Pub Date : 2025-12-23DOI: 10.1016/j.compeleceng.2025.110915
Adil Zohaib , Faraz Akram , Sohail Khalid , Hamid Nawaz , Mujeeb Ur Rehman
Microgrids offer a promising paradigm for sustainable and decentralized energy management; however, they face operational challenges due to fluctuating load profiles and the intermittency of renewable energy sources. This paper proposes a two-phase framework to address these challenges through accurate short-term load forecasting (STLF) and an advanced energy management system (EMS) for grid-connected multi-microgrids. In Phase I, STLF was performed using residential metering infrastructure data from the PRECON dataset. A hybrid deep learning model, Prophet-Long Short-Term Memory (PLSTM), was developed and outperformed benchmarks, including LSTM, XGBoost, SARIMA, and Prophet, reducing the error by 12%–18%. In Phase II, an AI-enhanced EMS is introduced, integrating PLSTM-based load forecasting, ANN-based photovoltaic generation prediction, adaptive self-learning weights, and deep Q-learning for forecast margin tuning. This robust hierarchical model predictive control strategy eliminates reliance on demand-side management and preserves user comfort. The simulation results demonstrate that the proposed framework outperforms conventional baseline EMS methods in terms of energy efficiency, reducing grid imports by 28%, adaptability with average SoC tracking improvement of 15%, and resilience indicated by a 22% increase in battery cycle longevity under uncertainties in load consumption and solar energy generation, offering a scalable solution for microgrid deployment in dynamic environments.
微电网为可持续和分散的能源管理提供了一个有希望的范例；然而，由于负荷波动和可再生能源的间歇性，它们面临着运营挑战。本文提出了一个两阶段框架，通过准确的短期负荷预测（STLF）和先进的并网多微电网能源管理系统（EMS）来解决这些挑战。在第一阶段，STLF使用来自PRECON数据集的住宅计量基础设施数据进行。开发了一种混合深度学习模型，Prophet- long - Short-Term Memory (PLSTM)，并优于LSTM、XGBoost、SARIMA和Prophet等基准，将误差降低了12%-18%。在第二阶段，引入了人工智能增强的EMS，集成了基于plstm的负荷预测、基于人工神经网络的光伏发电预测、自适应自学习权值以及用于预测裕度调整的深度q学习。这种鲁棒的分层模型预测控制策略消除了对需求侧管理的依赖，并保持了用户的舒适性。仿真结果表明，该框架在能效方面优于传统的基线EMS方法，减少了28%的电网进口，平均SoC跟踪提高了15%的适应性，在负载消耗和太阳能发电不确定的情况下，电池循环寿命增加了22%，为动态环境下的微电网部署提供了可扩展的解决方案。
{"title":"Hybrid deep learning based load forecasting and AI-driven energy management for grid-connected multi-microgrids","authors":"Adil Zohaib , Faraz Akram , Sohail Khalid , Hamid Nawaz , Mujeeb Ur Rehman","doi":"10.1016/j.compeleceng.2025.110915","DOIUrl":"10.1016/j.compeleceng.2025.110915","url":null,"abstract":"<div><div>Microgrids offer a promising paradigm for sustainable and decentralized energy management; however, they face operational challenges due to fluctuating load profiles and the intermittency of renewable energy sources. This paper proposes a two-phase framework to address these challenges through accurate short-term load forecasting (STLF) and an advanced energy management system (EMS) for grid-connected multi-microgrids. In Phase I, STLF was performed using residential metering infrastructure data from the PRECON dataset. A hybrid deep learning model, <em>Prophet</em>-Long Short-Term Memory (PLSTM), was developed and outperformed benchmarks, including LSTM, XGBoost, SARIMA, and Prophet, reducing the error by 12%–18%. In Phase II, an AI-enhanced EMS is introduced, integrating PLSTM-based load forecasting, ANN-based photovoltaic generation prediction, adaptive self-learning weights, and deep Q-learning for forecast margin tuning. This robust hierarchical model predictive control strategy eliminates reliance on demand-side management and preserves user comfort. The simulation results demonstrate that the proposed framework outperforms conventional baseline EMS methods in terms of energy efficiency, reducing grid imports by 28%, adaptability with average SoC tracking improvement of 15%, and resilience indicated by a 22% increase in battery cycle longevity under uncertainties in load consumption and solar energy generation, offering a scalable solution for microgrid deployment in dynamic environments.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"131 ","pages":"Article 110915"},"PeriodicalIF":4.9,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145801922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-23DOI: 10.1016/j.compeleceng.2025.110908
Binu Jose A. , Pranesh Das , Ebrahim Ghaderpour , Paolo Mazzanti
Unsupervised Domain Adaptation (UDA) is the process of learning knowledge from a labelled source domain to an unlabelled target domain, particularly in the context of remote sensing scene classification. The primary challenge in this process is the substantial cost associated with labelling and the significant discrepancies between domains. However, existing UDA methods degrade under severe domain shift and scene diversity, yielding noisy pseudo-labels and unstable target structure discovery. To address these issues, a novel UDA framework is proposed. The main focus of the framework is to develop a mapping using clustering-based pseudo-labelling that can provide a reliable and interpretable pseudo-labels to the target dataset. A deep learning-based Pareto font-driven feature-selection module is also added to fine-tune the source and