Anomaly Detection in Power Markets and Systems

• URL: http://arxiv.org/abs/2212.02182v1
• Date: Mon, 5 Dec 2022 11:38:25 GMT
• Title: Anomaly Detection in Power Markets and Systems
• Authors: Ugur Halden, Umit Cali, Ferhat Ozgur Catak, Salvatore D'Arco, Francisco Bilendo
• Abstract summary: A cyber-physical system, such as the electrical grid, may experience anomalies for a number of different reasons. The goal of this study is to emphasize what the most common incidents are with power systems and to give an overview and classification of the most common ways to find problems. In addition, this article aimed to discuss the methods and techniques, such as artificial intelligence (AI) that are used to identify anomalies in the power systems and markets.
• Score: 0.41998444721319217
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The widespread use of information and communication technology (ICT) over the course of the last decades has been a primary catalyst behind the digitalization of power systems. Meanwhile, as the utilization rate of the Internet of Things (IoT) continues to rise along with recent advancements in ICT, the need for secure and computationally efficient monitoring of critical infrastructures like the electrical grid and the agents that participate in it is growing. A cyber-physical system, such as the electrical grid, may experience anomalies for a number of different reasons. These may include physical defects, mistakes in measurement and communication, cyberattacks, and other similar occurrences. The goal of this study is to emphasize what the most common incidents are with power systems and to give an overview and classification of the most common ways to find problems, starting with the consumer/prosumer end working up to the primary power producers. In addition, this article aimed to discuss the methods and techniques, such as artificial intelligence (AI) that are used to identify anomalies in the power systems and markets.

Related papers

• Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning [52.6706505729803]
  We introduce Federated Learning (FL) to collaboratively train a decentralized shared model of Intrusion Detection Systems (IDS) FLEKD enables a more flexible aggregation method than conventional model fusion techniques. Experiment results show that the proposed approach outperforms local training and traditional FL in terms of both speed and performance.
  arXiv  Detail & Related papers  (2024-01-22T14:16:37Z)
• Multi-Tier Computing-Enabled Digital Twin in 6G Networks [50.236861239246835]
  In Industry 4.0, industries such as manufacturing, automotive, and healthcare are rapidly adopting DT-based development. The main challenges to date have been the high demands on communication and computing resources, as well as privacy and security concerns. To achieve low latency and high security services in the emerging DT, multi-tier computing has been proposed by combining edge/fog computing and cloud computing.
  arXiv  Detail & Related papers  (2023-12-28T13:02:53Z)
• Cybersecurity Challenges of Power Transformers [3.509488301177195]
  The dependency of new power grid technology on information, data analytic and communication systems make the entire electricity network vulnerable to cyber threats. Power transformers play a critical role within the power grid and are now commonly enhanced through factory add-ons or intelligent monitoring systems. This paper explores the vulnerabilities and the attack vectors of power transformers within electricity networks, the possible attack scenarios and the risks associated with these attacks.
  arXiv  Detail & Related papers  (2023-02-25T21:08:59Z)
• Exploring the Use of Data-Driven Approaches for Anomaly Detection in the Internet of Things (IoT) Environment [4.724825031148412]
  The Internet of Things (IoT) is a system that connects physical computing devices, sensors, software, and other technologies. Data can be collected, transferred, and exchanged with other devices over the network without requiring human interactions. Research on anomaly detection in the IoT environment has become popular and necessary in recent years.
  arXiv  Detail & Related papers  (2022-12-31T06:28:58Z)
• Hybrid AI-based Anomaly Detection Model using Phasor Measurement Unit Data [0.41998444721319217]
  Using phasor measurement units (PMUs) to surveil the power system is one of the technologies that have a promising future. The increased cyber-physical interaction offers both benefits and drawbacks, where one of the drawbacks comes in the form of anomalies in the measurement data. This paper aims to develop a hybrid AI-based model that is based on various methods for anomaly detection in PMUs data.
  arXiv  Detail & Related papers  (2022-09-21T11:22:01Z)
• Ranking-Based Physics-Informed Line Failure Detection in Power Grids [66.0797334582536]
  Real-time and accurate detecting of potential line failures is the first step to mitigating the extreme weather impact and activating emergency controls. Power balance equations nonlinearity, increased uncertainty in generation during extreme events, and lack of grid observability compromise the efficiency of traditional data-driven failure detection methods. This paper proposes a Physics-InformEd Line failure Detector (FIELD) that leverages grid topology information to reduce sample and time complexities and improve localization accuracy.
  arXiv  Detail & Related papers  (2022-08-31T18:19:25Z)
• Machine learning applications for electricity market agent-based models: A systematic literature review [68.8204255655161]
  Agent-based simulations are used to better understand the dynamics of the electricity market. Agent-based models provide the opportunity to integrate machine learning and artificial intelligence. We review 55 papers published between 2016 and 2021 which focus on machine learning applied to agent-based electricity market models.
  arXiv  Detail & Related papers  (2022-06-05T14:52:26Z)
• A review of Federated Learning in Intrusion Detection Systems for IoT [0.15469452301122172]
  Intrusion detection systems are evolving into intelligent systems that perform data analysis searching for anomalies in their environment. Deep learning technologies opened the door to build more complex and effective threat detection models. Current approaches rely on powerful centralized servers that receive data from all their parties. This paper focuses on the application of Federated Learning approaches in the field of Intrusion Detection.
  arXiv  Detail & Related papers  (2022-04-26T17:00:07Z)
• Machine Learning for Massive Industrial Internet of Things [69.52379407906017]
  Industrial Internet of Things (IIoT) revolutionizes the future manufacturing facilities by integrating the Internet of Things technologies into industrial settings. With the deployment of massive IIoT devices, it is difficult for the wireless network to support the ubiquitous connections with diverse quality-of-service (QoS) requirements. We first summarize the requirements of the typical massive non-critical and critical IIoT use cases. We then identify unique characteristics in the massive IIoT scenario, and the corresponding machine learning solutions with its limitations and potential research directions.
  arXiv  Detail & Related papers  (2021-03-10T20:10:53Z)
• Analyzing Power Grid, ICT, and Market Without Domain Knowledge Using Distributed Artificial Intelligence [0.0]
  Cyber-physical systems, such as our energy infrastructure, are becoming increasingly complex. This paper introduces the concept for an application of distributed artificial intelligence as a self-adaptive analysis tool.
  arXiv  Detail & Related papers  (2020-06-10T21:32:39Z)
• Survey of Network Intrusion Detection Methods from the Perspective of the Knowledge Discovery in Databases Process [63.75363908696257]
  We review the methods that have been applied to network data with the purpose of developing an intrusion detector. We discuss the techniques used for the capture, preparation and transformation of the data, as well as, the data mining and evaluation methods. As a result of this literature review, we investigate some open issues which will need to be considered for further research in the area of network security.
  arXiv  Detail & Related papers  (2020-01-27T11:21:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.