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Related Concept Videos

Control Systems01:10

Control Systems

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Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
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Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
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Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
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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.
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Functional Classification of Joints01:09

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Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
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A data-driven state identification method for intelligent control of the joint station export system.

Guangli Xu1,2, Yifu Wang1, Zhihao Zhou1

  • 1School of Oil & Natural Gas Engineering, Southwest Petroleum University, Chengdu, 610500, Sichuan, China.

Scientific Reports
|January 22, 2025
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Summary

This study introduces an optimized Backpropagation Neural Network (PSO-GWO-BP) for predicting pressure drop in joint station systems. The advanced model accurately identifies abnormal conditions, enabling intelligent control and adaptive operational adjustments.

Keywords:
BP neural networkGWOPSOParameter predictionState identification

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Area of Science:

  • Petroleum Engineering
  • Artificial Intelligence
  • Control Systems

Background:

  • Intelligent control of joint stations requires accurate system state identification and adaptive operational adjustments.
  • Current methods for identifying abnormal conditions and optimizing operations often lack precision and adaptability.

Purpose of the Study:

  • To develop an accurate pressure drop prediction model for joint station export systems.
  • To establish an intelligent method for identifying abnormal working conditions using a dynamic threshold.
  • To enhance the adaptive control capabilities of joint station export systems.

Main Methods:

  • A hybrid optimization algorithm combining Particle Swarm Optimization (PSO) and Gray Wolf Optimizer (GWO) was used to optimize a Backpropagation Neural Network (BP) model, creating the PSO-GWO-BP model.
  • A pressure drop prediction model was established using the PSO-GWO-BP approach.
  • A state identification method based on a dynamic threshold was developed, utilizing the PSO-GWO-BP pressure drop prediction model.

Main Results:

  • The PSO-GWO-BP model demonstrated superior prediction accuracy compared to traditional hydraulic calculation modified (THCM) models and other machine learning algorithms.
  • The proposed dynamic threshold method successfully identified abnormal working conditions in the joint station.
  • The effectiveness and accuracy of the developed method were verified using production and operation data.

Conclusions:

  • The PSO-GWO-BP model offers significant advantages in pressure drop prediction accuracy for joint station export systems.
  • The dynamic threshold-based state identification method enables intelligent recognition of system operation states.
  • This approach enhances the ability to detect abnormal conditions and adaptively adjusts operational schemes, improving the overall intelligence of the system.