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Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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Distributed Loads01:19

Distributed Loads

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Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
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Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
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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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Distribution Reliability and Automation01:25

Distribution Reliability and Automation

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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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The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
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A Secure IIoT Environment That Integrates AI-Driven Real-Time Short-Term Active and Reactive Load Forecasting with

Md Ibne Joha1, Md Minhazur Rahman1, Md Shahriar Nazim1

  • 1Department of Electronics Engineering, Kookmin University, Seoul 02707, Republic of Korea.

Sensors (Basel, Switzerland)
|December 17, 2024
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Summary

This study introduces a secure Industrial Internet of Things (IIoT) framework for real-time energy load prediction and anomaly detection. The system uses advanced AI models for enhanced energy efficiency and operational reliability in industrial settings.

Keywords:
Industrial Internet of Things (IIoT)anomaly detectioncloud serveredge serverenergy management systemload forecastingsecurity

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Area of Science:

  • Energy Systems Engineering
  • Artificial Intelligence
  • Cybersecurity

Background:

  • The Industrial Internet of Things (IIoT) integrates AI-driven analytics with real-time monitoring to optimize energy usage and efficiency.
  • Secure and reliable industrial operations require robust systems for data acquisition, monitoring, control, and protection.

Purpose of the Study:

  • To propose a secure IIoT framework capable of simultaneous active and reactive load prediction and anomaly detection.
  • To optimize the framework for real-time deployment on edge and cloud servers.
  • To ensure secure and reliable industrial operations through integrated smart systems.

Main Methods:

  • A Temporal Convolutional Networks-Gated Recurrent Unit-Attention (TCN-GRU-Attention) model was developed for predicting active and reactive energy loads.
  • An optimized Isolation Forest model was implemented for anomaly detection, considering appliance transient conditions.
  • Transport Layer Security (TLS) and Secure Sockets Layer (SSL) protocols, along with hash-encoded credentials, were used for system security.

Main Results:

  • The TCN-GRU-Attention model achieved superior performance in load forecasting with low Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE).
  • The Isolation Forest model demonstrated high performance in anomaly detection, achieving 95% Precision, 98% Recall, 96% F1 Score, and nearly 100% Accuracy.
  • The proposed framework ensures secure and reliable industrial operations through integrated security protocols.

Conclusions:

  • The developed IIoT framework effectively predicts energy loads and detects anomalies in real-time.
  • The TCN-GRU-Attention and optimized Isolation Forest models offer superior performance for their respective tasks.
  • The integrated security measures enhance the reliability and trustworthiness of industrial operations within the IIoT ecosystem.