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Related Concept Videos

Power System Three-Phase Short Circuits01:21

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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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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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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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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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Multimachine Stability01:25

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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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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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Predictive models for short-term load forecasting in the UK's electrical grid.

Yusuf A Sha'aban1

  • 1Department of Electrical Engineering, University of Hafr Al Batin, Hafr Al Batin, Kingdom of Saudi Arabia.

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Summary

Accurate short-term load forecasting is crucial for managing increased electricity demand from electric vehicles (EVs). Machine learning models, specifically Support Vector Regression (SVR) and Artificial Neural Networks (ANN), show high precision for predicting grid loads.

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Energy Systems

Background:

  • Global deployment of electric vehicles (EVs) is accelerating the energy transition.
  • Increased EV adoption raises concerns about potential strain on electricity grids due to higher demand.
  • Accurate short-term load forecasting is essential for efficient grid planning, operation, and control.

Purpose of the Study:

  • To develop and evaluate robust machine learning models for short-term load forecasting in the UK's power system.
  • To compare the performance of Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Gaussian Process Regression (GPR) for load prediction.
  • To assess the accuracy of forecasting models for both half-hourly and hourly electricity demand.

Main Methods:

  • Utilized net imports data from the UK's power system spanning 2010-2020.
  • Applied machine learning techniques including Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Gaussian Process Regression (GPR).
  • Evaluated model performance using metrics such as Root Mean Square Error (RMSE), Mean Absolute Prediction Error (MAPE), Mean Absolute Deviation (MAD), and Correlation of Determination (R2).

Main Results:

  • Support Vector Regression (SVR) demonstrated the highest accuracy for half-hourly load forecasts, achieving an R-value of 99.85%.
  • Artificial Neural Networks (ANN) provided the best performance for hourly load forecasts, with an R-value of 99.71%.
  • Gaussian Process Regression (GPR) also showed strong predictive capabilities for both forecast horizons.

Conclusions:

  • Machine learning methods, including SVR, ANN, and GPR, are highly reliable and precise for short-term load forecasting.
  • The SVR model is particularly effective for half-hourly predictions, while the ANN model excels in hourly predictions.
  • Accurate load forecasting is vital for integrating renewable energy and managing the evolving demands of e-mobility.