Harmonic Control Measures in Yili Rolling Mill

Liu, Bo; Liu, Chenyang; Liu, Jia; Zheng, Xianchun; Xu, Guohai; Wang, Baomin

doi:10.1109/ciced.2018.8592585

Cited by 4 publications

(6 citation statements)

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“…Paper [6] focuses on harmonic and electromagnetic interference and power factor at a rolling mill factory and concludes that the 5th, 7th, 11th, and 13th harmonics are the primary sources of power pollution.…”

Section: Literature Reviewmentioning

confidence: 99%

“…These consumers can be found in metallurgical plants like electric arc furnaces (EAF) [3,4], ladle furnaces [5], and rolling mills [1]. These high power consumers use a lot of electricity and can have an impact on power quality via reactive power, low power factor, and electric current harmonics [6][7][8]. The largest consumers of electric power in steel plants are EAF [5,9,10], but rolling mills also have significant power and produce harmonic currents [11,12], overcurrents that cause reactive power and low power factor [13,14] or flicker [12].…”

Section: Introductionmentioning

confidence: 99%

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Power Factor Modelling and Prediction at the Hot Rolling Mills’ Power Supply Using Machine Learning Algorithms

Panoiu,

Ivascanu

2024

Mathematics

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The power supply is crucial in the present day due to the negative impacts of poor power quality on the electric grid. In this research, we employed deep learning methods to investigate the power factor, which is a significant indicator of power quality. A multi-step forecast was developed for the power factor in the power supply installation of a hot rolling mill, extending beyond the horizontal line. This was conducted using data obtained from the respective electrical supply system. The forecast was developed via hybrid RNN (recurrent neural networks) incorporating LSTM (long short-term memory) and GRU (gated recurrent unit) layers. This research utilized hybrid recurrent neural network designs with deep learning methods to build several power factor models. These layers have advantages for time series forecasting. After conducting time series forecasting, qualitative indicators of the prediction were identified, including the sMAPE (Symmetric Mean Absolute Percentage Error) and regression coefficient. In this paper, the authors examined the quality of applied models and forecasts utilizing these indicators, both in the short term and long term.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Power Factor Modelling and Prediction at the Hot Rolling Mills’ Power Supply Using Machine Learning Algorithms

Panoiu,

Ivascanu

2024

Mathematics

View full text Add to dashboard Cite

show abstract

“…These consumers can be found in metallurgical plants like Electric Arc furnaces [3], [4], Laddle furnaces [5], and rolling mills [1]. These high power consumers use a lot of electricity and can have an impact on power quality via reactive power, low power factor, and electric current harmonics [6][7][8][9][10]. Harmonic currents are caused in large part by the widespread use of power converters, which are found in many electrical and electronic systems, including household appliances.…”

Section: Introductionmentioning

confidence: 99%

Researches regarding power quality at the hot rolling mills’ power supply

Ivascanu,

Militaru,

Panoiu

et al. 2024

J. Phys.: Conf. Ser.

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The study described in the paper was carried out through measurements taken at a feed station in a hot rolling line. The aforementioned measures are appropriate for the purpose of analyzing power quality. The measurements were taken using a three-phase power quality analyzer. Measurements were taken for voltage and current values, as well as for active, reactive, and apparent powers. The analysis of the measurements taken indicates that the power quality is affected by the nonlinearity of the load. As a consequence, the level of reactive power becomes higher, leading to a reduced power factor.

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“…The nowadays social media provided a place for the people to spread his statements rapidly on the internet; however, the rumors have also proliferated there. The proliferation of rumors has caused significant damage to individuals and society [1]. The social media operators and the government have established the platforms to deal with the rumors, such as WeiboPiyao, but these platforms rely on manual inspection, so they are inefficient.…”

Section: Introduction1mentioning

confidence: 99%

Improving the Early Rumor Detection Performance of the Deep Learning Models By CGAN

Dong¹,

Zhu²,

Wan³

et al. 2022

Proceedings of the 34th International Conference on Software Engineering and Knowledge Engineering

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Deep learning models are recently applied to detect rumors on social media based on the information in the posts. However, at the early stage of rumor propagation, due to the lack of responses, the performance of these models often degrades. In this paper, we propose a method based on the conditional generative adversarial network, which can generate the responses like data and help the deep learning models in early detection. On two large-scale Sina Weibo datasets, the proposed method is applied on the existing convolution neural network model, the recurrent neural network model, and the recursive neural network model. The results show that the proposed method can significantly improve the performance of the models in the case of zero response, and has performance superiority in a certain early period.

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Harmonic Control Measures in Yili Rolling Mill

Cited by 4 publications

References 0 publications

Power Factor Modelling and Prediction at the Hot Rolling Mills’ Power Supply Using Machine Learning Algorithms

Power Factor Modelling and Prediction at the Hot Rolling Mills’ Power Supply Using Machine Learning Algorithms

Researches regarding power quality at the hot rolling mills’ power supply

Improving the Early Rumor Detection Performance of the Deep Learning Models By CGAN

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