Roadmap toward Smart Grids in Hydro and Thermal Power System : A Case study of the Ghanaian Power System

The evolution of Smart Grid flings fresh applications and opportunities to enhance the efficiency of power distribution networks. Network operators have the opportunity to make use of different sources of power. Communication between the network operators and the consumersis constantly permitted to allow optimization and balancing of energy usage. This paper seeks to evaluate the state of the Ghanaian Electric Distribution Network with respect to Smart Grid. We evaluate the performance of the traditional distribution network since its partial incorporation with the Smart Grid elements. The operations of the Supervisory Control and Data Acquisition, the Automated Meter Infrastructure and Circuit Breakers are specifically addressed. Road map to optimizing the distribution network in Ghana is presented. It is concluded that optimizing these key elements will transform the role of the distribution system and ensure a safe and reliable power network.©2020 IJAREEIE.fi=vertaisarvioimaton|en=nonPeerReviewed

Proposed algorithm for smart grid DDoS detection based on deep learning

The Smart Grid’s objective is to increase the electric grid’s dependability, security, and efficiency through extensive digital information and control technology deployment. As a result, it is necessary to apply real-time analysis and state estimation-based techniques to ensure efficient controls are implemented correctly. These systems are vulnerable to cyber-attacks, posing significant risks to the Smart Grid’s overall availability due to their reliance on communication technology. Therefore, effective intrusion detection algorithms are required to mitigate such attacks. In dealing with these uncertainties, we propose a hybrid deep learning algorithm that focuses on Distributed Denial of Service attacks on the communication infrastructure of the Smart Grid. The proposed algorithm is hybridized by the Convolutional Neural Network and the Gated Recurrent Unit algorithms. Simulations are done using a benchmark cyber security dataset of the Canadian Institute of Cybersecurity Intrusion Detection System. According to the simulation results, the proposed algorithm outperforms the current intrusion detection algorithms, with an overall accuracy rate of 99.7%.© 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Cyber Security in Power Systems Using Meta-Heuristic and Deep Learning Algorithms

Supervisory Control and Data Acquisition system linked to Intelligent Electronic Devices over a communication network keeps an eye on smart grids’ performance and safety. The lack of algorithms protecting the power system communication protocols makes them vulnerable to cyberattacks, which can result in a hacker introducing false data into the operational network. This can result in delayed attack detection, which might harm the infrastructure, cause financial loss, or even result in fatalities. Similarly, attackers may be able to feed the system with fake information to hoax the operator and the algorithm into making bad decisions at crucial moments. This paper attempts to identify and classify such cyber-attacks by using numerous deep learning algorithms and optimizing the data features with a metaheuristic algorithm. We proposed a Restricted Boltzmann Machine-based nature-inspired artificial root foraging optimization algorithm. Using a publicly available dataset produced in Mississippi State University’s Oak Ridge National Laboratory, simulations are run on the Jupiter Notebook. Traditional supervised machine learning algorithms like Artificial Neural Networks, Convolutional Neural Networks, and Support Vector Machines are measured with the proposed algorithm to demonstrate the effectiveness of the algorithms. Simulations show that the proposed algorithm produced superior results, with an accuracy of 97.8% for binary classification, 95.6% for three-class classification, and 94.3% for multi-class classification. Thereby outperforming its counterpart algorithms in terms of accuracy, precision, recall, and f1 score.©2023 Authors. Published by IEEE. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/fi=vertaisarvioitu|en=peerReviewed

Evaluation of Optimization Algorithms for Customers Load Schedule

This paper introduces a novel concept for customer load scheduling in the Smart Grid (SG). The concept is based on the forthcoming internet of things (IoT). Approximate optimization algorithms are deduced for optimum customer load scheduling, maximization of electric power suppliers performance, and fairness in scheduling customers load. Using these approximate optimization algorithms as constraints, some loads are given priority. Other loads are scheduled in order to control the maximum demand load and electricity bills. To evaluate the effectiveness of the algorithms, we utilize the Mixed Integer Linear Programming (MILP). Simulations are carried out and the impact on reducing the peak-to-average power ratio (PAPR), the electricity bills, and ensuring fairness in customers load schedules are investigated. Simulation results establish that our algorithms significantly cut down on electricity bills, maximizes utility performance, and deliver fairness in customers load schedules.©2021 International Association of Engineers (IAENG).fi=vertaisarvioitu|en=peerReviewed

