An Automatic Identification Framework for Complex Power Quality Disturbances Based on Multifusion Convolutional Neural Network

Qiu, Wei; Tang, Qiu; Liu, Jie; Yao, Wenxuan

doi:10.1109/tii.2019.2920689

Cited by 87 publications

(25 citation statements)

References 25 publications

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“…However, better identification of PQDs has been found with convolutional network structure in different noise levels [218]. Multifusion convolutional neural network for complex PQDs in the noisy environment has been presented in [219]. The comparative performance of detection and classification techniques in the noisy and non-noisy environment are illustrated in Table 8.…”

Section: Hybrid Classifiermentioning

confidence: 99%

Comprehensive Review on Detection and Classification of Power Quality Disturbances in Utility Grid With Renewable Energy Penetration

et al. 2020

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The global concern with power quality is increasing due to the penetration of renewable energy (RE) sources to cater the energy demands and meet de-carbonization targets. Power quality (PQ) disturbances are found to be more predominant with RE penetration due to the variable outputs and interfacing converters. There is a need to recognize and mitigate PQ disturbances to supply clean power to the consumer. This paper presents a critical review of techniques used for detection and classification PQ disturbances in the utility grid with renewable energy penetration. The broad perspective of this review paper is to provide various concepts utilized for extraction of the features to detect and classify the PQ disturbances even in the noisy environment. More than 220 research publications have been critically reviewed, classified and listed for quick reference of the engineers, scientists and academicians working in the power quality area.

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Section: Hybrid Classifiermentioning

confidence: 99%

Comprehensive Review on Detection and Classification of Power Quality Disturbances in Utility Grid With Renewable Energy Penetration

et al. 2020

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“…At the same time, there are also a large number of nonlinear loads in the power grid (such as automotive charging piles, power transfer switches). The power grid is showing a power electronic trend, and the power quality problem of the distribution network is becoming more and more serious (Qiu et al, 2020). Frequent occurrences of power quality events cause a lot of economic losses and bring great inconvenience to people's lives.…”

Section: Introductionmentioning

confidence: 99%

Classification of Power Quality Disturbance Based on S-Transform and Convolution Neural Network

Liu

Deng-ke

et al. 2021

Front. Energy Res.

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The accurate classification of power quality disturbance (PQD) signals is of great significance for the establishment of a real-time monitoring system of modern power grids, ensuring the safe and stable operation of the power system and ensuring the electricity safety of users. Traditional power quality disturbance signal classification methods are susceptible to noise interference, feature selection, etc. In order to further improve the accuracy of power quality disturbance signal classification methods, this paper proposes a power quality disturbance classification method based on S-transform and Convolutional Neural Network (CNN). Firstly, S-transform is used to extract disturbance signals to obtain the time-frequency matrix with characteristics of the disturbance signals. As an extension of wavelet transform and Fourier transform, S-transform can avoid the disadvantages of difficult window function selection and fixed window width. At the same time, the feature extracted by S-transform has better noise immunity. Secondly, CNN is used to perform secondary feature extraction on the obtained high-dimensional time-frequency modulus matrix to reduce data dimensions and obtain the main features of the disturbance signal, then the main features extracted are classified by using the SoftMax classifier. Finally, after a series of simulation experiments, the results show that the proposed algorithm can accurately classify single disturbance signals with different signal-to-noise ratios and composite disturbance signals composed of single disturbance signals, and it also has good noise immunity. Compared with other classification methods, the algorithm proposed in this paper has better timeliness and higher accuracy, and it is an efficient and feasible power quality disturbance signal classification method.

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“…Typically, PQD can occur in different forms, i.e., voltage sag, swell, interruption, harmonic, spike, transient, notch, or their combination [9][10][11]. To prevent the damage to the power system due to PQD, several tools have been proposed to identify the type of disturbances [12][13][14][15][16]. Signal processing is the main technique used in PQD classification since it is capable of accurately characterizing the type of signals [17].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Salp Swarm Algorithm as Optimal Feature Selection for Power Quality Disturbance Classification

et al. 2021

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Power quality disturbance (PQD) is an influential situation that significantly declines the reliability of electrical distribution systems. Therefore, PQD classification is an important process for preventing system reliability degradation. This paper introduces a novel algorithm called “adaptive salp swarm algorithm (SSA)” as an optimal feature selection algorithm for PQD classification. Feature extraction and classifier of the proposed classification system were based on the discrete wavelet and the probabilistic neural network, respectively. The classification was focused on the 13 types of power quality signals. The optimal number of selected features for the proposed classification system was firstly determined. Then, it demonstrated that the optimally selected features resulted in the highest classification accuracy of 98.77%. High performance of the proposed classification system in the noisy environment, as well as based on the real dataset was also verified. Furthermore, the proposed SSA indicates a very high convergence rate compared to other well-known algorithms. A comparison of the proposed classification system’s performance to existing works was also carried out, revealing that the proposed system’s accuracy is on a high-range scale. Hence, the adaptive SSA becomes another efficient optimal feature selection algorithm for PQD classification.

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An Automatic Identification Framework for Complex Power Quality Disturbances Based on Multifusion Convolutional Neural Network

Cited by 87 publications

References 25 publications

Comprehensive Review on Detection and Classification of Power Quality Disturbances in Utility Grid With Renewable Energy Penetration

Comprehensive Review on Detection and Classification of Power Quality Disturbances in Utility Grid With Renewable Energy Penetration

Classification of Power Quality Disturbance Based on S-Transform and Convolution Neural Network

Adaptive Salp Swarm Algorithm as Optimal Feature Selection for Power Quality Disturbance Classification

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