Pub Date : 2025-01-29DOI: 10.1016/j.prime.2025.100917
Bright K. Banzie , John K. Annan , Francis B. Effah
In this paper, we present a novel GaN-based discrete current mirror active gate driver (AGD) for closed-loop dv/dt control, designed specifically for megahertz (MHz) frequency power converters employing power devices with low reverse transfer capacitance () values. The proposed AGD circuit, implemented using four N-channel GaN FETs, addresses the limitations of existing dv/dt control methods by providing a high-bandwidth, high-gain solution without the complexity of integrated circuits or reliance on conventional complementary current mirror configuration. Experimental validation in a 10 MHz, 24 V buck converter demonstrates a significant reduction in the turn-on dv/dt of the low-side switch from -15 V/ns to -11 V/ns, achieved with a small 0.1 pF sensor capacitor. This reduction was realised while maintaining sub-nanosecond-level response time and ensuring effective dv/dt regulation during the turn-on switching transient. Simulation results, verified through PSpice models, confirm the AGD's ability to generate feedback currents several orders of magnitude higher using the small sensor capacitor, thereby reducing gate current and enhancing system stability. The circuit design also benefits from using GaN technology, enabling higher switching frequencies and improved power conversion efficiency. This work offers a promising solution for discrete dv/dt control in MHz frequency applications, providing a foundation for future advancements in GaN-based AGD systems.
{"title":"A new discrete GaN-based dv/dt control circuit for megahertz frequency power converters","authors":"Bright K. Banzie , John K. Annan , Francis B. Effah","doi":"10.1016/j.prime.2025.100917","DOIUrl":"10.1016/j.prime.2025.100917","url":null,"abstract":"<div><div>In this paper, we present a novel GaN-based discrete current mirror active gate driver (AGD) for closed-loop dv/dt control, designed specifically for megahertz (MHz) frequency power converters employing power devices with low reverse transfer capacitance (<span><math><msub><mi>C</mi><mrow><mi>R</mi><mi>S</mi><mi>S</mi></mrow></msub></math></span>) values. The proposed AGD circuit, implemented using four N-channel GaN FETs, addresses the limitations of existing dv/dt control methods by providing a high-bandwidth, high-gain solution without the complexity of integrated circuits or reliance on conventional complementary current mirror configuration. Experimental validation in a 10 MHz, 24 V buck converter demonstrates a significant reduction in the turn-on dv/dt of the low-side switch from -15 V/ns to -11 V/ns, achieved with a small 0.1 pF sensor capacitor. This reduction was realised while maintaining sub-nanosecond-level response time and ensuring effective dv/dt regulation during the turn-on switching transient. Simulation results, verified through PSpice models, confirm the AGD's ability to generate feedback currents several orders of magnitude higher using the small sensor capacitor, thereby reducing gate current and enhancing system stability. The circuit design also benefits from using GaN technology, enabling higher switching frequencies and improved power conversion efficiency. This work offers a promising solution for discrete dv/dt control in MHz frequency applications, providing a foundation for future advancements in GaN-based AGD systems.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"11 ","pages":"Article 100917"},"PeriodicalIF":0.0,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-29DOI: 10.1016/j.prime.2025.100910
Prashant Kumar, Sabha Raj Arya
The complexity of distribution power system becomes a menace for utility person and causing severe power quality issues. The polluted voltage power quality seriously affects the performance of critical equipment's and causing load disruptions. Dynamic voltage restorer (DVR) is recommended to address the voltage Power Quality (PQ) in distribution network using advance control scheme of Least Mean Square such as Exponential Function Least Mean Square control (EFLMS). It employs an exponential function for step size update to extract the fundamental quantity and this excels the speed of convergence, more adaptable in environments of noise and reduces the error coefficients associated with non-ideal grid. The proposed techniques derive the fundamental component using learning factor which is dependent on exponential error function and minimizes the error to estimate the reference load voltage. Secondly, the voltage ripples and fluctuation of DC and AC link are stabilized with a controller called Fractional-Order Proportional-Integral-Derivative (FOPID). The proposed FOPID have five gain values to adjust the voltage oscillations and integrated with Tyrannosaurus optimization to reduce the computation effort and achieves the superior performance over classical PI with a settle time, peak overshoot, undershoot of 0.15 s, 2.45 % and 5.7 % respectively. This auto-tuning scheme restrict the load harmonic voltage as per IEEE-519 standard and results of Tyrannosaurus proves its efficacy during fine-tuning of FOPID variables. Nonetheless, the proposed methodology was tested and validated with the MATLAB and experimental captured results and highlights the DVR compensation effectiveness under different grid voltage issues.
