Anomaly Detection in Smart Grids using Machine Learning

Shabad, Prem Kumar Reddy; Alrashide, Abdulmueen; Mohammed, Osama A.

doi:10.1109/iecon48115.2021.9589851

Cited by 7 publications

(1 citation statement)

References 27 publications

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“…Machine learning techniques are employed to train models that can make accurate predictions and adapt to changing conditions. By leveraging AI, energy companies can minimize downtime, reduce maintenance costs, and ensure a reliable supply of electricity [4].…”

Section: Ai In Grid Managementmentioning

confidence: 99%

Transforming the Energy Sector: Addressing Key Challenges through Generative AI, Digital Twins, AI, Data Science and Analysis

Tomar,

Grover

2024

EAI Endorsed Trans Energy Web

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The energy sector, both in the UK and globally, faces significant challenges in the pursuit of sustainability and efficient resource utilization. Climate change, resource depletion, and the need for decarbonization demand innovative solutions. This analytical research paper examines the key challenges in the energy sector and explores how generative AI, digital twins, AI, and data science can play a transformative role in addressing these challenges. By leveraging advanced technologies and data-driven approaches, the energy sector can achieve greater efficiency, optimize operations, and facilitate informed decision-making. Artificial Intelligence (AI) involves replicating human-like intelligence in machines, enabling them to execute tasks that typically demand human cognitive capabilities like perception, reasoning, learning, and problem[1]solving. AI encompasses various methodologies and technologies, such as machine learning, natural language processing, computer vision, and robotics. Its adoption in the energy sector carries significant promise for addressing critical concerns and revolutionizing the industry. An overarching challenge in the energy sector revolves around enhancing energy efficiency, and AI emerges as a pivotal tool for optimizing energy utilization and curbing wastage. By analyzing vast amounts of data from various sources such as sensors, smart meters, and historical energy consumption patterns, AI algorithms can identify patterns and anomalies that humans may not detect. This enables the development of predictive models and algorithms that optimize energy consumption, leading to significant energy savings.

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Section: Ai In Grid Managementmentioning

confidence: 99%

Transforming the Energy Sector: Addressing Key Challenges through Generative AI, Digital Twins, AI, Data Science and Analysis

Tomar,

Grover

2024

EAI Endorsed Trans Energy Web

View full text Add to dashboard Cite

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A survey of anomaly detection methods for power grids

Madabhushi

Dewri

2023

Int. J. Inf. Secur.

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Distributed and explainable GHSOM for anomaly detection in sensor networks

Mignone,

Corizzo,

Ceci

2024

Mach Learn

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The identification of anomalous activities is a challenging and crucially important task in sensor networks. This task is becoming increasingly complex with the increasing volume of data generated in real-world domains, and greatly benefits from the use of predictive models to identify anomalies in real time. A key use case for this task is the identification of misbehavior that may be caused by involuntary faults or deliberate actions. However, currently adopted anomaly detection methods are often affected by limitations such as the inability to analyze large-scale data, a reduced effectiveness when data presents multiple densities, a strong dependence on user-defined threshold configurations, and a lack of explainability in the extracted predictions. In this paper, we propose a distributed deep learning method that extends growing hierarchical self-organizing maps, originally designed for clustering tasks, to address anomaly detection tasks. The SOM-based modeling capabilities of the method enable the analysis of data with multiple densities, by exploiting multiple SOMs organized as a hierarchy. Our map-reduce implementation under Apache Spark allows the method to process and analyze large-scale sensor network data. An automatic threshold-tuning strategy reduces user efforts and increases the robustness of the method with respect to noisy instances. Moreover, an explainability component resorting to instance-based feature ranking emphasizes the most salient features influencing the decisions of the anomaly detection model, supporting users in their understanding of raised alerts. Experiments are conducted on five real-world sensor network datasets, including wind and photovoltaic energy production, vehicular traffic, and pedestrian flows. Our results show that the proposed method outperforms state-of-the-art anomaly detection competitors. Furthermore, a scalability analysis reveals that the method is able to scale linearly as the data volume presented increases, leveraging multiple worker nodes in a distributed computing setting. Qualitative analyses on the level of anomalous pollen in the air further emphasize the effectiveness of our proposed method, and its potential in determining the level of danger in raised alerts.

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Anomaly Detection in Smart Grids using Machine Learning

Cited by 7 publications

References 27 publications

Transforming the Energy Sector: Addressing Key Challenges through Generative AI, Digital Twins, AI, Data Science and Analysis

Transforming the Energy Sector: Addressing Key Challenges through Generative AI, Digital Twins, AI, Data Science and Analysis

A survey of anomaly detection methods for power grids

Distributed and explainable GHSOM for anomaly detection in sensor networks

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