Novel optimization parameters of power quality disturbances using novel bio-inspired algorithms: A comparative approach

Jeevitha, S.; Mabel, M. Carolin

doi:10.1016/j.bspc.2018.02.003

Cited by 14 publications

(7 citation statements)

References 21 publications

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“…The MRA is the process that uses the scaling function and orthogonal wavelet function to decompose and reconstruct signals at various resolution levels. This process benefits for filtering the propitious information of input signals, so the resulting signals are easier to be further implemented and require less execution and memory [22]. In this work, we used the MRA for decomposing the PQD signals before constructing the feature vectors.…”

Section: Multi-resolution Analysismentioning

confidence: 99%

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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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Section: Multi-resolution Analysismentioning

confidence: 99%

“…Feature extraction is extensively known as an effective signal processing technique to classify the type of PQDs through their features [18][19][20][21][22][23]. In the literature, there were many signal processing methods performed in the feature extraction.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…The Institute of Electrical and Electronics Engineers' (IEEE) definition for voltage sag is as follows: the root mean square (RMS) voltage drops to 10-90% of the rated value, and the duration is 10 ms-60 s [4]. At present, the data collection, detection, classification, and identification of voltage sags are the focus of research; see [5][6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Clustering Analysis of Voltage Sag Events Based on Waveform Matching

Hao

Jin

2022

Processes

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Voltage sags are a serious problem within power supplies, which pose threats to both residential electricity and industrial manufacturing. Since any one sag may be recorded by multiple monitoring devices from different substations, the issue of redundant information in data arises. In this regard, a novel method for voltage sag events based on projection technology, shape dynamic time warping (shapeDTW), and spectral clustering is proposed. The main contributions of this paper may be summarized as follows: (1) We present a new method for extracting the voltage anomaly waveform, which is a fast projection segmentation algorithm (FPSA). The voltage sag waveform is only a part of the voltage anomaly waveform, so the voltage anomaly waveform contains more information. (2) ShapeDTW and spectral clustering are used to match and cluster voltage anomaly waveforms, so as to achieve the normalization of voltage sag events. (3) In practical engineering, the proposed method in the paper can be used to obtain the impact of voltage sags, reduce computational complexity, and ease the workload of the operation and maintenance engineers. Experiments were conducted using voltage sag data from voltage sag events recorded by the 10 kV monitoring points in Beijing, China. The results showed the effectiveness and reliability of our proposed methods.

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“…Power quality transient disturbance signal recognition problems used to be processed using signal processing techniques such as Fourier transform, wavelet transform, Hilbert transform and S transform [1][2][3][4][5][6][7][8][9][10].The author [1] proposed a simplified mathematical method to detect and identify different types of power quality categories, the mathematical methods include wavelet transform, Hilbert transform, and an overall algorithm for power system monitoring; In [3], using wavelet transform to detect signal interference in the quality of power; On the basis of these transformations, many experts and scholars improve wavelet transform and S transform [8][9];In [9], an improved discrete S transform is proposed for detecting power quality disturbances. Of course, there are also many new algorithms and theories for power quality detection [10][11][12][13][14][15][16][17][18][19][20][21].The author [10] proposed a new method for power quality disturbance detection and classification, Singular spectrum analysis and Curvelet are used for signal decomposition and extraction features, and are classified by deep learning and multi-class SVM, which can be applied to many types of Power quality disturbance signal;An adaptive process noise covariance Kalman filter [11] is used to detect power quality disturbances present in a distorted power signal; The author [12] proposed a method based on Histogram of Oriented Gradients and support vector machine to check power quality events. In [16], the authors propose a method for detecting and classifying power quality interference by a method of initializing Stockwell transform and Fuzzy C-means clustering by decision tree.…”

Section: Introductionmentioning

confidence: 99%

Identification of Power Quality Transient Disturbance Based on S Transform and Wavelet Transform

Zhang¹,

Huang²,

Li³

et al. 2019

dtcse

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With the rapid advancement of the country's industrialization, intelligence, and information technology, the nonlinearity, asymmetry, and impact of power system load are becoming increasingly prominent. On the other hand, users' demands for power quality are increasing. The national grid inevitably faces severe challenges. The power quality problem in circuit systems is particularly important. The type identification of power quality transient disturbances is of great significance for analyzing power quality problems and solving power quality problems. In this paper, a method based on S transform and wavelet transform for transient power quality disturbance identification is proposed. Firstly, the wavelet signal is used to denoise the power signal. Then, after obtaining the feature vector of the power signal by the S transform and the wavelet transform, The Relieff algorithm is used to obtain the feature weights of the feature vectors generated by S transform and wavelet transform and to select features. Finally, training and classification are performed using the Support Vector Machine (SVM) classifier. The experimental results show that the power quality transient disturbance classification method proposed in this paper can accurately identify the type of disturbance and have strong robustness, and fully meet the requirements of the relevant industry for the accuracy of power quality transient disturbance recognition.

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Novel optimization parameters of power quality disturbances using novel bio-inspired algorithms: A comparative approach

Cited by 14 publications

References 21 publications

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

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

Clustering Analysis of Voltage Sag Events Based on Waveform Matching

Identification of Power Quality Transient Disturbance Based on S Transform and Wavelet Transform

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