target features, thereby significantly improving the performance of the scene classification model. An adaptive density-based clustering method with a two-step neural network in the clustering module is utilized to determine whether adjacent clusters should be merged, thereby maintaining clear class boundaries. To reduce the pseudo-label noise, an uncertainty-aware soft pseudo-labelling approach is implemented, based on a dynamic confidence threshold. The framework is evaluated on four remote-sensing datasets namely AID (A), NWPU (N), RSSCN7 (R), and UC Merced (U) across various domain-adaptation tasks (A R, R A, A U, U A, R U, U R, N R, and R N). The proposed approach achieves accuracy improvements of 5.80%, 1.8%, 2.79%, 7.56%, 3.50%, 7.39%, 5.35%, and 3.12% over some of the baseline methods. These results show the superiority of the proposed approach in managing domain shifts, reducing pseudo-label noise, and improving target recognition without the need for labelled target data. The source code is available at https://github.com/BinuJoseA/UDA.
{"title":"An unsupervised domain adaptation approach for remote sensing scene classification using adaptive incremental density-based clustering and multi-objective optimization","authors":"Binu Jose A. , Pranesh Das , Ebrahim Ghaderpour , Paolo Mazzanti","doi":"10.1016/j.compeleceng.2025.110908","DOIUrl":"10.1016/j.compeleceng.2025.110908","url":null,"abstract":"<div><div>Unsupervised Domain Adaptation (UDA) is the process of learning knowledge from a labelled source domain to an unlabelled target domain, particularly in the context of remote sensing scene classification. The primary challenge in this process is the substantial cost associated with labelling and the significant discrepancies between domains. However, existing UDA methods degrade under severe domain shift and scene diversity, yielding noisy pseudo-labels and unstable target structure discovery. To address these issues, a novel UDA framework is proposed. The main focus of the framework is to develop a mapping using clustering-based pseudo-labelling that can provide a reliable and interpretable pseudo-labels to the target dataset. A deep learning-based Pareto font-driven feature-selection module is also added to fine-tune the source and target features, thereby significantly improving the performance of the scene classification model. An adaptive density-based clustering method with a two-step neural network in the clustering module is utilized to determine whether adjacent clusters should be merged, thereby maintaining clear class boundaries. To reduce the pseudo-label noise, an uncertainty-aware soft pseudo-labelling approach is implemented, based on a dynamic confidence threshold. The framework is evaluated on four remote-sensing datasets namely AID (A), NWPU (N), RSSCN7 (R), and UC Merced (U) across various domain-adaptation tasks (A <span><math><mo>→</mo></math></span> R, R <span><math><mo>→</mo></math></span> A, A <span><math><mo>→</mo></math></span> U, U <span><math><mo>→</mo></math></span> A, R <span><math><mo>→</mo></math></span> U, U <span><math><mo>→</mo></math></span> R, N <span><math><mo>→</mo></math></span> R, and R <span><math><mo>→</mo></math></span> N). The proposed approach achieves accuracy improvements of 5.80%, 1.8%, 2.79%, 7.56%, 3.50%, 7.39%, 5.35%, and 3.12% over some of the baseline methods. These results show the superiority of the proposed approach in managing domain shifts, reducing pseudo-label noise, and improving target recognition without the need for labelled target data. The source code is available at <span><span>https://github.com/BinuJoseA/UDA</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"131 ","pages":"Article 110908"},"PeriodicalIF":4.9,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145842706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-22DOI: 10.1016/j.compeleceng.2025.110917
G.Y. Sree Varshini , S. Latha , G.Y. Rajaa Vikhram , Sanjeevikumar Padmanaban
A modern interconnected power grid known as a cyber-physical power system (CPPS) integrates traditional power systems with information and communication technology. The primary purpose of a CPPS is to enhance the efficiency and security of the power grid via real-time monitoring, control, and data-informed decision-making. To attain its objective of self-healing, the CPPS must autonomously detect faults, respond to them, reorganize, and restore power delivery during disturbances or outages. Therefore, anomaly detection is essential for system recovery. This research examines the effects of physical and cyber disturbances through time-domain and frequency-domain simulations in MATLAB/SIMULINK. Different disturbance scenarios namely physical disturbances, and cyber disturbances such as data integrity attack (DIA), data availability attack (DAA) and coordinated attack are considered and detected using four data-driven methods such as support vector machine (SVM), random forest(RF), K-nearest neighbour(KNN) and convolutional neural network(CNN). The WSCC 3-machine 9-bus system demonstrates the effectiveness of several classifiers for attack detection.
