Monitoring electrical systems data-network equipment by means ofFuzzy
and Paraconsistent Annotated Logic
- URL: http://arxiv.org/abs/2105.07579v1
- Date: Mon, 17 May 2021 02:33:45 GMT
- Title: Monitoring electrical systems data-network equipment by means ofFuzzy
and Paraconsistent Annotated Logic
- Authors: Hyghor Miranda Cortes, Paulo Eduardo Santos, Joao Inacio da Silva
Filho
- Abstract summary: This paper develops a prototype of an expert system to monitor the status of equipment of datanetworks in electrical systems.
The expert system is developed with algorithms defined a combination of Fuzzy logic and Paraconsistent Annotated Logic.
A prototype of this expert system was installed on a virtualised server with CLP500 software.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The constant increase in the amount and complexity of information obtained
from IT data networkelements, for its correct monitoring and management, is a
reality. The same happens to data net-works in electrical systems that provide
effective supervision and control of substations and hydro-electric plants.
Contributing to this fact is the growing number of installations and new
environmentsmonitored by such data networks and the constant evolution of the
technologies involved. This sit-uation potentially leads to incomplete and/or
contradictory data, issues that must be addressed inorder to maintain a good
level of monitoring and, consequently, management of these systems. Inthis
paper, a prototype of an expert system is developed to monitor the status of
equipment of datanetworks in electrical systems, which deals with
inconsistencies without trivialising the inferences.This is accomplished in the
context of the remote control of hydroelectric plants and substationsby a
Regional Operation Centre (ROC). The expert system is developed with algorithms
definedupon a combination of Fuzzy logic and Paraconsistent Annotated Logic
with Annotation of TwoValues (PAL2v) in order to analyse uncertain signals and
generate the operating conditions (faulty,normal, unstable or inconsistent /
indeterminate) of the equipment that are identified as importantfor the remote
control of hydroelectric plants and substations. A prototype of this expert
systemwas installed on a virtualised server with CLP500 software (from the
EFACEC manufacturer) thatwas applied to investigate scenarios consisting of a
Regional (Brazilian) Operation Centre, with aGeneric Substation and a Generic
Hydroelectric Plant, representing a remote control environment.
Related papers
- Graph Neural Networks for Electric and Hydraulic Data Fusion to Enhance Short-term Forecasting of Pumped-storage Hydroelectricity [6.675805308519987]
Pumped-storage hydropower plants (PSH) actively participate in grid power-frequency control.
Predicting dynamically changing states is essential for comprehending the underlying sensor and machine conditions.
This work introduces the application of spectral-temporal graph neural networks, which leverage self-attention mechanisms to concurrently capture and learn meaningful subsystem interdependencies.
arXiv Detail & Related papers (2024-04-04T11:09:49Z) - Grid Monitoring and Protection with Continuous Point-on-Wave
Measurements and Generative AI [47.19756484695248]
This article presents a case for a next-generation grid monitoring and control system, leveraging recent advances in generative artificial intelligence (AI) and machine learning.
We argue for a monitoring and control framework based on the streaming of continuous point-on-wave (CPOW) measurements with AI-powered data compression and fault detection.
arXiv Detail & Related papers (2024-03-11T17:28:46Z) - 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) - Autonomous Point Cloud Segmentation for Power Lines Inspection in Smart
Grid [56.838297900091426]
An unsupervised Machine Learning (ML) framework is proposed, to detect, extract and analyze the characteristics of power lines of both high and low voltage.
The proposed framework can efficiently detect the power lines and perform PLC-based hazard analysis.
arXiv Detail & Related papers (2023-08-14T17:14:58Z) - Delayed Propagation Transformer: A Universal Computation Engine towards
Practical Control in Cyber-Physical Systems [68.75717332928205]
Multi-agent control is a central theme in the Cyber-Physical Systems.
This paper presents a new transformer-based model that specializes in the global modeling of CPS.
With physical constraint inductive bias baked into its design, our DePT is ready to plug and play for a broad class of multi-agent systems.
arXiv Detail & Related papers (2021-10-29T17:20:53Z) - Federated Learning for Intrusion Detection System: Concepts, Challenges
and Future Directions [0.20236506875465865]
Intrusion detection systems play a significant role in ensuring security and privacy of smart devices.
The present paper aims to present an extensive and exhaustive review on the use of FL in intrusion detection system.
arXiv Detail & Related papers (2021-06-16T13:13:04Z) - Towards AIOps in Edge Computing Environments [60.27785717687999]
This paper describes the system design of an AIOps platform which is applicable in heterogeneous, distributed environments.
It is feasible to collect metrics with a high frequency and simultaneously run specific anomaly detection algorithms directly on edge devices.
arXiv Detail & Related papers (2021-02-12T09:33:00Z) - Multi-Source Data Fusion for Cyberattack Detection in Power Systems [1.8914160585516038]
We show that fusing information from multiple data sources can help identify cyber-induced incidents and reduce false positives.
We perform multi-source data fusion for training IDS in a cyber-physical power system testbed.
Results are presented using the proposed data fusion application to infer False Data and Command injection-based Man-in- The-Middle attacks.
arXiv Detail & Related papers (2021-01-18T06:34:45Z) - Decentralized Control with Graph Neural Networks [147.84766857793247]
We propose a novel framework using graph neural networks (GNNs) to learn decentralized controllers.
GNNs are well-suited for the task since they are naturally distributed architectures and exhibit good scalability and transferability properties.
The problems of flocking and multi-agent path planning are explored to illustrate the potential of GNNs in learning decentralized controllers.
arXiv Detail & Related papers (2020-12-29T18:59:14Z) - Defending Against Adversarial Attacks in Transmission- and
Distribution-level PMU Data [2.5365237338254816]
Phasor measurement units (PMUs) provide high-fidelity data that improve situation awareness of electric power grid operations.
As PMU data become more available and increasingly reliable, these devices are found in new roles within control systems.
We present a comprehensive analysis of multiple machine learning techniques to detect malicious data injection within PMU data streams.
arXiv Detail & Related papers (2020-08-20T18:44:37Z) - Unionized Data Governance in Virtual Power Plants [7.008490462870144]
We focus on the central role of virtual power plants in flexible electricity networks.
We propose a unionized data governance model for virtual power plants.
arXiv Detail & Related papers (2020-06-04T09:03:26Z)
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.