Backpropagation Neural Network for Interval Prediction of Three-Phase Ampacity Level in Power Systems

2020 ◽  
pp. 883-904
Author(s):  
Rafik Fainti ◽  
Miltiadis Alamaniotis ◽  
Lefteri H. Tsoukalas
Keyword(s):  
Power Systems ◽  
Smart Grids ◽  
Single Point ◽  
Stable State ◽  
Power Grids ◽  
Energy Prices ◽  
Smart Power ◽  
Energy Needs ◽  
High Volatility ◽  
High Degree

The modern way of living depends on a very high degree on electricity utilization. People take for granted that their energy needs will be satisfied 24/7 which mandates the maintaining of the power grid in stable state. To that end, the development of precise methods for monitoring and predicting events that might disturb its uninterrupted operation is immense. Moreover, the evolvement of power grids into smart grids where the end users continuously participate in the power market by forming energy prices and/or by adjusting their energy needs according to their own agenda, adds high volatility to load demand. In that sense, with regard to predictive methods, a plain single point prediction application may not be enough. The aim of this study is to develop and evaluate a method in order to further enhance this type of applications by providing Predictive Intervals (PIs) regarding ampacity overloading in smart power systems through the use of Artificial Neural Networks (ANNs).

Backpropagation Neural Network for Interval Prediction of Three-Phase Ampacity Level in Power Systems

2016 ◽  
Vol 4 (3) ◽  
pp. 1-20 ◽  
Author(s):  
Rafik Fainti ◽  
Miltiadis Alamaniotis ◽  
Lefteri H. Tsoukalas
Keyword(s):  
Power Systems ◽  
Smart Grids ◽  
Single Point ◽  
Stable State ◽  
Power Grids ◽  
Energy Prices ◽  
Smart Power ◽  
Energy Needs ◽  
High Volatility ◽  
High Degree

The modern way of living depends on a very high degree on electricity utilization. People take for granted that their energy needs will be satisfied 24/7 which mandates the maintaining of the power grid in stable state. To that end, the development of precise methods for monitoring and predicting events that might disturb its uninterrupted operation is immense. Moreover, the evolvement of power grids into smart grids where the end users continuously participate in the power market by forming energy prices and/or by adjusting their energy needs according to their own agenda, adds high volatility to load demand. In that sense, with regard to predictive methods, a plain single point prediction application may not be enough. The aim of this study is to develop and evaluate a method in order to further enhance this type of applications by providing Predictive Intervals (PIs) regarding ampacity overloading in smart power systems through the use of Artificial Neural Networks (ANNs).

Securing SDN-Enabled Smart Power Grids

2018 ◽  
pp. 79-98
Author(s):  
Uttam Ghosh ◽  
Pushpita Chatterjee ◽  
Sachin Shetty
Keyword(s):  
Smart Grid ◽  
Smart Grids ◽  
Single Point ◽  
Performance Comparison ◽  
Power Grids ◽  
Smart Power ◽  
Control Center ◽  
Grid Networks ◽  
Result Show ◽  
Sdn Controller

Software-defined networking (SDN) provides flexibility in controlling, managing, and dynamically reconfiguring the distributed heterogeneous smart grid networks. Considerably less attention has been received to provide security in SDN-enabled smart grids. Centralized SDN controller protects smart grid networks against outside attacks only. Furthermore, centralized SDN controller suffers from a single point of compromise and failure which is detrimental to security and reliability. This chapter presents a framework with multiple SDN controllers and security controllers that provides a secure and robust smart grid architecture. The proposed framework deploys a local IDS to provide security in a substation. Whereas a global IDS is deployed to provide security in control center and overall smart grid network, it further verifies the consequences of control-commands issued by SDN controller and SCADA master. Performance comparison and simulation result show that the proposed framework is efficient as compared to existing security frameworks for SDN-enabled smart grids.

Securing SDN-Enabled Smart Power Grids

2022 ◽  
pp. 1028-1046
Author(s):  
Uttam Ghosh ◽  
Pushpita Chatterjee ◽  
Sachin Shetty
Keyword(s):  
Smart Grid ◽  
Smart Grids ◽  
Single Point ◽  
Performance Comparison ◽  
Power Grids ◽  
Smart Power ◽  
Control Center ◽  
Grid Networks ◽  
Result Show ◽  
Sdn Controller

