What does this research mean for the field?

The proposed framework achieves a classification accuracy of 99.50% for power quality disturbance detection under noisy conditions, effectively identifying 23 types of interference. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This research aims to enhance the detection of power quality disturbances using a novel framework combining time-frequency analysis and AdaBoost classification.

March 13, 2026Open Access

Adaptive Instantaneous Frequency Synchrosqueezing Transform and Enhanced AdaBoost for Power Quality Disturbance Detection

Key Points

This research aims to enhance the detection of power quality disturbances using a novel framework combining time-frequency analysis and AdaBoost classification.
Developed an instantaneous frequency adaptive Fourier synchrosqueezing transform (IFAFSST) with a custom adaptive operator.
Decomposed IFAFSST outputs into low-frequency and high-frequency components, extracting 16 discriminative features from each.
Introduced an improved AdaBoost classifier with forward feature selection and Hyperband-based hyperparameter optimization.
Achieved 99.50% accuracy on simulated data with 30 dB white noise.
Achieved 98.30% accuracy on data from a hardware platform.
Effectively handled 23 types of interference, including single, double, triple, and quadruple compound interferences.

Abstract

The integration of renewable energy and power electronics has intensified the occurrence of complex power quality disturbances (PQDs), which increasingly threaten grid stability. To address the challenges of multi-class PQD identification under noisy conditions, this paper proposes a novel framework that combines an enhanced time–frequency analysis method with an optimized AdaBoost decision tree. The main contributions are three-fold: (1) We develop an instantaneous frequency adaptive Fourier synchrosqueezing transform (IFAFSST) equipped with a custom adaptive operator that aligns closely with the frequency modulation patterns in PQD signals, thereby improving time–frequency energy localization. (2) The IFAFSST outputs are decomposed into low-frequency and high-frequency components, from each of which a set of 16 discriminative features is extracted. (3) An improved AdaBoost classifier is introduced, incorporating forward feature selection and Hyperband-based hyperparameter optimization to enhance classification performance. Hyperband accelerates the optimization process by dynamically allocating computing resources and iteratively eliminating suboptimal configurations, thereby enabling efficient determination of the optimal hyperparameters. The method proposed in this paper achieved an accuracy rate of 99.50% on simulated data containing 30 dB white noise and 98.30% on hardware platform data. This framework can effectively handle 23 types of interference, including seven types of single interference, 12 types of double compound interference, three types of triple compound interference, and one type of quadruple compound interference. It performs particularly well in identifying composite interference scenarios. This research has made a significant contribution to power quality analysis, providing a powerful solution with high accuracy and practical applicability, and offering great potential for the implementation of smart grid monitoring systems and the integration of renewable energy.

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Cite This Study

He et al. (Tue,) studied this question.

synapsesocial.com/papers/69b3ac7002a1e69014cce2c2 https://doi.org/https://doi.org/10.3390/sym18030475

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