Pub Date : 2025-07-07DOI: 10.1016/j.prime.2025.101067
Luka Herc , Luka Perković , Tomislav Pukšec , Neven Duić
This research presents a novel method for the statistical evaluation of the synthetic driving cycles for small-to-medium vehicles, based on the real driving cycles recorded with a GPS tracker with a resolution of five seconds. The recorded data is processed so it can be used as input for energy planning, namely the estimation of battery electric vehicles' energy demand and charging strategies in the dump, smart and V2G regimes. Initial statistical analysis shows that hourly distribution among various vehicles is best represented with gamma distribution. However, due to the lower amount of data recorded from the GPS, synthetic driving cycles match the data measurement with a correlation of 0,5 and 0,8 for workdays and weekends, respectively. This drawback can be avoided with more data being recorded during the research on the topic and consequent re-tuning of the distribution parameters. Also, the variations in the process are presented with the use of different combinations of statistical distributions and machine learning.
{"title":"Modelling decarbonisation of the transport sector with method for assessing vehicle driving cycles based on real GPS data","authors":"Luka Herc , Luka Perković , Tomislav Pukšec , Neven Duić","doi":"10.1016/j.prime.2025.101067","DOIUrl":"10.1016/j.prime.2025.101067","url":null,"abstract":"<div><div>This research presents a novel method for the statistical evaluation of the synthetic driving cycles for small-to-medium vehicles, based on the real driving cycles recorded with a GPS tracker with a resolution of five seconds. The recorded data is processed so it can be used as input for energy planning, namely the estimation of battery electric vehicles' energy demand and charging strategies in the dump, smart and V2G regimes. Initial statistical analysis shows that hourly distribution among various vehicles is best represented with gamma distribution. However, due to the lower amount of data recorded from the GPS, synthetic driving cycles match the data measurement with a correlation of 0,5 and 0,8 for workdays and weekends, respectively. This drawback can be avoided with more data being recorded during the research on the topic and consequent re-tuning of the distribution parameters. Also, the variations in the process are presented with the use of different combinations of statistical distributions and machine learning.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"13 ","pages":"Article 101067"},"PeriodicalIF":0.0,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144605022","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}
Pub Date : 2025-07-07DOI: 10.1016/j.prime.2025.101061
Fredelino A. Galleto Jr. , Aaron Don M. Africa
Microstrip antennas emerged as a prevalent class of patch antennas due to their low profile, conformable structures, and ease of integration. However, limitations in their form factor and operating bandwidth present challenges in achieving optimal performance, which is contingent upon the proper design of antenna properties. Working against this gap, this article presents a machine learning-based pseudocode algorithm to optimize orthogonal microstrip antennas. Seeking to improve antenna S11 gain and radiation efficiency as a contribution to innovative communication systems. Results showed that after fuzzifying antenna gain and parameters using MATLAB, OriginPro, and ROSE software, the method of approximation, which creates rough classifications and handles the scale and complexity of the datasets, produced 16 classes with an entropy of 95.76 % and a -8.69874 dB S11 gain magnitude at a 3.378 GHz frequency. A high entropy percentage indicates a high-quality result, as it simplifies complex calculations from machine-driven outcomes. A significant reduction in rules of 97.68 % was also achieved, wherein the large dataset of antenna parameters’ fuzzy values was condensed into a more concise set of rules, highlighting the significance of the processing technique. The empirical testing of the developed rules yielded a 100 % validity rate, denoting the accuracy of rules in data classification. The pseudocode algorithm is structured into sections to enhance clarity, offering a detailed framework for optimizing MSA design and providing valuable contributions to the design of next-generation wireless systems.
