• Energy Research

ABSTRACTDeep learning has become a vital tool for addressing complex challenges in power systems, particularly fault detection and classification in transmission lines. This study presents a comparative analysis of three advanced time‐series models like temporal convolutional networks (TCN), bidirectional long short‐term memory (BiLSTM), and gated recurrent units (GRU) for fault classification. Leveraging a comprehensive data set encompassing diverse fault scenarios like single‐phase to ground fault (AG), double line to ground fault (ABG), three‐phase fault (ABC) from both simulated and real transmission line data, the study provides a rigorous evaluation of these models’ performance under realistic conditions. The results demonstrate that TCN achieves a fault classification accuracy of 99.9%, significantly outperforming BiLSTM (92.31%) and GRU (95.27%), while also excelling in precision, recall, F1 score, and training efficiency. Additionally, this study incorporates feature extraction techniques like discrete wavelet transform (CWT) to establish new benchmarks for fault classification. The findings underscore TCN's robustness in handling the dynamic nature of transmission line signals and its practical potential for real‐time applications, contributing to the development of more reliable and efficient power system fault classification systems.

AbstractEnergy is a crucial need in today's world for powering homes, businesses, transportation, and industrial processes. Fossil fuels, such as oil, coal, and natural gas, have been the primary sources of energy for decades. However, there is growing recognition of the negative environmental impact of fossil fuels and the need to transition to cleaner and more sustainable sources of energy. Distributed Energy Resources , such as wind and solar offer several benefits including, reducing energy costs, increasing resiliency, and decreasing carbon emissions. However, the integration of () into the grid requires advanced communication and secure control strategies to ensure a stable and reliable grid operations. In this regard, a blockchain‐based industrial wireless sensor network can provide secure and resilience data transmission to facilitate intelligent integration, monitoring, and control of in the smart grid. In this research, a smart contracts framework in Solana called Advanced Solana Blockchain () is proposed for in the smart grid. The proposed scheme enables resilient and secure real‐time control and monitoring of in smart grids. The performance evaluations and security analysis illustrated that this scheme is secure, reliable, and suitable in terms of lightweight data sharing between in smart grids.

AbstractThe rapid and green energy transition is essential to deal with the fast‐growing energy needs in both public and industrial sectors. This has paved the way to integrate distributed renewable energy resources () such as solar, hydro, wind, and geothermal into the power grid (). Wind and solar are free, zero‐carbon emission, and everlasting power sources that contribute 5% and 7% of global electricity generation, respectively. Therefore, the fast, secure, and reliable integration of these green is critical to achieve the instant energy demands. Smart grid due to inherited characteristics such as intelligent sensing, computing, and communication technologies can effectively integrate the . However, the existing smart grid communication architecture faces various cyberattacks, resulting in poor integration, monitoring, and control of . In this respect, blockchain technology can provide fast, secure, and efficient end‐to‐end communication between in the smart grid. In this study, the authors propose a blockchain‐based resilient and secure scheme called for wireless sensor networks ‐based events monitoring and control in . Experimental studies and performance analyses are carried out to predict the efficiency of the proposed scheme by considering numerous standard metrics. The extensive numerical results demonstrated that the proposed scheme is significant in terms of secure, resilient, and reliable information transmission for in .

AbstractThe authors explore the various obstacles and possible approaches that the UK may take to fulfil its goal of having net‐zero greenhouse gas emissions by 2050. The paper thoroughly examines several aspects of this project, such as the modernisation of infrastructure, the energy transition, the economic effects, the obstacles to research and development, changes in behaviour, and the frameworks for policy and regulation. With a 44% decrease from 1990 levels by 2021, it showcases the UK's noteworthy achievement in lowering emissions and its ambitious initiatives, such as the £12 billion Ten Point Plan, to accelerate this development. The difficulties of switching from reliance on fossil fuels to renewable energy sources, their implications for the economy, and the necessity of green technology innovation are all covered in the article. It also discusses the behavioural sides of this shift, highlighting the need to change one's lifestyle and engage the public. To address these issues, the importance of international cooperation and policymaking is emphasised. Insights into potential remedies are provided by the article, which includes energy efficiency initiatives, investments in renewable energy, assistance for clean technology R&D, green funding options, public awareness campaigns, international cooperation, and regulatory frameworks. Every one of these alternatives is examined for possible effects and obstacles. The article concludes that reaching net zero in the UK is a complex but necessary objective that calls for a concerted strategy that strikes a balance between social and economic concerns and environmental sustainability.

