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Related Concept Videos

Multimachine Stability01:25

Multimachine Stability

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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.
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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Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

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The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
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Response Surface Methodology01:16

Response Surface Methodology

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Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
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Wind Turbine Machine Models01:24

Wind Turbine Machine Models

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In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
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Power System Three-Phase Short Circuits01:21

Power System Three-Phase Short Circuits

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Determining the subtransient fault current in a power system involves representing transformers by their leakage reactances, transmission lines by their equivalent series reactances, and synchronous machines as constant voltage sources behind their subtransient reactances. In this analysis, certain elements are excluded, such as winding resistances, series resistances, shunt admittances, delta-Y phase shifts, armature resistance, saturation, saliency, non-rotating impedance loads, and small...
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Three-Phase Short Circuit—Unloaded Synchronous Machine01:21

Three-Phase Short Circuit—Unloaded Synchronous Machine

114
Conducting a three-phase short circuit test on an unloaded synchronous machine helps understand its impact on the system. The AC fault current's oscillogram, with the DC offset removed, reveals that the waveform amplitude decreases from an initially high value to a steady-state level for one phase of the machine.
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Surrogate Model Development for Digital Experiments in Welding
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Manufacturing cycle prediction using structural equation model toward industrial early warning system simulation: The

Tirta Wisnu Permana1, Gatot Yudoko1, Eko Agus Prasetio1

  • 1School of Business and Management, Institute of Technology Bandung (ITB), Bandung, Indonesia.

Heliyon
|January 27, 2025
PubMed
Summary

Integrating short-term, medium-term, and long-term Composite Leading Indices (CLIs) enhances the prediction of Indonesia's Manufacturing Cycle (ManC). Interconnected CLIs provide superior forecasting power over individual indices, validated by Partial Least Squares-Structural Equation Modeling.

Keywords:
Composite leading indices (CLI)IndonesiaManufacturing cyclePLS-SEMTime series data

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Area of Science:

  • Economics
  • Econometrics
  • Economic Forecasting

Background:

  • Composite Leading Indices (CLIs) are crucial for economic cycle prediction.
  • Existing research often focuses on individual CLIs, potentially limiting predictive accuracy.
  • Understanding the interplay of short, medium, and long-term CLIs is vital for robust economic forecasting.

Purpose of the Study:

  • To integrate short-term, medium-term, and long-term Composite Leading Indices (CLIs) for enhanced predictive capabilities.
  • To investigate the relationships among CLIs for forecasting Indonesia's Manufacturing Cycle (ManC).
  • To validate the application of Partial Least Squares-Structural Equation Modeling (PLS-SEM) in ManC forecasting.

Main Methods:

  • Utilized quarterly data from Q1 2010 to Q2 2022.
  • Employed Partial Least Squares-Structural Equation Modeling (PLS-SEM) for analysis.
  • Incorporated five constructs representing key economic sectors influencing the manufacturing cycle, including Short Leading Economic Index (SLEI), International Trade Channel (ITC), Fiscal Cycle (FC), and Monetary Cycle (MC).

Main Results:

  • Demonstrated that interconnected CLIs offer enhanced predictive capabilities compared to individual CLIs.
  • Successfully forecasted Indonesia's Manufacturing Cycle (ManC) using the integrated CLI approach.
  • Validated the effectiveness of PLS-SEM in analyzing complex economic relationships for forecasting.

Conclusions:

  • The integration of diverse Composite Leading Indices significantly improves the accuracy of economic cycle forecasting.
  • PLS-SEM is a suitable methodology for modeling the intricate relationships within economic indicators to predict the Manufacturing Cycle.
  • This study provides a robust framework for utilizing interconnected CLIs in economic policy and planning.