On the performance metrics for cyber-physical attack detection in smart grid

Supervisory Control and Data Acquisition (SCADA) systems play an important role in Smart Grid. Though the rapid evolution provides numerous advantages it is one of the most desired targets for malicious attackers. So far security measures deployed for SCADA systems detect cyber-attacks, however, the performance metrics are not up to the mark. In this paper, we have deployed an intrusion detection system to detect cyber-physical attacks in the SCADA system concatenating the Convolutional Neural Network and Gated Recurrent Unit as a collective approach. Extensive experiments are conducted using a benchmark dataset to validate the performance of the proposed intrusion detection model in a smart metering environment. Parameters such as accuracy, precision, and false-positive rate are compared with existing deep learning models. The proposed concatenated approach attains 98.84% detection accuracy which is much better than existing techniques.©The Author(s) 2022 This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.fi=vertaisarvioitu|en=peerReviewed

SCADA securing system using deep learning to prevent cyber infiltration

Supervisory Control and Data Acquisition (SCADA) systems are computer-based control architectures specifically engineered for the operation of industrial machinery via hardware and software models. These systems are used to project, monitor, and automate the state of the operational network through the utilization of ethernet links, which enable two-way communications. However, as a result of their constant connectivity to the internet and the lack of security frameworks within their internal architecture, they are susceptible to cyber-attacks. In light of this, we have proposed an intrusion detection algorithm, intending to alleviate this security bottleneck. The proposed algorithm, the Genetically Seeded Flora (GSF) feature optimization algorithm, is integrated with Transformer Neural Network (TNN) and functions by detecting changes in operational patterns that may be indicative of an intruder’s involvement. The proposed Genetically Seeded Flora Transformer Neural Network (GSFTNN) algorithm stands in stark contrast to the signature-based method employed by traditional intrusion detection systems. To evaluate the performance of the proposed algorithm, extensive experiments are conducted using the WUSTL-IIOT-2018 ICS SCADA cyber security dataset. The results of these experiments indicate that the proposed algorithm outperforms traditional algorithms such as Residual Neural Networks (ResNet), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM) in terms of accuracy and efficiency.© 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