{"title":"Optimization of microstrip antenna S11 gain using fuzzy rough set-based pseudocode algorithm","authors":"Fredelino A. Galleto Jr. , Aaron Don M. Africa","doi":"10.1016/j.prime.2025.101061","DOIUrl":"10.1016/j.prime.2025.101061","url":null,"abstract":"<div><div>Microstrip antennas emerged as a prevalent class of patch antennas due to their low profile, conformable structures, and ease of integration. However, limitations in their form factor and operating bandwidth present challenges in achieving optimal performance, which is contingent upon the proper design of antenna properties. Working against this gap, this article presents a machine learning-based pseudocode algorithm to optimize orthogonal microstrip antennas. Seeking to improve antenna S11 gain and radiation efficiency as a contribution to innovative communication systems. Results showed that after fuzzifying antenna gain and parameters using MATLAB, OriginPro, and ROSE software, the method of approximation, which creates rough classifications and handles the scale and complexity of the datasets, produced 16 classes with an entropy of 95.76 % and a -8.69874 dB S11 gain magnitude at a 3.378 GHz frequency. A high entropy percentage indicates a high-quality result, as it simplifies complex calculations from machine-driven outcomes. A significant reduction in rules of 97.68 % was also achieved, wherein the large dataset of antenna parameters’ fuzzy values was condensed into a more concise set of rules, highlighting the significance of the processing technique. The empirical testing of the developed rules yielded a 100 % validity rate, denoting the accuracy of rules in data classification. The pseudocode algorithm is structured into sections to enhance clarity, offering a detailed framework for optimizing MSA design and providing valuable contributions to the design of next-generation wireless systems.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"13 ","pages":"Article 101061"},"PeriodicalIF":0.0,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144595718","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}
Pub Date : 2025-07-06DOI: 10.1016/j.prime.2025.101060
Anwar Jarndal , Lutfi Albasha , Hasan Mir
Thermal effects present a major challenge for all semiconductor devices, especially high-power transistors such as GaN High Electron Mobility Transistors (HEMTs). The combined internal and external temperatures significantly impact the device's small- and large-signal characteristics, leading to performance degradation. In this paper, a 10 × 200-µm GaN-on-Si substrate depletion-mode HEMT was characterized using DC (Direct Current) and pulsed IV measurement setups at different ambient temperatures and quiescent voltages. These measurements were used to investigate the influence of temperature on the drain current and to develop an electrothermal model for the device. The results show that the drain current is highly sensitive to temperature, exhibiting a significant reduction at higher temperatures, which in turn affects large-signal output power, gain, and power-added efficiency. Additionally, temperature has a stronger impact on parasitic resistances, indirectly affecting the DC and RF (Radio Frequency) characteristics of the device. This investigation highlights the critical role of thermal effects and underscores the need for effective thermal management strategies.
{"title":"On the temperature-dependence characterization and modeling of GaN HEMTs","authors":"Anwar Jarndal , Lutfi Albasha , Hasan Mir","doi":"10.1016/j.prime.2025.101060","DOIUrl":"10.1016/j.prime.2025.101060","url":null,"abstract":"<div><div>Thermal effects present a major challenge for all semiconductor devices, especially high-power transistors such as GaN High Electron Mobility Transistors (HEMTs). The combined internal and external temperatures significantly impact the device's small- and large-signal characteristics, leading to performance degradation. In this paper, a 10 × 200-µm GaN-on-Si substrate depletion-mode HEMT was characterized using DC (Direct Current) and pulsed IV measurement setups at different ambient temperatures and quiescent voltages. These measurements were used to investigate the influence of temperature on the drain current and to develop an electrothermal model for the device. The results show that the drain current is highly sensitive to temperature, exhibiting a significant reduction at higher temperatures, which in turn affects large-signal output power, gain, and power-added efficiency. Additionally, temperature has a stronger impact on parasitic resistances, indirectly affecting the DC and RF (Radio Frequency) characteristics of the device. This investigation highlights the critical role of thermal effects and underscores the need for effective thermal management strategies.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"13 ","pages":"Article 101060"},"PeriodicalIF":0.0,"publicationDate":"2025-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144596381","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}
Pub Date : 2025-07-05DOI: 10.1016/j.prime.2025.101059
Arnab Talukder , Mohiminur Rahman Ifty , Abdullah Al Fahad
The emerging need for high-frequency, high-power electronics and biosensors necessitates the demand for high electron mobility transistors (HEMTs) that outperform the mainstream silicon and other direct bandgap materials. Gallium Nitride (GaN)-based HEMTs can operate in both depletion-mode (D-mode) and enhancement-mode (E-mode), and have garnered significant attention for their superior performance in these applications. These wide band-gap semiconductors exhibit significant outcomes in DC as well as RF applications, such as a higher threshold voltage of 8.6 V, transconductance of 680 S/mm with OIP3 (output third-order intercept point) of 41.2 dB, cut-off frequency (fT) of 391 GHz compared to the conventional devices. There are also found some meticulous parameters e.g. breakdown voltage (Vbr) of 1513 V, drain saturation current of 3.41 kA/cm2 with an equivalent noise resistance (Rn) of 1.21 dB and 20 Ω at 20 GHz, a low on-resistance (RON) of 0.00269 Ω-mm, at gate length (LG) of 100 nm in a GaN HEMT by using quaternary InAlGaN barrier is achieved maximum drain current (IDS, max) of 1940 mA/mm while another HEMT with Carbon doped GaN buffer as well as AlGaN back barrier gets Vbr around 2900 V. The RF metrics, like a fT of 200 GHz with moderate LG of 80 nm for AlGaN/GaN HEMT with Si substrate of plasma molecular beam epitaxy, a maximum oscillation frequency (fmax) of 308 GHz, show great impact on High-frequency and microwave applications. Nevertheless, the E-mode outperforms the D-mode HEMTs for secured operations with low leakage loss; there are still some challenges, such as current collapse, short-channel effects, and pinch-off phenomena that persist, impacting device reliability. This review article examines recent advancements in GaN HEMT architectures, emerging materials, and their applications in power and radio-frequency devices, as well as explores future applications in biosensing, satellite, and optical communications.