AbstractThis study focuses on improving the quality of MAPbI3‐based perovskite films by adjusting the concentration ratios of the methylammonium iodide (MAI) precursor using a two‐step sequential deposition method. The primary objective is to explore how altering the MAI concentration influences the microstrain, dislocation density, perovskite film quality, and their subsequent impact on the performance of perovskite solar cells. The examined device configuration CdS/MAPbI3/Spiro‐OMeTAD demonstrates impressive power conversion efficiency of 12.05%, Voc of 1.02 V, Jsc of 16.2 mA cm−2, and fill factor of 0.73. X‐ray diffraction and scanning electron microscope analyses show improved crystal quality and surface characteristics with reduced microstrain, dislocation density, larger crystal grains, and minimized pinholes. The investigation of MAPbI3's optical and electrical characteristics provides in‐depth insights, facilitating the optimization of MAI precursor concentrations for improved perovskite film development and enhanced solar cell performance.

AbstractIndustry 4.0 is a term used to refer to the fourth industrial revolution, characterized by the introduction of new technologies, such as the Internet of Things, Big Data, and artificial intelligence (AI). As the number of connected devices in industrial settings grows, energy optimization of such sensors becomes increasingly essential. This paper proposes an energy optimization framework for sensor nodes in Industry 4.0. The framework is based on energy efficiency, energy conservation, and energy harvesting principles. It is designed to optimize the energy consumption of sensor nodes while maintaining their performance. The framework includes dynamic power management, scheduling, and harvesting techniques to reduce energy consumption while maintaining performance. In addition, the framework provides a comprehensive approach to energy optimization, including advanced analytics and AI to predict energy consumption and optimize energy use. The proposed model reached 96.93% sensitivity, 91.36% false discovery rate, 11.28% false omission rate, 90.12% prevalence threshold, and 91.24% threat score. The proposed framework is expected to improve the performance of sensor nodes in Industry 4.0, enabling increased efficiency and cost savings.

