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

{"title":"Enhancing grid-connected photovoltaic systems' power quality through a dynamic voltage restorer equipped with an innovative sliding mode and PR control system","authors":"Negin Shahidi,&nbsp;Ebrahim Salary","doi":"10.1016/j.prime.2024.100875","DOIUrl":"10.1016/j.prime.2024.100875","url":null,"abstract":"<div><div>This study focuses on enhancing power quality in on-grid photovoltaic (PV) schemes through an innovative dynamic voltage restorer (DVR) that integrates two control strategies with a multi-level inverter. The DVR, a power electronic compensator, corrects voltage disturbances such as sag and swell by adjusting the voltage at the point of common coupling (PCC). It utilizes sliding mode control (SMC) for normal operations and proportional resonance (PR) during voltage disruptions, ensuring accurate voltage correction. The system's multi-level inverter offers a distinct advantage over traditional high-frequency inverters, which typically suffer from significant switching losses in high-power applications. The multi-level inverter effectively detects and mitigates voltage issues while minimizing harmonic distortion. The study evaluates the DVR's performance under various conditions, including voltage swell, disruption, mild sag, and severe sag. Simulation results using the Simulink tool demonstrate that the proposed DVR significantly reduces voltage disturbances and enhances power quality in the PV panel's output, the PCC, and the injected voltage. The findings suggest that this dual-control DVR designe, with its efficient use of a multi-level inverter, is a promising solution for improving power quality and stability in on-grid PV arrangements.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"11 ","pages":"Article 100875"},"PeriodicalIF":0.0,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177061","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}

{"title":"AI and Machine Learning in V2G technology: A review of bi-directional converters, charging systems, and control strategies for smart grid integration","authors":"Nagarajan Munusamy,&nbsp;Indragandhi Vairavasundaram","doi":"10.1016/j.prime.2024.100856","DOIUrl":"10.1016/j.prime.2024.100856","url":null,"abstract":"<div><div>Electric Vehicles (EVs) are transforming the transportation sector, and their integration with the grid is crucial for a sustainable energy future. EVs can serve as distributed energy resources, aiding in peak shaving, frequency management, and voltage support, thus enhancing grid stability. This comprehensive review explores the transformative potential of EVs in the power grid, focusing on Vehicle-to-Grid (V2 G) technology. We discuss different bidirectional Converter types, including AC-DC and DC-DC converters, to optimize power flow and voltage regulation. AC-DC converters rectify AC grid power for DC charging, while DC-DC converters optimize DC power flow and voltage regulation. Charging station safety is paramount, with electrical shock protection, fire protection, and cybersecurity measures essential for ensuring safe and reliable charging. The review also delves into energy trading and security in blockchain management, highlighting the use of blockchain technology to address hacking vulnerabilities. We explore the potential of Artificial Intelligence (AI) and Machine Learning (ML) algorithms to optimize V2 G performance. By leveraging AI and ML, we can improve the efficiency, reliability, and scalability of V2 G systems. AI-powered predictive analytics can forecast energy demand and supply, enabling proactive charging and discharging strategies. ML algorithms can optimize charging rates, battery health, and grid stability while also detecting anomalies and preventing potential faults. By integrating AI and ML into V2 G systems, we can unlock new possibilities for sustainable energy management, grid resilience, and electric vehicle adoption.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"10 ","pages":"Article 100856"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142748428","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}

{"title":"A novel low complexity, low latency rate 1/2 FEC code","authors":"Maan A.S. Al-Adwany,&nbsp;Mohammed H. Al-Jammas,&nbsp;Hind Th. Hamdoon","doi":"10.1016/j.prime.2024.100838","DOIUrl":"10.1016/j.prime.2024.100838","url":null,"abstract":"<div><div>In this paper, a relatively simple and low complexity rate <span><math><mrow><mn>1</mn><mo>/</mo><mn>2</mn></mrow></math></span> FEC (Forward Error Correction) code has been proposed. The proposed encoder combines the effect of the low cross correlation of two orthogonal sequences along with the effect of the quadrature phase to achieve the desired performance. A mathematical modeling for the proposed code has been accomplished which indicates that the code is able to deliver a <span><math><mrow><mtext>3</mtext><mspace></mspace><mtext>dB</mtext></mrow></math></span> coding gain. The obtained results revealed that the performance of the proposed code is comparable to that of the Convolutional Codes (CCs). Interestingly, the latency analysis showed that, unlike polar codes and convolutional codes where latency is correlated with the data block size or traceback depth (<span><math><mi>TB</mi></math></span>), the proposed code exhibits a decoding latency of a single clock cycle. Furthermore, the proposed code and the CC have been implemented on an Field Programmable Gate Array (FPGA) platform to evaluate the overhead in terms of usability of hardware resources. The experimental results showed that the proposed code can achieve a <span><math><mrow><mtext>3</mtext><mspace></mspace><mtext>dB</mtext></mrow></math></span> coding gain, which is in agreement with the outcomes of the mathematical analyses. Moreover, the proposed code showed relatively fewer usability of hardware resources. Accordingly, the proposed code is suitable for applications that require a good balance between error correction and data rate.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"10 ","pages":"Article 100838"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142748360","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}

