Detection and classification of power quality disturbances using GWO ELM

Subudhi, Umamani; Dash, Sambit

doi:10.1016/j.jii.2021.100204

Cited by 32 publications

(12 citation statements)

References 24 publications

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“…Therefore, it has been widely used for the optimization of various intelligence algorithms. For example, the hybrid GWO-ELM algorithm [23,24] has been successfully used for solving complex problems in different areas and shows better performance than other algorithms.…”

Section: No Notationsmentioning

confidence: 99%

Forecasting the Collapse-Induced Ground Vibration Using a GWO-ELM Model

et al. 2022

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Blasting demolition is a popular method in the area of building demolishing. Due to the complex process of the building components’ collapse, it is difficult to predict the collapse-induced ground vibrations. As the accuracy of the empirical equation in predicting the collapse-induced ground vibration is not high, there is a significant risk of damage to the surrounding structures. To mitigate this risk, it is necessary to control and predict the peak particle velocity (PPV) and dominant frequency of ground vibration with higher accuracy. In this study, the parameters on the PPV and frequency of collapse-induced ground vibration are analyzed based on the Hertz theory. Then, fall tests are performed to simulate the collapse process of structural components and to investigate the characteristics of influential parameters on PPV and frequency. Using kernel density estimation (KDE) and Pearson correlation, the PPV and frequency are correlated with the distance from the falling point to the monitored point (R) and the mass of the falling structural component (M). Using recorded ground vibration data, the PPV and frequency are predicted using an extreme learning machine in combination with gray wolf optimization. The efficiency of the proposed algorithm is compared with other predictive models. The results indicate that the accuracy pre-diction of the proposed algorithm is better than those of plain extreme learning machines and the empirical equations, which indicates that the approach can be applied for PPV and frequency prediction of collapse-induced ground vibrations during blasting demolition.

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Section: No Notationsmentioning

confidence: 99%

Forecasting the Collapse-Induced Ground Vibration Using a GWO-ELM Model

et al. 2022

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“…Thus, power quality (PQ) has been recognized as one of most crucial issue in modern power systems. The extensive use of various types of aforementioned devices creates unprecedented challenges in the functioning of the reliable and stable operation of the power system, resulting many PQ disturbance events [1], [2].…”

Section: Introductionmentioning

confidence: 99%

Classification of Power Quality Disturbance Using Segmented and Modified S-Transform and DCNN-MSVM Hybrid Model

et al. 2023

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In this paper, a novel approach to classify the signals of power quality (PQ) disturbance is proposed based on segmented and modified S-transform (SMST), deep convolutional neural network (DCNN), and multiclass support vector machine (MSVM). The idea of frequency segmentation with different adjustable parameters was used in the Gaussian window function. The accurate time-frequency localization and efficient feature extraction of different PQ disturbances then could be achieved. Firstly, the SMST was used to analyze the PQ disturbance signals and obtained two-dimensional (2D) contour maps with high time-frequency resolution. Then, the DCNN was employed to automatically extract features from the 2D contour maps. Finally, the MSVM classifier was developed for the classification of single and complex signals of PQ disturbance. In order to demonstrate the effectiveness and robustness of the proposed model, eight single and thirteen complex waveforms of PQ disturbances were considered without noise and with different noise level, respectively. Extensive simulations were performed and compared to other existing methods. The simulation results show that the proposed method has better performance than several stateof-the-art algorithms in classifying PQ disturbances under different noise level.

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“…Equipment used in industrial processes is sensitive to the quality of power. Hence, bad or poor power quality (PQ) affects the efficiency of industrial production and the quality of the finished products [1]. Hence, PQ is identified as a major challenge for the utilities and customers [2].…”

Section: Introductionmentioning

confidence: 99%

Identification and Classification of Multiple Power Quality Disturbances Using a Parallel Algorithm and Decision Rules

Swarnkar¹,

Mahela²,

Khan³

et al. 2022

Energy Engineering

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A multiple power quality (MPQ) disturbance has two or more power quality (PQ) disturbances superimposed on a voltage signal. A compact and robust technique is required to identify and classify the MPQ disturbances. This manuscript investigated a hybrid algorithm which is designed using parallel processing of voltage with multiple power quality (MPQ) disturbance using stockwell transform (ST) and hilbert transform (HT). This will reduce the computational time to identify the MPQ disturbances, which makes the algorithm fast. A MPQ identification index (IPI) is computed using statistical features extracted from the voltage signal using the ST and HT. IPI has different patterns for various types of MPQ disturbances which effectively identify the MPQ disturbances. A MPQ time location index (IPL) is computed using the features extracted from the voltage signal using ST and HT. IPL effectively identifies the initiation and end of PQ disturbances and thereby locates the MPQ events with respect to time. Classification of MPQ disturbances is performed using decision rules in both the noise-free and noisy environments with a 20 dB noise to signal ratio (SNR). The performance of the proposed hybrid algorithm using ST and HT with rule-based decision tree (RBDT) is better compared to the ST and RBDT techniques in terms of accuracy of classification of MPQ disturbances. MATLAB software is used to perform the study.

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Detection and classification of power quality disturbances using GWO ELM

Cited by 32 publications

References 24 publications

Forecasting the Collapse-Induced Ground Vibration Using a GWO-ELM Model

Forecasting the Collapse-Induced Ground Vibration Using a GWO-ELM Model

Classification of Power Quality Disturbance Using Segmented and Modified S-Transform and DCNN-MSVM Hybrid Model

Identification and Classification of Multiple Power Quality Disturbances Using a Parallel Algorithm and Decision Rules

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