{"title":"Exponential function LMS and fractional order pid based voltage power quality enhancement in distribution network","authors":"Prashant Kumar, Sabha Raj Arya","doi":"10.1016/j.prime.2025.100910","DOIUrl":"10.1016/j.prime.2025.100910","url":null,"abstract":"<div><div>The complexity of distribution power system becomes a menace for utility person and causing severe power quality issues. The polluted voltage power quality seriously affects the performance of critical equipment's and causing load disruptions. Dynamic voltage restorer (DVR) is recommended to address the voltage Power Quality (PQ) in distribution network using advance control scheme of Least Mean Square such as Exponential Function Least Mean Square control (EFLMS). It employs an exponential function for step size update to extract the fundamental quantity and this excels the speed of convergence, more adaptable in environments of noise and reduces the error coefficients associated with non-ideal grid. The proposed techniques derive the fundamental component using learning factor which is dependent on exponential error function and minimizes the error to estimate the reference load voltage. Secondly, the voltage ripples and fluctuation of DC and AC link are stabilized with a controller called Fractional-Order Proportional-Integral-Derivative (FOPID). The proposed FOPID have five gain values to adjust the voltage oscillations and integrated with Tyrannosaurus optimization to reduce the computation effort and achieves the superior performance over classical PI with a settle time, peak overshoot, undershoot of 0.15 s, 2.45 % and 5.7 % respectively. This auto-tuning scheme restrict the load harmonic voltage as per IEEE-519 standard and results of Tyrannosaurus proves its efficacy during fine-tuning of FOPID variables. Nonetheless, the proposed methodology was tested and validated with the MATLAB and experimental captured results and highlights the DVR compensation effectiveness under different grid voltage issues.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"11 ","pages":"Article 100910"},"PeriodicalIF":0.0,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-28DOI: 10.1016/j.prime.2025.100911
Sagar Babu Mitikiri , Vedantham Lakshmi Srinivas , Mayukha Pal
The electrification of the transportation sector involves the widespread adoption of electric vehicles (EVs), to achieve global decarbonization. However, the increasing deployment of EV charging infrastructures (EVCI) introduces cybersecurity challenges, particularly concerning the different vulnerabilities associated with them, leading to cyberattacks. Charging ports are the crucial vulnerable points in the EVCI, which are connecting points between the EVs and EVCI. Intruders pose potential risks to the security, reliability, and functionality of the EVCI, by spoofing the data through these charging ports leading to anomalies in the data. This paper proposes an effective approach in detecting anomalies in the current magnitude of charging ports. An EVCI system is simulated in the MATLAB/SIMULINK environment for various scenarios of data generation. A Long Short Term Memory (LSTM) based autencoder model is used for predicting the charging port current magnitudes that capture the temporal dependencies in the sequential EVCI data. For generating the abnormalities in the data, the Fast-Gradient Sign Method (FGSM) is used, through which adversarial inputs are obtained, and these adversarial inputs are fed to the proposed LSTM autoencoder to obtain the anomalous data. To detect anomalies, the distributions of the sliding windows of the predicted and the observed charging port current magnitudes are compared through Kolmogorov–Smirnov (KS) test. The results demonstrate the model’s robust performance and predictive capabilities in forecasting the current magnitudes and identifying anomalies in them with an accuracy of 98.5%, enhancing the security and reliability of EVCI.