{"title":"Multi-scale energy planning for the global transition: Local, regional, and global insights","authors":"Felipe Feijoo ,&nbsp;Matteo Giacomo Prina ,&nbsp;Marko Mimica ,&nbsp;Neven Duić","doi":"10.1016/j.prime.2024.100841","DOIUrl":"10.1016/j.prime.2024.100841","url":null,"abstract":"<div><div>The global shift towards sustainable energy systems and decarbonization presents a complex set of challenges that require technological innovation, policy integration, and regional adaptation. This editorial synthesizes the contributions from four papers in this special issue of e-Prime - Advances in Electrical Engineering, Electronics and Energy, each providing unique insights into key aspects of the energy transition. The first paper assesses the economic feasibility of floating offshore wind energy projects in the European Atlantic and Mediterranean, emphasizing regional cost differences and resource availability. The second paper examines the dynamics of renewable energy investments in Croatia, highlighting the critical role of policy support, technology costs, and energy flexibility in driving the transition. The third paper utilizes a simulation-based optimization approach to explore global decarbonization pathways, employing Integrated Assessment Models (IAMs) to analyze policy trade-offs and long-term impacts. Finally, the fourth paper focuses on the energy transition challenges of Mediterranean islands, exploring their dependency on imported fossil fuels and the role of local governance in promoting renewable energy solutions. Together, these studies underscore the need for an integrated, multi-faceted approach that combines policy, technology, and localized strategies to accelerate the transition towards a sustainable and resilient global energy future. Hence, this editorial discusses that while technological advancements are critical, only a combined strategy involving regulation, technology, and societal engagement will ensure the global success of the energy transition.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"10 ","pages":"Article 100841"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143140293","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}

{"title":"Fast frequency response constrained stochastic scheduling of flexible loads in low inertia grids","authors":"Ashish Mathur,&nbsp;Sumit Nema,&nbsp;Vivek Prakash,&nbsp;Jyotsna Singh","doi":"10.1016/j.prime.2024.100852","DOIUrl":"10.1016/j.prime.2024.100852","url":null,"abstract":"<div><div>This paper presents a modeling approach to address fast frequency response (FFR) requirements within a stochastic scheduling framework. The work integrates FFR response from flexible loads and propose advance modeling techniques to characterize renewable generation uncertainty. The study incorporates scenario-based uncertainty modeling to capture the inherent unpredictability of renewable energy resources (RES), enhancing the accuracy of scheduling in low inertia grids. Central to the proposed methodology is the integration of flexible loads such as interruptible loads (ILs), deferrable loads (DLs), and electric vehicles (EVs) strategically modeled to contribute to FFR capabilities. By embedding uncertainty modeling within the proposed stochastic scheduling framework, the research offers a comprehensive strategy to effectively handle the challenges posed by renewable generation fluctuations and reduces the RES curtailment. The paper underscores the significance of FFR in maintaining grid balancing, particularly in the presence of RES. The inclusion of flexible loads further contributes to enhancement of grid resilience by enabling rapid adjustments in response to frequency disturbances. The proposed framework not only accommodates renewable generation uncertainties but also leverages smart flexible loads to bolster FFR capabilities paving the way for a reliable and sustainable power systems.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"10 ","pages":"Article 100852"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142748359","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}

