100 research outputs found
Survey on synchrophasor data quality and cybersecurity challenges, and evaluation of their interdependencies
Synchrophasor devices guarantee situation awareness for real-time monitoring and operational visibility of smart grid. With their widespread implementation, significant challenges have emerged, especially in communication, data quality and cybersecurity. The existing literature treats these challenges as separate problems, when in reality, they have a complex interplay. This paper conducts a comprehensive review of quality and cybersecurity challenges for synchrophasors, and identifies the interdependencies between them. It also summarizes different methods used to evaluate the dependency and surveys how quality checking methods can be used to detect potential cyberattacks. This paper serves as a starting point for researchers entering the fields of synchrophasor data analytics and security
Cybersecurity Challenges of Power Transformers
The rise of cyber threats on critical infrastructure and its potential for
devastating consequences, has significantly increased. 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 added later
to improve the condition monitoring of critical and long lead time assets such
as transformers. However, the increased connectivity of those power
transformers opens the door to more cyber attacks. Therefore, the need to
detect and prevent cyber threats is becoming critical. The first step towards
that would be a deeper understanding of the potential cyber-attacks landscape
against power transformers. Much of the existing literature pays attention to
smart equipment within electricity distribution networks, and most methods
proposed are based on model-based detection algorithms. Moreover, only a few of
these works address the security vulnerabilities of power elements, especially
transformers within the transmission network. To the best of our knowledge,
there is no study in the literature that systematically investigate the
cybersecurity challenges against the newly emerged smart transformers. This
paper addresses this shortcoming by exploring the vulnerabilities and the
attack vectors of power transformers within electricity networks, the possible
attack scenarios and the risks associated with these attacks.Comment: 11 page
Towards computer vision technologies: Semi-automated reading of automated utility meters
In this report we analysed a possibility of using computer vision techniques
for automated reading of utility meters. In our study, we focused on two
computer vision techniques: an open-source solution Tensorflow Object Detection
(Tensorflow) and a commercial solution Anyline. This report extends our
previous publication: We start with presentation of a structured analysis of
related approaches. After that we provide a detailed comparison of two computer
vision technologies, Tensorflow Object Detection (Tensorflow) and Anyline,
applied to semi-automated reading of utility meters. In this paper, we discuss
limitations and benefits of each solution applied to utility meters reading,
especially focusing on aspects such as accuracy and inference time. Our goal
was to determine the solution that is the most suitable for this particular
application area, where there are several specific challenges
Combined network intrusion and phasor data anomaly detection for secure dynamic control centers
The dynamic operation of power transmission systems requires the acquisition of reliable and accurate measurement and state information. The use of TCP/IP-based communication protocols such as IEEE C37.118 or IEC 61850 introduces different gateways to launch cyber-attacks and to compromise major system operation functionalities. Within this study, a combined network intrusion and phasor data anomaly detection system is proposed to enable a secure system operation in the presence of cyber-attacks for dynamic control centers. This includes the utilization of expert-rules, one-class classifiers, as well as recurrent neural networks to monitor different network packet and measurement information. The effectiveness of the proposed network intrusion and phasor data anomaly detection system is shown within a real-time simulation testbed considering multiple operation and cyber-attack conditions
Blockchain technology research and application: a systematic literature review and future trends
Blockchain, as the basis for cryptocurrencies, has received extensive
attentions recently. Blockchain serves as an immutable distributed ledger
technology which allows transactions to be carried out credibly in a
decentralized environment. Blockchain-based applications are springing up,
covering numerous fields including financial services, reputation system and
Internet of Things (IoT), and so on. However, there are still many challenges
of blockchain technology such as scalability, security and other issues waiting
to be overcome. This article provides a comprehensive overview of blockchain
technology and its applications. We begin with a summary of the development of
blockchain, and then give an overview of the blockchain architecture and a
systematic review of the research and application of blockchain technology in
different fields from the perspective of academic research and industry
technology. Furthermore, technical challenges and recent developments are also
briefly listed. We also looked at the possible future trends of blockchain
Upgrading the Power Grid Functionalities with Broadband Power Line Communications: Basis, Applications, Current Trends and Challenges
This article reviews the basis and the main aspects of the recent evolution of Broadband Power Line Communications (BB-PLC or, more commonly, BPL) technologies. The article starts describing the organizations and alliances involved in the development and evolution of BPL systems, as well as the standardization institutions working on PLC technologies. Then, a short description of the technical foundation of the recent proposed technologies and a comparison of the main specifications are presented; the regulatory activities related to the limits of emissions and immunity are also addressed. Finally, some representative applications of BPL and some selected use cases enabled by these technologies are summarized, together with the main challenges to be faced.This work was financially supported in part by the Basque Government under the grants IT1426-22, PRE_2021_1_0006, and PRE_2021_1_0051, and by the Spanish Government under the grants PID2021-124706OB-I00 and RTI2018-099162-B-I00 (MCIU/AEI/FEDER, UE, funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”)
Adaptive individual residential load forecasting based on deep learning and dynamic mirror descent
With a growing penetration of renewable energy generation in the modern power networks, it has become highly challenging for network operators to balance electricity supply and demand. Residential load forecasting nowadays plays an increasingly important role in this aspect and facilitates various interactions between power networks and electricity users. While numerous research works have been proposed targeting at aggregate residential load forecasting, only a few efforts have been made towards individual residential load forecasting. The issue of volatility of individual residential load has never been addressed in forecasting. Thus, to fill this gap, this paper presents a deep learning method empowered with dynamic mirror descent for adaptive individual residential load forecasting. The proposed method is evaluated on a real-life Irish residential load dataset, and the experimental results show that it improves the prediction accuracy by 9.1% and 11.6% in the aspects of RMSE and MAE respectively in comparison with a benchmark method
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