Special Issue Wind Energy Conversion Systems)

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19 pages, 2075 KiB

Open AccessArticle

Minimizing the Energy Cost of Offshore Wind Farms by Simultaneously Optimizing Wind Turbines and Their Layout

by Longfu Luo, Xiaofeng Zhang, Dongran Song, Weiyi Tang, Li Li and Xiaoyu Tian

Appl. Sci. 2019, 9(5), 835; https://doi.org/10.3390/app9050835 - 26 Feb 2019

Cited by 13 | Viewed by 3239

Abstract

The construction and gradual installation of turbines on wind farms has been hindered by the high cost of the energy production. An effective way to minimize energy costs is via the optimal design of wind turbines and their layout, but relevant and synthetic [...] Read more.

The construction and gradual installation of turbines on wind farms has been hindered by the high cost of the energy production. An effective way to minimize energy costs is via the optimal design of wind turbines and their layout, but relevant and synthetic studies are lacking. This paper proposes a method to minimize the energy cost of offshore wind farms by simultaneously optimizing the rated wind speed, the rotor radius of wind turbines and their layout. Firstly, a new, mixed mathematical formulation of the energy cost is presented, considering the Weibull distribution for wind, the characterizing parameters of wind turbines and the distance between two turbines. Secondly, to obtain the minimum energy cost, a composite optimization algorithm was developed, which consists of an iterative method and an improved particle swarm optimization algorithm. The former was used to search the minimal energy costs that relate to the design parameters of a single wind turbine, while the latter was adopted for optimizing the layout of the wind turbines iteratively. Finally, the proposed method was applied to three case studies with variable wind speed and constant wind direction. Results of the case studies show that the reduced energy cost after optimization has a range of 0–0.001 $/kWh, which confirms the effectiveness of the proposed approach. Meanwhile, the layout of the wind turbines after optimization tends to locate the two wind turbines with the biggest spacing in the wind direction, which justifies the utilization of layout optimization. Furthermore, exploring the optimally designed parameters of wind turbines revealed that the wind farms with a high mean wind speed can have a wider range of turbine capacity than those with a low wind speed, which offers more freedom for the designers when constructing offshore wind farms at wind sites with rich wind resources. Full article

(This article belongs to the Special Issue Wind Energy Conversion Systems)

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19 pages, 6698 KiB

Open AccessArticle

Predicting the Extreme Loads in Power Production of Large Wind Turbines Using an Improved PSO Algorithm

by Caicai Liao, Kezhong Shi and XiaoLu Zhao

Appl. Sci. 2019, 9(3), 521; https://doi.org/10.3390/app9030521 - 03 Feb 2019

Cited by 11 | Viewed by 3236

Abstract

Predicting the extreme loads in power production for the preliminary-design of large-scale wind turbine blade is both important and time consuming. In this paper, a simplified method, called Particle Swarm Optimization-Extreme Load Prediction Model (PSO-ELPM), is developed to quickly assess the extreme loads. [...] Read more.

Predicting the extreme loads in power production for the preliminary-design of large-scale wind turbine blade is both important and time consuming. In this paper, a simplified method, called Particle Swarm Optimization-Extreme Load Prediction Model (PSO-ELPM), is developed to quickly assess the extreme loads. This method considers the extreme loads solution as an optimal problem. The rotor speed, wind speed, pitch angle, yaw angle, and azimuth angle are selected as design variables. The constraint conditions are obtained by considering the influence of the aeroelastic property and control system of the wind turbine. An improved PSO algorithm is applied. A 1.5 MW and a 2.0 MW wind turbine are chosen to validate the method. The results show that the extreme root load errors between PSO-ELPM and FOCUS are less than 10%, while PSO-ELPM needs much less computational cost than FOCUS. The distribution of flapwise bending moments are close to the results of FOCUS. By analyzing the loads, we find that the extreme flapwise bending moment of the blade root in chord coordinate (CMF_ROOT) is largely reduced because of the control system, with the extreme edgewise bending moment of the blade root in chord coordinate (CME_ROOT) almost unchanged. Furthermore, higher rotor speed and smaller pitch angle will generate larger extreme bending moments at the blade root. Full article

(This article belongs to the Special Issue Wind Energy Conversion Systems)