{"title":"Advanced parameter extraction optimization technique for the four-diode model approach","authors":"Bhanu Prakash Saripalli ,&nbsp;Bharati Gamgula ,&nbsp;Revathi Ravilisetty ,&nbsp;Prashant Kumar ,&nbsp;Gagan Singh ,&nbsp;Sonika Singh","doi":"10.1016/j.prime.2024.100861","DOIUrl":"10.1016/j.prime.2024.100861","url":null,"abstract":"<div><div>Accurate performance estimation of photovoltaic devices is important in optimizing the efficiency and cost of the photovoltaic systems. The one-diode and two-diode models are used because they concisely represent the current-voltage (I-V) variations. However, these models mainly focus on the fundamental mechanism of diffusion and Shockley-Read-Hall recombination. Four-diode model (FDM) is developed from the standard three-diode model used to enhance the precision of PV system performance estimations. However, the better-detailed FDM offers modeling of other recombination and leakage currents that occur in rather complex solar cells or advanced cells like heterojunction, multijunction, or perovskite ones. This model helps to get a more accurate picture of the PV cell operation as several diodes are added to model recombination processes and defects. This work makes use of sophisticated forms of parameter extraction aimed at promoting the optimization of algorithms such as the one known as Advanced Dynamic Inertia-Particle Swarm Optimization with Velocity Clamping or ADIPSO-VC. For comparison with FDM, a three-diode model (THDM) is utilized, and the outcome of the former is then analyzed against the latter. In addition, as a confirmation of the reliability and repeatability of the results obtained by applying the developed algorithm for parameter extraction, FDM is compared with classical methods. To demonstrate the efficacy of the proposed method it is tested against the other algorithms Simulated annealing, and conventional PSO. Based on the comparison, it is evident that ADIPSO-VC surpasses the other methods by demonstrating lower error rates and shorter computational time.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"10 ","pages":"Article 100861"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143140310","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}

{"title":"Directivity enhancement of microstrip antennas for high-resolution brain tumor imaging using characteristic modes theory and the confocal microwave image reconstruction algorithm","authors":"Mouad El Moudden ,&nbsp;Badiaa Ait Ahmed ,&nbsp;Ibtisam Amdaouch ,&nbsp;Mohamed Zied Chaari ,&nbsp;Juan Ruiz-Alzola ,&nbsp;Otman Aghzout","doi":"10.1016/j.prime.2024.100854","DOIUrl":"10.1016/j.prime.2024.100854","url":null,"abstract":"<div><div>The increasing prevalence of brain tumors necessitates the development of advanced diagnostic techniques to enhance detection and characterization. This paper presents innovative methodologies for designing and optimizing antenna characteristics using characteristic modes theory (CMT), specifically adapted for high-resolution imaging in medical applications. Our research focuses on the critical goal of improving the accuracy and precision of brain tumor detection through a confocal microwave image reconstruction algorithm. The study begins with an in-depth modeling of essential antenna elements, examining their behavior to understand their interactions within the overall structure. This comprehensive analysis enhances our understanding of antenna performance and characteristics. The introduction of CMT is pivotal, as it facilitates the identification of resonance frequencies that exhibit exceptional radiation efficiency. Moreover, the antenna’s directivity is significantly enhanced through a thorough investigation of the effects of various substrate materials and patch shapes on the performance of the radiated antenna modes. This study prioritizes the optimization of the dominant directive mode to improve tumor imaging resolution, ultimately leading to superior quality imaging results. To compare and analyze the impact of different antenna directivity modes on the imaging resolution of brain tumors, two optimized antennas with distinct patch shapes and radiation patterns are integrated into a microwave imaging system. This advanced system is carefully designed to accurately locate and characterize brain tumors, enhancing diagnostic precision. The confocal imaging algorithm demonstrates that the dominant mode with high directivity radiation produces high-resolution images that significantly improve tumor detection and diagnosis.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"10 ","pages":"Article 100854"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142757145","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}

