Short-term forecasting of power quality distortions in electrical energy systems with long short-term memory and gated recurrent unit networks

Document Type : Research Article

Authors

1 Department of Technical Administration, Bandırma Organized Industrial Zone, Bandırma, Turkey.

2 Department of Electrical Engineering, Bandırma Onyedi Eylül University, Bandırma, Turkey.

3 Department of Computer Engineering, Bandırma Onyedi Eylül University, Bandırma, Turkey.

10.24200/sci.2023.61430.7300

Abstract

Technological development has led to a diversification of loads in transmission and distribution systems. The rise of non-linear loads in the system is one of the biggest effects of this variation as semiconductor technology develops. Nonlinear loads are characterized by current and voltage characteristics that are not purely sinusoidal, also known as harmonics. Harmonics cause the system insulation to degrade and increase energy loss. Therefore, it's crucial to get rid of harmonics before they occur. This study intends to lower the risk of distribution system damage by employing complex harmonic forecasting methods. A Recurrent Neural Network (RNN)-based forecasting algorithm has been created by using actual system power quality data obtained from the organized industrial zone in Bandırma, Turkey. Parameters that are most likely to be neglected in simulation studies are also taken into account in the calculation by using actual data. Active power data, current harmonic data, and calendar data were used together to design a harmonic forecasting model. Graphs and calculations were used to discuss the results. The obtained minimum values of the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) are 2,116, 0,666, and 11,619, respectively. The convergence as a result of these calculations has allowed high forecasting performance of power quality distortions.

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Main Subjects

References

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Scientia Iranica
Volume 32, Issue 15
Transactions on Computer Science & Engineering and Electrical Engineering
July and August 2025 Article ID:7300

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  • Receive Date: 10 November 2022
  • Revise Date: 24 May 2023
  • Accept Date: 22 August 2023

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