对高频、高功率电子和生物传感器的新需求要求对高电子迁移率晶体管（hemt）的需求，这种晶体管的性能优于主流硅和其他直接带隙材料。氮化镓（GaN）基hemt可以在耗尽模式（d模式）和增强模式（e模式）下工作，并因其在这些应用中的优异性能而受到广泛关注。与传统器件相比，这些宽带隙半导体在直流和射频应用中表现出显著的成果，例如更高的阈值电压8.6 V，跨导680 S/mm， OIP3（输出三阶截距点）为41.2 dB，截止频率（fT）为391 GHz。还发现有一些细致的参数如击穿电压1513 V (Vbr),排水饱和电流的3.41 kA / cm2等效噪声电阻(Rn)为1.21 dB和20Ω20 GHz,低导通电阻(罗恩)0.00269Ωmm,在门的长度(LG)的100 nm GaN HEMT利用第四纪InAlGaN障碍达到最大漏电流(id、max) 1940 mA / mm而另一个HEMT与碳掺杂氮化镓缓冲区以及沃甘障碍得到Vbr约2900 V。等离子体分子束外延的Si衬底AlGaN/GaN HEMT的射频指标，如fT为200 GHz， LG为80 nm，最大振荡频率（fmax）为308 GHz，对高频和微波应用有很大影响。尽管如此，在安全操作方面，E-mode的性能优于D-mode hemt，且泄漏损耗低；目前仍存在一些挑战，如电流崩溃、短通道效应和持续存在的掐断现象，影响设备的可靠性。本文综述了GaN HEMT架构、新兴材料及其在功率和射频器件中的应用的最新进展，并探讨了其在生物传感、卫星和光通信方面的未来应用。
{"title":"Comprehensive review of GaN HEMTs: Architectures, recent developments, reliability concerns, challenges, and multifaceted applications","authors":"Arnab Talukder , Mohiminur Rahman Ifty , Abdullah Al Fahad","doi":"10.1016/j.prime.2025.101059","DOIUrl":"10.1016/j.prime.2025.101059","url":null,"abstract":"<div><div>The emerging need for high-frequency, high-power electronics and biosensors necessitates the demand for high electron mobility transistors (HEMTs) that outperform the mainstream silicon and other direct bandgap materials. Gallium Nitride (GaN)-based HEMTs can operate in both depletion-mode (D-mode) and enhancement-mode (E-mode), and have garnered significant attention for their superior performance in these applications. These wide band-gap semiconductors exhibit significant outcomes in DC as well as RF applications, such as a higher threshold voltage of 8.6 V, transconductance of 680 S/mm with OIP3 (output third-order intercept point) of 41.2 dB, cut-off frequency (f<sub>T</sub>) of 391 GHz compared to the conventional devices. There are also found some meticulous parameters e.g. breakdown voltage (V<sub>br</sub>) of 1513 V, drain saturation current of 3.41 kA/cm<sup>2</sup> with an equivalent noise resistance (R<sub>n</sub>) of 1.21 dB and 20 Ω at 20 GHz, a low on-resistance (R<sub>ON</sub>) of 0.00269 Ω-mm, at gate length (L<sub>G</sub>) of 100 nm in a GaN HEMT by using quaternary InAlGaN barrier is achieved maximum drain current (I<sub>DS, max</sub>) of 1940 mA/mm while another HEMT with Carbon doped GaN buffer as well as AlGaN back barrier gets V<sub>br</sub> around 2900 V. The RF metrics, like a f<sub>T</sub> of 200 GHz with moderate L<sub>G</sub> of 80 nm for AlGaN/GaN HEMT with Si substrate of plasma molecular beam epitaxy, a maximum oscillation frequency (f<sub>max</sub>) of 308 GHz, show great impact on High-frequency and microwave applications. Nevertheless, the E-mode outperforms the D-mode HEMTs for secured operations with low leakage loss; there are still some challenges, such as current collapse, short-channel effects, and pinch-off phenomena that persist, impacting device reliability. This review article examines recent advancements in GaN HEMT architectures, emerging materials, and their applications in power and radio-frequency devices, as well as explores future applications in biosensing, satellite, and optical communications.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"13 ","pages":"Article 101059"},"PeriodicalIF":0.0,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144579288","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}
The integration of Terahertz (THz) technology into 6 G networks represents a significant advancement in wireless communication, particularly within the Internet of Things (IoT) sector. Terahertz’s frequencies offer wider bandwidths and faster data transmission, crucial for applications such as high-definition video streaming, IoT security systems, and healthcare devices. This work introduces a high-performance THz microstrip patch antenna engineered for IoT and 6 G applications, utilizing Graphene-based patches and polyimide substrates. We demonstrate the antenna's performance through machine learning (ML)–enhanced design optimization, achieving a gain of 14.3 dB, an efficiency of 97.7 %, and over 31 dB of isolation across an extensive bandwidth (1 THz to 5.4 THz). To validate the regression machine learning model for THz MIMO antenna design, a comprehensive dataset was generated using full-wave electromagnetic simulations. This dataset comprises six features based on the geometric and material parameters of the antenna. The implementation of various machine-learning techniques, including Extreme Gradient Boosting (XGB) regression, yielded outstanding outcomes. XGB achieved an R-squared value and variance scores of 98 %, demonstrating