{"title":"Anomaly detection of adversarial cyber attacks on electric vehicle charging stations","authors":"Sagar Babu Mitikiri , Vedantham Lakshmi Srinivas , Mayukha Pal","doi":"10.1016/j.prime.2025.100911","DOIUrl":"10.1016/j.prime.2025.100911","url":null,"abstract":"<div><div>The electrification of the transportation sector involves the widespread adoption of electric vehicles (EVs), to achieve global decarbonization. However, the increasing deployment of EV charging infrastructures (EVCI) introduces cybersecurity challenges, particularly concerning the different vulnerabilities associated with them, leading to cyberattacks. Charging ports are the crucial vulnerable points in the EVCI, which are connecting points between the EVs and EVCI. Intruders pose potential risks to the security, reliability, and functionality of the EVCI, by spoofing the data through these charging ports leading to anomalies in the data. This paper proposes an effective approach in detecting anomalies in the current magnitude of charging ports. An EVCI system is simulated in the MATLAB/SIMULINK environment for various scenarios of data generation. A Long Short Term Memory (LSTM) based autencoder model is used for predicting the charging port current magnitudes that capture the temporal dependencies in the sequential EVCI data. For generating the abnormalities in the data, the Fast-Gradient Sign Method (FGSM) is used, through which adversarial inputs are obtained, and these adversarial inputs are fed to the proposed LSTM autoencoder to obtain the anomalous data. To detect anomalies, the distributions of the sliding windows of the predicted and the observed charging port current magnitudes are compared through Kolmogorov–Smirnov (KS) test. The results demonstrate the model’s robust performance and predictive capabilities in forecasting the current magnitudes and identifying anomalies in them with an accuracy of 98.5%, enhancing the security and reliability of EVCI.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"11 ","pages":"Article 100911"},"PeriodicalIF":0.0,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143211244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-27DOI: 10.1016/j.prime.2025.100908
Chiho Jimba, Yutaro Akimoto, Keiichi Okajima
Distributed renewable energy systems are emerging in communities and buildings in response to global warming. Concurrently, the frequency of large-scale power outages, which are often triggered by natural disasters and heavy rainfall, has increased. This trend highlights the necessity to develop robust energy systems that integrate distributed renewable energy with other sources. Economic and resilience assessments of photovoltaic (PV) and battery systems within infrastructure contexts have been extensively studied. However, resilience evaluation at the building level is necessary. Given that energy resilience encompasses various assessment aspects, the importance of employing multiple indicators for a comprehensive quantitative evaluation of resilience in PV and battery installations is increasing. This study introduced a methodology to quantitatively assess the resilience of energy systems. It employs multiple resilience indicators and simulates a power outage scenario in a positive energy building (PEB) equipped with PV and batteries. Additionally, this study analyzed the impact of different weather conditions on resilience. An assessment using multiple resilience indicators reveals that energy systems are more resilient on favorable weather days and that resilience is maximized when supply interruptions coincide with daylight hours. Furthermore, while no single indicator can measure resilience, this study shows that the use of multiple indicators enables the clarification and comparison of electricity supply and demand conditions during a disaster.
