###### Abstract  
This article proposes a multiscale classification framework for detecting voltage disturbances in electrical distribution systems using artificial neural networks (ANNs) combined with the Hilbert-Huang transform (HHT). The framework targets four core power quality (PQ) events defined in the IEEE 1159-2019 standard: normal operation and voltage sag, swell, and interruption. Unlike traditional methods that operate on a fixed disturbance duration, our approach incorporates multiple time scales (0.2 s, 0.4 s, and 0.8 s) to improve detection robustness across varied event lengths, a critical factor in real-world scenarios where disturbance durations are unpredictable. Features are extracted using empirical mode decomposition (EMD) and Hilbert spectral analysis, enabling accurate representation of the signals' non-stationary and nonlinear characteristics. The ANN is trained using statistical descriptors derived from the first two intrinsic mode functions (IMFs), capturing both amplitude and frequency content. The method was validated in MATLAB on the IEEE 33-bus radial distribution test system using simulated disturbances. The proposed model achieved a classification accuracy of 94.09% and demonstrated consistent performance across all time windows, supporting its suitability for real-time monitoring in smart distribution networks. This study contributes a scalable and adaptable solution for automated PQ event classification under variable conditions. p 2  
common manifestation of this category. More specifically, these are voltage sags and swells and complete loss of power [3]. Such processes may result from pulse transients, changes in frequency, equipment, or out-of-order devices and may affect system performance from increased wear of electric parts to irreversible and prolonged suspension of intended system operations [4]. There exists a problem in the effective monitoring structures in the context of inter-dependent electrical grids due to their intricate nature and the intelligent technologies associated with them. In other cases, more advanced devices such as artificial neural networks are employed for detecting and analyzing power system disturbances [4]. Neural networks have been proven to aid in problem solving due to their ability to complexly identify patterns and modify themselves according to continuously changing settings [5]. This paper proposes a methodological approach in the form of disturbance detection and classification at the level of the artificial neural network application paired with Hilbert-Huang transform-based power signal processing according to IEEE 1159 [5; 6; 7]. This methodology in the IEEE 1159 standard allows for monitoring the power supply and identifying phenomena that can impact the quality of electrical power supplied, hence categorizing them into electrical events. The proposed methodology allows for unrivaled depth analysis and classification in addition to disturbance detection, which further strengthens the existing electrical frameworks [5; 6; 7]. In this case, neural networks enhance detection capabilities and provide great value through effective data collection and analysis throughout large datasets. The Hilbert-Huang transform is capable of detailed spectral analysis of non-stationary signals decomposition enabling identifying characteristic patterns of disturbances [6; 7]. This proposal stands out as it attempts to solve one of the almost perennial problems lingering in modern electrical systems: the early detection and accurate classification of disturbances. Precise detection allows for remedial action to be taken well ahead of the disturbances degrading grid performance, thereby minimizing the impact to users [6; 7]. Accurate event classification enhances understanding of the phenomenon which can help in designing specific mitigation strategies. This proposal aims to provide groundwork for subsequent advanced research which integrates highly sophisticated techniques for dynamic and flexible monitoring of electrical systems.