exceptional accuracy. It also showed minimal error rates in efficiency prediction, with a reassuringly low Mean Absolute Error (MAE) of 1.62 %, a Mean Squared Error (MSE) of 0.37 %, and a Root Mean Squared Error (RMSE) of 2.78 %. The antenna design is rigorously tested using CST and ADS simulation tools, confirming its superior performance compared to existing systems. The study explores multi-objective optimization, covering efficiency, bandwidth, and compactness, which are crucial for future wireless communication systems. This study highlights the potential of integrating THz technology with machine learning to enhance antenna design, presenting a novel framework for the evolution of future wireless networks with improved performance and energy efficiency.
{"title":"A high-gain THz microstrip patch antenna designed for IoT and 6G communications with predicted efficiency using machine learning approaches","authors":"Md Sharif Ahammed , Redwan A. Ananta , Jun-Jiat Tiang , Mouaaz Nahas , Narinderjit Singh Sawaran Singh , Md. Ashraful Haque","doi":"10.1016/j.prime.2025.101058","DOIUrl":"10.1016/j.prime.2025.101058","url":null,"abstract":"<div><div>The integration of Terahertz (THz) technology into 6 G networks represents a significant advancement in wireless communication, particularly within the Internet of Things (IoT) sector. Terahertz’s frequencies offer wider bandwidths and faster data transmission, crucial for applications such as high-definition video streaming, IoT security systems, and healthcare devices. This work introduces a high-performance THz microstrip patch antenna engineered for IoT and 6 G applications, utilizing Graphene-based patches and polyimide substrates. We demonstrate the antenna's performance through machine learning (ML)–enhanced design optimization, achieving a gain of 14.3 dB, an efficiency of 97.7 %, and over 31 dB of isolation across an extensive bandwidth (1 THz to 5.4 THz). To validate the regression machine learning model for THz MIMO antenna design, a comprehensive dataset was generated using full-wave electromagnetic simulations. This dataset comprises six features based on the geometric and material parameters of the antenna. The implementation of various machine-learning techniques, including Extreme Gradient Boosting (XGB) regression, yielded outstanding outcomes. XGB achieved an R-squared value and variance scores of 98 %, demonstrating exceptional accuracy. It also showed minimal error rates in efficiency prediction, with a reassuringly low Mean Absolute Error (MAE) of 1.62 %, a Mean Squared Error (MSE) of 0.37 %, and a Root Mean Squared Error (RMSE) of 2.78 %. The antenna design is rigorously tested using CST and ADS simulation tools, confirming its superior performance compared to existing systems. The study explores multi-objective optimization, covering efficiency, bandwidth, and compactness, which are crucial for future wireless communication systems. This study highlights the potential of integrating THz technology with machine learning to enhance antenna design, presenting a novel framework for the evolution of future wireless networks with improved performance and energy efficiency.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"13 ","pages":"Article 101058"},"PeriodicalIF":0.0,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144595717","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}
Pub Date : 2025-06-25DOI: 10.1016/j.prime.2025.101044
Ahmed Reda Mohamed , Muneer A. Al-Absi
This paper presents the realization of a CMOS-grounded positive and negative capacitance multiplier (CM) with an extremely high multiplication factor. The proposed CM is primarily constructed by cascading configurable modified second-generation current conveyors (M-CCII) that offer flexible configuration during CM integration. The functionality of the proposed design is validated using Cadence with the 180 nm TSMC CMOS process technology. The design is powered by a 1.8 V supply voltage and consumes 250 W of power. Simulation results indicate that the multiplication factor () is 50,625 with a maximum relative error of 5% and the proposed CM occupies a silicon area of 0.026 mm. Furthermore, the influence of non-ideal factors is analyzed to assess the parasitic effects on performance. The pre- and post-layout simulation results are closely matched and consistent. Moreover, statistical analyses using Monte Carlo (MC) and process-voltage-temperature (PVT) variations are conducted to verify reliable performance in the manufacturing process going forward. Furthermore, as evidenced by the comparative table and overall performance, the figures of merit (FOMs) indicate that this work outperforms previous designs. A low-pass filter with a corner frequency of 6.4 Hz, designed using the proposed CM, is implemented to suppress power line interference during the acquisition of the photoplethysmography (PPG) signal. In the end, to verify the reconfigurability and reusability of the proposed design, commercial ICs such as the LMC6482, ALD11007, and ALD11006 are employed in experimental setups.
