Dissertation/Thesis
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Research on Power Load Prediction and Dynamic Power Management of Trailing Suction Hopper Dredger

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Research on Power Load Prediction and Dynamic Power Management of Trailing Suction Hopper Dredger
Συγγραφείς: Zhengtao Xia, Zhanjing Hong, Runkang Tang, Song Song, Changjiang Li, Shuxia Ye
Πηγή: Symmetry ; Volume 17 ; Issue 9 ; Pages: 1446
Στοιχεία εκδότη: Multidisciplinary Digital Publishing Institute
Έτος έκδοσης: 2025
Συλλογή: MDPI Open Access Publishing
Θεματικοί όροι: Dilated-LSTM, Improved Grey Wolf Optimization, load prediction, Power Management System (PMS), power distribution, trailing suction hopper dredger
Περιγραφή: During the continuous operation of trailing suction hopper dredger (TSHD), equipment workload exhibits significant time-varying characteristics. Maintaining dynamic symmetry between power generation and consumption is crucial for ensuring system stability and preventing power supply failures. Key challenges lie in dynamic perception, accurate prediction, and real-time power management to achieve this equilibrium. To address this issue, this paper proposes and constructs a “prediction-driven dynamic power management method.” Firstly, to model the complex temporal dependencies of the workload sequence, we introduce and improve a dilated convolutional long short-term memory network (Dilated-LSTM) to build a workload prediction model with strong long-term dependency awareness. This model significantly improves the accuracy of workload trend prediction. Based on the accurate prediction results, a dynamic power management strategy is developed: when the predicted total power consumption is about to exceed a preset margin threshold, the Power Management System (PMS) automatically triggers power reduction operations for adjusfigure loads, aiming to maintain grid balance without interrupting critical loads. If the power that the generator can produce is still less than the required power after the power is reduced, and there is still a risk of supply-demand imbalance, the system uses an Improved Grey Wolf Optimization (IGWO) algorithm to automatically disconnect some non-critical loads, achieving real-time dynamic symmetry matching of generation capacity and load demand. Experimental results show that this mechanism effectively prevents generator overloads or ship-wide power failures, significantly improving system stability and the reliability of power supply to critical loads. The research results provide effective technical support for intelligent energy efficiency management and safe operation of TSHDs and other vessels with complex working conditions.
Τύπος εγγράφου: Thesis
Περιγραφή αρχείου: application/pdf
Γλώσσα: English
Relation: Engineering and Materials; https://dx.doi.org/10.3390/sym17091446
DOI: 10.3390/sym17091446
Διαθεσιμότητα: https://doi.org/10.3390/sym17091446
Rights: https://creativecommons.org/licenses/by/4.0/
Αριθμός Καταχώρησης: edsbas.1995E310
Βάση Δεδομένων: BASE
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  – Url: https://doi.org/10.3390/sym17091446#
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PubType: Dissertation/Thesis
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IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Research on Power Load Prediction and Dynamic Power Management of Trailing Suction Hopper Dredger
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Zhengtao+Xia%22">Zhengtao Xia</searchLink><br /><searchLink fieldCode="AR" term="%22Zhanjing+Hong%22">Zhanjing Hong</searchLink><br /><searchLink fieldCode="AR" term="%22Runkang+Tang%22">Runkang Tang</searchLink><br /><searchLink fieldCode="AR" term="%22Song+Song%22">Song Song</searchLink><br /><searchLink fieldCode="AR" term="%22Changjiang+Li%22">Changjiang Li</searchLink><br /><searchLink fieldCode="AR" term="%22Shuxia+Ye%22">Shuxia Ye</searchLink>
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  Label: Source
  Group: Src
  Data: Symmetry ; Volume 17 ; Issue 9 ; Pages: 1446
– Name: Publisher
  Label: Publisher Information
  Group: PubInfo
  Data: Multidisciplinary Digital Publishing Institute
– Name: DatePubCY
  Label: Publication Year
  Group: Date
  Data: 2025
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  Label: Collection
  Group: HoldingsInfo
  Data: MDPI Open Access Publishing
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  Data: <searchLink fieldCode="DE" term="%22Dilated-LSTM%22">Dilated-LSTM</searchLink><br /><searchLink fieldCode="DE" term="%22Improved+Grey+Wolf+Optimization%22">Improved Grey Wolf Optimization</searchLink><br /><searchLink fieldCode="DE" term="%22load+prediction%22">load prediction</searchLink><br /><searchLink fieldCode="DE" term="%22Power+Management+System+%28PMS%29%22">Power Management System (PMS)</searchLink><br /><searchLink fieldCode="DE" term="%22power+distribution%22">power distribution</searchLink><br /><searchLink fieldCode="DE" term="%22trailing+suction+hopper+dredger%22">trailing suction hopper dredger</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: During the continuous operation of trailing suction hopper dredger (TSHD), equipment workload exhibits significant time-varying characteristics. Maintaining dynamic symmetry between power generation and consumption is crucial for ensuring system stability and preventing power supply failures. Key challenges lie in dynamic perception, accurate prediction, and real-time power management to achieve this equilibrium. To address this issue, this paper proposes and constructs a “prediction-driven dynamic power management method.” Firstly, to model the complex temporal dependencies of the workload sequence, we introduce and improve a dilated convolutional long short-term memory network (Dilated-LSTM) to build a workload prediction model with strong long-term dependency awareness. This model significantly improves the accuracy of workload trend prediction. Based on the accurate prediction results, a dynamic power management strategy is developed: when the predicted total power consumption is about to exceed a preset margin threshold, the Power Management System (PMS) automatically triggers power reduction operations for adjusfigure loads, aiming to maintain grid balance without interrupting critical loads. If the power that the generator can produce is still less than the required power after the power is reduced, and there is still a risk of supply-demand imbalance, the system uses an Improved Grey Wolf Optimization (IGWO) algorithm to automatically disconnect some non-critical loads, achieving real-time dynamic symmetry matching of generation capacity and load demand. Experimental results show that this mechanism effectively prevents generator overloads or ship-wide power failures, significantly improving system stability and the reliability of power supply to critical loads. The research results provide effective technical support for intelligent energy efficiency management and safe operation of TSHDs and other vessels with complex working conditions.
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  Data: Engineering and Materials; https://dx.doi.org/10.3390/sym17091446
– Name: DOI
  Label: DOI
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  Data: 10.3390/sym17091446
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  Data: https://doi.org/10.3390/sym17091446
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  Data: https://creativecommons.org/licenses/by/4.0/
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  Data: edsbas.1995E310
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        Value: 10.3390/sym17091446
    Languages:
      – Text: English
    Subjects:
      – SubjectFull: Dilated-LSTM
        Type: general
      – SubjectFull: Improved Grey Wolf Optimization
        Type: general
      – SubjectFull: load prediction
        Type: general
      – SubjectFull: Power Management System (PMS)
        Type: general
      – SubjectFull: power distribution
        Type: general
      – SubjectFull: trailing suction hopper dredger
        Type: general
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      – TitleFull: Research on Power Load Prediction and Dynamic Power Management of Trailing Suction Hopper Dredger
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              Y: 2025
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