Research on Equivalent Modeling of Double-Fed Wind Farm Study

2014 ◽  
Vol 521 ◽  
pp. 147-150
Author(s):  
Chao Zhong ◽  
Ran Li
Keyword(s):  
Wind Speed ◽  
Transmission Line ◽  
Output Power ◽  
Average Method ◽  
Wind Farm ◽  
Weighted Average ◽  
Equivalent Model ◽  
Weighted Average Method ◽  
Ground Fault ◽  
Three Phase

This paper used double-fed wind farm as the object. The output power of the double-fed wind farm fluctuates because the wind speed differs and changes and the three-phase shorted to ground fault occurs in the transmission line. The weighted average method was used in double-fed wind farm grid equivalent modeling. By comparing power fluctuations in the actual model and the equivalent model, it is verified the proposed equivalent modeling method is effective.

Modelling Wind Farm with Doubly Fed Induction Generators Based on Characteristic Fusion

2014 ◽  
Vol 494-495 ◽  
pp. 1820-1824
Author(s):  
Dong Ning Wei ◽  
Xue Min Zhang ◽  
Jian Min Ye
Keyword(s):  
Wind Speed ◽  
Wind Farm ◽  
Measurement Data ◽  
Equivalent Model ◽  
Modelling Framework ◽  
Induction Generators ◽  
Doubly Fed Induction Generators ◽  
Wake Model ◽  
Basic Characteristics ◽  
Doubly Fed

In this paper, a novel modelling approach based on characteristic fusion is proposed and used to build a static equivalent model of wind farm. Firstly, the modelling framework based on characteristic fusion is given. Secondly, the basic characteristics of wind farm including characteristic of wind turbine generator (WTG), wind speed spatial distribution and characteristic of wind farm are analyzed according to the framework. Then detailed modelling process is provided utilizing SVR as a fusion tool. This approach combines the advantages of both mechanism and non-mechanism methods with both satisfactory fitting ability and generalization ability. It only requires the maximum and minimum value of wind speed among the wind farm, rather than accurate wake model as mechanism method nor massive measurement data as non-mechanism method. Numerical simulation indicates the effectiveness and robustness of the proposed method. When available data is reduced or includes bad measurement, the proposed method can still keep favorable performance.

Front vehicle detection based on improved fusion method for lidar and visual image

2022 ◽  
pp. 095440702110685
Author(s):  
Aijuan Li ◽  
Zhenghong Chen ◽  
Donghong Ning ◽  
Xin Huang ◽  
Gang Liu
Keyword(s):  
Average Method ◽  
Weighted Average ◽  
Visual Image ◽  
Vehicle Detection ◽  
Detection Accuracy ◽  
The Real ◽  
Weighted Average Method ◽  
Weighted Fusion ◽  
Vehicle Test ◽  
Real Vehicle

In order to ensure the detection accuracy, an improved adaptive weighted (IAW) method is proposed in this paper to fuse the data of images and lidar sensors for the vehicle object’s detection. Firstly, the IAW method is proposed in this paper and the first simulation is conducted. The unification of two sensors’ time and space should be completed at first. The traditional adaptive weighted average method (AWA) will amplify the noise in the fusion process, so the data filtered with Kalman Filter (KF) algorithm instead of with the AWA method. The proposed IAW method is compared with the AWA method and the Distributed Weighted fusion KF algorithm in the data fusion simulation to verify the superiority of the proposed algorithm. Secondly, the second simulation is conducted to verify the robustness and accuracy of the IAW algorithm. In the two experimental scenarios of sparse and dense vehicles, the vehicle detection based on image and lidar is completed, respectively. The detection data is correlated and merged through the IAW method, and the results show that the IAW method can correctly associate and fuse the data of the two sensors. Finally, the real vehicle test of object vehicle detection in different environments is carried out. The IAW method, the KF algorithm, and the Distributed Weighted fusion KF algorithm are used to complete the target vehicle detection in the real vehicle, respectively. The advantages of the two sensors can give full play, and the misdetection of the target objects can be reduced with proposed method. It has great potential in the application of object acquisition.

scholarly journals Evaluation of a plot-scale methane emission model using eddy covariance observations and footprint modelling

2014 ◽  
Vol 11 (17) ◽  
pp. 4651-4664 ◽  
Author(s):  
A. Budishchev ◽  
Y. Mi ◽  
J. van Huissteden ◽  
L. Belelli-Marchesini ◽  
G. Schaepman-Strub ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Methane Emission ◽  
Average Method ◽  
Temporal Dynamics ◽  
Weighted Average ◽  
Ambient Atmosphere ◽  
Emission Model ◽  
Closed Chamber ◽  
Weighted Average Method ◽  
Emission Models

Abstract. Most plot-scale methane emission models – of which many have been developed in the recent past – are validated using data collected with the closed-chamber technique. This method, however, suffers from a low spatial representativeness and a poor temporal resolution. Also, during a chamber-flux measurement the air within a chamber is separated from the ambient atmosphere, which negates the influence of wind on emissions. Additionally, some methane models are validated by upscaling fluxes based on the area-weighted averages of modelled fluxes, and by comparing those to the eddy covariance (EC) flux. This technique is rather inaccurate, as the area of upscaling might be different from the EC tower footprint, therefore introducing significant mismatch. In this study, we present an approach to validate plot-scale methane models with EC observations using the footprint-weighted average method. Our results show that the fluxes obtained by the footprint-weighted average method are of the same magnitude as the EC flux. More importantly, the temporal dynamics of the EC flux on a daily timescale are also captured (r2 = 0.7). In contrast, using the area-weighted average method yielded a low (r2 = 0.14) correlation with the EC measurements. This shows that the footprint-weighted average method is preferable when validating methane emission models with EC fluxes for areas with a heterogeneous and irregular vegetation pattern.

Research on Optimization Model for Power Coal Blending Based on Genetic Algorithm

2005 ◽  
Author(s):  
Yanfen Liao ◽  
Changhong Wu ◽  
Xiaoqian Ma
Keyword(s):  
Genetic Algorithm ◽  
Average Method ◽  
Power Plants ◽  
Weighted Average ◽  
Weighted Average Method ◽  
Blended Coal ◽  
Coal Blending ◽  
Two Factors ◽  
Great Pressure ◽  
Coal Properties

The slagging process is a popular problem in coal-fired power plants because the coal properties deviate from designed condition, at the same time, power plants is enduring a great pressure with the increasing of coal prices. Power coal blending provides an effective way to solve these two problems. In some traditional methods, blended-coal properties were usually treated by the weighted average method which induced the optimization solutions deviating from the actual results. The reason is that different coal property indexes are based on different benchmarks; for example, the sulphur content in coal is based on applied basis, while the slagging properties of blended-coal are calculated on air-dried basis, which was influenced by the contents of moisture and ash in each coal. In order to study the effects, based on the genetic algorithm, a model considering these two factors was build up to optimum the coal-blending scheme. Compared with the traditional weighted average method, the new model got higher slagging property indexes, as means the former method may include some coal blending schemes into the optimizing process, in which the real slagging parameters go beyond constraint standards. Therefore, in the case of coal-blending optimization to prevent slagging in furnace, these two factors are especially important and should be considered carefully to ensure the precise of slagging parameters, so as to obtain the optimum results both in the prices of coals and in slagging property.

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