{"title":"Realization of a resistor-less CMOS super capacitor-multiplier using modified-current conveyors","authors":"Ahmed Reda Mohamed , Muneer A. Al-Absi","doi":"10.1016/j.prime.2025.101044","DOIUrl":"10.1016/j.prime.2025.101044","url":null,"abstract":"<div><div>This paper presents the realization of a CMOS-grounded positive and negative capacitance multiplier (CM) with an extremely high multiplication factor. The proposed CM is primarily constructed by cascading configurable modified second-generation current conveyors (M-CCII) that offer flexible configuration during CM integration. The functionality of the proposed design is validated using Cadence with the 180 nm TSMC CMOS process technology. The design is powered by a 1.8 V supply voltage and consumes 250 <span><math><mi>μ</mi></math></span>W of power. Simulation results indicate that the multiplication factor (<span><math><mi>K</mi></math></span>) is 50,625 with a maximum relative error of 5% and the proposed CM occupies a silicon area of 0.026 mm<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>. Furthermore, the influence of non-ideal factors is analyzed to assess the parasitic effects on performance. The pre- and post-layout simulation results are closely matched and consistent. Moreover, statistical analyses using Monte Carlo (MC) and process-voltage-temperature (PVT) variations are conducted to verify reliable performance in the manufacturing process going forward. Furthermore, as evidenced by the comparative table and overall performance, the figures of merit (FOMs) indicate that this work outperforms previous designs. A low-pass filter with a corner frequency of 6.4 Hz, designed using the proposed CM, is implemented to suppress power line interference during the acquisition of the photoplethysmography (PPG) signal. In the end, to verify the reconfigurability and reusability of the proposed design, commercial ICs such as the LMC6482, ALD11007, and ALD11006 are employed in experimental setups.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"13 ","pages":"Article 101044"},"PeriodicalIF":0.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144480430","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}
Distributed Generation has become an integral part of the microgrid system predominantly powered by solar PV systems. Electric Vehicles, renewable energy sources, and household appliances are just a few examples of the increasing number of DC loads that are driving the growing significance of Low-Voltage DC distribution networks. Higher power transfer capacity than AC, lower energy conversion losses, and increased efficiency and dependability are some benefits of low voltage DC systems. It has become very essential that faults that occur in such systems must be detected and the type of fault must be identified accurately so that the system’s reliability can be further increased. The literature provides many methodologies for identifying and classifying the faults in AC transmission systems and also in LVDC distribution systems. In off grid LVDC distribution systems, approaches such as deep learning based identification and classification of faults is presented in literature, which majorly concentrates on small electrification and poor internet coverage area of Sub-Saharan Africa. A methodology based on power electronic converter is also presented in literature for fault diagnosis, this methodology includes signal injection, which may lead to line interferences. To overcome these challenges, this paper proposes a new methodology for identifying and classifying the faults in renewable based LVDC distribution systems using machine learning algorithms such as k-Nearest Neighbour (kNN) and Decision Tree (DT). Literature presents a maximum of 99 % of accuracy in identifying and classifying the faults whereas, the proposed methodology achieves 100 % accuracy in identifying and classifying the faults in LVDC distribution system with 100 % precision.