{"title":"Detection of coordinated attack using data driven approach in cyber physical power system (CPPS)","authors":"G.Y. Sree Varshini , S. Latha , G.Y. Rajaa Vikhram , Sanjeevikumar Padmanaban","doi":"10.1016/j.compeleceng.2025.110917","DOIUrl":"10.1016/j.compeleceng.2025.110917","url":null,"abstract":"<div><div>A modern interconnected power grid known as a cyber-physical power system (CPPS) integrates traditional power systems with information and communication technology. The primary purpose of a CPPS is to enhance the efficiency and security of the power grid via real-time monitoring, control, and data-informed decision-making. To attain its objective of self-healing, the CPPS must autonomously detect faults, respond to them, reorganize, and restore power delivery during disturbances or outages. Therefore, anomaly detection is essential for system recovery. This research examines the effects of physical and cyber disturbances through time-domain and frequency-domain simulations in MATLAB/SIMULINK. Different disturbance scenarios namely physical disturbances, and cyber disturbances such as data integrity attack (DIA), data availability attack (DAA) and coordinated attack are considered and detected using four data-driven methods such as support vector machine (SVM), random forest(RF), K-nearest neighbour(KNN) and convolutional neural network(CNN). The WSCC 3-machine 9-bus system demonstrates the effectiveness of several classifiers for attack detection.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"131 ","pages":"Article 110917"},"PeriodicalIF":4.9,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145801921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-20DOI: 10.1016/j.compeleceng.2025.110914
Pranav Prakash Singh , Ravi Shankar , S.N. Singh
This work focuses on developing an Improved Frequency Regulation (IFR) strategy for a multi-area hybrid power system which includes reheat thermal power plant, biogas power plant, nuclear power plant and Electric Vehicle (EV). The tested system also incorporates the dynamic behaviour of wind and solar sources and its comprehensive impact on proposed IFR. Additionally, for a more realistic approach of the proposed work, incorporate nonlinearities such as Generation Dead-Band (GDB) and Generation Rate Constraint (GRC). A newly modified Chaotic Quasi-Opposition based Crayfish Optimization Algorithm (CQOCOA) is investigated along with suggested controller. For getting the different optimal gain parameters of the system, Integrated Time Absolute Error (ITAE) is considered as performance index. A CQOCOA based modified Linear Active Disturbance Rejection Control (LADRC) cascade with Three Degrees of Freedom Proportional Fractional Integral Derivative Filter (3DoF-PFIDN) controller is also proposed and investigated. The efficacy and robustness of the proposed system are further validated and implemented successfully on different load perturbations, system uncertainties, physical constraints, renewable penetrations and on bigger power system i.e., IEEE-118 test bus system. A comprehensive sensitivity analysis is performed to evaluate the resilience of the proposed controller by varying ±25 % and ± 50 % parametric uncertainties for investigated system. The proposed IFR strategy is also analysed for the physical cyber threats and its different possibilities. These cyber threats also explore the deep learning-based Attack Detection and Mitigation (ADM) system which enhance the system performance and reliability. Furthermore, the whole setup has been performed on OPAL-RT OP4510 platform, which demonstrate the supremacy of enhanced IFR strategy and validate the resilient control structure for the proposed hybrid power system under restructured scenario.
