e-Prime - Advances in Electrical Engineering, Electronics and Energy最新文献

{"title":"A novel framework for multi-sensor data fusion in bearing fault diagnosis using continuous wavelet transform and transfer learning","authors":"Iman Makrouf ,&nbsp;Mourad Zegrari ,&nbsp;Khalid Dahi ,&nbsp;Ilias Ouachtouk","doi":"10.1016/j.prime.2025.101025","DOIUrl":"10.1016/j.prime.2025.101025","url":null,"abstract":"<div><div>Intelligent fault diagnosis (IFD) is crucial in industrial settings, leveraging big data from various sensors and machine learning advancements to monitor critical components such as rolling bearings. While IFD-based deep learning and multi-sensor fusion offer promising solutions, challenges remain in integrating heterogeneous data and managing computational complexity. Transfer learning from pre-trained models can mitigate these issues, particularly with limited labeled datasets common in industrial applications. However, integrating transfer learning with multi-sensor fusion for diagnosing complex fault scenarios, especially combined bearing defects under varying operational conditions, remains underexplored in current research. This paper proposes a novel multi-sensor fusion approach for bearing fault diagnosis that combines vibration and acoustic signals within a transfer learning framework. Continuous Wavelet Transform (CWT) is applied to multi-sensor inputs, and the resulting wavelet coefficients are fused using the Maximum Energy to Shannon Entropy Ratio (ME-to-SER) criterion to fine-tune pre-trained Convolutional Neural Networks (CNNs). The effectiveness of the proposed method is validated on the Spectra Quest Machinery Fault Simulator (MFS) across various bearing fault scenarios, including combined faults, under variable speeds. The proposed approach achieves high accuracy (up to 100%) using multi-modal fused data, outperforming single-modality methods. It excels in complex fault classification and maintains robustness under various operational conditions. The fusion approach efficiently handles heterogeneous data to enhance diagnostic reliability, whereas transfer learning effectively addresses limited labeled datasets and reduces the computational burden of training deep CNNs from scratch.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"13 ","pages":"Article 101025"},"PeriodicalIF":0.0,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144240974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stochastic optimization for siting and sizing of renewable distributed generation and D-STATCOMs","authors":"Alejandro Valencia-Díaz,&nbsp;Sebastián García H.,&nbsp;Ricardo A. Hincapie I.","doi":"10.1016/j.prime.2025.101026","DOIUrl":"10.1016/j.prime.2025.101026","url":null,"abstract":"<div><div>This paper presents a novel methodology for the optimal placement and sizing of distributed renewable generators and D-STATCOMs in electrical distribution systems. The problem is formulated as a mixed-integer second-order cone stochastic model with an objective function that minimizes the investment costs of purchasing and installing D-STATCOMs, wind turbines, photovoltaic systems, and small hydropower plants, as well as the expected value of energy purchase cost by the distribution company. A two-stage stochastic programming formulation addresses uncertainties in electrical demand, energy prices, wind-based distributed generation, solar-based distributed generation, and small hydropower-based distributed generation. Stochastic scenarios are generated using the k-means clustering technique. Moreover, a relaxed convex model is proposed to reduce the number of candidate nodes for installation, significantly improving computational efficiency while ensuring optimality. The proposed methodology’s accuracy, efficiency, and robustness are validated on two benchmark distribution systems with 70 and 136 nodes, respectively. The results demonstrate that the simultaneous integration of distributed renewable generators and D-STATCOMs effectively reduces operational costs and energy losses, achieving a loss reduction of 42.3% and 13.6% for the 70-node and 136-node test systems, respectively, while enhancing voltage regulation and improving the loading of network components. Furthermore, the model estimates the cost reductions required for solar and wind technologies to become economically viable under uncertainty, providing a practical tool for policymakers to design effective financial incentives. This feature is particularly relevant for developing countries, where high capital costs and limited public resources hinder renewable energy integration.