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Multimachine Stability01:25

Multimachine Stability

Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:

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Multi-node knowledge graph assisted distributed fault detection for large-scale industrial processes based on graph

Qing Li1, Yangfan Wang2, Jie Dong3

  • 1Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, University of Science and Technology Beijing, Beijing, 100083, PR China; School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, PR China.

Neural Networks : the Official Journal of the International Neural Network Society
|February 28, 2024
PubMed
Summary

A new distributed graph attention network-bidirectional long short-term memory (D-GATBLSTM) model enhances fault detection in large industrial processes. This approach improves precision and recall for critical systems like water treatment plants.

Keywords:
Bidirectional long short-term memoryDistributed fault detectionGraph attention networksMulti-node knowledge graph (MNKG)

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Area of Science:

  • Industrial Process Control
  • Artificial Intelligence
  • Water Treatment Technology

Background:

  • Large-scale industrial processes feature complex, interconnected subsystems, making plant-wide fault detection challenging.
  • Existing methods struggle with the intricate correlations in multi-field processes, such as those found in water treatment.
  • Accurate fault detection is crucial for operational safety, efficiency, and reliability in modern industrial settings.

Purpose of the Study:

  • To propose a novel distributed graph attention network-bidirectional long short-term memory (D-GATBLSTM) model for robust fault detection in large-scale industrial processes.
  • To address the limitations of current fault detection techniques in handling complex, coupled subsystems.
  • To enhance the precision and recall of fault detection in critical infrastructure like water treatment plants.

Main Methods:

  • Construction of a multi-node knowledge graph (MNKG) using a hybrid data and knowledge-driven strategy.
  • Development of a global feature extractor using graph attention networks (GATs) and decomposition into sub-blocks based on the MNKG.
  • Implementation of local feature extractors using bidirectional long short-term memory (Bi-LSTM) for each sub-block, considering inter-sub-block correlations.
  • A multi-sub-block fusion collaborative prediction model with grid search for final fault detection.

Main Results:

  • The proposed D-GATBLSTM model demonstrated superior performance in fault detection compared to baseline methods.
  • In a secure water treatment process case study, the model achieved a 27% improvement in precision.
  • The model also showed a 15% increase in recall and an overall F-score enhancement of 0.22.

Conclusions:

  • The D-GATBLSTM model offers a significant advancement in fault detection for complex, large-scale industrial systems.
  • The integration of graph attention networks and bidirectional LSTMs effectively captures intricate process correlations.
  • The model's validated effectiveness in a water treatment scenario highlights its potential for diverse industrial applications.