{"title":"Chaotic Quasi-opposition based 3DoF (PFIDN)-LADRC for enhanced frequency regulation in hybrid restructured power system considering cyber threats","authors":"Pranav Prakash Singh , Ravi Shankar , S.N. Singh","doi":"10.1016/j.compeleceng.2025.110914","DOIUrl":"10.1016/j.compeleceng.2025.110914","url":null,"abstract":"<div><div>This work focuses on developing an Improved Frequency Regulation (IFR) strategy for a multi-area hybrid power system which includes reheat thermal power plant, biogas power plant, nuclear power plant and Electric Vehicle (EV). The tested system also incorporates the dynamic behaviour of wind and solar sources and its comprehensive impact on proposed IFR. Additionally, for a more realistic approach of the proposed work, incorporate nonlinearities such as Generation Dead-Band (GDB) and Generation Rate Constraint (GRC). A newly modified Chaotic Quasi-Opposition based Crayfish Optimization Algorithm (CQO<img>COA) is investigated along with suggested controller. For getting the different optimal gain parameters of the system, Integrated Time Absolute Error (ITAE) is considered as performance index. A CQO<img>COA based modified Linear Active Disturbance Rejection Control (LADRC) cascade with Three Degrees of Freedom Proportional Fractional Integral Derivative Filter (3DoF-PFIDN) controller is also proposed and investigated. The efficacy and robustness of the proposed system are further validated and implemented successfully on different load perturbations, system uncertainties, physical constraints, renewable penetrations and on bigger power system i.e., IEEE-118 test bus system. A comprehensive sensitivity analysis is performed to evaluate the resilience of the proposed controller by varying ±25 % and ± 50 % parametric uncertainties for investigated system. The proposed IFR strategy is also analysed for the physical cyber threats and its different possibilities. These cyber threats also explore the deep learning-based Attack Detection and Mitigation (ADM) system which enhance the system performance and reliability. Furthermore, the whole setup has been performed on OPAL-RT OP4510 platform, which demonstrate the supremacy of enhanced IFR strategy and validate the resilient control structure for the proposed hybrid power system under restructured scenario.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"130 ","pages":"Article 110914"},"PeriodicalIF":4.9,"publicationDate":"2025-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145789699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The present study proposes a novel bi-level coordinated planning and scheduling framework of Vehicle-to-Grid (V2G)-enabled Electric Vehicle (EV) Parking Lot Charging Infrastructure in a renewable integrated smart grid. Developed in collaboration with the Distribution System Operator and EV aggregator, the proposed model leverages PV and V2G to reduce grid dependency, mitigate PV intermittency, enhance EV battery longevity, and address storage challenges. To fully realize the scheduling process in the long-term planning, an interactive hybrid optimization technique is developed that combines Harris Hawk Optimization and Linear Programming at the upper-level and lower-level of the model, respectively. The model optimizes the number of chargers and their hourly charging-discharging power, considering the key factors such as energy losses, investment costs, and charging expenses while adhering to security constraints. To ensure participation of EV users in the V2G program, monetary incentives are designed. As V2G operations negatively impact battery life, EV battery degradation and lifecycle assessments are conducted using Rain flow Counting Algorithm. To prove the efficacy of the proposed model, transient and frequency stability analysis is performed. The proposed approach is validated in 33-bus network, achieves a 30 % reduction in energy losses, a 37 % decrease in charging costs, enhanced system voltage profile, extended battery life, and hence a 21 % reduction in replacement costs of EV batteries.