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"13 ","pages":"Article 101026"},"PeriodicalIF":0.0,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144253680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Potential for climate protection in hospitals","authors":"Oliver Wagner,&nbsp;Lena Tholen","doi":"10.1016/j.prime.2025.101037","DOIUrl":"10.1016/j.prime.2025.101037","url":null,"abstract":"<div><div>Achieving national and international climate protection targets is a major challenge for many stakeholders. An often overlooked sector in this context is the healthcare sector. This is particularly significant because hospitals play a crucial role in sustainability. On the one hand, climate change poses the greatest global health threat of the 21^st century. Hospitals will inevitably face increasing challenges due to climate change, such as the emergence of new pathogens or if extreme heat exacerbates preexisting cardiovascular conditions, leading to more health complications. On the other hand, hospitals are energy-intensive and significantly contribute to climate change. In Germany, the healthcare sector accounts for 5.2 percent of CO₂ emissions, with hospitals being a major source. The need for energy-efficient modernization in hospitals is urgent, especially since they are part of our critical infrastructure. Ensuring their energy supply, even in crises, is vital for a resilient and independent energy system. Given the importance of climate protection in the healthcare sector, this article analyses which strategies hospitals should pursue to achieve climate protection targets and which measures should be prioritized. In addition, the analysis includes an estimation of the associated costs, providing a basis for understanding the financial implications of climate mitigation efforts in hospital settings. Through stakeholder workshops and a narrative literature review, we have identified 10 suitable climate protection measures for the hospital sector. Our initial cost analysis indicates that implementing these measures would require an investment of approximately 7.1 billion euros for the 315 hospitals in North Rhine-Westphalia.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"13 ","pages":"Article 101037"},"PeriodicalIF":0.0,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144253681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Junction temperature prediction Model Development with Co-simulation","authors":"Mustafa Ozden ,&nbsp;Gokhan Ozkan ,&nbsp;S M Imrat Rahman ,&nbsp;Elutunji Buraimoh ,&nbsp;Laxman Timilsina ,&nbsp;Behnaz Papari ,&nbsp;Christopher S. Edrington","doi":"10.1016/j.prime.2025.101033","DOIUrl":"10.1016/j.prime.2025.101033","url":null,"abstract":"<div><div>This study examines the thermal behavior and junction temperature of MOSFET modules under varying operating conditions using ANSYS/Fluent software, with simulations managed through Python/Jupyter Notebook. Two different approaches are evaluated: the Temperature-Responsive Power Loss Calculation (TRPLC) and the Temperature-Agnostic Power Loss Calculation (TAPLC). In the TRPLC approach, power loss is calculated as a func- tion of the junction temperature, which is updated at each time step. In contrast, the TAPLC approach relies on four predefined power loss curves derived from the MOSFET datasheet, with each curve simulated separately. Unlike TRPLC, this method does not account for the relationship between junction temperature and power loss, resulting in significantly high junction temperature values at higher power loss levels. By dynamically recalculat- ing power loss at every step, the TRPLC approach provides more realistic results compared to TAPLC. These findings underscore the importance of incorporating temperature-dependent calculations to enhance the accuracy of thermal performance predictions under practical operational scenarios.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"12 ","pages":"Article 101033"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144212680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advanced signal processing algorithm for fault classification and localization in VSC-HVDC based Offshore wind farm","authors":"Rehana Perveen","doi":"10.1016/j.prime.2025.101031","DOIUrl":"10.1016/j.prime.2025.101031","url":null,"abstract":"<div><div>This work provides real-time validation on the RTDS platform by the use of S-transform, Ensemble Empirical Mode Decomposition (EEMD), and SVM, for quick and reliable detection of AC/DC faults. Next for classification, features extracted through intrinsic mode function decomposed by EMD and EEMD and classified distinctly using support vector machine techniques. The simulation results reveal that S-transform and IMF1-H in association with MPNN and LSSVM can effectively detect and classify AC/DC faults even under raw signal conditions. This paper also presents the fault localization in the high-voltage direct current cable line connected to OWF by traveling wave and EEMD. The detection and classification are carried out on an offshore wind farm (OWF) system integrated to an onshore grid through a voltage source converter-high voltage direct current (VSC-HVDC) in MATLAB, as well as in RTDS(real time digital simulation).