AI-Driven Predictive Analytics Framework for Electric Grid Reliability and Infrastructure Resilience
Keywords:
predictive analytics, electric grid reliability, infrastructure resilience, machine learning, deep learning, fault detection, cyberattack detection, load forecasting, renewable energy forecasting, extreme weather, smart grid, ensemble learningAbstract
The need for predictive, rather than reactive, solutions to managing the electric grid has grown, due to the increasing frequency of extreme weather events, the growing complexity of electric transmission and distribution networks, and the rise in exposure to cyber-physical threats. This paper is a synthesis of 24 peer-reviewed studies to suggest an integrated artificial intelligence (AI) based predictive analytics framework to improve grid reliability and infrastructure resilience. This framework brings together four layers of analysis: data acquisition, feature engineering, predictive modelling, resilience and risk quantification, and leverages ensemble learning techniques (such as random forests (Breiman, 2001) and extreme gradient boosting (Chen & Guestrin, 2016)), deep sequential networks (long short-term memory (LSTM) networks (Hochreiter & Schmidhuber, 1997; Kong et al., 2019)), and probabilistic fragility modelling approaches for extreme-weather impact assessment (Panteli & Mancarella, 2015; Panteli, Pickering, et al., 2017). Various applications such as transformer lifetime prediction (Aizpurua et al., 2019), transmission-line fault classification (Shi et al., 2019), cyberattack and false-data-injection detection (He et al., 2017; Karimipour et al., 2019), and renewable-energy forecasting (Voyant et al., 2017; Yagli et al., 2019) are also included in the synthesis. The reported behaviour of these methods implies that deep sequential models outperform classical statistical models in short-term load forecasting and/or detection by several points, particularly in the case of mean absolute percentage error; and detection accuracy is often higher than ninety percent across several classes of cyber-physical threats. The paper proposes that the resilience-metric quantification (Panteli, Mancarella et al., 2017) combined with real-time predictive modelling provides a scalable solution to proactive grid management. It ends with identifying data quality, interoperability and regulatory challenges for widespread implementation, and proposing a path towards having standardised resilience metrics across utilities.
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