学习多模态时空数据以提升智能电网韧性：机器学习方法

Aug 5, 2026

The electric grid’s rapid expansion in size and technology, combined with aging infrastructure, has intensified reliability challenges, including a growing number of long-duration outages and substantial customer-hour losses. Severe weather is a major driver of large-scale outages in the United States, while increasing renewable integration pushes the grid toward decentralization, adding control complexity. Although sensing and data storage enable new opportunities, smart-grid data is high-dimensional, multi-modal, noisy, incomplete, and often inaccurately labeled, making manual analysis infeasible and reducing model interpretability. This dissertation develops machine learning models for solar generation forecasting, fault detection and explanation, outage prediction with explainable precursors, and spatiotemporal precursor extraction to support mitigation planning.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages136

Reading Time5 h 43 min

AccessFree to download

Abstract

Smart grid reliability challenges

Multi-modal spatiotemporal data and data quality issues

Proposed machine learning models

Solar generation prediction

Power fault detection, de-noising, and explanation

Outage prediction and explainable event precursors

LEARNING FROM MULTI-MODAL SPATIOTEMPORAL DATA: MACHINE LEARNING APPROACHES TO ADVANCE RESILIENCE IN SMART GRIDS | A Dissertation
Submitted to the Temple University Graduate Board | | --- | | In Partial Fulﬁllment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY | by Mohammad Khaled Alqudah December 2023 Examining committee members: Dr. Zoran Obradovic, Advisory Chair, Department of Computer & Information Sciences Dr. Slobodan Vucetic, Department of Computer & Information Sciences Dr. Hongchang Gao, Department of Computer & Information Sciences Dr. Liang Du, External Member, Department of Electrical Engineering © Copyright 2023 by Mohammad Khaled Alqudah All Rights Reserved ii ABSTRACT The electric grid has been expanding both in size and the technologies used. As of the 2020s, the United States power grid consists of more than 9,200 electric generating units with more than 1 million megawatts of generating capacity connected to more than 300,000 miles of transmission lines. The United States electricity grid has rapidly expanded in recent decades, and the majority (over 70%) of its infrastructure has exceeded 25 years of age. Due to its size and age, several challenges have emerged. Widespread power outages have been increasing across the United States. Between 2018 and 2020, more than 231,000 power outages occurred in the United States that lasted more than one hour, out of which 17,484 lasted at least eight hours. In the same period, the power outages resulted in an annual loss of 520 million customer hours across 2,447 U.S. counties. Moreover, and with the rapidly changing climate, between 2000 and 2021, approximately 83% of signiﬁcant power outages impacting a minimum of 50,000 customers in the United States were attributed to severe weather conditions. Lastly, the increasing use of renewables and other non-traditional generation methods forces the power system towards a more decentralized model, with many integrated systems constantly added to the grid. This decentralization adds additional burdens on controlling systems and grid operators. The rapid growth of technology and data storage allowed the deployment of sensing devices across the electric grid. Such technologies present a golden opportunity to tackle many of the electric grid’s challenges. Despite that, such technologies presented many challenges simultaneously. With the large amounts of data, it became humanly impossible to comprehend, analyze, and use all collected data manually. While machine learning can be used to analyze smart grid data, this can be challenged by the nature of its data. Smart grid produces highdimensional spatiotemporal data, and many applications require multi-modal data. Moreover, power systems’ data quality challenges add complexities to model development. The data is noisy, contains missing segments, and usually has incomplete and inaccurate labels. In addition, interpreting machine learning models in the context of smart grids poses unique challenges. To address these challenges, different models for multiple smart-grid applications were introduced in this research, where each model focused on producing practical solutions for the challenges facing current-day smart grids. Using spatiotemporal data, a solar generation prediction model was proposed in (Alqudah et al., 2020) . The solution combined spatial and temporal data, then utilized machine learning embeddings to build datasets to train downstream models. This resulted in accurate prediction of solar generation across several settings. In addition to solar generation prediction, several models were introduced to detect, predict and explain power grid faults. A neural model is introduced in (Alqudah et al., 2022b) to detect power faults from Phasor Measurement Unit (PMU) data. A novel method is introduced to preprocesses, de-noise, and combine high dimensional data, then this data is used to train novel neural methods that detect faults in m

FAQs

为什么需要从多模态时空数据学习来提升智能电网韧性？

电网规模与技术持续增长、基础设施老化以及极端天气导致的停电问题日益突出，而可用传感数据同时面临噪声、缺失与不完整标签等困难，使得需要更有效的数据驱动方法与韧性提升策略。

论文指出智能电网数据有哪些关键建模挑战？

数据通常是高维时空与多模态，存在噪声、缺失片段，并且标签往往不完整或不准确；此外，还需要能在电网语境下解释模型结果。

研究如何把学习结果用于电网运行与故障/停电缓解？

研究提出用于太阳能发电预测、功率故障检测与解释、以及提前预测停电并提取可解释的事件先兆的方法；这些先兆可帮助运行人员缩小缓解计划并降低大范围停电风险。

Aug 5, 2026

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