{"title":"Assessment methodology for the resilience of energy systems in positive energy buildings","authors":"Chiho Jimba, Yutaro Akimoto, Keiichi Okajima","doi":"10.1016/j.prime.2025.100908","DOIUrl":"10.1016/j.prime.2025.100908","url":null,"abstract":"<div><div>Distributed renewable energy systems are emerging in communities and buildings in response to global warming. Concurrently, the frequency of large-scale power outages, which are often triggered by natural disasters and heavy rainfall, has increased. This trend highlights the necessity to develop robust energy systems that integrate distributed renewable energy with other sources. Economic and resilience assessments of photovoltaic (PV) and battery systems within infrastructure contexts have been extensively studied. However, resilience evaluation at the building level is necessary. Given that energy resilience encompasses various assessment aspects, the importance of employing multiple indicators for a comprehensive quantitative evaluation of resilience in PV and battery installations is increasing. This study introduced a methodology to quantitatively assess the resilience of energy systems. It employs multiple resilience indicators and simulates a power outage scenario in a positive energy building (PEB) equipped with PV and batteries. Additionally, this study analyzed the impact of different weather conditions on resilience. An assessment using multiple resilience indicators reveals that energy systems are more resilient on favorable weather days and that resilience is maximized when supply interruptions coincide with daylight hours. Furthermore, while no single indicator can measure resilience, this study shows that the use of multiple indicators enables the clarification and comparison of electricity supply and demand conditions during a disaster.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"11 ","pages":"Article 100908"},"PeriodicalIF":0.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-25DOI: 10.1016/j.prime.2025.100914
Peter Anuoluwapo Gbadega, Olufunke Abolaji Balogun
This study presents an advanced Transactive Energy Management (TEM) approach employing the Slime Mould Algorithm (SMA) to optimize scheduling and storage utilization in grid-connected renewable energy microgrids. SMA's adaptability enables effective management of renewable variability, maximizing energy efficiency while minimizing operational costs and emissions. The study evaluates SMA's performance through simulations of two scenarios: with and without battery storage. In the non-storage scenario, SMA reduces operational costs by optimizing distributed generation and grid transactions. However, in the storage-integrated scenario, SMA demonstrates substantial advantages, achieving 20–48% cost savings by leveraging optimal charging and discharging cycles. This underscores the critical role of energy storage in stabilizing costs and reducing reliance on grid power during high-price intervals. Additionally, the inclusion of storage contributes to 25–38% emission reductions by enhancing renewable energy utilization and minimizing dependency on fossil-fuel-generated electricity. Comparative analysis reveals that SMA consistently outperforms conventional methods such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) in terms of convergence speed and computational efficiency, making it particularly suitable for real-time energy management. SMA achieves faster convergence, ensuring timely decision-making even in dynamic market conditions. This research highlights the critical role of advanced energy management strategies and battery storage in improving economic and operational efficiency in renewable energy microgrids.
{"title":"Transactive energy management for efficient scheduling and storage utilization in a grid-connected renewable energy-based microgrid","authors":"Peter Anuoluwapo Gbadega, Olufunke Abolaji Balogun","doi":"10.1016/j.prime.2025.100914","DOIUrl":"10.1016/j.prime.2025.100914","url":null,"abstract":"<div><div>This study presents an advanced Transactive Energy Management (TEM) approach employing the Slime Mould Algorithm (SMA) to optimize scheduling and storage utilization in grid-connected renewable energy microgrids. SMA's adaptability enables effective management of renewable variability, maximizing energy efficiency while minimizing operational costs and emissions. The study evaluates SMA's performance through simulations of two scenarios: with and without battery storage. In the non-storage scenario, SMA reduces operational costs by optimizing distributed generation and grid transactions. However, in the storage-integrated scenario, SMA demonstrates substantial advantages, achieving 20–48% cost savings by leveraging optimal charging and discharging cycles. This underscores the critical role of energy storage in stabilizing costs and reducing reliance on grid power during high-price intervals. Additionally, the inclusion of storage contributes to 25–38% emission reductions by enhancing renewable energy utilization and minimizing dependency on fossil-fuel-generated electricity. Comparative analysis reveals that SMA consistently outperforms conventional methods such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) in terms of convergence speed and computational efficiency, making it particularly suitable for real-time energy management. SMA achieves faster convergence, ensuring timely decision-making even in dynamic market conditions. This research highlights the critical role of advanced energy management strategies and battery storage in improving economic and operational efficiency in renewable energy microgrids.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"11 ","pages":"Article 100914"},"PeriodicalIF":0.0,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-23DOI: 10.1016/j.prime.2025.100912
Katyayani Chauhan, Deepika Bansal
This article proposed the use of an efficient ternary multiplexer as a building block in the implementation of ternary adders and multipliers. These designs aim to reduce the power consumption and minimize the transistor counts while maintaining low noise sensitivity. All ternary circuits use carbon nanotube field-effect transistor technology to achieve all levels due to the variable threshold property. The proposed ternary circuits have been evaluated and compared to state-of-the-art designs in the literature using the HSPICE simulator. The average power consumption of the proposed ternary multiplexer has improved up to 95 %. The average power of the proposed ternary half adder is improved by 99 % and the power-delay product of it is reduced up to 99 %. The proposed ternary multiplier and ternary half adder have reduced the transistor count by up to 60 % and 36 % respectively, in comparison to existing designs. The delay of the proposed ternary multiplier and ternary half adder has been reduced by up to 13 % and 93 %, respectively.