{"title":"Collaborative planning and scheduling framework of EV charging infrastructure with Vehicle-to-Grid facility in a renewable integrated smart grid","authors":"Sriparna Roy Ghatak , Sasmita Tripathy , Chandrashekhar Narayan Bhende , Sharmistha Nandi , Surajit Sannigrahi , Parimal Acharjee","doi":"10.1016/j.compeleceng.2025.110911","DOIUrl":"10.1016/j.compeleceng.2025.110911","url":null,"abstract":"<div><div>The present study proposes a novel bi-level coordinated planning and scheduling framework of Vehicle-to-Grid (V2G)-enabled Electric Vehicle (EV) Parking Lot Charging Infrastructure in a renewable integrated smart grid. Developed in collaboration with the Distribution System Operator and EV aggregator, the proposed model leverages PV and V2G to reduce grid dependency, mitigate PV intermittency, enhance EV battery longevity, and address storage challenges. To fully realize the scheduling process in the long-term planning, an interactive hybrid optimization technique is developed that combines Harris Hawk Optimization and Linear Programming at the upper-level and lower-level of the model, respectively. The model optimizes the number of chargers and their hourly charging-discharging power, considering the key factors such as energy losses, investment costs, and charging expenses while adhering to security constraints. To ensure participation of EV users in the V2G program, monetary incentives are designed. As V2G operations negatively impact battery life, EV battery degradation and lifecycle assessments are conducted using Rain flow Counting Algorithm. To prove the efficacy of the proposed model, transient and frequency stability analysis is performed. The proposed approach is validated in 33-bus network, achieves a 30 % reduction in energy losses, a 37 % decrease in charging costs, enhanced system voltage profile, extended battery life, and hence a 21 % reduction in replacement costs of EV batteries.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"130 ","pages":"Article 110911"},"PeriodicalIF":4.9,"publicationDate":"2025-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145789758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-18DOI: 10.1016/j.compeleceng.2025.110909
Chinmay Bera , Rajib Mandal , Amitesh Kumar
Accurate estimation of the State-of-Charge (SoC) in lithium-ion batteries (LIBs) is essential for optimizing performance, ensuring safety, and prolonging battery life in Battery Management Systems (BMS) for Electric Vehicles (EVs). While Long Short-Term Memory (LSTM) networks have shown significant promise for SoC estimation, they often rely on manual hyperparameter tuning, leading to inconsistent accuracy and reduced adaptability. To overcome these limitations, this study introduces a robust, noise-resilient, and adaptive deep learning framework—MRFOSA-LSTM, that combines Manta Ray Foraging Optimization (MRFO) with Simulated Annealing (SA) to automate LSTM hyperparameter tuning. The hybrid MRFOSA enhances convergence and avoids local optima, while the addition of controlled noise during training improves the model’s robustness to external interference. The proposed method is rigorously analyzed and validated using multiple real-world driving cycles and evaluated across a wide range of initial SoC levels. Comparative analysis against baseline methods, including EKF, Particle swarm optimization (PSO) based LSTM, Genetic algorithm (GA) based LSTM, MRFO-LSTM, Transformer and Bi-LSTM methods, confirms the superior performance of MRFOSA-LSTM, achieving a Mean Absolute Error (MAE) of 0.25% and Root Mean Square Error (RMSE) of 0.36%. This framework offers a highly accurate and resilient solution for real-time SoC estimation in LIBs.
{"title":"A noise-resilient adaptive deep learning framework for accurate state-of-charge prediction in lithium-ion batteries for electric vehicles","authors":"Chinmay Bera , Rajib Mandal , Amitesh Kumar","doi":"10.1016/j.compeleceng.2025.110909","DOIUrl":"10.1016/j.compeleceng.2025.110909","url":null,"abstract":"<div><div>Accurate estimation of the State-of-Charge (SoC) in lithium-ion batteries (LIBs) is essential for optimizing performance, ensuring safety, and prolonging battery life in Battery Management Systems (BMS) for Electric Vehicles (EVs). While Long Short-Term Memory (LSTM) networks have shown significant promise for SoC estimation, they often rely on manual hyperparameter tuning, leading to inconsistent accuracy and reduced adaptability. To overcome these limitations, this study introduces a robust, noise-resilient, and adaptive deep learning framework—MRFOSA-LSTM, that combines Manta Ray Foraging Optimization (MRFO) with Simulated Annealing (SA) to automate LSTM hyperparameter tuning. The hybrid MRFOSA enhances convergence and avoids local optima, while the addition of controlled noise during training improves the model’s robustness to external interference. The proposed method is rigorously analyzed and validated using multiple real-world driving cycles and evaluated across a wide range of initial SoC levels. Comparative analysis against baseline methods, including EKF, Particle swarm optimization (PSO) based LSTM, Genetic algorithm (GA) based LSTM, MRFO-LSTM, Transformer and Bi-LSTM methods, confirms the superior performance of MRFOSA-LSTM, achieving a Mean Absolute Error (MAE) of 0.25% and Root Mean Square Error (RMSE) of 0.36%. This framework offers a highly accurate and resilient solution for real-time SoC estimation in LIBs.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"130 ","pages":"Article 110909"},"PeriodicalIF":4.9,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145789757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-18DOI: 10.1016/j.compeleceng.2025.110895
Vikas Kumar Jain , Meenakshi Tripathi
Smart contracts, a key component of blockchain technology, automate and enforce contractual agreements, facilitating trustless transactions across decentralized networks. However, their immutable and decentralized nature introduces unique security challenges, making vulnerability analysis crucial as vulnerabilities can lead to financial losses, exploitation, and manipulation. This research presents a comprehensive survey of smart contract vulnerability analysis techniques, encompassing rule-based, machine learning, and large language model-driven approaches. The study systematically categorizes vulnerabilities by their nature and impact into coding flaws, logical inconsistencies, and dependency-based risks, providing a unified taxonomy. Comparative evaluations reveal that while machine learning models excel in pattern-based detection, large language models demonstrate superior performance in semantic reasoning and contextual understanding of complex vulnerabilities. Additionally, the study identifies open research gaps, proposes a standardized evaluation framework, and outlines future directions for analyzing smart contract vulnerabilities. The findings of this survey aim to provide valuable insights into smart contract vulnerability analysis for researchers, developers, and practitioners and contribute to the advancement of more secure blockchain-based applications.