</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"12 ","pages":"Article 101031"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144222364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling and optimization of HS-IMPATT diode execution enriched with Si/SiC using ANN","authors":"Mamata Rani Swain ,&nbsp;Pravash Ranjan Tripathy","doi":"10.1016/j.prime.2025.101017","DOIUrl":"10.1016/j.prime.2025.101017","url":null,"abstract":"<div><div>This study offers a precise, expandable, and effective ANN (Artificial Neural Network) model to evaluate and calculate the important device parameters like breakdown voltage, efficiency, negative conductance, negative resistance, susceptance, and RF power of a heterostructure Si/3C-SiC-based IMPATT diode at an operating frequency of 94 GHz. The authors have compared the simulation and optimization of a Si/SiC-based heterostructure IMPATT diode with the neural network techniques for CW operation. The experimental data are almost 85 % to 90 % the same as computer simulation outcomes and provide numerically agreed results regarding breakdown voltage, efficiency, negative conductance, and power. Owing to several factors such as temperature, parasitic impacts, and appropriate hit sink arrangements, there is a 10 %–15 % discrepancy between the theoretical simulation result and the experimental output. This newly developed ANN technique, developed by the authors for the first time, was found to be in close agreement with the experimental findings available at 94.0 GHz. The simulation result gives the breakdown voltage of the IMPATT device as 188 V as compared with the experimental results of 185 V. Similarly, the neural network model shows approximately 183 V. The RF power of the simulated device is 2.5 W as compared to the experimental result of 2.0 W at 94 GHz, whereas the neural network model gives 2.2 W, which shows the validity of the model. The assessed outcomes clearly demonstrate the effectiveness of the device parameter estimations and optimizing IMPATT device design efficiently, and the findings will benefit missile and radar technology.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"12 ","pages":"Article 101017"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144203097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Short-term and long-term inertia forecasting with low-inertia event prediction in IBR-integrated power systems using a deep learning approach","authors":"Santosh Diggikar,&nbsp;Arunkumar Patil,&nbsp;Katkar Siddhant Satyapal,&nbsp;Kunal Samad","doi":"10.1016/j.prime.2025.101021","DOIUrl":"10.1016/j.prime.2025.101021","url":null,"abstract":"<div><div>The integration of renewable energy sources (RES), particularly inverter-based resources (IBRs) such as solar and wind power, has significantly reduced dependence on conventional synchronous generators, thereby decreasing system-wide spinning inertia. This reduction results in rapid changes in the rate of change of frequency (RoCoF), heightening the risk of grid instability. Accurate inertia forecasting is essential for ensuring grid stability, particularly in systems such as the Great Britain (GB) power system, where inertia levels occasionally fall below critical thresholds. However, most traditional and online estimation techniques provide reactive inertia assessments, limiting their effectiveness for proactive grid management. Moreover, existing machine learning (ML)-based models primarily focus on either short-term or long-term forecasting and are often trained on limited datasets, which undermines their robustness and generalisation capabilities. Critically, these models do not prioritise the detection of low-inertia events, which are key moments requiring swift action from grid operators to maintain system stability. To address these limitations, this study proposes a novel hybrid deep learning neural network (DLNN) model that integrates bidirectional long short-term memory (Bi-LSTM) and bidirectional gated recurrent unit (Bi-GRU) architectures to effectively learn complex temporal dependencies in power system dynamics. The model is benchmarked against baseline architectures, including Bi-LSTM, Bi-GRU, and convolutional neural networks (CNNs). The proposed hybrid model achieves superior predictive performance, with a mean absolute percentage error (MAPE) of 2.74%, mean absolute error (MAE) of 4.55 GVAs, root mean square error (RMSE) of 6.65 GVAs, mean squared error (MSE) of 44.22 GVAs<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>, and combined accuracy (CA) of 3.70 GVAs. It consistently outperforms the baseline models across seasonal scenarios, achieving MAPE values of 2.09% for Spring, 2.23% for Summer, 2.62% for Autumn, and 2.53% for Winter. For short-term forecasts, the model achieves MAPE values of 1.01% for 12 h and 1.21% for 24 h horizons. In the task of low-inertia event detection, the model demonstrates high precision (0.9538), recall (0.9687), and F1-score (0.9612), highlighting its practical utility in enhancing grid operator decision-making, maintaining frequency stability, and optimising power system operation.