Noise Reduction for Nonlinear Nonstationary Time Series Data using Averaging Intrinsic Mode Function

Premanode, Bhusana; Vongprasert, Jumlong; Toumazou, C.

doi:10.3390/a6030407

Cited by 13 publications

(4 citation statements)

References 23 publications

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“…So before starting analysis one should clean the data so as to avoid the wrong conclusion. Noise removing can be handled by using traditional signal processing techniques such as digital filters or wavelet thresholding [26]. To filter outliers, k-nearest neighbor clustering [27] is widely used.…”

Section: Proposed Methodologymentioning

confidence: 99%

Intelligent multiperiod wind power forecast model using statistical and machine learning model

Galphade¹,

Nikam²,

Banerjee

et al. 2022

Bulletin EEI

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With the rapidly increasing integration of wind energy into the modern energy grid system, wind energy prediction (WPP) is playing an important role in the planning and operation of an electrical distribution system. However, the time series data of wind energy always has nonlinear and non-stationary characteristics, which is still a great challenge to be accurately predicted. This paper proposes the intelligent wind power forecast model and evaluates to forecast long term, short term and medium term wind power. It uses statistical and machine learning approach for finding the best model for multiperiod forecasting. The model has been tested on Sotavento wind farm historical data, located in Galicia, Spain. The experimental results show that random forest has better accuracy than other models for long term, short term and medium term forecasting. The power prediction accuracy of the proposed model has been evaluated on RMSE, and MAE metrics. The proposed model has shown better accuracy for medium term and long term forecast. The accuracy is improved by 72.12% in case of medium term and 50.49% in case of long term.

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Section: Proposed Methodologymentioning

confidence: 99%

Intelligent multiperiod wind power forecast model using statistical and machine learning model

Galphade¹,

Nikam²,

Banerjee

et al. 2022

Bulletin EEI

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“…Some scholars combine these methods for research, such as Wang et al [3] proposed an improved threshold wavelet denoising algorithm based on EMD. Some scholars have proposed some new methods, such as Premanode et al [4] using the averaging intrinsic mode function to denoise the nonlinear and nonstationary time series data. Due to the complexity of the algorithm and the heavy workload of calculation, it needs long-term exploration to extend the above methods to practical application.…”

Section: Introductionmentioning

confidence: 99%

Filter Realization of the Time‐Domain Average Denoising Method for a Mechanical Signal

Feng

Wang

Chen

et al. 2021

Shock and Vibration

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Time-domain averaging (TDA) is an effective signal processing technique in fault diagnosis that can extract the periodic components of interest from signals mixed with noise interference while suppressing other irrelevant periodic signals. However, there are two obvious shortcomings to TDA: first, the acquisition of keyphasor signals is often restricted by the application environment and conditions. Even if the signal is obtained by TDA, owing to the existence of periodic truncation errors, satisfactory results cannot be obtained. Second, due to the velocity fluctuation, the actual mechanical signal is easy to produce a large error in TDA stacking. To solve the above challenges, first, based on the disadvantage of using traditional resampling to solve the TDA synchronization problem, this paper proposes a new method of subsection resampling, which improves the analysis effect of the traditional TDA. Second, to further expand the range of the practical applications, according to the amplitude-frequency map of TDA, a method for realizing TDA function by using the FIR multiband filter is proposed. This approach effectively avoids the requirement of traditional methods to collect the keyphasor signals and broadens the application in practical engineering. Finally, the improved TDA method is compared with the filter implementation technology, and their respective application conditions are given.

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“…These methods are usually combined with the use of the yaw rate gyro and lateral acceleration sensor. However, these sensors usually contain a bias and noise [18]. In addition, a lateral accelerometer cannot provide a good identification of vehicle lateral acceleration and the gravity component of acceleration.…”

Section: Introductionmentioning

confidence: 99%

Data Fusion Modeling for an RT3102 and Dewetron System Application in Hybrid Vehicle Stability Testing

Miao

Zhang

2015

Algorithms

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More and more hybrid electric vehicles are driven since they offer such advantages as energy savings and better active safety performance. Hybrid vehicles have two or more power driving systems and frequently switch working condition, so controlling stability is very important. In this work, a two-stage Kalman algorithm method is used to fuse data in hybrid vehicle stability testing. First, the RT3102 navigation system and Dewetron system are introduced. Second, a modeling of data fusion is proposed based on the Kalman filter. Then, this modeling is simulated and tested on a sample vehicle, using Carsim and Simulink software to test the results. The results showed the merits of this modeling.

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Noise Reduction for Nonlinear Nonstationary Time Series Data using Averaging Intrinsic Mode Function

Cited by 13 publications

References 23 publications

Intelligent multiperiod wind power forecast model using statistical and machine learning model

Intelligent multiperiod wind power forecast model using statistical and machine learning model

Filter Realization of the Time‐Domain Average Denoising Method for a Mechanical Signal

Data Fusion Modeling for an RT3102 and Dewetron System Application in Hybrid Vehicle Stability Testing

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