On cyber security evaluations in smart grid using machine learning

The smart grid aims to enhance the electric grid’s dependability, security, and effciency by deploying digital information and control technology. However, the increasing reliance on communication technology exposes these systems to cyberattacks, posing signifcant cyber threats to the availability and functionality of the smart grid. To mitigate such threats, effective intrusion detection algorithms are crucial. In this context, we propose a hybrid deep learning algorithm that focuses on distributed denial of service (DDoS) attacks on the communication infrastructure of the smart grid. The proposed algorithm combines convolutional neural network (CNN) and gated recurrent unit (GRU) algorithms to provide real-time analysis and state estimation-based techniques for effcient control implementation. We conduct simulations using a benchmark cyber-security dataset from the Canadian institute of cybersecurity intrusion detection system. The results demonstrate that our hybrid deep learning algorithm outperforms existing intrusion detection algorithms, achieving an impressive overall accuracy rate of 99.7 %. In the context of supervisory control and data acquisition (SCADA) systems, which monitor and control industrial machinery, communication network vulnerabilities can lead to cyber-attacks introducing false data into the operational network. We propose a restricted Boltzmann machine-based nature-inspired artifcial root foraging optimization algorithm for identifying and classifying cyber-attacks to address this issue. We optimize data features using this algorithm and evaluate its performance against traditional supervised machine learning algorithms such as artifcial neural networks, convolutional neural networks, and support vector machines. The proposed algorithm outperforms its counterparts in accuracy, precision, recall, and f1 score. Furthermore, we address the security vulnerabilities in SCADA systems by introducing the genetically seeded fora transformer neural network (GSFTNN) intrusion detection algorithm. Unlike signature-based methods, GSFTNN detects changes in operational patterns indicative of intruder involvement. We evaluate the proposed algorithm using the WUSTL IIOT 2018 ICS SCADA cyber security dataset and demonstrate its superiority over traditional algorithms like residual neural networks, recurrent neural networks, and long short-term memory (LSTM) in terms of accuracy and effciency.Älyverkko pyrkii parantamaan sähköverkon luotettavuutta, turvallisuutta ja tehokkuutta käyttämällä digitaalista tieto- ja ohjausteknologiaa. Kasvava riippuvuus viestintätekniikasta altistaa kuitenkin nämä järjestelmät kyberhyökkäyksille, mikä aiheuttaa merkittäviä kyberuhkia älyverkon saatavuudelle ja toiminnallisuudelle. Kyetäksemme vähentämään tällaisia uhkia, tehokkaat tunkeutumisen havaitsemisalgoritmit ovat ratkaisevan tärkeitä. Tässä yhteydessä ehdotamme hybridi syväoppimisalgoritmia, joka keskittyy hajautettuihin palvelunestohyökkäyksiin (DDoS) älyverkon viestintäinfrastruktuurissa. Ehdotettu algoritmi yhdistää konvolutionaalisen neuroverkon (CNN) ja portitetun toistoyksikön (GRU) algoritmit tarjotakseen reaaliaikaista analyysia ja tila-arviopohjaisia tekniikoita tehokkaalle ohjausten toteutukselle. Työssä suoritetaan simulointeja käyttäen Kanadan Kyberturvallisuusinstituutin tunkeutumisen havaitsemisjärjestelmän vertailutietojoukkoa. Tulokset osoittavat, että hybridi syväoppimisalgoritmimme suoriutuu paremmin kuin olemassa olevat tunkeutumisen havaitsemisalgoritmit, saavuttaen vaikuttavan kokonaistarkkuuden 99,7 prosenttia. Teollisuuden koneita valvovien ja ohjaavien valvonta- ja tiedonkeruujärjestelmien (SCADA) yhteydessä tietoliikenneverkkojen haavoittuvuudet voivat johtaa kyberhyökkäyksiin, joissa väärää tietoa tuodaan operatiiviseen verkkoon. Ehdotamme rajoitettuun Boltzmannin koneeseen perustuvaa ja luonnon inspiroimaa juurten etsinnän optimointialgoritmia kyber-hyökkäysten tunnistamiseen ja luokitteluun. Optimoimme dataominaisuuksia tällä algoritmilla ja arvioimme sen suorituskykyä perinteisiä valvotun koneoppimisen algoritmeja, kuten tekoälyä hyödyntävät neuroverkot, konvolutionaaliset neuroverkot ja tuen vektorikoneet, vastaan. Ehdotettu algoritmi päihittää vertailukohteensa tarkkuudessa, toistettavuudessa ja f1-pisteissä. Lisäksi työssä käsitellään SCADA-järjestelmien tietoturvaaukkoja esittelemällä geneettisesti alustetun muuntavan neuroverkon (GSFTNN) tunkeutumisen havaitsemisalgoritmin. Toisin kuin allekirjoituksiin perustuvat menetelmät, GS-FTNN havaitsee muutokset toiminnallisten mallien perusteella, jotka viittaavat tunkeutujan osallistumiseen verkkoliikenteessä. Ehdotettua algoritmia arvioidaan käyttäen WUSTLIIOT-2018 ICS SCADA -kyberturvallisuus tietojoukkoa. Työssä osoitetaan sen ylivoimaisuus perinteisiin algoritmeihin, kuten jäännösneuroverkkoihin, toistaviin neuroverkkoihin ja pitkäkestoisiin lyhytaikamuisteihin, verrattuna tarkkuuden ja tehokkuuden suhteen.fi=vertaisarvioitu|en=peerReviewed

Risk Accessment of Machine Learning Algorithms on Manipulated Dataset in Power Systems

The emergence of the communication infrastructure in power systems has increased the variety and sophistication of network assaults. Intrusion Detection Systems’ (IDS) importance has increased in relation to network security. IDS, however, is no longer secure when confronted with adversarial examples, and attackers can boost assault success rates by tricking the IDS. As a result, resilience must be increased. This paper assesses the Decision Tree, Logistic regression, Support Vector Machines (SVM), Naïve Bayes, K-Nearest Neighbours (KNN), and Ensemble’s effectiveness. Using the WUSTL-IIoT-2021 dataset and CIC-IDS2017 dataset, we train the algorithms on the unmanipulated dataset and then train the algorithms on the manipulated dataset. Per the simulation results, the accuracy and prediction speed drop on the manipulated dataset while the training time rises.©2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed