International Journal of Energy Technology and Policy (13 papers in press)

Regular Issues

• Open Access

  Current-sensing-array-based dielectric health monitoring system for secondary power circuits ( Free Full-text Access )

  by Hengchu Shi, Xiaofan Chen, Hao You, Shoudong Xu, Xiao Hu 
  Abstract: The secondary power system plays a crucial role in ensuring reliable power supply and safe production of the power system. The system consists of secondary equipment and secondary circuits, where the integrity of the secondary circuits and the correctness of wiring are crucial for the regular operation of protection and automation devices. If there are problems with the secondary circuit, especially wiring errors, it will directly affect the response of relay protection and automation devices, which may lead to system failures or accidents. In the actual operation of substations, multiple grounding points on the secondary sides of PT (voltage transformer) and current transformer (CT) often occur, which may cause misoperation or the refusal of relay protection devices, significantly increasing the risk of system operation. To address this issue, this paper proposes an online monitoring system based on a distributed zero-sequence current monitoring method for real-time monitoring of the insulation status of secondary AC circuits. This system effectively reduces the workload of operation and maintenance personnel, improves operational and maintenance efficiency and monitoring accuracy, thereby ensuring the reliable operation of the substation’s secondary circuit and providing strong guarantees for the stability and safety of the power system.
  Keywords: secondary power circuits; online monitoring; dielectric monitoring system; distributed sampling; current sensing.
  DOI: 10.1504/IJETP.2026.10076937
   
  
• Power grid safety monitoring system based on machine learning algorithms  
  by Yaoshan Zhang, Zhuangwei Chen, Meihong Wang, Liang Zhang, Yue Zhou 
  Abstract: This article presents a power grid safety monitoring system based on embedded machine learning algorithms to improve the accuracy and real-time performance of power grid operations. A module for analysing and determining changes in optical channel performance was designed, and a long short-term memory (LSTM) model was used for analysis and prediction; a module for analysing and predicting the types of hidden danger degradation in optical channel performance was constructed, and a random forest model was used for identification and prediction. By integrating the outputs of the above modules using a cascaded model, the operational time of the optical channel was predicted. Thirty sets of comparative tests were conducted between traditional monitoring systems and embedded algorithms in the experiment. Experimental results showed that the embedded algorithm achieved anomaly detection accuracy of 89.1% to 99.2%, an error rate of 0.26% to 0.87%, and a response time of 0.71 seconds to 1.27 seconds, all of which were better than those of traditional monitoring systems.
  Keywords: power grid safety monitoring system; embedded machine learning algorithms; LSTM model; random forest model; optical channel performance.
  DOI: 10.1504/IJETP.2026.10076803
   
  
• Dynamic prediction of grid flexible current-carrying capacity based on wind power fluctuations and parameter self-correction  
  by Feng Zhang, Chengcheng Rao, Huawei Meng 
  Abstract: With the proposal of the ‘dual-carbon’ goals and the advancement of the green and low-carbon transformation of energy, the large-scale integration of renewable energy sources such as wind energy has posed severe challenges to the stability and reliability of the new power system. To address this issue, this paper proposes an integrated dynamic wind power prediction algorithm that combines machine learning and optimisation algorithms. Furthermore, by incorporating a self-correcting parameter estimation process, a hybrid model is constructed. This model can adapt to changes in wind power fluctuations by dynamically adjusting the transmission capacity of the power grid in real-time. The model dynamically adjusts system parameters according to actual fluctuation conditions, ensuring the optimal operation of the power grid in complex and changing environments. Simulation results based on actual data show that the proposed method exhibits significant advantages in improving prediction accuracy and power grid operation efficiency. It can effectively enhance the power grid’s capacity to withstand wind power fluctuations and ensure grid stability. This method provides practical technical support for the efficient integration of wind energy into power systems.
  Keywords: wind power prediction; flexible current-carrying capacity; artificial intelligence; parameter self-correction; smart grid; dynamic line rating; DLR.
  DOI: 10.1504/IJETP.2026.10076936
   