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19 pages, 1955 KiB

Open AccessArticle

Variable-Constrained Model Predictive Control of Coordinated Active Power Distribution for Wind-Turbine Cluster

by Zhenyu Chen, Jizhen Liu, Zhongwei Lin and Chenzhi Qu

Appl. Sci. 2019, 9(1), 112; https://doi.org/10.3390/app9010112 - 29 Dec 2018

Cited by 7 | Viewed by 3135

Abstract

In this paper, a wind-turbine active power control strategy is proposed from the cluster level to optimize the active power set-point for each wind turbine in a specific cluster. The wind turbine power tracking characteristic is described as an inertial link to establish [...] Read more.

In this paper, a wind-turbine active power control strategy is proposed from the cluster level to optimize the active power set-point for each wind turbine in a specific cluster. The wind turbine power tracking characteristic is described as an inertial link to establish the power tracking predictive model, and the model predictive control (MPC) method is used to optimize the cluster power demand and output. Time-varying constraints are proposed to coordinate the power output for different time-scale wind turbines and sustain that the cluster has enough fast-tracking capacity when the cluster power demand changes. Under different scenarios, the proposed strategy is tested to verify the effectiveness in improving the power output stability and frequency support ability. Full article

(This article belongs to the Special Issue Wind Energy Conversion Systems)

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14 pages, 7096 KiB

Open AccessArticle

Numerical and Experimental Methods for the Assessment of Wind Turbine Control Upgrades

by Davide Astolfi, Francesco Castellani, Francesco Berno and Ludovico Terzi

Appl. Sci. 2018, 8(12), 2639; https://doi.org/10.3390/app8122639 - 16 Dec 2018

Cited by 12 | Viewed by 2742

Abstract

Megawatt-scale wind turbine technology is nowadays mature and, therefore, several technical improvements in order to optimize the efficiency of wind power conversion have been recently spreading in the industry. Due to the nonstationary conditions to which wind turbines are subjected because of the [...] Read more.

Megawatt-scale wind turbine technology is nowadays mature and, therefore, several technical improvements in order to optimize the efficiency of wind power conversion have been recently spreading in the industry. Due to the nonstationary conditions to which wind turbines are subjected because of the stochastic nature of the source, the quantification of the impact of wind turbine power curve upgrades is a complex task and in general, it has been observed that the efficiency of the upgrades can vary considerably depending on the wind flow conditions at the microscale level. In this work, a test case of wind turbine control system improvement was studied numerically and through operational data. The wind turbine is multi-megawatt; it is part of a wind farm sited in a complex terrain in Italy, featuring 17 wind turbines. The analyzed control upgrade is an optimization of the revolutions per minute (rpm) management. The impact of this upgrade was quantified through a method based on operational data: It consists of the study, before and after the upgrade, of the residuals between the measured power output of the wind turbine of interest and an appropriate model of the power output itself. The input variables for the model were selected to be some operational parameters of the nearby wind turbines: They were selected from the data set at disposal with a stepwise regression algorithm. This work also includes a numerical characterization of the problem, by means of aeroelastic simulations performed with the FAST software: By mimicking the pre- and post-upgrade generator rpm–generator torque curve, it is subsequently possible to estimate how the wind turbine power curve changes. The main result of this work is that the two estimates of production improvement have the same order of magnitude (1.0% of the production below rated power). In general, this study sheds light on the perspective of employing not only operational data, but also a sort of digital replica of the wind turbine of interest, in order to reliably quantify the impact of control system upgrades. Full article

(This article belongs to the Special Issue Wind Energy Conversion Systems)

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16 pages, 4097 KiB

Open AccessArticle

Inertia Estimation of Wind Power Plants Based on the Swing Equation and Phasor Measurement Units

by Omar Beltran, Rafael Peña, Juan Segundo, Aaron Esparza, Eduard Muljadi and David Wenzhong

Appl. Sci. 2018, 8(12), 2413; https://doi.org/10.3390/app8122413 - 28 Nov 2018

Cited by 34 | Viewed by 5204

Abstract

High penetration of wind power plants may have an adverse impact on power systems’ stability by reducing the inertia, and problems like frequency stability could appear due to total inertia in the system being reduced, making the power system more vulnerable to disturbances. [...] Read more.

High penetration of wind power plants may have an adverse impact on power systems’ stability by reducing the inertia, and problems like frequency stability could appear due to total inertia in the system being reduced, making the power system more vulnerable to disturbances. However, most recent grid codes include an emulation inertia requirement for wind power plants, because modern wind turbines are capable of providing virtual inertia through power electronic converter controls to improve frequency stability issues. Because of this, it is necessary that the inertia estimation analyze and quantify the impact of the inertia reduction in power systems. In this paper, an implementation of a methodology for the inertia estimation of wind power plants is presented. It is evaluated through synchrophasor measurements obtained from a Real-Time Digital Simulator (RTDS) implementation, using industrial Phasor Measurement Units (PMUs). This methodology is based on the swing equation. Furthermore, a comparison of the results obtained between two professional tools RSCAD and DIgSILENT PowerFactory is performed, in order to evaluate the accuracy and the robustness of the methodology. This methodology is applied for the inertia estimation of an equivalent of the southeast zone of the Mexican power system, where there is a large-scale penetration of wind power plants. The results demonstrate that this methodology can be applied in real power systems using PMUs. Full article

(This article belongs to the Special Issue Wind Energy Conversion Systems)

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18 pages, 1034 KiB

Open AccessArticle

Wind Turbine Power Curves Based on the Weibull Cumulative Distribution Function

by Neeraj Bokde, Andrés Feijóo and Daniel Villanueva

Appl. Sci. 2018, 8(10), 1757; https://doi.org/10.3390/app8101757 - 28 Sep 2018

Cited by 28 | Viewed by 5536

Abstract

The representation of a wind turbine power curve by means of the cumulative distribution function of a Weibull distribution is investigated in this paper, after having observed the similarity between such a function and real WT power curves. The behavior of wind speed [...] Read more.

The representation of a wind turbine power curve by means of the cumulative distribution function of a Weibull distribution is investigated in this paper, after having observed the similarity between such a function and real WT power curves. The behavior of wind speed is generally accepted to be described by means of Weibull distributions, and this fact allows researchers to know the frequency of the different wind speeds. However, the proposal of this work consists of using these functions in a different way. The goal is to use Weibull functions for representing wind speed against wind power, and due to this, it must be clear that the interpretation is quite different. This way, the resulting functions cannot be considered as Weibull distributions, but only as Weibull functions used for the modeling of WT power curves. A comparison with simulations carried out by assuming logistic functions as power curves is presented. The reason for using logistic functions for this validation is that they are very good approximations, while the reasons for proposing the use of Weibull functions are that they are continuous, simpler than logistic functions and offer similar results. Additionally, an explanation about a software package has been discussed, which makes it easy to obtain Weibull functions for fitting WT power curves. Full article

(This article belongs to the Special Issue Wind Energy Conversion Systems)

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14 pages, 2644 KiB

Open AccessArticle

Congestion Risk-Averse Stochastic Unit Commitment with Transmission Reserves in Wind-Thermal Power Systems

by Yu Huang, Qingshan Xu and Guang Lin

Appl. Sci. 2018, 8(10), 1726; https://doi.org/10.3390/app8101726 - 22 Sep 2018

Cited by 10 | Viewed by 2723

Abstract

The great proliferation of wind power generation has brought about great challenges to power system operations. To mitigate the ramifications of wind power uncertainty on operational reliability, predictive scheduling of generation and transmission resources is required in the day-ahead and real-time markets. In [...] Read more.

The great proliferation of wind power generation has brought about great challenges to power system operations. To mitigate the ramifications of wind power uncertainty on operational reliability, predictive scheduling of generation and transmission resources is required in the day-ahead and real-time markets. In this regard, this paper presents a risk-averse stochastic unit commitment model that incorporates transmission reserves to flexibly manage uncertainty-induced congestion. In this two-settlement market framework, the key statistical features of line flows are extracted using a high-dimensional probabilistic collocation method in the real-time dispatch, for which the spatial correlation between wind farms is also considered. These features are then used to quantify transmission reserve requirements in the transmission constraints at the day-ahead stage. Comparative studies on the IEEE 57-bus system demonstrate that the proposed method outperforms the conventional unit commitment (UC) to enhance the system reliability with wind power integration while leading to more cost-effective operations. Full article