{"title":"Fault detection and classification in a DG powered LVDC distribution system using machine learning algorithm","authors":"Ankush Kumar M․, Shubham T․M․, Farha Naz, Rajkumar Jhapte, Vishal Moyal","doi":"10.1016/j.prime.2025.101055","DOIUrl":"10.1016/j.prime.2025.101055","url":null,"abstract":"<div><div>Distributed Generation has become an integral part of the microgrid system predominantly powered by solar PV systems. Electric Vehicles, renewable energy sources, and household appliances are just a few examples of the increasing number of DC loads that are driving the growing significance of Low-Voltage DC distribution networks. Higher power transfer capacity than AC, lower energy conversion losses, and increased efficiency and dependability are some benefits of low voltage DC systems. It has become very essential that faults that occur in such systems must be detected and the type of fault must be identified accurately so that the system’s reliability can be further increased. The literature provides many methodologies for identifying and classifying the faults in AC transmission systems and also in LVDC distribution systems. In off grid LVDC distribution systems, approaches such as deep learning based identification and classification of faults is presented in literature, which majorly concentrates on small electrification and poor internet coverage area of Sub-Saharan Africa. A methodology based on power electronic converter is also presented in literature for fault diagnosis, this methodology includes signal injection, which may lead to line interferences. To overcome these challenges, this paper proposes a new methodology for identifying and classifying the faults in renewable based LVDC distribution systems using machine learning algorithms such as k-Nearest Neighbour (kNN) and Decision Tree (DT). Literature presents a maximum of 99 % of accuracy in identifying and classifying the faults whereas, the proposed methodology achieves 100 % accuracy in identifying and classifying the faults in LVDC distribution system with 100 % precision.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"13 ","pages":"Article 101055"},"PeriodicalIF":0.0,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144502006","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}
Pub Date : 2025-06-23DOI: 10.1016/j.prime.2025.101053
Evangelos E. Pompodakis , Georgios I. Orfanoudakis
Voltage Source Converters (VSCs) operating as active rectifiers inherently lack current-limiting capabilities for faults occurring on the DC side due to the presence of freewheeling diodes in IGBT or MOSFET structures. This limitation leads to uncontrolled fault currents, flowing from the AC to the DC network, that can jeopardize the safety of power electronic components. Additionally, the challenge is compounded in DC networks, where DC circuit breakers must interrupt high fault currents, fed by the AC side, without the benefit of current zero crossings. To address these issues, this paper presents a novel topology that integrates a classical buck DC/DC converter into a VSC to regulate the currents of faults occurring in DC network, thereby improving the protection of the converter and aiding DC circuit breakers in interrupting the fault. The advantage of the proposed topology lies in that under normal operating conditions, the buck converter is totally bypassed, thus improving the efficiency of the topology. When a fault is detected within the DC microgrid, the buck converter is connected in series with the VSC to control the current flowing from the AC to DC network. Simulation results using MATLAB/Simulink validate the effectiveness of the proposed topology in completely controlling the current at the DC side of converter, thus demonstrating significant improvements in fault management, system reliability, and converter protection.
