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  • Open Access

    ARTICLE

    An Efficient MPPT Tracking in Solar PV System with Smart Grid Enhancement Using CMCMAC Protocol

    B. Jegajothi1,*, Sundaram Arumugam2, Neeraj Kumar Shukla3, I. Kathir4, P. Yamunaa5, Monia Digra6

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2417-2437, 2023, DOI:10.32604/csse.2023.038074

    Abstract Renewable energy sources like solar, wind, and hydro are becoming increasingly popular due to the fewer negative impacts they have on the environment. Because, Since the production of renewable energy sources is still in the process of being created, photovoltaic (PV) systems are commonly utilized for installation situations that are acceptable, clean, and simple. This study presents an adaptive artificial intelligence approach that can be used for maximum power point tracking (MPPT) in solar systems with the help of an embedded controller. The adaptive method incorporates both the Whale Optimization Algorithm (WOA) and the Artificial Neural Network (ANN). The WOA… More >

  • Open Access

    REVIEW

    A Review on Intelligent Detection and Classification of Power Quality Disturbances: Trends, Methodologies, and Prospects

    Yanjun Yan, Kai Chen*, Hang Geng, Wenqian Fan, Xinrui Zhou

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1345-1379, 2023, DOI:10.32604/cmes.2023.027252

    Abstract With increasing global concerns about clean energy in smart grids, the detection of power quality disturbances (PQDs) caused by energy instability is becoming more and more prominent. It is well acknowledged that the PQD effects on power grid equipment are destructive and hazardous, which causes irreversible damage to underlying electrical/electronic equipment of the concerned intelligent grids. In order to ensure safe and reliable equipment implementation, appropriate PQD detection technologies must be adopted to avoid such adverse effects. This paper summarizes the newly proposed and traditional PQD detection techniques in order to give a quick start to new researchers in the… More > Graphic Abstract

    A Review on Intelligent Detection and Classification of Power Quality Disturbances: Trends, Methodologies, and Prospects

  • Open Access

    ARTICLE

    SFSDA: Secure and Flexible Subset Data Aggregation with Fault Tolerance for Smart Grid

    Dong Chen1, Tanping Zhou1,2,3,*, Xu An Wang1,2, Zichao Song1, Yujie Ding1, Xiaoyuan Yang1,2

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2477-2497, 2023, DOI:10.32604/iasc.2023.039238

    Abstract Smart grid (SG) brings convenience to users while facing great challenges in protecting personal private data. Data aggregation plays a key role in protecting personal privacy by aggregating all personal data into a single value, preventing the leakage of personal data while ensuring its availability. Recently, a flexible subset data aggregation (FSDA) scheme based on the Paillier homomorphic encryption was first proposed by Zhang et al. Their scheme can dynamically adjust the size of each subset and obtain the aggregated data in the corresponding subset. In this paper, firstly, an efficient attack with both theorems proving and experimentative verification is… More >

  • Open Access

    ARTICLE

    Optimizing Decision-Making of A Smart Prosumer Microgrid Using Simulation

    Oussama Accouche1,*, Rajan Kumar Gangadhari2

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 151-173, 2023, DOI:10.32604/cmc.2023.038648

    Abstract Distributed renewable energy sources offer significant alternatives for Qatar and the Arab Gulf region’s future fuel supply and demand. Microgrids are essential for providing dependable power in difficult-to-reach areas while incorporating significant amounts of renewable energy sources. In energy-efficient data centers, distributed generation can be used to meet the facility’s overall power needs. This study primarily focuses on the best energy management practices for a smart microgrid in Qatar while taking demand-side load management into account. This article looked into a university microgrid in Qatar that primarily aimed to get all of its energy from the grid. While diesel generators… More >

  • Open Access

    ARTICLE

    Design and Development of an Intelligent Energy Management System for a Smart Grid to Enhance the Power Quality

    Nisha Vasudevan1,*, Vasudevan Venkatraman2, A. Ramkumar1, T. Muthukumar3, A. Sheela4, M. Vetrivel5, R. J. Vijaya Saraswathi6, F. T. Josh7

    Energy Engineering, Vol.120, No.8, pp. 1747-1761, 2023, DOI:10.32604/ee.2023.027821

    Abstract MigroGrid (MG) has emerged to resolve the growing demand for energy. But because of its inconsistent output, it can result in various power quality (PQ) issues. PQ is a problem that is becoming more and more important for the reliability of power systems that use renewable energy sources. Similarly, the employment of nonlinear loads will introduce harmonics into the system and, as a result, cause distortions in the current and voltage waveforms as well as low power quality issues in the supply system. Thus, this research focuses on power quality enhancement in the MG using hybrid shunt filters. However, the… More >

