Artificial Intelligence-Based Voltage Stability Assessment in Modern Power Systems: Edge Intelligence for Real-Time Monitoring of Smart Electrical Networks

Abstract

Voltage stability has become a critical operational concern in modern power systems because of rising renewable energy penetration, decentralised generation and heavier line loadings have narrowed the margin between secure operation and voltage collapse. While traditional model-based indices are accurate but slow, centralised machine-learning methods suffer from communication latency, which restricts their real-time applicability. This paper introduces an Edge-Intelligence framework, where a small hybrid Convolutional Neural Network — Long Short-Term Memory (CNN-LSTM) classifier is placed on edge nodes in the substation to provide millisecond-scale voltage-stability assessment based on phasor measurement unit (PMU) data. The pipeline is tested on 12,000 operating scenarios generated on IEEE 14-, 30- and 118-bus test systems for normal, stressed, contingency and post-fault conditions. The proposed model achieves 94.5% accuracy, macro-F1 of 0.941 and ROC-AUC of 0.999 on the combined test set, outperforming four strong baselines (logistic regression, RBF-SVM, random forest and gradient boosting) by 12-14 percentage points in accuracy, while requiring only 186 kB of memory and delivering submillisecond inference on a Jetson-class edge device. In accordance with classical L-index theory, the explainability analysis shows that the most important predictors are bus-voltage magnitude, reactive-power injection and load factor. The results show that Edge-Intelligence is a viable, low latency, and low footprint path towards always-on voltage-stability monitoring in smart electrical networks.

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