Software-defined networking (SDN) provides flexibility in controlling, managing, and dynamically reconfiguring the distributed heterogeneous smart grid networks. Considerably less attention has been received to provide security in SDN-enabled smart grids. Centralized SDN controller protects smart grid networks against outside attacks only. Furthermore, centralized SDN controller suffers from a single point of compromise and failure which is detrimental to security and reliability. This chapter presents a framework with multiple SDN controllers and security controllers that provides a secure and robust smart grid architecture. The proposed framework deploys a local IDS to provide security in a substation. Whereas a global IDS is deployed to provide security in control center and overall smart grid network, it further verifies the consequences of control-commands issued by SDN controller and SCADA master. Performance comparison and simulation result show that the proposed framework is efficient as compared to existing security frameworks for SDN-enabled smart grids.

scholarly journals Advances in the Application of Machine Learning Techniques for Power System Analytics: A Survey

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4776
Author(s):  
Seyed Mahdi Miraftabzadeh ◽  
Michela Longo ◽  
Federica Foiadelli ◽  
Marco Pasetti ◽  
Raul Igual
Keyword(s):  
Machine Learning ◽  
Power Systems ◽  
Smart Grids ◽  
State Of The Art ◽  
Smart Cities ◽  
Power Grids ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
New Research ◽  
Traditional Approaches

The recent advances in computing technologies and the increasing availability of large amounts of data in smart grids and smart cities are generating new research opportunities in the application of Machine Learning (ML) for improving the observability and efficiency of modern power grids. However, as the number and diversity of ML techniques increase, questions arise about their performance and applicability, and on the most suitable ML method depending on the specific application. Trying to answer these questions, this manuscript presents a systematic review of the state-of-the-art studies implementing ML techniques in the context of power systems, with a specific focus on the analysis of power flows, power quality, photovoltaic systems, intelligent transportation, and load forecasting. The survey investigates, for each of the selected topics, the most recent and promising ML techniques proposed by the literature, by highlighting their main characteristics and relevant results. The review revealed that, when compared to traditional approaches, ML algorithms can handle massive quantities of data with high dimensionality, by allowing the identification of hidden characteristics of (even) complex systems. In particular, even though very different techniques can be used for each application, hybrid models generally show better performances when compared to single ML-based models.

scholarly journals Analysis of Cyber Attacks Vulnerabilities In Electrical Power Systems

2019 ◽  
Vol 8 (9) ◽  
pp. 925-928
Keyword(s):  
Power Systems ◽  
Smart Grids ◽  
Electrical Power ◽  
Cyber Attacks ◽  
Power Grids ◽  
Electrical Power System ◽  
Smart Meters ◽  
Electrical Power Systems ◽  
Different Types ◽  
Intelligent Electronic Devices

The term “Smart grid” is used for the modernized electrical power system grids. Power grids as we know it is a collection of generation units and load centers that are connected through power lines. Smart grids are a newer version of power grids which basically is the digitalization of the infrastructure with the involvement of smart meters, sensors and different types of IED’s (Intelligent Electronic Devices). As the grids become smart they become vulnerable to attacks over the internet i.e., cyber attacks

The Application Prospect of 4G Communication Technology in Smart Grids

2014 ◽  
Vol 687-691 ◽  
pp. 4068-4071
Author(s):  
Song Hai Fan ◽  
Shu Hong Yang
Keyword(s):  
Electric Power ◽  
Communication Technology ◽  
Spectral Efficiency ◽  
Communication System ◽  
Smart Grids ◽  
Power Grids ◽  
Smart Power ◽  
Smart Power Grids ◽  
Key Technologies ◽  
Application Fields

At first, this paper analyzed the key technologies of the 4G communication system and the main advantages in comparison with the 3G communication system in communication rate, time-delay, spectral efficiency, etc. And then, it analyzed the smart power grids’ demand for communication technology and the limitations of traditional electric power communication. By Comparing with the traditional electric power communication, it demonstrated that the 4G communication system is suitable for smart power grids. At last, this paper prospected the application fields of the 4G communication system in the smart power grids, and also raised the issues and challenges that electric power enterprises will face.

scholarly journals Fault detection in power grids based on improved supervised machine learning binary classification

2021 ◽  
Vol 72 (5) ◽  
pp. 315-322
Author(s):  
Mohammed Wadi
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Power Systems ◽  
Smart Grids ◽  
Binary Classification ◽  
Power Grids ◽  
Supervised Machine Learning ◽  
Learning Approaches ◽  
Smart Meters ◽  
Data Set

Abstract With the increased complexity of power systems and the high integration of smart meters, advanced sensors, and high-level communication infrastructures within the modern power grids, the collected data becomes enormous and requires fast computation and outstanding analyzing methods under normal conditions. However, under abnormal conditions such as faults, the challenges dramatically increase. Such faults require timely and accurate fault detection, identification, and location approaches for guaranteeing their desired performance. This paper proposes two machine learning approaches based on the binary classification to improve the process of fault detection in smart grids. Besides, it presents four machine learning models trained and tested on real and modern fault detection data set designed by the Technical University of Ostrava. Many evaluation measures are applied to test and compare these approaches and models. Moreover, receiver operating characteristic curves are utilized to prove the applicability and validity of the proposed approaches. Finally, the proposed models are compared to previous studies to confirm their superiority.

