The fault diagnostic simulation and research of wind generator electrical signal in dierent conditions

2015 ◽  
pp. 585-590
Keyword(s):  
Electrical Signal ◽  
Wind Generator ◽  
Fault Diagnostic ◽  
Diagnostic Simulation

Analysis of magneto rheological elastomers for energy harvesting systems

2020 ◽  
Vol 64 (1-4) ◽  
pp. 439-446
Author(s):  
Gildas Diguet ◽  
Gael Sebald ◽  
Masami Nakano ◽  
Mickaël Lallart ◽  
Jean-Yves Cavaillé
Keyword(s):  
Magnetic Field ◽  
Energy Harvesting ◽  
Shear Strain ◽  
Magnetic Induction ◽  
Magnetic Particles ◽  
Homogeneous Magnetic Field ◽  
Electrical Signal ◽  
Harvesting Systems ◽  
Dc Bias ◽  
Magneto Rheological

Magneto Rheological Elastomers (MREs) are composite materials based on an elastomer filled by magnetic particles. Anisotropic MRE can be easily manufactured by curing the material under homogeneous magnetic field which creates column of particles. The magnetic and elastic properties are actually coupled making these MREs suitable for energy conversion. From these remarkable properties, an energy harvesting device is considered through the application of a DC bias magnetic induction on two MREs as a metal piece is applying an AC shear strain on them. Such strain therefore changes the permeabilities of the elastomers, hence generating an AC magnetic induction which can be converted into AC electrical signal with the help of a coil. The device is simulated with a Finite Element Method software to examine the effect of the MRE parameters, the DC bias magnetic induction and applied shear strain (amplitude and frequency) on the resulting electrical signal.

Adaptive identification methods of nonlinear random electrical signal

2020 ◽  
Vol 64 (1-4) ◽  
pp. 431-438
Author(s):  
Jian Liu ◽  
Lihui Wang ◽  
Zhengqi Tian
Keyword(s):  
Wavelet Transform ◽  
Electric Vehicle ◽  
Signal To Noise Ratio ◽  
Sudden Change ◽  
Electrical Signal ◽  
Signal Recognition ◽  
State Variable ◽  
Signal Characteristics ◽  
The Matrix ◽  
Kalman Filter Algorithm

The nonlinearity of the electric vehicle DC charging equipment and the complexity of the charging environment lead to the complex and changeable DC charging signal of the electric vehicle. It is urgent to study the distortion signal recognition method suitable for the electric vehicle DC charging. Focusing on the characteristics of fundamental and ripple in DC charging signal, the Kalman filter algorithm is used to establish the matrix model, and the state variable method is introduced into the filter algorithm to track the parameter state, and the amplitude and phase of the fundamental waves and each secondary ripple are identified; In view of the time-varying characteristics of the unsteady and abrupt signal in the DC charging signal, the stratification and threshold parameters of the wavelet transform are corrected, and a multi-resolution method is established to identify and separate the unsteady and abrupt signals. Identification method of DC charging distortion signal of electric vehicle based on Kalman/modified wavelet transform is used to decompose and identify the signal characteristics of the whole charging process. Experiment results demonstrate that the algorithm can accurately identify ripple, sudden change and unsteady wave during charging. It has higher signal to noise ratio and lower mean root mean square error.

Development of the algorithm for the selection of a wind generator-based autonomous power supply system

2019 ◽  
Vol 2 (1) ◽  
pp. 8-16 ◽  
Author(s):  
P. A. Khlyupin ◽  
G. N. Ispulaeva
Keyword(s):  
Wind Turbines ◽  
Power Supply ◽  
Alternative Energy ◽  
Power Supply System ◽  
Supply System ◽  
Power Generators ◽  
Energy Devices ◽  
Wind Generator ◽  
Alternative Energy Sources ◽  
Selection Of

Introduction: The co-authors provide an overview of the main types of wind turbines and power generators installed into wind energy devices, as well as advanced technological solutions. The co-authors have identified the principal strengths and weaknesses of existing wind power generators, if applied as alternative energy sources. The co-authors have proven the need to develop an algorithm for the selection of a wind generator-based autonomous power supply system in the course of designing windmill farms in Russia. Methods: The co-authors have analyzed several types of wind turbines and power generators. Results and discussions: The algorithm for the selection of a wind generator-based autonomous power supply system is presented as a first approximation. Conclusion: The emerging algorithm enables designers to develop an effective wind generator-based autonomous power supply system.

THE INFLUENCE OF THE WIND SPEED PREDICTION ERROR ON THE SIZE OF THE STORAGE CONTROLLED OPERATION ZONE IN THE SYSTEM WITH THE WIND GENERATOR

2020 ◽  
Vol 2020 (57) ◽  
pp. 35-41
Author(s):  
K.S. Klen ◽  
◽  
M.K. Yaremenko ◽  
V.Ya. Zhuykov ◽  
◽  
...  
Keyword(s):  
Wind Speed ◽  
Prediction Error ◽  
Average Energy ◽  
Interpolation Formula ◽  
Wind Speed Prediction ◽  
Wind Generator ◽  
Prediction Horizon ◽  
Speed Prediction ◽  
Using Data ◽  
Predictor Corrector

The article analyzes the influence of wind speed prediction error on the size of the controlled operation zone of the storage. The equation for calculating the power at the output of the wind generator according to the known values of wind speed is given. It is shown that when the wind speed prediction error reaches a value of 20%, the controlled operation zone of the storage disappears. The necessity of comparing prediction methods with different data discreteness to ensure the minimum possible prediction error and determining the influence of data discreteness on the error is substantiated. The equations of the "predictor-corrector" scheme for the Adams, Heming, and Milne methods are given. Newton's second interpolation formula for interpolation/extrapolation is given at the end of the data table. The average relative error of MARE was used to assess the accuracy of the prediction. It is shown that the prediction error is smaller when using data with less discreteness. It is shown that when using the Adams method with a prediction horizon of up to 30 min, within ± 34% of the average energy value, the drive can be controlled or discharged in a controlled manner. References 13, figures 2, tables 3.

Study of the vortex trail behind the model of rotoe wind generator

2012 ◽  
pp. 344-349
Author(s):  
Ivan Kabardin ◽  
Igor Naumov ◽  
R Mikelson ◽  
K Velte
Keyword(s):  
Wind Generator

Effect of Neglecting Stator Transients in Squirrel Cage Wind Generator Model

2005 ◽  
Vol 1 (03) ◽  
pp. 453-457
Author(s):  
S. Martín ◽  
◽  
M.P. Comech ◽  
S. Borroy ◽  
M. García-Gracia
Keyword(s):  
Wind Generator ◽  
Squirrel Cage ◽  
Generator Model
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