{"title":"Ethereum smart contract security: Vulnerabilities, analysis techniques, challenges and research directions","authors":"Vikas Kumar Jain , Meenakshi Tripathi","doi":"10.1016/j.compeleceng.2025.110895","DOIUrl":"10.1016/j.compeleceng.2025.110895","url":null,"abstract":"<div><div>Smart contracts, a key component of blockchain technology, automate and enforce contractual agreements, facilitating trustless transactions across decentralized networks. However, their immutable and decentralized nature introduces unique security challenges, making vulnerability analysis crucial as vulnerabilities can lead to financial losses, exploitation, and manipulation. This research presents a comprehensive survey of smart contract vulnerability analysis techniques, encompassing rule-based, machine learning, and large language model-driven approaches. The study systematically categorizes vulnerabilities by their nature and impact into coding flaws, logical inconsistencies, and dependency-based risks, providing a unified taxonomy. Comparative evaluations reveal that while machine learning models excel in pattern-based detection, large language models demonstrate superior performance in semantic reasoning and contextual understanding of complex vulnerabilities. Additionally, the study identifies open research gaps, proposes a standardized evaluation framework, and outlines future directions for analyzing smart contract vulnerabilities. The findings of this survey aim to provide valuable insights into smart contract vulnerability analysis for researchers, developers, and practitioners and contribute to the advancement of more secure blockchain-based applications.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"130 ","pages":"Article 110895"},"PeriodicalIF":4.9,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145789700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-18DOI: 10.1016/j.compeleceng.2025.110893
Jiangbing Sun , Yan Zhang , Jie Chen , Ruoting Xiong , Wei Ren
Given two or more sets with elements that are in plain text, it is straightforward to compute the intersection. If the elements are encrypted, then it becomes non-trivial. In this paper, we formally define the computational problem for private set intersection (PSI), and its corresponding security. We propose a general method for computing PSI with only semantics of encryption. Our method is lightweight so as to be feasible for sets with a large scale elements. We extensively analyze the security and performance to justify that our method can protect the privacy yet maintain the feasibility.
{"title":"A general and lightweight method for private set intersection computation","authors":"Jiangbing Sun , Yan Zhang , Jie Chen , Ruoting Xiong , Wei Ren","doi":"10.1016/j.compeleceng.2025.110893","DOIUrl":"10.1016/j.compeleceng.2025.110893","url":null,"abstract":"<div><div>Given two or more sets with elements that are in plain text, it is straightforward to compute the intersection. If the elements are encrypted, then it becomes non-trivial. In this paper, we formally define the computational problem for private set intersection (PSI), and its corresponding security. We propose a general method for computing PSI with only semantics of encryption. Our method is lightweight so as to be feasible for sets with a large scale elements. We extensively analyze the security and performance to justify that our method can protect the privacy yet maintain the feasibility.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"130 ","pages":"Article 110893"},"PeriodicalIF":4.9,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145789846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-17DOI: 10.1016/j.compeleceng.2025.110913
Ravi Anand , Rajib Kumar Mandal
This paper presents a novel seven-level switched-capacitor (SC) inverter topology configuration that incorporates a triple-boost mechanism. The proposed design significantly reduces component count by utilizing only six switches, four diodes, four capacitors, and two inductors, while simultaneously mitigating capacitor charging currents and minimizing voltage stress across the devices. Capacitor voltage self-balancing is achieved without the need for additional sensors through PD-PWM modulation. The topology effectively supports a wide modulation index range, dynamic load variations, and different operating frequencies, demonstrating high versatility. The system’s performance is confirmed by both simulations and experiments, which show that it has a low output current THD of 0.78% and an efficiency of 98.13%. A hardware prototype confirms stable operation under transient and steady-state conditions. Comparative analysis highlights the proposed inverter’s superior performance in terms of reduced total standing voltage (TSV), higher efficiency, and fewer components required compared to existing seven-level inverters. This makes the topology a promising choice for integration into renewable energy systems and other advanced power electronic applications.