  
• A self-healing method for faults in low and medium voltage substations during large-scale power outages  
  by Rongsheng Zhou, Jiaxin Lv, Guanquan Dai, Zhaoyu Wu, Shaozhong Xiang 
  Abstract: To address the coordination gap between distributed generation anti-islanding protection and feeder automation, this study proposes a self-healing method for low/medium-voltage faults during large-scale outages. A multi-domain (time, frequency, wavelet) detection approach was developed using zero-sequence current analysis and a single-phase grounding fault model. Feature selection via random forest and fault zone identification with LightGBM enhance fault response efficiency. A distributed self-healing strategy based on smart terminal collaboration achieves autonomous fault isolation via associated feeder partitioning. Combining switch transfer and DG islanding restores power to non-fault zones. A topological adjacency matrix and load-based repair priority model optimise fault location, network reconfiguration, and repair scheduling. Experimental results demonstrate 50ms fault detection (0.5%-1% false positives) and 95%-99% load recovery, improving grid resilience.
  Keywords: large-scale power outage; low to medium pressure platform area; fault self-healing; zero sequence current characteristics.
  DOI: 10.1504/IJETP.2026.10076998
   
  
• Techno-economic feasibility and CO₂ emission analysis of a novel biogas to blue gold multigeneration process: a simulation of cryogenic processes for efficient CO₂ management and energy production  
  by Xiuyan Wang 
  Abstract: This work presents an eco-friend novel biomass-based integrated energy system that combines cryogenic separation of biomass with gas turbines, steam Rankine, absorption chillers, and water electrolyser units. The main contribution of system is the utilisation nitrogen-based cryogenic cycle, which guarantees improved thermal and power integration by allowing for simultaneous production of high-purity biomethane and liquid CO₂ from biomass. This study utilises a thorough 4E evaluation that includes thermodynamic, environmental, and exergo-economic analyses. Based on the results, cryogenic process successfully recovers 99.61% of the biomethane, which results strong resource utilisation by efficiency of 59.59% for energy and efficiency of 29.16% for exergy. Moreover, the proposed process exhibits both economic viability and efficient emission management by achieving a low footprint of 0.1669 kg/kWh and a specific product cost of $39.81/GJ, due to its high biomethane recovery and efficient liquid CO₂ capture.
  Keywords: specific carbon dioxide emission; CO₂; biogas upgrading; exergo-economic evaluation; cryogenic separation; hydrogen.
  DOI: 10.1504/IJETP.2026.10077525
   
  
• The effect of zinc oxide nanoparticles and aluminium oxide nanoparticles in the performance and emissions of canola biodiesel in diesel engine  
  by Anbarasu Athimoolam 
  Abstract: The present experimental work analyses the effect of zinc oxide and aluminium oxide nanoparticles addition in canola biodiesel in single-cylinder water cooled direct injection four stroke compression ignition engine. The nano fluids are prepared from 50 ppm of zinc oxide and aluminium oxide nanoparticles with distilled water through ultrasonication process. Various engine loads of 0, 7, 14, 21 and 28 Nm at a constant engine speed of 1,500 rpm were applied during engine testing. The fuel outcomes were obtained by calculating the average three times repetition of engine testing. Findings reveal that the highest maximum pressure of zinc oxide and aluminium oxide nanoparticles fuel increased by 12.3% compared to canola test fuel. Other than that, the engine brake thermal efficiency and specific fuel consumption show a significant increase by 23% and 9.3%, respectively. Meanwhile, the emissions of zinc oxide and aluminium oxide nanoparticles fuel show a large NOx decrease of 41.6%, 28.5% HC and 40% CO. Addition of zinc oxide and aluminium oxide nanoparticles in biodiesel show positive improvements when used in compression ignition engines.
  Keywords: Al₂O₃ nanoparticle; zinc oxide nanoparticle; canola biodiesel; transesterification; emissions.
  DOI: 10.1504/IJETP.2026.10077675
   
