Wei Chen, Yang Wu^*, Tingting Pei, Jie Lin, Guojing Yuan

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.070905 - 27 January 2026

Abstract In view of the insufficient utilization of condition-monitoring information and the improper scheduling often observed in conventional maintenance strategies for photovoltaic (PV) modules, this study proposes a predictive maintenance (PdM) strategy based on Remaining Useful Life (RUL) estimation. First, a RUL prediction model is established using the Transformer architecture, which enables the effective processing of sequential degradation data. By employing the historical degradation data of PV modules, the proposed model provides accurate forecasts of the remaining useful life, thereby supplying essential inputs for maintenance decision-making. Subsequently, the RUL information obtained from the prediction process is… More >

Pralay Roy¹, Pabitra Kumar Biswas¹, Chiranjit Sain^2,*, Taha Selim Ustun^3,*

Energy Engineering, Vol.122, No.10, pp. 4035-4060, 2025, DOI:10.32604/ee.2025.068989 - 30 September 2025

Abstract This study presents the use of an innovative population-based algorithm called the Sine Cosine Algorithm and its metaheuristic form, Quasi Oppositional Sine Cosine Algorithm, to automatic generation control of a multiple-source-based interconnected power system that consists of thermal, gas, and hydro power plants. The Proportional-Integral-Derivative controller, which is utilized for automated generation control in an interconnected hybrid power system with a DC link connecting two regions, has been tuned using the proposed optimization technique. An Electric Vehicle is taken into consideration only as an electrical load. The Quasi Oppositional Sine Cosine method’s performance and efficacy… More >

Asrar Mahboob¹, Muhammad Rashad¹, Ghulam Abbas¹, Zohaib Mushtaq², Tehseen Mazhar^3,*, Ateeq Ur Rehman^4,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3805-3823, 2025, DOI:10.32604/cmc.2025.067615 - 23 September 2025

Abstract This paper presents a novel blockchain-embedded cybersecurity framework for industrial solar power systems, integrating immutable machine learning (ML) with distributed ledger technology. Our contribution focused on three factors, Quantum-resistant feature engineering using the UNSW-NB15 dataset adapted for solar infrastructure anomalies. An enhanced Light Gradient Boosting Machine (LightGBM) classifier with blockchain-validated decision thresholds, and A cryptographic proof-of-threat (PoT) consensus mechanism for cyber attack verification. The proposed Immutable LightGBM model with majority voting and cryptographic feature encoding achieves 96.9% detection accuracy with 0.97 weighted average of precision, recall and F1-score, outperforming conventional intrusion detection systems (IDSs) by… More >

Ghadah Aldehim, Shakila Basheer, Ala Saleh Alluhaidan, Sapiah Sakri^*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3599-3619, 2025, DOI:10.32604/cmc.2025.065643 - 23 September 2025

Abstract Although digital changes in power systems have added more ways to monitor and control them, these changes have also led to new cyber-attack risks, mainly from False Data Injection (FDI) attacks. If this happens, the sensors and operations are compromised, which can lead to big problems, disruptions, failures and blackouts. In response to this challenge, this paper presents a reliable and innovative detection framework that leverages Bidirectional Long Short-Term Memory (Bi-LSTM) networks and employs explanatory methods from Artificial Intelligence (AI). Not only does the suggested architecture detect potential fraud with high accuracy, but it also… More >

Zekun Liu¹, Junwei Ma^1,2,*, Xin Gong¹, Xiu Liu¹, Bingbing Liu¹, Long An¹

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 983-999, 2025, DOI:10.32604/cmc.2025.065978 - 29 August 2025

Abstract With the ongoing digitalization and intelligence of power systems, there is an increasing reliance on large-scale data-driven intelligent technologies for tasks such as scheduling optimization and load forecasting. Nevertheless, power data often contains sensitive information, making it a critical industry challenge to efficiently utilize this data while ensuring privacy. Traditional Federated Learning (FL) methods can mitigate data leakage by training models locally instead of transmitting raw data. Despite this, FL still has privacy concerns, especially gradient leakage, which might expose users’ sensitive information. Therefore, integrating Differential Privacy (DP) techniques is essential for stronger privacy protection.… More >

