TITLE:
Hybrid Singular Parameter Phasor Measurement Based on the Discrete Fourier Transform (DFT)-Adaptive Artificial Neural Network (ADALINE) Algorithm
AUTHORS:
Gabriel Musonda, Charles Lubobya, Ackim Zulu
KEYWORDS:
Phasor Measurement Unit, Discrete Fourier Transform (DFT), Adaptive Linear Neuron Network (ADALINE), Correlation Coefficient
JOURNAL NAME:
Journal of Power and Energy Engineering,
Vol.13 No.9,
September
24,
2025
ABSTRACT: The PMU’s performance relies on its ability to detect and provide accurate measurements of both steady-state and dynamic conditions. The phasor estimation algorithm assesses the accuracy and quality of measuring grid parameters under various operating conditions. The Discrete Fourier Transform (DFT) algorithm is the most commonly used because of its low computational complexity and ease of implementation on any hardware. However, DFT has limitations in accurately obtaining a phasor during off-nominal grid frequency conditions caused by inter-harmonics, intermodulation, and frequency ramps. This paper introduces the design of a Hybrid DFT-ADALINE model. The ADALINE is a linear activation function where the input is directly proportional to the output, and it can learn from its environment and adjust its weights to minimize errors. Recently, many researchers have focused on applying an adaptive linear neural network (ADALINE) for parameter estimation due to its low computational complexity, minimal tracking error, and faster convergence rate. ADALINE is widely used as a harmonic estimator because of its simple structure and ability to track nonstationary signal parameters. A key feature of this approach is the integration of ADALINE, which tracks the estimated frequency and the phase angle error output of the PMU, converting it into a correlation coefficient. The ADALINE deep learning AI analyzes the changing pattern of frequency relative to the nominal grid frequency, comparing it to the phase angle error to determine the average correlation. The model is calibrated so that it reflects the average correlation for frequencies between 49.5 Hz and 50.5 Hz as normal grid conditions, ensuring the PMU does not generate alarms under these off-nominal conditions. This study demonstrates how a single PMU measurement parameter, the correlation coefficient, can be used to predict the behavior of the power grid under dynamic conditions, and how it can trigger grid voltage and frequency adjustments, significantly simplifying the fault detection logic of the power system and reducing computation latency.