(This article belongs to the Special Issue Wind Energy Conversion Systems)

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18 pages, 14621 KiB

Open AccessArticle

Modeling and Control of a 600 kW Closed Hydraulic Wind Turbine with an Energy Storage System

by Liejiang Wei, Zengguang Liu, Yuyang Zhao, Gang Wang and Yanhua Tao

Appl. Sci. 2018, 8(8), 1314; https://doi.org/10.3390/app8081314 - 07 Aug 2018

Cited by 31 | Viewed by 4991

Abstract

In this paper, an innovative closed hydraulic wind turbine with an energy storage system is proposed. The hydraulic wind turbine consists of the wind rotor, the variable pump, the hydraulic bladder accumulator, the variable motor, and the synchronous generator. The wind energy captured [...] Read more.

In this paper, an innovative closed hydraulic wind turbine with an energy storage system is proposed. The hydraulic wind turbine consists of the wind rotor, the variable pump, the hydraulic bladder accumulator, the variable motor, and the synchronous generator. The wind energy captured by the wind rotor is converted into hydraulic energy by the variable pump, and then the hydraulic energy is transformed into electrical energy by the variable motor and generator. In order to overcome the fluctuation and intermittence shortcomings of wind power, the hydraulic bladder accumulator is used as an energy storage system in this system to store and release hydraulic energy. A double-loop speed control scheme is presented to allow the wind rotor to operate at optimal aerodynamic performance for different wind speeds and hold the motor speed at the synchronous speed to product constant frequency electrical power regardless of the changes of wind speed and load power. The parameter design and modeling of 600 kW hydraulic wind turbine are accomplished according to the Micon 600 kW wind turbine. Ultimately, time-domain simulations are completed to analyze the dynamic response of the hydraulic wind turbine under the step change conditions of wind speed, rotor speed input, and load power. The simulation results validate the efficiency of the hydraulic wind turbine and speed control scheme presented, moreover, they also show that the systems can achieve the automatic matching among turbine energy, accumulator energy, and generator output energy. Full article

(This article belongs to the Special Issue Wind Energy Conversion Systems)

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Review

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22 pages, 5467 KiB

Open AccessReview

Overview of Wind Power Industry Value Chain Using Diamond Model: A Case Study from China

by Jicheng Liu, Qiushuang Wei, Qiongjie Dai and Chunyan Liang

Appl. Sci. 2018, 8(10), 1900; https://doi.org/10.3390/app8101900 - 12 Oct 2018

Cited by 14 | Viewed by 10198

Abstract

Sustainable energy development has gained worldwide attention, in part thanks to the wind power industry value chain that focuses on overall value creation and innovation, especially in China. This paper aims to construct a wind power industry value chain model and comprehensively analyze [...] Read more.

Sustainable energy development has gained worldwide attention, in part thanks to the wind power industry value chain that focuses on overall value creation and innovation, especially in China. This paper aims to construct a wind power industry value chain model and comprehensively analyze factors that have significant influences on it using a modified diamond model, which has remained nebulous. Focused on the value-adding effect of constructed value chains, we offer key ideas from different angles. A factor condition lays the foundation of the value chain, and shows that China is experiencing energy structure adjustment in which wind power will play a key role; its resource potential is huge, but with mismatched distribution. Demand conditions reveal an increasing demand for wind but serious wind rejection as well; this is where the value-adding probability exists. Related and support departments collaborate to determine the overall value creation process. Firm strategy, structure, and rivalry are terms that describe possible value-adding subjects considering the wind industry as a whole. Government and opportunity provide robust prices and non-price policies to support value integration, and Technology is an effective factor in cost reduction and value creation as a high value-adding sector. Furthermore, a comparison of wind power industry value chains in China and Japan is conducted. Our findings underscore that a gap exists between actual performance and the expected wind power industry value chain, and corresponding measurements to promote the performance are discussed, including encouraging diversified business models, enhancing R&D and independent innovation, professional cultivation, effectively reducing wind rejection rate, and the full range of government support. Full article

(This article belongs to the Special Issue Wind Energy Conversion Systems)

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