{"title":"Noise tolerant and power optimized ternary combinational circuits for arithmetic logic unit","authors":"Katyayani Chauhan, Deepika Bansal","doi":"10.1016/j.prime.2025.100912","DOIUrl":"10.1016/j.prime.2025.100912","url":null,"abstract":"<div><div>This article proposed the use of an efficient ternary multiplexer as a building block in the implementation of ternary adders and multipliers. These designs aim to reduce the power consumption and minimize the transistor counts while maintaining low noise sensitivity. All ternary circuits use carbon nanotube field-effect transistor technology to achieve all levels due to the variable threshold property. The proposed ternary circuits have been evaluated and compared to state-of-the-art designs in the literature using the HSPICE simulator. The average power consumption of the proposed ternary multiplexer has improved up to 95 %. The average power of the proposed ternary half adder is improved by 99 % and the power-delay product of it is reduced up to 99 %. The proposed ternary multiplier and ternary half adder have reduced the transistor count by up to 60 % and 36 % respectively, in comparison to existing designs. The delay of the proposed ternary multiplier and ternary half adder has been reduced by up to 13 % and 93 %, respectively.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"11 ","pages":"Article 100912"},"PeriodicalIF":0.0,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-23DOI: 10.1016/j.prime.2025.100913
Yurii Biletskyi, Ihor Shchur
One of the promising ways of using solar energy to generate low-power electricity is standalone solar PV water pumping systems (SPVWPS) designed for irrigation and urban/rural water supplies. The simplest and most common among such SPVWPSs are direct driven systems, which do not have expensive and unreliable batteries. This paper investigates the basic traditional configuration that consists of a PV module, a DC-DC converter performing its maximum power point tracking (MPPT), and a brushless DC motor (BLDCM) driving a centrifugal pump (CP). The conducted simulation studies on the developed model in the SimScape multiphysical library of the Matlab/Simulink environment shows that such a direct driven SPVWPS is characterized by a very low overall efficiency, and its operating range of solar irradiance starts from only 450 W/m2. Taking into account that the water pumping process itself is an accumulation of energy, this work proposes applying a pulsating mode of pump operation with nominal power in each pulse enabled by the introduction of an intermediate supercapacitor buffer. It is permanently connected to the PV module and periodically connected to the BLDCM with the CP using an electronic switch. The on-off algorithm of this switch operation is designed in such a way as to ensure the MPPT of the PV module. In the paper, the operation regularities of the SPVWPS of the proposed configuration were investigated, and the method of determining its operating parameters was developed. The conducted simulation studies showed that the pulsating operation of the proposed SPVWPS provides a constant maximum value of total system efficiency across the entire range of solar irradiance. The water pumping performance of the investigated hydraulic system during a sunny summer day in the latitudes of Ukraine in the proposed SPVWPS is on 64 % higher than in the SPVWPS of basic traditional configuration. Experimental studies conducted on the created SPVWPS sample confirmed the effectiveness of the proposed pulsating operation of the CP: in conditions of reduced insolation, the efficiency of the operation of the CP together with its electric drive was 25 % higher than in the basic configuration.