Une communication fiable, résiliente et sécurisée basée sur la blockchain pour les ressources énergétiques distribuées (DER) est essentielle dans le réseau intelligent (SG). La blockchain Solana, en raison de sa grande stabilité, de son évolutivité et de son débit, ainsi que de sa faible latence, est conçue pour améliorer la fiabilité, la résilience et la sécurité des DER dans les SG. Cet article présente de grands ensembles de données axés sur l'injection SQL, l'usurpation d'identité et les cyberattaques Man-in-the-Middle (MitM), qui ont été collectés auprès des réseaux de capteurs sans fil industriels (IWSN) basés sur la blockchain Solana pour la surveillance et le contrôle des événements dans les DER. Les ensembles de données fournis comprennent des données brutes (non traitées) et raffinées (traitées), qui mettent en évidence des tendances distinctes en matière de cyberattaques dans les DER. Ces modèles distinctifs démontrent des problèmes tels que la génération de données de masse superflue, la transmission de paquets invalides, l'envoi de paquets de données trompeurs, l'utilisation intensive de la bande passante du réseau, le réacheminement, la surcharge de mémoire, les frais généraux et la création d'une latence élevée. Ces problèmes entraînent une surveillance et un contrôle inefficaces des événements en temps réel des DER dans les SG. La nature approfondie de ces ensembles de données devrait jouer un rôle crucial dans l'identification et l'atténuation d'un large éventail de cyberattaques dans différentes applications de réseau intelligent. La comunicación confiable, resistente y segura basada en blockchain para los Recursos de Energía Distribuida (der) es esencial en la Red Inteligente (SG). Se prevé que la cadena de bloques de Solana, debido a su alta estabilidad, escalabilidad y rendimiento, junto con su baja latencia, mejore la confiabilidad, la resiliencia y la seguridad de los der en los SG. Este documento presenta grandes conjuntos de datos centrados en la inyección SQL, la suplantación y los ciberataques Man-in-the-Middle (MitM), que se han recopilado de las redes de sensores inalámbricos industriales (IWSN) basadas en la cadena de bloques de Solana para el monitoreo y control de eventos en der. Los conjuntos de datos proporcionados incluyen datos sin procesar (sin procesar) y refinados (procesados), que destacan distintas tendencias en los ciberataques en los der. Estos patrones distintivos demuestran problemas como la generación masiva de datos superfluos, la transmisión de paquetes no válidos, el envío de paquetes de datos engañosos, el uso intensivo del ancho de banda de la red, el reencaminamiento, el desbordamiento de la memoria, los gastos generales y la creación de una alta latencia. Estos problemas dan como resultado un monitoreo y control ineficaces de los eventos en tiempo real de los der en los SG. Se espera que la naturaleza exhaustiva de estos conjuntos de datos desempeñe un papel crucial en la identificación y mitigación de una amplia gama de ciberataques en diferentes aplicaciones de redes inteligentes. Blockchain-based reliable, resilient, and secure communication for Distributed Energy Resources (DERs) is essential in Smart Grid (SG). The Solana blockchain, due to its high stability, scalability, and throughput, along with low latency, is envisioned to enhance the reliability, resilience, and security of DERs in SGs. This paper presents big datasets focusing on SQL Injection, Spoofing, and Man-in-the-Middle (MitM) cyberattacks, which have been collected from Solana blockchain-based Industrial Wireless Sensor Networks (IWSNs) for events monitoring and control in DERs. The datasets provided include both raw (unprocessed) and refined (processed) data, which highlight distinct trends in cyberattacks in DERs. These distinctive patterns demonstrate problems like superfluous mass data generation, transmitting invalid packets, sending deceptive data packets, heavily using network bandwidth, rerouting, causing memory overflow, overheads, and creating high latency. These issues result in ineffective real-time events monitoring and control of DERs in SGs. The thorough nature of these datasets is expected to play a crucial role in identifying and mitigating a wide range of cyberattacks across different smart grid applications. يعد التواصل الموثوق والمرن والآمن القائم على البلوك تشين لموارد الطاقة الموزعة (DERs) أمرًا ضروريًا في الشبكة الذكية (SG). من المتصور أن تعمل سلسلة كتل سولانا، نظرًا لاستقرارها العالي وقابليتها للتوسع وإنتاجيتها، جنبًا إلى جنب مع الكمون المنخفض، على تعزيز موثوقية ومرونة وأمن DERs في SGs. تقدم هذه الورقة مجموعات بيانات كبيرة تركز على حقن SQL، وانتحال الشخصية، والهجمات الإلكترونية من نوع Man - in - the - middle (MitM)، والتي تم جمعها من شبكات الاستشعار اللاسلكية الصناعية القائمة على سلسلة الكتل (IWSNs) لرصد الأحداث والتحكم فيها في DERs. تتضمن مجموعات البيانات المقدمة كلاً من البيانات الأولية (غير المعالجة) والمنقحة (المعالجة)، والتي تسلط الضوء على الاتجاهات المتميزة في الهجمات الإلكترونية في DERs. تُظهر هذه الأنماط المميزة مشاكل مثل توليد البيانات الضخمة الزائدة عن الحاجة، ونقل الحزم غير الصالحة، وإرسال حزم البيانات الخادعة، واستخدام النطاق الترددي للشبكة بكثافة، وإعادة التوجيه، والتسبب في تجاوز الذاكرة، والنفقات العامة، وخلق زمن انتقال عالٍ. تؤدي هذه المشكلات إلى مراقبة الأحداث في الوقت الفعلي غير الفعالة والتحكم في DERs في SGs. من المتوقع أن تلعب الطبيعة الشاملة لمجموعات البيانات هذه دورًا حاسمًا في تحديد وتخفيف مجموعة واسعة من الهجمات الإلكترونية عبر تطبيقات الشبكة الذكية المختلفة.

Lack of synchronization between high voltage DC systems linking offshore wind farms and the onshore grid is a natural consequence owing to the stochastic nature of wind energy. The poor synchronization results in increased system disturbances, grid contingencies, power loss, and frequency instability. Emphasizing frequency stability analysis, this research investigates a dynamic coordination control technique for a Double Fed Induction Generator (DFIG) consisting of OWFs integrated with a hybrid multi-terminal HVDC (MTDC) system. Line commutated converters (LCC) and voltage source converters (VSC) are used in the suggested control method in order to ensure frequency stability. The adaptive neuro-fuzzy inference approach is used to accurately predict wind speed in order to further improve frequency stability. The proposed HVDC system can integrate multiple distributed OWFs with the onshore grid system, and the control strategy is designed based on this concept. In order to ensure the transient stability of the HVDC system, the DFIG-based OWF is regulated by a rotor side controller (RSC) and a grid side controller (GSC) at the grid side using a STATCOM. The devised HVDC (MTDC) is simulated in MATLAB/SIMULINK, and the performance is evaluated in terms of different parameters, such as frequency, wind power, rotor and stator side current, torque, speed, and power. Experimental results are compared to a conventional optimal power flow (OPF) model to validate the performance.