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Pub Date : 2025-12-16DOI: 10.1016/j.compeleceng.2025.110912
G.K. Jaiswal, U. Nangia, N.K. Jain
This research introduces a novel hybrid algorithm that combines opposition-based strategies with Differential Evolution and the Giza Pyramids Construction algorithm to address the deterministic and stochastic Optimal Reactive Power Dispatch (ORPD) problem in power systems. This novel algorithm is initially evaluated on thirteen benchmark functions, including unimodal and multimodal functions. It is then applied to single-objective deterministic ORPD problems in IEEE 30-bus and IEEE 57-bus systems, and further extended to a stochastic ORPD problem in a modified IEEE 30-bus system. In the stochastic ORPD problem, the uncertainties in load demand, wind speed, solar irradiation, and small-hydro inflows are considered. These uncertainties account for the continuous fluctuations and intrinsic intermittency of solar irradiation, wind speed, water flow rate and demand fluctuation. To demonstrate the robustness of the proposed hybrid algorithm, a comparative analysis is conducted against the recently introduced Giza Pyramids Construction Algorithm (GPC), Honey Badger Algorithm (HBA), and COOT Algorithm (COOT). For the deterministic ORPD problem, the proposed method achieves the highest savings among all four methods for , VD and VSI that are 21.75%, 92.54%, and 32.95% for the IEEE 30-bus system and 18.12%, 61.51% and 38.42% for the IEEE 57-bus system, respectively. For the stochastic ORPD problem, the proposed method obtained the expected sum of , VD and VSI as 3.8425 MW, 0.0592 p.u., and 0.0771 p.u., respectively.
{"title":"A novel hybrid optimization approach for stochastic reactive power dispatch in hybrid energy systems","authors":"G.K. Jaiswal, U. Nangia, N.K. Jain","doi":"10.1016/j.compeleceng.2025.110912","DOIUrl":"10.1016/j.compeleceng.2025.110912","url":null,"abstract":"<div><div>This research introduces a novel hybrid algorithm that combines opposition-based strategies with Differential Evolution and the Giza Pyramids Construction algorithm to address the deterministic and stochastic Optimal Reactive Power Dispatch (ORPD) problem in power systems. This novel algorithm is initially evaluated on thirteen benchmark functions, including unimodal and multimodal functions. It is then applied to single-objective deterministic ORPD problems in IEEE 30-bus and IEEE 57-bus systems, and further extended to a stochastic ORPD problem in a modified IEEE 30-bus system. In the stochastic ORPD problem, the uncertainties in load demand, wind speed, solar irradiation, and small-hydro inflows are considered. These uncertainties account for the continuous fluctuations and intrinsic intermittency of solar irradiation, wind speed, water flow rate and demand fluctuation. To demonstrate the robustness of the proposed hybrid algorithm, a comparative analysis is conducted against the recently introduced Giza Pyramids Construction Algorithm (GPC), Honey Badger Algorithm (HBA), and COOT Algorithm (COOT). For the deterministic ORPD problem, the proposed method achieves the highest savings among all four methods for <span><math><msub><mrow><mi>P</mi></mrow><mrow><mi>L</mi><mi>o</mi><mi>s</mi><mi>s</mi></mrow></msub></math></span>, VD and VSI that are 21.75%, 92.54%, and 32.95% for the IEEE 30-bus system and 18.12%, 61.51% and 38.42% for the IEEE 57-bus system, respectively. For the stochastic ORPD problem, the proposed method obtained the expected sum of <span><math><msub><mrow><mi>P</mi></mrow><mrow><mi>L</mi><mi>o</mi><mi>s</mi><mi>s</mi></mrow></msub></math></span>, VD and VSI as 3.8425 MW, 0.0592 p.u., and 0.0771 p.u., respectively.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"130 ","pages":"Article 110912"},"PeriodicalIF":4.9,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145789847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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