Pure harmonics extracting from time-varying power signal based on improved empirical mode decomposition

Section: Energy Measurement Algorithm Based On Fftmentioning

confidence: 99%

An Energy Measurement Method of Electric Vehicle Based on FFT and Wavelet Transform

Tian¹,

Jian²,

Xu³

et al. 2017

IJCTE

“…

is the estimate sources using independent component analysis. A widely-applied method for harmonic identification is independent component analysis (ICA); its principle and realization have been explained in [ 41 ]. In this paper, this method is utilized to testify to the effects of disordered sequences on harmonic source identification accuracy.…”

Section: Preliminariesmentioning

confidence: 99%

“…The 3 channels are linked to the same point of common coupling (PCC), and their harmonics affect each other. Harmonic source identification will separate the sources according to the independent component analysis method introduced in [ 41 ]. The 3rd–9th odd harmonics are measured to examine the effects of the OOSM algorithm on harmonic identification.…”

Section: Experiments and Analysismentioning

confidence: 99%

Data-Aware Retrodiction for Asynchronous Harmonic Measurement in a Cyber-Physical Energy System

Wang

et al. 2016

Sensors

Self Cite

Cyber-physical energy systems provide a networked solution for safety, reliability and efficiency problems in smart grids. On the demand side, the secure and trustworthy energy supply requires real-time supervising and online power quality assessing. Harmonics measurement is necessary in power quality evaluation. However, under the large-scale distributed metering architecture, harmonic measurement faces the out-of-sequence measurement (OOSM) problem, which is the result of latencies in sensing or the communication process and brings deviations in data fusion. This paper depicts a distributed measurement network for large-scale asynchronous harmonic analysis and exploits a nonlinear autoregressive model with exogenous inputs (NARX) network to reorder the out-of-sequence measuring data. The NARX network gets the characteristics of the electrical harmonics from practical data rather than the kinematic equations. Thus, the data-aware network approximates the behavior of the practical electrical parameter with real-time data and improves the retrodiction accuracy. Theoretical analysis demonstrates that the data-aware method maintains a reasonable consumption of computing resources. Experiments on a practical testbed of a cyber-physical system are implemented, and harmonic measurement and analysis accuracy are adopted to evaluate the measuring mechanism under a distributed metering network. Results demonstrate an improvement of the harmonics analysis precision and validate the asynchronous measuring method in cyber-physical energy systems.

“…Modern methods based on artificial neural networks (ANN) [ 9 , 10 ], adaptive linear neuron (ADALINE) network [ 11 , 21 ], independent component analysis (ICA) [ 29 , 30 ] and empirical mode decomposition (EMD) [ 31 , 32 ] have emerged in this field recently. ANN and ADALINE can be applied in real-time for time-varying power signals; however, traditional ADALINE and ANN methods are only capable of calculating the harmonic component’s amplitude and phase angle with the frequency known a priori, and noise and interharmonics have to be pre-filtered for accuracy, thus limiting the measurement of interharmonics.…”

Section: Introductionmentioning

confidence: 99%

“…[ 30 ] proposes to leave the computation to the design stage to obtain the best separation row offline, and achieves accurate estimation results at the sacrifice of the adaptability to noise. EMD-based methods such as improved EMD with masking signals (IM-EMD) [ 32 ] also aim at extracting single-frequency harmonics from distorted time-varying power signals, but the masking parameters for IM-EMD are not consistent in different conditions and lack of self-adaption.…”

Section: Introductionmentioning

confidence: 99%

Resolution-Enhanced Harmonic and Interharmonic Measurement for Power Quality Analysis in Cyber-Physical Energy System

Wang

et al. 2016

Sensors

Self Cite

Power quality analysis issues, especially the measurement of harmonic and interharmonic in cyber-physical energy systems, are addressed in this paper. As new situations are introduced to the power system, the impact of electric vehicles, distributed generation and renewable energy has introduced extra demands to distributed sensors, waveform-level information and power quality data analytics. Harmonics and interharmonics, as the most significant disturbances, require carefully designed detection methods for an accurate measurement of electric loads whose information is crucial to subsequent analyzing and control. This paper gives a detailed description of the power quality analysis framework in networked environment and presents a fast and resolution-enhanced method for harmonic and interharmonic measurement. The proposed method first extracts harmonic and interharmonic components efficiently using the single-channel version of Robust Independent Component Analysis (RobustICA), then estimates the high-resolution frequency from three discrete Fourier transform (DFT) samples with little additional computation, and finally computes the amplitudes and phases with the adaptive linear neuron network. The experiments show that the proposed method is time-efficient and leads to a better accuracy of the simulated and experimental signals in the presence of noise and fundamental frequency deviation, thus providing a deeper insight into the (inter)harmonic sources or even the whole system.