  • Open Access

    ARTICLE

    Artificial Neural Network-Based Development of an Efficient Energy Management Strategy for Office Building

    Payal Soni, J. Subhashini*

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1225-1242, 2023, DOI:10.32604/iasc.2023.038155

    Abstract In the current context, a smart grid has replaced the conventional grid through intelligent energy management, integration of renewable energy sources (RES) and two-way communication infrastructures from power generation to distribution. Energy management from the distribution side is a critical problem for balancing load demand. A unique energy management strategy (EMS) is being developed for office building equipment. That includes renewable energy integration, automation, and control based on the Artificial Neural Network (ANN) system using Matlab Simulink. This strategy reduces electric power consumption and balances the load demand of the traditional grid. This strategy is developed by taking inputs from… More >

  • Open Access

    ARTICLE

    A Distributed Power Trading Scheme Based on Blockchain and Artificial Intelligence in Smart Grids

    Yue Yu1, Junhua Wu1,*, Guangshun Li1, Wangang Wang2

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 583-598, 2023, DOI:10.32604/iasc.2023.037875

    Abstract As an emerging hot technology, smart grids (SGs) are being employed in many fields, such as smart homes and smart cities. Moreover, the application of artificial intelligence (AI) in SGs has promoted the development of the power industry. However, as users’ demands for electricity increase, traditional centralized power trading is unable to well meet the user demands and an increasing number of small distributed generators are being employed in trading activities. This not only leads to numerous security risks for the trading data but also has a negative impact on the cost of power generation, electrical security, and other aspects.… More >

  • Open Access

    REVIEW

    Technologies Behind the Smart Grid and Internet of Things: A System Survey

    Kuldeep Sharma1, Arun Malik1, Isha Batra1, A. S. M. Sanwar Hosen2, Md Abdul Latif Sarker3, Dong Seog Han4,*

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5049-5072, 2023, DOI:10.32604/cmc.2023.035638

    Abstract Electric smart grids enable a bidirectional flow of electricity and information among power system assets. For proper monitoring and controlling of power quality, reliability, scalability and flexibility, there is a need for an environmentally friendly system that is transparent, sustainable, cost-saving, energy-efficient, agile and secure. This paper provides an overview of the emerging technologies behind smart grids and the internet of things. The dependent variables are identified by analyzing the electricity consumption patterns for optimal utilization and planning preventive maintenance of their legacy assets like power distribution transformers with real-time parameters to ensure an uninterrupted and reliable power supply. In… More >

  • Open Access

    ARTICLE

    Metaheuristic Optimization with Deep Learning Enabled Smart Grid Stability Prediction

    Afrah Al-Bossly*

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6395-6408, 2023, DOI:10.32604/cmc.2023.028433

    Abstract Due to the drastic increase in global population as well as economy, electricity demand becomes considerably high. The recently developed smart grid (SG) technology has the ability to minimize power loss at the time of power distribution. Machine learning (ML) and deep learning (DL) models can be effectually developed for the design of SG stability techniques. This article introduces a new Social Spider Optimization with Deep Learning Enabled Statistical Analysis for Smart Grid Stability (SSODLSA-SGS) prediction model. Primarily, class imbalance data handling process is performed using Synthetic minority oversampling technique (SMOTE) technique. The SSODLSA-SGS model involves two stages of pre-processing… More >

  • Open Access

    ARTICLE

    Physics-Informed AI Surrogates for Day-Ahead Wind Power Probabilistic Forecasting with Incomplete Data for Smart Grid in Smart Cities

    Zeyu Wu1, Bo Sun1,2, Qiang Feng2,*, Zili Wang1, Junlin Pan1

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 527-554, 2023, DOI:10.32604/cmes.2023.027124

    Abstract Due to the high inherent uncertainty of renewable energy, probabilistic day-ahead wind power forecasting is crucial for modeling and controlling the uncertainty of renewable energy smart grids in smart cities. However, the accuracy and reliability of high-resolution day-ahead wind power forecasting are constrained by unreliable local weather prediction and incomplete power generation data. This article proposes a physics-informed artificial intelligence (AI) surrogates method to augment the incomplete dataset and quantify its uncertainty to improve wind power forecasting performance. The incomplete dataset, built with numerical weather prediction data, historical wind power generation, and weather factors data, is augmented based on generative… More > Graphic Abstract

    Physics-Informed AI Surrogates for Day-Ahead Wind Power Probabilistic Forecasting with Incomplete Data for Smart Grid in Smart Cities

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