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"12 ","pages":"Article 101021"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144222365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Shunt active power filter using model predictive control with stability guarantee","authors":"Jhon Pérez-Ramírez ,&nbsp;Diego Montoya-Acevedo ,&nbsp;Walter Gil-González ,&nbsp;Oscar Danilo Montoya ,&nbsp;Carlos Restrepo","doi":"10.1016/j.prime.2025.101029","DOIUrl":"10.1016/j.prime.2025.101029","url":null,"abstract":"<div><div>This paper presents a model predictive control (MPC) strategy to solve an optimization problem based on system state and control variables, subject to constraints imposed by the control objective. The cost function exhibits convex characteristics, ensuring a unique control law. Additionally, the proposed control technique includes a formal analysis that ensures stability in the sense of Lyapunov. The control strategy is applied to a shunt active power filter (SAPF) connected to a voltage source and a nonlinear load, aiming to suppress the harmonics demanded by the load while ensuring that the source current remains sinusoidal and balanced, with a total harmonic distortion (THD) within regulatory standards. Simulation and hardware-in-the-loop (HIL) tests are conducted to evaluate the proposed approach. In both tests, two cases are considered: the first assumes balanced voltage conditions, while the second includes unbalanced voltage conditions and harmonic distortion. The simulation test, carried out in MATLAB/SIMULINK, demonstrates that our MPC reduces the THD from 26.71% to 1.59% in Case 1 and to 1.95% in Case 2. The HIL test, implemented using two RT boxes, shows a THD reduction from 24.57% to 2.77% in Case 1 and to 2.96% in Case 2. These results highlight the effectiveness of the proposed strategy, achieving values below regulatory standards for distribution networks. Furthermore, the MPC demonstrates superior performance in comparison with a passivity-based control strategy that employs interconnection and damping assignment (IDA-PBC), further emphasizing its efficiency and practicality.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"12 ","pages":"Article 101029"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144230485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ultra-fast all-optical memory based on hexagonal photonic crystal lattice on a GaAs substrate","authors":"Dariush Jafari,&nbsp;Mohammad Danaie","doi":"10.1016/j.prime.2025.101034","DOIUrl":"10.1016/j.prime.2025.101034","url":null,"abstract":"<div><div>In this paper, a novel photonic crystal (PhC) all-optical memory is proposed. It is based on a hexagonal lattice of air holes on a GaAs substrate. The structure is numerically simulated using the finite difference time domain (FDTD) method. This structure utilizes the nonlinear Kerr effect properties of a photonic crystal cavity to achieve high-speed performance with a rise time of &lt;0.21 ps. The high Q factor of the hexagonal cavity, combined with the use of Kerr material, results in reduced power consumption for both the data and bias signals, reaching below 3 μW/μm. Additionally, the reduction in the dimensions of the structure to approximately 30 μm² is significant and noteworthy compared to recent works. Our innovative design highlights significant advancements in speed and power efficiency for the designed all-optical memory, suggesting promising applications in photonic integrated circuits.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"12 ","pages":"Article 101034"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimized Cascode LNA design for low noise and high gain at 5 GHz","authors":"Md. Hasan Maruf,&nbsp;Arif Hossain,&nbsp;Abdullah Al Mahfuz,&nbsp;Md. Mohi Uddin Mohin,&nbsp;Md. Sabbir Alam,&nbsp;Md. Shakib Ibne Ashrafi,&nbsp;M M Naushad Ali,&nbsp;Md Ashraful Islam","doi":"10.1016/j.prime.2025.101030","DOIUrl":"10.1016/j.prime.2025.101030","url":null,"abstract":"<div><div>The receiver plays a critical role in wireless communication systems, especially for low-power signals known for their durability and speed. As a receiver's front-end component, a Low Noise Amplifier (LNA) amplifies signals to increase power levels while maintaining the Signal-to-Noise Ratio (SNR). With the increasing demand for high-performance and energy-efficient wireless networks, the design of LNA architectures has become paramount. However, during amplification, the signal encounters challenges such as the ‘Miller effect,’ which reduces frequency and bandwidth, and residual noise at the output. In this work, LNA designs for CMOS-based wireless communication systems are thoroughly analyzed, emphasizing resolving the issues of attaining low noise figure and high power gain. A Cascode LNA circuit is suggested, which provides better performance in terms of noise figure and power gain than previous designs. The proposed LNA, implemented and analyzed using 130 nm CMOS technology in Advanced Design System (ADS) software, operates at a 5 GHz frequency with a 1 V supply voltage. The design achieves an input reflection coefficient (s11) of less than -10 dB, a power gain of 15.088 dB, and a noise figure of 0.541 dB, demonstrating its effectiveness for high-performance wireless communication applications.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"12 ","pages":"Article 101030"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144203230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}