Modernization of the Electric Drive and Automation System of the Sewage Pumping Station

Klokov, O.A.; Pushkina, A. A.

doi:10.1109/fareastcon50210.2020.9271382

Cited by 4 publications

(2 citation statements)

References 37 publications

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“…These stations analyze on-site data collected by sensors, providing real-time information on pump station operation status to users, central monitoring centers, or relevant institutions. Klokov et al, for instance, improved the efficiency of sewage pumping stations by optimizing electric motor units and developing a modern sewage pumping station with real-time visualization technology and remote control capabilities [13].…”

Section: Introductionmentioning

confidence: 99%

Visual Analysis Method for Unmanned Pumping Stations on Dynamic Platforms based on Data Fusion Technology

Liu,

Chen,

Zhang

et al. 2024

Preprint

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As the scale of water conservancy projects continues to expand, the amount and complexity of analytical data have also correspondingly increased. Currently, As the scale of water conservancy projects continues to expand, the amount and complexity of analytical data have also correspondingly increased. Currently, It is difficult to realize project management decision support based on a single data source, and most manual analysis methods not only have high labor costs, but also are prone to the risk of misjudgment and misjudgement, resulting in huge property losses. Based on this problem, this paper proposes a visualization and analysis method for unmanned pumping stations on a dynamic platform based on data fusion technology. First, the method uses the transfer learning method to make ResNet18 model obtain generalization ability. Secondly, the method uses the ResNet18 model to extract image features, and outputs fixed length sequence data as the input of long short-term memory (LSTM) model, Finally, the method uses the LSTM model outputs the classification results. The experimental results show that the algorithm model can achieve an accuracy of 99.032%, effectively recognize and classify pump station images, and reduce the occurrence of pump station accidents.

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Section: Introductionmentioning

confidence: 99%

Visual Analysis Method for Unmanned Pumping Stations on Dynamic Platforms based on Data Fusion Technology

Liu,

Chen,

Zhang

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…These stations analyze on-site data collected by sensors, providing real-time information on pumping station operation status to users, central monitoring centers or relevant institutions. Klokov et al, for instance, improved the efficiency of sewage pumping stations by optimizing electric motor units and developing a modern sewage pumping station with real-time visualization technology and remote control capabilities [16]. Tlabu et al proposed a centralized intelligent digital water management system that utilizes a data-centric pump infrastructure and a comprehensive system architecture model to achieve data security, real-time monitoring and digitization to provide comprehensive support for business operations [17].…”

mentioning

confidence: 99%

Visual analysis method for unmanned pumping stations on dynamic platforms based on data fusion technology

Liu,

Chen,

Zhang

et al. 2024

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

As the scale of water conservancy projects continues to expand, the amount and complexity of analytical data have also correspondingly increased. At present, it is difficult to realize project management decision support based on a single data source, and most manual analysis methods not only have high labor costs, but also are prone to the risk of misjudgment, resulting in huge property losses. Based on this problem, this paper proposes visual analysis method for unmanned pumping stations on dynamic platforms based on data fusion technology. First, the method uses the transfer learning method to enable ResNet18 obtain generalization ability. Secondly, the method uses ResNet18 to extract image features, and outputs fixed length sequence data as the input of long short-term memory (LSTM). Finally, the method uses LSTM outputs the classification results. The experimental results demonstrate that the algorithm model can achieve an impressive accuracy of 99.032%, outperforming the combination of traditional feature extraction and machine learning methods. This model effectively recognizes and classifies images of pumping stations, significantly reducing the risk of accidents in these facilities.

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Study of operational modes of an "electric drive–turbomachinery–pipeline" technological complex

Pavlov

2022

jour

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A developed mathematical model of an asynchronous motor – turbomachinery pipeline technological complex is presented. An analysis of starting conditions is carried out by a nonlinear differential calculus and graphic analytical method. The calculations for the model were performed using the MATLAB software package. The flow, head, efficiency of the pump mechanism and entire pump unit, stator current, angular frequency and torque of the asynchronous motor were calculated at pump start-up and with an increase of pipeline resistance coefficient by 2, 5, 10, and 1000 times. At an increase in the pipeline resistance coefficient by 10 times, the pump efficiency is shown to be reduced by 2.8 times, while the head is increased by 1.28 times; meanwhile, the torque, stator current, and rotational speed of the asynchronous motor change insignificantly. The torque and current decrease by 1.167 and 1.034 times, respectively, while the speed increases by 1.0046 times; the efficiency of the pump mechanism and pump assembly (including motor) decreases by 1.78 and 1.89 times, respectively. The start-up time of the pump motor equals 0.5 s; the maximum stator current at start-up exceeds the nominal value by 4.39 times; the steady-state stator current comprises no more than 59.3 % of the nominal value. The developed mathematical model of the asynchronous motor – turbomachinery – pipeline technological complex is established to allow the operational and energy parameters of the unit to be quantitatively estimated at start-up, while the pump capacity is capable of being controlled by throttling.

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Modernization of the Electric Drive and Automation System of the Sewage Pumping Station

Cited by 4 publications

References 37 publications

Visual Analysis Method for Unmanned Pumping Stations on Dynamic Platforms based on Data Fusion Technology

Visual Analysis Method for Unmanned Pumping Stations on Dynamic Platforms based on Data Fusion Technology

Visual analysis method for unmanned pumping stations on dynamic platforms based on data fusion technology

Study of operational modes of an "electric drive–turbomachinery–pipeline" technological complex

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