{"title":"Efficiency improvement in standalone solar PV water pumping system by pulsating pump operation based on intermediate supercapacitor buffer","authors":"Yurii Biletskyi, Ihor Shchur","doi":"10.1016/j.prime.2025.100913","DOIUrl":"10.1016/j.prime.2025.100913","url":null,"abstract":"<div><div>One of the promising ways of using solar energy to generate low-power electricity is standalone solar PV water pumping systems (SPVWPS) designed for irrigation and urban/rural water supplies. The simplest and most common among such SPVWPSs are direct driven systems, which do not have expensive and unreliable batteries. This paper investigates the basic traditional configuration that consists of a PV module, a DC-DC converter performing its maximum power point tracking (MPPT), and a brushless DC motor (BLDCM) driving a centrifugal pump (CP). The conducted simulation studies on the developed model in the SimScape multiphysical library of the Matlab/Simulink environment shows that such a direct driven SPVWPS is characterized by a very low overall efficiency, and its operating range of solar irradiance starts from only 450 W/m<sup>2</sup>. Taking into account that the water pumping process itself is an accumulation of energy, this work proposes applying a pulsating mode of pump operation with nominal power in each pulse enabled by the introduction of an intermediate supercapacitor buffer. It is permanently connected to the PV module and periodically connected to the BLDCM with the CP using an electronic switch. The on-off algorithm of this switch operation is designed in such a way as to ensure the MPPT of the PV module. In the paper, the operation regularities of the SPVWPS of the proposed configuration were investigated, and the method of determining its operating parameters was developed. The conducted simulation studies showed that the pulsating operation of the proposed SPVWPS provides a constant maximum value of total system efficiency across the entire range of solar irradiance. The water pumping performance of the investigated hydraulic system during a sunny summer day in the latitudes of Ukraine in the proposed SPVWPS is on 64 % higher than in the SPVWPS of basic traditional configuration. Experimental studies conducted on the created SPVWPS sample confirmed the effectiveness of the proposed pulsating operation of the CP: in conditions of reduced insolation, the efficiency of the operation of the CP together with its electric drive was 25 % higher than in the basic configuration.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"11 ","pages":"Article 100913"},"PeriodicalIF":0.0,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes a deep learning (DL)-based high-resolution hybrid time-division multiplexing (TDM) and code-division multiplexing (CDM) multiple input multiple output (MIMO) automotive radar to enhance the discrimination capabilities of the radar in a cluttered environment. The hybrid TDM-CDM approach is implemented by partitioning the transmit and receive arrays into subarrays, applying CDM across the subarrays, while TDM is used within each subarray. On the other hand, the DL-based scheme utilizes the SqueezeNet deep convolutional neural network (DCNN), which treats the angle, range, and Doppler estimations of the extracted targets as a multi-label classification problem. Compared to CDM-MIMO radars, this approach requires fewer spreading codes, alleviating the challenge of spreading and despreading over each element. Compared to TDM-MIMO radars, it requires fewer time slots, increasing the refresh rate. Our approach outperforms existing DL-based TDM-MIMO radar systems and performs similarly to DL-based CDM-MIMO radar systems but with reduced complexity. Simulation results show that an angular resolution of 0.25° was achieved using 12-element transmit and receive arrays, each partitioned into three subarrays.
{"title":"High-resolution hybrid TDM-CDM MIMO automotive radar","authors":"Zakaria Benyahia , Mostafa Hefnawi , Mohamed Aboulfatah , Hassan Abdelmounim , Jamal Zbitou","doi":"10.1016/j.prime.2025.100897","DOIUrl":"10.1016/j.prime.2025.100897","url":null,"abstract":"<div><div>This paper proposes a deep learning (DL)-based high-resolution hybrid time-division multiplexing (TDM) and code-division multiplexing (CDM) multiple input multiple output (MIMO) automotive radar to enhance the discrimination capabilities of the radar in a cluttered environment. The hybrid TDM-CDM approach is implemented by partitioning the transmit and receive arrays into subarrays, applying CDM across the subarrays, while TDM is used within each subarray. On the other hand, the DL-based scheme utilizes the SqueezeNet deep convolutional neural network (DCNN), which treats the angle, range, and Doppler estimations of the extracted targets as a multi-label classification problem. Compared to CDM-MIMO radars, this approach requires fewer spreading codes, alleviating the challenge of spreading and despreading over each element. Compared to TDM-MIMO radars, it requires fewer time slots, increasing the refresh rate. Our approach outperforms existing DL-based TDM-MIMO radar systems and performs similarly to DL-based CDM-MIMO radar systems but with reduced complexity. Simulation results show that an angular resolution of 0.25° was achieved using 12-element transmit and receive arrays, each partitioned into three subarrays.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"11 ","pages":"Article 100897"},"PeriodicalIF":0.0,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-21DOI: 10.1016/j.prime.2025.100909
Santosh Nirmal , Pramod Patil , Sagar Shinde
Electricity theft has become a major problem worldwide and is a significant headache for utility companies. It not only results in revenue loss but also disrupts the quality of electricity, increases generation costs, and raises overall electricity prices. Electricity or Energy theft detection (ETD) systems utilizing machine learning, particularly those employing neural networks, have high accuracy and have become popular in literature, achieving higher detection performance. Recent studies reveal that machine learning and deep learning models are vulnerable. Day by day, different attack techniques are coming up in different fields, including energy, financial, etc. As the use of machine learning for energy theft detection has grown, it has become important to explore its weaknesses. Research has shown that most of the ETD models are vulnerable to evasion attacks (EA). Its goal is to reduce electricity costs by deceiving the model into recognizing a fraudulent customer as legitimate.