{"title":"Efficient integration of buck converter into an active rectifier for DC-fault current limitation in DC networks","authors":"Evangelos E. Pompodakis , Georgios I. Orfanoudakis","doi":"10.1016/j.prime.2025.101053","DOIUrl":"10.1016/j.prime.2025.101053","url":null,"abstract":"<div><div>Voltage Source Converters (VSCs) operating as active rectifiers inherently lack current-limiting capabilities for faults occurring on the DC side due to the presence of freewheeling diodes in IGBT or MOSFET structures. This limitation leads to uncontrolled fault currents, flowing from the AC to the DC network, that can jeopardize the safety of power electronic components. Additionally, the challenge is compounded in DC networks, where DC circuit breakers must interrupt high fault currents, fed by the AC side, without the benefit of current zero crossings. To address these issues, this paper presents a novel topology that integrates a classical buck DC/DC converter into a VSC to regulate the currents of faults occurring in DC network, thereby improving the protection of the converter and aiding DC circuit breakers in interrupting the fault. The advantage of the proposed topology lies in that under normal operating conditions, the buck converter is totally bypassed, thus improving the efficiency of the topology. When a fault is detected within the DC microgrid, the buck converter is connected in series with the VSC to control the current flowing from the AC to DC network. Simulation results using MATLAB/Simulink validate the effectiveness of the proposed topology in completely controlling the current at the DC side of converter, thus demonstrating significant improvements in fault management, system reliability, and converter protection.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"13 ","pages":"Article 101053"},"PeriodicalIF":0.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144491513","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}
Pub Date : 2025-06-23DOI: 10.1016/j.prime.2025.101052
Ali H. Ramadan
In this paper, the S-parameters of a balanced topology that employs two identical branch amplifiers are formulated and evaluated for a 2.5 GHz broadband amplifier. A prototype is fabricated and measured to validate the performance of the designed amplifier, where a good agreement between simulated and measured results is noticed as follows. and are less than -13 dB over 2.5 GHz ± 5 % and reveal a broadband operation for the balanced amplifier. is below -20 dB over the 1.5–3.5 GHz frequency span, whereas has a value around 12.4 dB at 2.5 GHz. The noise figure, output compression point, and output third-order intercept point of the designed amplifier are found to be , , and , respectively. Additionally, a distortion of 3rd order harmonics is attained. The versatile gain margin and gain flatness tuning attributes of a balanced amplifier topology are then looked into through a parametric study that accounts for tuning the values of CS and LL of the incorporated (LC) and (series-L) matching blocks in individual and dual manners. The tuning process reveals that the gain margin can span from 9.75 dB to 14.65 dB with various gain flatness intervals over 2.5 GHz ± 5 %, and therefore promotes the candidacy of balanced amplifiers for use in applications where gain margin and/or gain flatness adjustment over broad frequency bands is demanding.
{"title":"A 2.5 GHz broadband balanced amplifier with gain margin and gain flatness tuning capability","authors":"Ali H. Ramadan","doi":"10.1016/j.prime.2025.101052","DOIUrl":"10.1016/j.prime.2025.101052","url":null,"abstract":"<div><div>In this paper, the S-parameters of a balanced topology that employs two identical branch amplifiers are formulated and evaluated for a 2.5 GHz broadband amplifier. A prototype is fabricated and measured to validate the performance of the designed amplifier, where a good agreement between simulated and measured results is noticed as follows. <span><math><mrow><mrow><mo>|</mo></mrow><msub><mi>S</mi><mn>11</mn></msub><mrow><mo>|</mo></mrow></mrow></math></span> and <span><math><mrow><mrow><mo>|</mo></mrow><msub><mi>S</mi><mn>22</mn></msub><mrow><mo>|</mo></mrow></mrow></math></span> are less than -13 dB over 2.5 GHz ± 5 % and reveal a broadband operation for the balanced amplifier. <span><math><mrow><mrow><mo>|</mo></mrow><msub><mi>S</mi><mn>12</mn></msub><mrow><mo>|</mo></mrow></mrow></math></span> is below -20 dB over the 1.5–3.5 GHz frequency span, whereas <span><math><mrow><mrow><mo>|</mo></mrow><msub><mi>S</mi><mn>21</mn></msub><mrow><mo>|</mo></mrow></mrow></math></span> has a value around 12.4 dB at 2.5 GHz. The noise figure, output <span><math><mrow><mn>1</mn><mtext>dB</mtext></mrow></math></span> compression point, and output third-order intercept point of the designed amplifier are found to be <span><math><mrow><mtext>NF</mtext><mo>=</mo><mn>1.3</mn><mrow><mspace></mspace><mtext>dB</mtext></mrow></mrow></math></span>, <span><math><mrow><mi>O</mi><msub><mi>P</mi><mrow><mn>1</mn><mtext>dB</mtext></mrow></msub><mo>≈</mo><mn>1</mn><mspace></mspace><mtext>dBm</mtext></mrow></math></span>, and <span><math><mrow><mtext>OIP</mtext><mn>3</mn><mo>≈</mo><mn>17.5</mn><mspace></mspace><mtext>dBm</mtext></mrow></math></span>, respectively. Additionally, a <span><math><mrow><mn>60.6</mn><mspace></mspace><mtext>dB</mtext></mrow></math></span> distortion of 3rd order harmonics is attained. The versatile gain margin and gain flatness tuning attributes of a balanced amplifier topology are then looked into through a parametric study that accounts for tuning the values of C<sub>S</sub> and L<sub>L</sub> of the incorporated <span><math><msub><mi>G</mi><mi>S</mi></msub></math></span> (LC) and <span><math><msub><mi>G</mi><mi>L</mi></msub></math></span> (series-L) matching blocks in individual and dual manners. The tuning process reveals that the gain margin can span from 9.75 dB to 14.65 dB with various gain flatness intervals over 2.5 GHz ± 5 %, and therefore promotes the candidacy of balanced amplifiers for use in applications where gain margin and/or gain flatness adjustment over broad frequency bands is demanding.