Xiaolong Xiao¹, Xiaoxing Lu^1,*, Ziran Guo¹, Jian Liu¹, Shenglong Wu², Ye Cai²

Energy Engineering, Vol.122, No.8, pp. 3117-3132, 2025, DOI:10.32604/ee.2025.065631 - 24 July 2025

Abstract With the popularization of microgrid construction and the connection of renewable energy sources to the power system, the problem of source and load uncertainty faced by the coordinated operation of multi-microgrid is becoming increasingly prominent, and the accuracy of typical scenario predictions is low. In order to improve the accuracy of scenario prediction under source and load uncertainty, this paper proposes a typical scenario identification model based on random forests and order parameters. Firstly, a method for ordinal parameter identification and quantification is provided for the coordinated operating mode of multi-microgrids, taking into account source-load… More >

Cuiping Li¹, Jiacheng Sun¹, Qiang Li², Qi Guo², Junhui Li^1,*, Shuo Yu², Jingbo Wang², Wenze Li²

Energy Engineering, Vol.122, No.8, pp. 3055-3077, 2025, DOI:10.32604/ee.2025.064440 - 24 July 2025

Abstract The penetration rate of new wind and photovoltaic energy in the power system has increased significantly, and the dramatic fluctuation of the net load of the grid has led to a severe lack of flexibility in the regional grid. This paper proposes a hierarchical optimal dispatch strategy for a high proportion of new energy power systems that considers the balanced response of grid flexibility. Firstly, various flexibility resource regulation capabilities on the source-load side are analyzed, and then flexibility demand and flexibility response are matched, and flexibility demand response assessment is proposed; then, a hierarchical… More >

Zuhaib Nishtar^1,*, Fangzong Wang¹, Fawwad Hassan Jaskani², Hussain Afzaal³

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 2919-2956, 2025, DOI:10.32604/cmes.2025.065098 - 30 June 2025

Abstract This research aims to address the challenges of fault detection and isolation (FDI) in digital grids, focusing on improving the reliability and stability of power systems. Traditional fault detection techniques, such as rule-based fuzzy systems and conventional FDI methods, often struggle with the dynamic nature of modern grids, resulting in delays and inaccuracies in fault classification. To overcome these limitations, this study introduces a Hybrid Neuro-Fuzzy Fault Detection Model that combines the adaptive learning capabilities of neural networks with the reasoning strength of fuzzy logic. The model’s performance was evaluated through extensive simulations on the… More >

Rodolfo V. C. Ramalho¹, Vitoria B. Portilho¹, Davi E. S. Souza¹, Gilton C. A. Furtado¹, Natália M. Graças², Manoel J. S. Sena², Cláudio J. C. Blanco², André L. Amarante Mesquita^1,*

Energy Engineering, Vol.122, No.6, pp. 2197-2213, 2025, DOI:10.32604/ee.2025.064196 - 29 May 2025

Abstract Despite significant Brazilian social programs to expand energy access, approximately one million people in rural Amazonia still lack electricity. Moreover, the existing rural electricity grid in the region is inadequate for supporting efficient small-scale production systems due to both the poor quality and high cost of supplied energy. In parallel, traditional wooden bridges in the Amazon have been progressively replaced by more durable concrete structures in recent years. In this context, this study explores the application of very low-head hydropower installations in the Amazon, focusing on integrating axial-flow turbines beneath small concrete bridges. The methodology… More > Graphic Abstract

Shitharth Selvarajan^1,2,3,*, Hariprasath Manoharan⁴, Taher Al-Shehari⁵, Hussain Alsalman⁶, Taha Alfakih⁷

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4339-4369, 2025, DOI:10.32604/cmc.2025.056100 - 06 March 2025

Abstract This research presents an analysis of smart grid units to enhance connected units’ security during data transmissions. The major advantage of the proposed method is that the system model encompasses multiple aspects such as network flow monitoring, data expansion, control association, throughput, and losses. In addition, all the above-mentioned aspects are carried out with neural networks and adaptive optimizations to enhance the operation of smart grid networks. Moreover, the quantitative analysis of the optimization algorithm is discussed concerning two case studies, thereby achieving early convergence at reduced complexities. The suggested method ensures that each communication More >