Research on classification of voltage sag sources based on recorded events

Wei, Pengfei; Xu, Yan; Wu, Ying; Li, Chenyi

doi:10.1049/oap-cired.2017.0907

Cited by 13 publications

(7 citation statements)

References 6 publications

(7 reference statements)

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“…The length and magnitude of the specific parameter anomaly from the input supply side are also taken into account [3]. Sagging situations, in particular, may be caused by the energising of an energy transformer, a large capacity load, an occurrence of lighting, a short circuit, or a combination of the foregoing [4]. Thermal behaviour aspects also have an effect on the drive system's functioning, which has an effect on the insulation of ac systems and hence on their own system's reliability.…”

Section: Technical Survey Of Current Industrial Drives -A Broad Overviewmentioning

confidence: 99%

Performance Assessment of Industrial Drives due to Power Quality Issues and its Potential Impacts on Electrical Drives

Vishnu¹,

Ashok²

2021

Proceedings of the First International Conference on Combinatorial and Optimization, ICCAP 2021, December 7-8 2021, Chennai, In

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Power quality difficulties have been predicted since electronically powered drives have been the main component of any manufacturing process. Evaluating the same's informative quality has proven to be a big challenge. Because of non-linearity on the load side and an over-reliance on semiconductor technology, a good power quality could be crucial. The capabilities of the drives section employed in the business are also attracting attention in the current circumstances. As a result of the concerns mentioned above, several literary scientific studies show disturbances in the load side and the efficiency of electric drives. Weakness resulting from power quality is simply too significant, making it difficult for even the most sophisticated engineering to function effectively. A deep insight examination of grid side characteristics was performed in conjunction with investigating the inverter drive section responsiveness to grid variation to confirm the grid side issues for the artificial intelligence technique. This study work emphasized and proposed an answer to the possible artificial wise approach for administering fault deterrence prediction algorithm after clear proof from analysis and accounting grid side quality concerns relevant to electronic powered drives. The result is an accurate forecast of abnormal grid side behavior to offer safe and trustworthy grid operation, reducing the likelihood of shutdown or failure to a greater extent. Improvements in forecast accuracy based on previous data have been viable feedback to business equipment manufacturers, in addition to the aforementioned technique.

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Section: Technical Survey Of Current Industrial Drives -A Broad Overviewmentioning

confidence: 99%

Performance Assessment of Industrial Drives due to Power Quality Issues and its Potential Impacts on Electrical Drives

Vishnu¹,

Ashok²

2021

Proceedings of the First International Conference on Combinatorial and Optimization, ICCAP 2021, December 7-8 2021, Chennai, In

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show abstract

“…As the most frequent power quality disturbance, voltage sags can have severe consequences spanning from malfunctioning of control system equipment, to disconnection or loss of efficiency in electric machines. For this reason, many researchers have focused on the analysis, classification and characterization of voltage sags (e.g., [4]- [10]). Not much attention, however, has been devoted to measurement uncertainty due to the presence of harmonics and additive noise.…”

Section: A Motivationmentioning

confidence: 99%

Probability Density Function of Three-Phase Ellipse Parameters for the Characterization of Noisy Voltage Sags

Bellan

2020

IEEE Access

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This work deals with the statistical analysis of additive noise impact on the space-vector ellipse parameters used to detect and classify three-phase voltage sags. In fact, since voltage waveforms are always corrupted by additive noise and harmonics, the space vector is pre-processed through the Discrete Fourier Transform to extract the power frequency components. Thus, harmonics can be readily discarded, but additive noise can still have significant impact on the elliptical trajectory of the space vector on the complex plane. Therefore, by modeling the ellipse parameters (i.e., the shape index and the inclination angle) as random variables, the related statistical characterization is derived in the paper. In particular, the main results and the novelty of the paper are given by the analytical derivation in closed-form of the probability density function, cumulative distribution function, mean value, and variance of the ellipse parameters as functions of the additive noise variance. Since the ellipse shape index and inclination angle are commonly used to detect and classify voltage sags, the results derived in the paper are useful for both uncertainty propagation analysis, and assessment of detection capability in case of voltage dips close to the minimum value defined in the IEEE Standard 1159. Analytical results are validated through numerical simulation of noisy voltage sags.

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“…In addition, the composite voltage sag causes created by mutual influence between them. The voltage sag signals generated by different causes have significantly different characteristics [3].…”

Section: Introductionmentioning

confidence: 99%

Recognition method of voltage sag causes based on two‐dimensional transform and deep learning hybrid model

Zheng

Wang

et al. 2020

IET Power Electronics

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The voltage sags' caused recognition is the basis for formulating governance plans and clarifying liabilities for accidents. The diversification of smart grid equipment, the grid-connected power generation of new energy sources and the regional differentiation of power consumption modes pose new challenges to the traditional methods. In this study, a method based on deep learning hybrid model is proposed. The convolutional neural network is used to flexibly receive the voltage after two-dimensional transformation, so as to automatically obtain the time series and spatial characteristics of the voltage sag signals. The deep belief network is used to replace the fully connected layers in convolutional neural network, thereby enhancing the multi-label classification ability of the model. The parameters obtained by the unsupervised training of the stacked sparse denoising auto-encoder are used to initialise the weight of deep belief network, thereby improving the convergence speed and the anti-noise performance of the model. Iterative training and repeated testing of the network using pre-processed simulation data and actual recorded data verify the high recognition accuracy and strong anti-noise performance of the hybrid model. Compared with the traditional methods, the hybrid model also has good generalisation ability and can be effectively applied in practical engineering.

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Research on classification of voltage sag sources based on recorded events

Cited by 13 publications

References 6 publications

Performance Assessment of Industrial Drives due to Power Quality Issues and its Potential Impacts on Electrical Drives

Performance Assessment of Industrial Drives due to Power Quality Issues and its Potential Impacts on Electrical Drives

Probability Density Function of Three-Phase Ellipse Parameters for the Characterization of Noisy Voltage Sags

Recognition method of voltage sag causes based on two‐dimensional transform and deep learning hybrid model

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