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"13 ","pages":"Article 101052"},"PeriodicalIF":0.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144491465","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}
Pub Date : 2025-06-23DOI: 10.1016/j.prime.2025.101054
Ahmed Khayat, Mohammed Kissaoui, Lhoussaine Bahatti, Abdelhadi Raihani, Khalid Errakkas, Youness Atifi
As distributed energy resources become increasingly integrated into power systems, accurate day-ahead load-forecasting is essential for effective microgrid (MG) management—enabling optimized energy generation, reduced reliance on the main grid. However, forecasting energy demand remains a significant challenge due to its inherent variability, nonlinear temporal patterns. Many existing models rely on external inputs such as temperature forecasts, which are often imprecise and introduce additional uncertainty. Moreover, energy consumption is influenced by delayed thermal responses in buildings, further complicating prediction accuracy. Traditional methods also struggle to capture sharp demand peaks with sufficient precision. To address these limitations, this study introduces a novel hybrid model based on Long Short-Term Memory (LSTM) networks optimized by the Grey Wolf Optimizer (GWO), referred to as LSTM-GWO. Unlike conventional approaches, the LSTM-GWO eliminates the need for exogenous variables by learning intrinsic seasonal patterns directly from historical consumption data. GWO is employed to automatically fine-tune key hyperparameters without manual intervention. The proposed model achieves a Mean Absolute Percentage Error (MAPE) of 8.69 %, with a peak prediction error of only 1.33 %, outperforming traditional baselines. Performance is further validated using Root Mean Square Error (RMSE) and the coefficient of determination (R²), confirming its ability to accurately capture complex temporal dependencies. In addition to its accuracy, the LSTM-GWO demonstrates high stability across multiple independent runs, ensuring consistent performance and reliability. By leveraging only historical load data, this approach reduces forecasting uncertainty, improves peak load anticipation, and provides a practical, efficient, and scalable solution for short-term load-forecasting in dynamic MG environment.
{"title":"Efficient day-ahead energy forecasting for microgrids using LSTM optimized by grey wolf algorithm","authors":"Ahmed Khayat, Mohammed Kissaoui, Lhoussaine Bahatti, Abdelhadi Raihani, Khalid Errakkas, Youness Atifi","doi":"10.1016/j.prime.2025.101054","DOIUrl":"10.1016/j.prime.2025.101054","url":null,"abstract":"<div><div>As distributed energy resources become increasingly integrated into power systems, accurate day-ahead load-forecasting is essential for effective microgrid (MG) management—enabling optimized energy generation, reduced reliance on the main grid. However, forecasting energy demand remains a significant challenge due to its inherent variability, nonlinear temporal patterns. Many existing models rely on external inputs such as temperature forecasts, which are often imprecise and introduce additional uncertainty. Moreover, energy consumption is influenced by delayed thermal responses in buildings, further complicating prediction accuracy. Traditional methods also struggle to capture sharp demand peaks with sufficient precision. To address these limitations, this study introduces a novel hybrid model based on Long Short-Term Memory (LSTM) networks optimized by the Grey Wolf Optimizer (GWO), referred to as LSTM-GWO. Unlike conventional approaches, the LSTM-GWO eliminates the need for exogenous variables by learning intrinsic seasonal patterns directly from historical consumption data. GWO is employed to automatically fine-tune key hyperparameters without manual intervention. The proposed model achieves a Mean Absolute Percentage Error (MAPE) of 8.69 %, with a peak prediction error of only 1.33 %, outperforming traditional baselines. Performance is further validated using Root Mean Square Error (RMSE) and the coefficient of determination (R²), confirming its ability to accurately capture complex temporal dependencies. In addition to its accuracy, the LSTM-GWO demonstrates high stability across multiple independent runs, ensuring consistent performance and reliability. By leveraging only historical load data, this approach reduces forecasting uncertainty, improves peak load anticipation, and provides a practical, efficient, and scalable solution for short-term load-forecasting in dynamic MG environment.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"13 ","pages":"Article 101054"},"PeriodicalIF":0.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144502005","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}
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