{"title":"Multi-port network based modeling and selection of capacitor for desired voltage regulation of a standalone six-phase short-shunt induction generator for application in remote areas","authors":"Saikat Ghosh,&nbsp;S.N. Mahato","doi":"10.1016/j.prime.2024.100859","DOIUrl":"10.1016/j.prime.2024.100859","url":null,"abstract":"<div><div>This paper gives a straightforward method to determine the values of excitation capacitors of a standalone short-shunt six-phase induction generator (SPIG) to maintain the voltage profile within predetermined percentage of voltage deviation (VD). In this envisioned study, the value of the capacitor is meticulously chosen to optimize the number of capacitor switching, ensuring minimal system cost and complexity. The theory of multi-port network analysis has been applied for modelling of the SPIG, thus, the complex mathematical derivation to obtain the model equations is avoided. The system is expressed as a multivariable nonlinear optimization problem. The resultant admittance of the SPIG is calculated from its per phase equivalent circuit and is used as an objective function, which is solved using Binary Search Algorithm (BSA). The main novelty of this work is the determination of the model equations of the SPIG system in an efficient and simple way using the multi-port network analysis approach. Along with this, the BSA is employed for optimal selection of excitation capacitors because of its simplicity and less computational time. The results, on a 3.7 kW induction machine, reveal that to maintain a 4 % VD, a fixed series capacitor of 140 µF and two switched shunt capacitors (34.4 µF, 91.8 µF) are required. For 2 % VD, four shunt capacitors (24.2µF, 36.2µF, 64.7µF, 91.2µF) are necessary. The performance of the machine is evaluated with the help of magnetic characteristics and other equations obtained from its per phase equivalent circuit. The experimentation has been carried out in a hardware prototype system developed in the laboratory. The experimental and the simulated results are compared and found that both are nearly same for different operating conditions, which indicates the efficacy of the proposed approach.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"10 ","pages":"Article 100859"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143140311","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}

{"title":"Modern deep neural networks for Direct Normal Irradiance forecasting: A classification approach","authors":"Muhammad Saud Ul Hassan ,&nbsp;Kashif Liaqat ,&nbsp;Laura Schaefer ,&nbsp;Alexander J. Zolan","doi":"10.1016/j.prime.2024.100853","DOIUrl":"10.1016/j.prime.2024.100853","url":null,"abstract":"<div><div>The escalating energy demand and the adverse environmental impacts of fossil-fuel use necessitate a shift towards cleaner and renewable alternatives. Concentrated Solar Power (CSP) technology emerges as a promising solution, offering a carbon-free alternative for power generation. The efficiency and profitability of CSP depend on the Direct Normal Irradiance (DNI) component of solar radiation; hence, accurate DNI forecasting can help optimize CSP plants’ operations and performance. The unpredictable nature of weather phenomena, particularly cloud cover, introduces uncertainty into DNI projections. Existing DNI forecasting models use meteorological factors, which are both challenging to estimate numerically over short prediction windows and expensive to model through data at a sufficiently high spatial and temporal resolution. This research addresses the challenge by presenting a novel approach that formulates DNI prediction as a multi-class classification problem, departing from conventional regression-based methods. The primary objective of this classification framework is to identify optimal periods aligning with specific operational thresholds for CSP plants, contributing to enhanced dispatch optimization strategies. We model the DNI classification problem using four advanced deep neural networks – rectified linear unit (ReLU) networks, 1D residual networks (ResNets), bidirectional long short-term memory (BiLSTM) networks, and transformers – achieving accuracies up to 93.5% without requiring meteorological parameters.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"10 ","pages":"Article 100853"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142748361","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}

{"title":"A deep learning model for fault detection in distribution networks with high penetration of electric vehicle chargers","authors":"Seyed Amir Hosseini ,&nbsp;Behrooz Taheri ,&nbsp;Seyed Hossein Hesamedin Sadeghi ,&nbsp;Adel Nasiri","doi":"10.1016/j.prime.2024.100845","DOIUrl":"10.1016/j.prime.2024.100845","url":null,"abstract":"<div><div>Integration of a significant number of domestic electrical vehicle (EV) charging stations into the power distribution infrastructure can give rise to several protection problems. Therefore, we propose a new method to detect short-circuit faults in distribution networks with high penetration of residential EV chargers. In this method, first, the features of voltage and current waveforms in various operational scenarios are extracted through a two-dimensional modeling. These features are then used to train a deep learning model based on black widow optimization bi-directional long short-term memory (BWO-BiLSTM) technique. In contrast with the conventional adaptive protection schemes, the proposed method can perform accurately in the presence of fast and unpredictable network topology, without requiring to determine a large number of threshold values to detect a fault, or relying on communication links. The effectiveness of the proposed method is investigated through a series of case studies on a modified IEEE 69-bus distribution network with a substantial penetration of residential EV chargers. The results show the proposed method's ability to detect all types of faults within 5 ms. Since it employs a machine learning algorithm for fault detection, the method's accuracy is 98.5 %, surpassing the accuracy of k-nearest neighbors (KNN) and conventional LSTM models. Additionally, the results confirm its optimal performance under noisy conditions. Even with noise in the sampled signals at a level of 10 dB, the method's accuracy remains higher than that of other methods, with a value of 96.9 %.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"10 ","pages":"Article 100845"},"PeriodicalIF":0.0,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720792","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}