Identification of multiple harmonic sources in power system containing inverter‐based distribution generations using empirical mode decomposition

Moradifar, Amir; Foroud, Asghar Akbari; Fouladi, Milad

doi:10.1049/iet-gtd.2018.5382

Cited by 13 publications

(19 citation statements)

References 25 publications

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“…Although one‐dimensional (1D) signal processing methods were widely used to analyze the PQDs, but two‐dimensional (2D) signal processing methods due to generating more feature groups than 1D signal processing and distinctive features can be better 3 . In recent studies regarding PQDs recognition, only 1D methods are used 4‐12 Simultaneous study of the current and voltage signals using 2D signal processing methods can help to better identify some PQDs, which is discussed in this article by using two‐dimensional discrete wavelet transform (2D‐DWT).…”

Section: Introductionmentioning

confidence: 99%

A novel flicker detection method for vertical axis wind turbine using two‐dimensional discrete wavelet transform

Doustmohammadi

Foroud

2020

Int Trans Electr Energ Syst

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Background Pattern recognition applications have increased dramatically over the past four decades. In power system, pattern recognition can be used in fields such as power quality. One of the important power quality aspects is flicker, which is caused by torque fluctuations in wind turbines. Aims In this paper, the various mechanical faults including single and double faults that cause flicker in vertical axis wind turbines (VAWTs) are modeled; then flicker caused by single and double faults are analyzed. Then, a novel method for pattern recognition is presented which results in more accuracy in flicker detection. Materials and Methods In this paper, features of voltage and current signals are extracted by the 2D‐DWT. The 2D‐DWT is capable of producing valuable features than the discrete wavelet transform (DWT) at a lower level of decomposition. The 2D‐DWT is even capable of compressing data that can be suitable for smart grids, which high volume data is one of their problems. In order to reduce the redundant data, dimension of features vector, and computational burden, a new method is presented for feature selection. The backbone of the presented feature selector is the used classifier in the corresponding pattern system; so it is compatible with the classifier. Results Results show the high accuracy of the proposed method in identifying the factors that cause flicker in VAWTs. Conclusion In this paper, it was shown that the features extracted from the current and voltage signal by the 2D‐DWT were superior to the features extracted from the only voltage signal by the DWT. Also, the advantage of the proposed feature selection over the Relieff method was shown.

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Section: Introductionmentioning

confidence: 99%

A novel flicker detection method for vertical axis wind turbine using two‐dimensional discrete wavelet transform

Doustmohammadi

Foroud

2020

Int Trans Electr Energ Syst

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“…The serious drawbacks of neural network-based methods include unavailability of suitable training data and inaccuracies due to model mismatches, especially when unknown and unmodelled harmonics are encountered by the method. Another method is presented in (Moradifar et al, 2019b), which uses K-nearest neighbours (KNN) classifier for harmonic source identification. This method is computationally complex due to empirical mode decomposition, which is used for feature extraction.…”

Section: Introductionmentioning

confidence: 99%

An improved active power direction method for harmonic source identification

Joshi

Jain

2020

Transactions of the Institute of Measurement and Control

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Due to significant increment in harmonic polluting loads in the power system, there has been enhanced attention of the power professionals towards the estimation of harmonic signals and identification of their sources in the system. Harmonic source identification is an important step for proper accountability, monitoring, and mitigation of any harmonic pollution. The active power direction (APD) method is one of the conventional approaches for harmonic source detection in the distribution system. Although it is simple and easy to implement, serious concerns were raised on its validity, as the direction of active power is dependent on the phase angle. In this paper, APD is augmented with distorting and non-distorting power to improve its accuracy and reliability for harmonic source identification. The distorting and non-distorting portions of the loads are separated, and the distorting and non-distorting powers are calculated at each node. These calculated powers, in addition to the direction of the harmonic active power, are used to formulate the logic required for deciding the severity index at each node. The validity of the method has been tested on a single-phase network, an IEEE-5 bus system, and an IEEE-14 bus system. It has been observed that the proposed method provides good results than conventional APD with the same measurement requirement.

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“…Five features with the most weight ratio as the input of the probabilistic neural network (PPN) classification network were selected. In addition, compared to reference [7], reference [17] only considered the voltage characteristics. The harmonic voltage data was decomposed by empirical mode decomposition (EMD), and the features are extracted from IMF1∼5.…”

Section: Introductionmentioning

confidence: 99%

“…According to previous work, the existing methods for determining the harmonic source type include three main stages which are the feature extraction to form a feature set, feature selection, and classification. The majority of the feature extraction in this work are implemented manually with the support of a wealth of expert knowledge, such as total harmonic distortion (THD) [14], fast Fourier transform (FFT) [15], Wavelet transform (WT) [16], active power [7], EMD [17], etc. These methods may not intuitively and effectively express the characteristics of each type of the harmonic source, and required a large amount of computation for feature extraction, which increases the complexity of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

Classification of the Type of Harmonic Source Based on Image-Matrix Transformation and Deep Convolutional Neural Network

Mei

Sha

2019

IEEE Access

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The classification of harmonic source types is a necessary step to alleviate harmonic pollution. This study proposes a method for the harmonic source classification based on 2-D image-matrix transformation (IMT) and deep convolutional neural network (CNN). The method firstly converts the V-I waveforms of different harmonic source types into a matrix by IMT, which was then applied to express the V-I waveform of each harmonic source as an input CNN model. Thereafter, features were extracted automatically from the harmonic source IMT matrix by the CNN model. Finally, favorable results are achieved after identifying the type of harmonic source via a classifier. In case study, a number of verifications were performed on the proposed method. Moreover, compared with existing harmonic source classification methods, the proposed strategy in this study not only avoids extracting features through expert knowledge, but also reduces the amount of data required for the harmonic source classification, and achieves higher accuracy than other methods. INDEX TERMS IMT, CNN, type of harmonic source, classification.

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Identification of multiple harmonic sources in power system containing inverter‐based distribution generations using empirical mode decomposition

Cited by 13 publications

References 25 publications

A novel flicker detection method for vertical axis wind turbine using two‐dimensional discrete wavelet transform

A novel flicker detection method for vertical axis wind turbine using two‐dimensional discrete wavelet transform

An improved active power direction method for harmonic source identification

Classification of the Type of Harmonic Source Based on Image-Matrix Transformation and Deep Convolutional Neural Network

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