Cyber-Physical System for Smart Grid

2021 ◽  
pp. 301-323
Author(s):  
Nagi Faroug M. Osman ◽  
Ali Ahmed A. Elamin ◽  
Elmustafa Sayed Ali Ahmed ◽  
Rashid A. Saeed
Keyword(s):  
Smart Grid ◽  
Power Systems ◽  
Smart Grids ◽  
Power Grid ◽  
Vital Role ◽  
Computing Methods ◽  
System Stability ◽  
Smart Meters ◽  
Smart Power ◽  
Power Resources

A smart grid is an advanced utility, stations, meters, and energy systems that comprises a diversity of power processes of smart meters, and various power resources. The cyber-physical systems (CPSs) can play a vital role boosting the realization of the smart power grid. Applied CPS techniques that comprise soft computing methods, communication network, management, and control into a smart physical power grid can greatly boost to realize this industry. The cyber-physical smart power systems (CPSPS) are an effective model system architecture for smart grids. Topics as control policies, resiliency methods for secure utility meters, system stability, and secure end-to-end communications between various sensors/controllers would be quite interested in CPSPS. One of the essential categories in CPSPS applications is the energy management system (EMS). The chapter will spotlight the model and design the relationship between the grid and EMS networks with standardization. The chapter also highlights some necessary standards in the context of CPSPS for the grid infrastructure.

scholarly journals Implementation of Smart Grids: Limitations and Regulations

2020 ◽  
Vol 8 (6) ◽  
pp. 5494-5498
Keyword(s):  
Smart Grids ◽  
Public Management ◽  
New Technologies ◽  
Legal Framework ◽  
Power Grids ◽  
Electricity Sector ◽  
Management Policy ◽  
Smart Power ◽  
Industrial Growth ◽  
Smart Power Grids

Smart grids change the business model of power companies for the benefit of the end consumer. Today, one of the challenges facing the electricity sector in Peru is meeting the demand and energy consumption of society due to urban and industrial growth. This research aimed to analyze the current situation of smart power grids; as well as the viability of the implementation of projects of these networks taking into account the restrictions and considerations within the legal framework in the Peruvian territory. The relevance of these projects will provide electricity to less favored sectors of society and will make current services more efficient. The research is based on a hermeneutical study structured in three stages. First, an analysis of the implementation in Peru. Second, challenges to implement Smart grids in Peru. Third, regulations for the implementation of Smart grids. Therefore, it is concluded that the implementation of Smart Grids must be part of the government's public management policy, taking into account the advantages, vulnerabilities and an appropriate form of regulation in the implementation of these new technologies.

Cyber-Physical System for Smart Grid

2022 ◽  
pp. 325-347
Author(s):  
Nagi Faroug M. Osman ◽  
Ali Ahmed A. Elamin ◽  
Elmustafa Sayed Ali Ahmed ◽  
Rashid A. Saeed
Keyword(s):  
Smart Grid ◽  
Power Systems ◽  
Smart Grids ◽  
Power Grid ◽  
Vital Role ◽  
Computing Methods ◽  
System Stability ◽  
Smart Meters ◽  
Smart Power ◽  
Power Resources

A smart grid is an advanced utility, stations, meters, and energy systems that comprises a diversity of power processes of smart meters, and various power resources. The cyber-physical systems (CPSs) can play a vital role boosting the realization of the smart power grid. Applied CPS techniques that comprise soft computing methods, communication network, management, and control into a smart physical power grid can greatly boost to realize this industry. The cyber-physical smart power systems (CPSPS) are an effective model system architecture for smart grids. Topics as control policies, resiliency methods for secure utility meters, system stability, and secure end-to-end communications between various sensors/controllers would be quite interested in CPSPS. One of the essential categories in CPSPS applications is the energy management system (EMS). The chapter will spotlight the model and design the relationship between the grid and EMS networks with standardization. The chapter also highlights some necessary standards in the context of CPSPS for the grid infrastructure.

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