Ensemble learning and deep learning-based defect detection in power generation plants

Abstract

One of the key factors driving a country’s economic development and ensuring the sustainability of its industries is the constant availability of electricity. This is normally provided by the national grid. However, the power supply is not always stable in developing countries where new businesses, including the telecommunications industry, are constantly emerging. Therefore, they must rely on generators to ensure their full functionality. These generators rely on fuel to function, and consumption is usually high if not properly monitored. Monitoring is usually done by a (non-expert) human. This can sometimes be a tedious process, as some companies have reported excessively high consumption rates. For anomaly detection in power generating plants, the studies by Mulongo et al. and Atemkeng and Jimoh used the same dataset to train a multilayer perceptron (MLP) and generative adversarial networks (GANs), respectively, achieving an accuracy of 96.1% with MLP and 98.9% with GAN. Through comparative analysis and the use of ensemble learning techniques, we found that ensemble learning models outperform both MLP and GAN as proposed by Mulongo et al. and Atemkeng and Jimoh using the same dataset. Furthermore, we investigated the potential of autoencoders to outperform MLPs, GANs, and ensemble learning models. To this end, we have introduced a label-assisted autoencoder approach for detecting anomalies in power-generating plants. This model includes a labelling assistance module that adjusts the thresholds. Our results indicate that the label-assisted autoencoder outperforms the MLP. However, GANs and all ensemble learning models outperformed the label-assisted autoencoder. Nevertheless, the use of a label-assisted autoencoder offers a distinct advantage in categorizing anomalies based on their severity, a capability not present in ensemble learning models and GANs.

Keywords

• electric grid
• fuel consumption
• autoencoder
• anomaly detection
• power generating plants
• gans
• ensemble learning