  
• Study on large vision model for key feature recognition in power equipment  
  by Huaqing Cao 
  Abstract: In this paper, a key feature recognition algorithm for power equipment based on visual large model is proposed. Firstly, collecting multi-source image data from devices through a multidimensional perception network, using an improved side window guided filtering method for image enhancement, and combining OTSU threshold segmentation to achieve target area extraction; then, use the DINOv2 visual big model for self supervised feature extraction; finally, the optimised whale swarm algorithm is introduced for key feature selection, which improves the accuracy of identifying key features of power equipment while reducing feature dimensions. The experimental results show that the proposed algorithm has a maximum signal-to-noise ratio of 48.75 dB for power equipment images, a maximum accuracy of 97.5% for key feature recognition, and a minimum recognition time of only 3.16 s.
  Keywords: visual big model; power equipment; key feature recognition; improved side window guided filtering; OTSU threshold segmentation; DINOv2 visual big model; optimised whale swarm algorithm.
  DOI: 10.1504/IJETP.2026.10077827
   
  
• Intelligent fault area identification in distribution networks: a joint graph convolutional network approach  
  by Yunyun Ma 
  Abstract: Traditional fault identification methods face challenges such as topological blind spots, computational bottlenecks, and hyperparameter sensitivity, hindering accurate and rapid fault area localisation. To address these limitations, this paper proposes an intelligent identification method based on a joint graph convolutional neural network. The approach constructs a feature matrix integrating multi-source measurement data and introduces a topological adjacency matrix to characterise structural correlations among nodes. By incorporating Bayesian classification and decision tree algorithms, adaptive allocation of feature weights is achieved. Furthermore, a fuzzy optimisation layer is embedded into the network to quantify fault feature uncertainty using membership functions, significantly enhancing model robustness under noisy or incomplete data conditions. Experimental results demonstrate that the proposed method achieves up to 95% accuracy and a response time of 0.42 seconds in fault section identification.
  Keywords: graph convolutional neural network; distribution network; fault area identification; decision tree algorithm.
  DOI: 10.1504/IJETP.2026.10077828
   
  
• Optimisation of new energy vehicle traffic flow and application of hybrid multi-objective evolutionary algorithm based on internet of things simulation of urban mobility simulation platform  
  by Tengfei Li 
  Abstract: The current optimisation of traffic flow faces challenges, such as increased congestion at intersections and the inability of traditional signal control strategies to adapt to dynamic mixed traffic. With the increasing proportion of new energy vehicles, the exhaust emission model needs to be reconstructed, and existing methods often focus on single objective optimisation, ignoring the synergistic trade-off between delay and emissions. This paper selects a multi-objective optimisation (MOO) model consisting of average vehicle delay and average exhaust emissions. It uses backpropagation neural network (BPNN) to improve non dominated sorting genetic algorithm II (NSGA-II), and utilises urban traffic simulation (SUMO) traffic model to establish and optimise micro road traffic models of intersections. According to the analysis of the optimisation results, the signal cycle at the optimised intersection has been shortened by 16.4%, the average delay time has been reduced by 14.9%, and the exhaust emissions have been reduced by 8.5%.
  Keywords: traffic flow optimisation; non-dominated sorting genetic algorithm II; simulation of urban mobility; backpropagation neural network; traffic signal time; multi-objective optimisation; MOO.
  DOI: 10.1504/IJETP.2026.10077886
   
  

Special Issue on: Multiscale Energy Systems for Renewable Energy Storage Part 3

• Open Access

  Enabling holistic insulation monitoring in secondary AC protection circuits: a diagnostic algorithm ( Free Full-text Access )

  by Hengchu Shi, Hao You, Xiaofan Chen, Shoudong Xu, Jing Chen 
  Abstract: This paper addresses the problem of insulation status monitoring in secondary alternating current (AC) circuits of power system relay protection. A comprehensive diagnostic algorithm based on multi-dimensional feature fusion is proposed. This algorithm integrates distributed zero-sequence current monitoring, waveform similarity analysis, and third harmonic analysis techniques to construct a multi-dimensional feature fusion diagnostic model. By real-time acquisition of zero-sequence current in each branch, combined with the similarity changes of current waveforms under normal and abnormal operating conditions and the characteristics of third harmonic content, early identification and accurate location of typical faults such as insulation degradation, single-point grounding, and multi-point grounding are achieved. Experimental results show that the algorithm achieves a 98.6% detection rate for insulation faults, a 94.3% accuracy rate for multi-point grounding location, an average diagnostic latency of only 43.63 ms, and a false alarm rate of 1.5% under normal operating conditions. This significantly improves the timeliness and accuracy of insulation fault diagnosis, providing effective technical support for the safe and reliable operation of secondary circuits in substation relay protection.
  Keywords: secondary circuit; insulation status; zero-sequence current; waveform similarity analysis; harmonic analysis; comprehensive diagnosis.
  DOI: 10.1504/IJETP.2026.10077374
   