In this paper, four different experiments are conducted in which we check the performance of Convolutional Neural Network and adaboost (CNN-Adaboost) ETD system. Then, we design an evasion attack to assess the model's performance under attack. The attack comprises two methods: the first is we originally propose a novel Adversarial Data Generation Method (ADGM), which is an algorithm designed to generate adversarial data, and the other is Fast Gradient Sign Method (FGSM). In the third scenario, test the attack success rate on different percentages of malicious consumers. Finally, the performance of CNN-Adaboost and other state-of-the-art methods is tested and compared using 10 % and 20 % adversarial data. Our proposed attack is validated with State Grid Corporation of China (SGCC) dataset.
ADGM and FGSM attack models generate adversarial evasion attack samples by modifying the benign sample along with already available malicious data. These samples are transferred to the surrogate model in order to test how efficiently it works on malicious data, and we forward only those data that successfully deceive the surrogate model. The CNN_Adaboost ETD model's overall performance significantly decreased for both methods. The accuracy reduced up to 53.61 % from 96.3 % for ADGM and 63.42 % for FGSM and the transferability rates are 95.82 % and 90.68 % for ADGM and FGSM, respectively. Our findings reveal that the attack success rate (ASR) of ADGM is 94.11 % which is better than FGSM. It is also observed that as the percentage of adversarial data increased, the accuracy of the models decreased. The accuracy of CNN-Adaboost, initially 96.3 %, decreased to 85.45 % and 79.43 % for 10 % and 20 % adversarial data, respectively. These adversaries are transferable and are useful for designing robust and secure machine learning (ML) models.
{"title":"Adversarial measurements for convolutional neural network-based energy theft detection model in smart grid","authors":"Santosh Nirmal , Pramod Patil , Sagar Shinde","doi":"10.1016/j.prime.2025.100909","DOIUrl":"10.1016/j.prime.2025.100909","url":null,"abstract":"<div><div>Electricity theft has become a major problem worldwide and is a significant headache for utility companies. It not only results in revenue loss but also disrupts the quality of electricity, increases generation costs, and raises overall electricity prices. Electricity or Energy theft detection (ETD) systems utilizing machine learning, particularly those employing neural networks, have high accuracy and have become popular in literature, achieving higher detection performance. Recent studies reveal that machine learning and deep learning models are vulnerable. Day by day, different attack techniques are coming up in different fields, including energy, financial, etc. As the use of machine learning for energy theft detection has grown, it has become important to explore its weaknesses. Research has shown that most of the ETD models are vulnerable to evasion attacks (EA). Its goal is to reduce electricity costs by deceiving the model into recognizing a fraudulent customer as legitimate.</div><div>In this paper, four different experiments are conducted in which we check the performance of Convolutional Neural Network and adaboost (CNN-Adaboost) ETD system. Then, we design an evasion attack to assess the model's performance under attack. The attack comprises two methods: the first is we originally propose a novel Adversarial Data Generation Method (ADGM), which is an algorithm designed to generate adversarial data, and the other is Fast Gradient Sign Method (FGSM). In the third scenario, test the attack success rate on different percentages of malicious consumers. Finally, the performance of CNN-Adaboost and other state-of-the-art methods is tested and compared using 10 % and 20 % adversarial data. Our proposed attack is validated with State Grid Corporation of China (SGCC) dataset.