Transformer-Based Prognostic Modeling for Smart Grid Health Monitoring

Smart power grids, pivotal to modern infrastructure, face increased vulnerability due to their complex and interconnected nature, often leading to disruptions that can result in widespread power outages and significant economic losses. Traditional grid monitoring systems frequently fall short in both predicting and mitigating these disruptions, creating a critical need for more advanced predictive methods that can foresee and interpret the progression of these anomalies into distinct failure modes. This paper introduces a transformer-based prognostic model based on Phasor Measurement Unit (PMU) data to enhance the predictive monitoring of smart grids. The method capitalizes on the advanced capabilities of transformer models to manage high-dimensional time-series data with complex dependencies. The methodology, validated rigorously against historical data, progresses through three main phases: (1) PMU data acquisition and preprocessing, (2) PMU-based feature engineering, and (3) prognostic modeling. In the data acquisition and preprocessing phase, high-resolution PMU data were collected, encompassing parameters such as voltage and current magnitudes, frequency, and phase angles. Next, we extracted 43 distinct features across four temporal windows, which were essential in characterizing the dynamic behavior of the grid. The prognostic modeling phase employed a transformer architecture with a sophisticated self-attention mechanism that efficiently handles the sequential PMU data, identifying subtle patterns indicative of potential grid failures. The prognostic model had a superior performance in predicting different types of grid failures-severe weather, lightning, and failed AC circuit equipment, which achieved an accuracy of up to 84.07% with low variability using five-fold cross-validation. This prognostic model enhances the predictive capabilities of smart grid monitoring systems and offers a robust framework for proactive management of grid health, potentially reducing the incidence and impact of power outages.