  

Special Issue on: Advancing Sustainable Development Banking Strategies Energy Transition and Green Economies

• Empowering communities: a case study of sustainable solutions to address load shedding in South Africa  
  by Chané De Bruyn  
  Abstract: South Africans have been plagued by varying stages of load shedding, with 2023 seeing a record-breaking 332 days of load shedding. This prolonged crisis has had severe repercussions, impacting local economic development, water services, food security, education and healthcare. As it affects businesses across all sectors, productivity, employment, and overall growth, addressing this issue is crucial for sustainable development and maintaining a thriving local economy. Using a case study approach, this paper assesses South Africa’s first 'smart town’, that through collaboration and innovative measures have been able to manage their own electricity demand, ensuring the continuation of business and economic activity. This study examines the significance of empowering local communities, discusses important tactics for encouraging community involvement, and provides a compelling case study of sustainable development projects led by empowered communities.
  Keywords: community empowerment; loadshedding; community led development; sustainable development; community; South Africa.
  DOI: 10.1504/IJETP.2025.10071921
   
  
• The impact of AI on China’s energy policy for EVs  
  by Klemens Katterbauer, Sema Yilmaz, Hassan Syed, Gözde Meral 
  Abstract: China’s national energy policy is fundamentally oriented toward achieving a low-carbon economy, with electric vehicles (EVs) serving as a key pillar in this transition. AI plays a crucial role in enhancing energy efficiency, optimizing grid integration, accelerating the widespread adoption of EVs. This report provides a comprehensive analysis of AI’s contributions to China’s EV-related energy policies, examining its applications, benefits, challenges, future developments. AI technologies are instrumental in facilitating China’s ambition to achieve carbon neutrality by 2060. In the energy distribution, AI significantly enhances grid stability through the implementation of smart charging systems, vehicle-to-grid(V2G) technologies, predictive analytics. However, several challenges must be addressed to realize these advantages. These are data security, the digital divide in rural areas, the high costs, the need for regulatory frameworks that balance innovation with compliance. Consequently, AI represents a transformative tool in advancing China’s EV adoption and aligning its energy policies with long-term sustainability objectives.
  Keywords: artificial intelligence; energy policy; electric vehicles; China; carbon neutrality.
  DOI: 10.1504/IJETP.2025.10072079
   
  
• NEOM Smart City the urban oasis in Saudi desert (green energy technologies, policies and strategies)  
  by Somayya Madakam, Shidhar M. Samant, Pragya Bhawsar 
  Abstract: Today, the cities are facing an energy crisis as day-to-day urban operations including home automation, manufacturing, transportation, water, entertainment and others depend on fossil fuel energy. These urban challenges are not just faced by a particular city, nation but also across the globe including Saudi Arabia. In light of all the above challenges, the present manuscript highlights how the new urban energy solutions can meet the present needs and can also help in sustainable urban development. The paper is based on the secondary data collected through reports, white papers, blogs, snaps, and videos on ‘NEOM’. The insights from the content analysis explores NEOM Smart City’s commitment to sustainable energy technologies that reflects its ambition to set new standards in urban sustainability and environmental stewardship. By harnessing renewable energy sources, implementing smart grid technologies, promoting energy efficiency, and fostering innovation, NEOM aims to create a model city that balances environmental preservation.
  Keywords: circular economy; green building; green energy technologies; green hydrogen economy; NEOM Smart City; quality of life; QoL; smart cities; smart grids; sustainable development.
  DOI: 10.1504/IJETP.2026.10075557