</div><div>ADGM and FGSM attack models generate adversarial evasion attack samples by modifying the benign sample along with already available malicious data. These samples are transferred to the surrogate model in order to test how efficiently it works on malicious data, and we forward only those data that successfully deceive the surrogate model. The CNN_Adaboost ETD model's overall performance significantly decreased for both methods. The accuracy reduced up to 53.61 % from 96.3 % for ADGM and 63.42 % for FGSM and the transferability rates are 95.82 % and 90.68 % for ADGM and FGSM, respectively. Our findings reveal that the attack success rate (ASR) of ADGM is 94.11 % which is better than FGSM. It is also observed that as the percentage of adversarial data increased, the accuracy of the models decreased. The accuracy of CNN-Adaboost, initially 96.3 %, decreased to 85.45 % and 79.43 % for 10 % and 20 % adversarial data, respectively. These adversaries are transferable and are useful for designing robust and secure machine learning (ML) models.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"11 ","pages":"Article 100909"},"PeriodicalIF":0.0,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-20DOI: 10.1016/j.prime.2025.100901
Fernando M. Camilo , Paulo J. Santos , Armando J. Pires
Wind energy plays a key role in the global shift towards renewable energy, requiring accurate prediction models for integration with power grids and effective energy distribution. This study validates the accuracy of wind speed forecasts from three widely used sources – European Centre for Medium-Range Weather Forecasts (ERA5), Modern-Era Retrospective Analysis for Research and Applications, MERRA-2 (NASA), and the Wind Atlas – against actual power generation data from the WindFloat Atlantic offshore wind farm near Viana do Castelo, Portugal, over the years 2022 and 2023. The results show that NASA’s forecasts were the most precise, with annual relative errors of 5 % for 2022 and 1.6% for 2023, outperforming the other models. This analysis underscores the importance of validated forecasting models to enhance renewable energy management through multi-year data for precise local calibration. The findings also emphasize the necessity of consistent short-term load forecasting models for reliable daily energy production. Overall, this research demonstrates that combining global wind datasets with local validation improves offshore wind prediction accuracy. In this context, NASA’s dataset emerges as the most reliable for operational and planning purposes in offshore renewable energy systems.
{"title":"A comparative analysis of real and theoretical data in offshore wind energy generation","authors":"Fernando M. Camilo , Paulo J. Santos , Armando J. Pires","doi":"10.1016/j.prime.2025.100901","DOIUrl":"10.1016/j.prime.2025.100901","url":null,"abstract":"<div><div>Wind energy plays a key role in the global shift towards renewable energy, requiring accurate prediction models for integration with power grids and effective energy distribution. This study validates the accuracy of wind speed forecasts from three widely used sources – European Centre for Medium-Range Weather Forecasts (ERA5), Modern-Era Retrospective Analysis for Research and Applications, MERRA-2 (NASA), and the Wind Atlas – against actual power generation data from the WindFloat Atlantic offshore wind farm near Viana do Castelo, Portugal, over the years 2022 and 2023. The results show that NASA’s forecasts were the most precise, with annual relative errors of 5 % for 2022 and 1.6% for 2023, outperforming the other models. This analysis underscores the importance of validated forecasting models to enhance renewable energy management through multi-year data for precise local calibration. The findings also emphasize the necessity of consistent short-term load forecasting models for reliable daily energy production. Overall, this research demonstrates that combining global wind datasets with local validation improves offshore wind prediction accuracy. In this context, NASA’s dataset emerges as the most reliable for operational and planning purposes in offshore renewable energy systems.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"11 ","pages":"Article 100901"},"PeriodicalIF":0.0,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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