Electronics

18 pages, 364 KB

Open AccessReview

Comparative Analysis of Passkeys (FIDO2 Authentication) on Android and iOS for GDPR Compliance in Biometric Data Protection

by Albert Carroll and Shahram Latifi

Electronics 2025, 14(20), 4018; https://doi.org/10.3390/electronics14204018 (registering DOI) - 13 Oct 2025

Abstract

Biometric authentication, such as facial recognition and fingerprint scanning, is now standard on mobile devices, offering secure and convenient access. However, the processing of biometric data is tightly regulated under the European Union’s General Data Protection Regulation (GDPR), where such data qualifies as [...] Read more.

Biometric authentication, such as facial recognition and fingerprint scanning, is now standard on mobile devices, offering secure and convenient access. However, the processing of biometric data is tightly regulated under the European Union’s General Data Protection Regulation (GDPR), where such data qualifies as “special category” personal data when used for uniquely identifying individuals. Compliance requires meeting strict conditions, including explicit consent and data protection by design. Passkeys, the modern name for FIDO2-based authentication credentials developed by the FIDO Alliance, enable passwordless login using public key cryptography. Its “match-on-device” architecture stores biometric data locally in secure hardware (e.g., Android’s Trusted Execution Environment, Apple’s Secure Enclave), potentially reducing the regulatory obligations associated with cloud-based biometric processing. This paper examines how Passkeys are implemented on Android and iOS platforms and their differences in architecture, API access, and hardware design, and how those differences affect compliance with the GDPR. Through a comparative analysis, we evaluate the extent to which each platform supports local processing, data minimization, and user control—key principles under GDPR. We find that while both platforms implement strong local protections, differences in developer access, trust models, and biometric isolation can influence the effectiveness and regulatory exposure of Passkeys deployment. These differences have direct implications for privacy risk, legal compliance, and implementation choices by app developers and service providers. Our findings highlight the need for platform-aware design and regulatory interpretation in the deployment of biometric authentication technologies. This work can help inform stakeholders, policymakers, and legal experts in drafting robust privacy and ethical policies—not only in the realm of biometrics but across AI technologies more broadly. By understanding platform-level implications, future frameworks can better align technical design with regulatory compliance and ethical standards. Full article

(This article belongs to the Special Issue Biometric Recognition: Latest Advances and Prospects, 2nd Edition)

19 pages, 4437 KB

Open AccessArticle

Research on Preventive Maintenance Technology for Highway Cracks Based on Digital Image Processing

by Zhi Chen, Zhuozhuo Bai, Xinqi Chen and Jiuzeng Wang

Electronics 2025, 14(20), 4017; https://doi.org/10.3390/electronics14204017 (registering DOI) - 13 Oct 2025

Abstract

Cracks are the initial manifestation of various diseases on highways. Preventive maintenance of cracks can delay the degree of pavement damage and effectively extend the service life of highways. However, existing crack detection methods have poor performance in identifying small cracks and are [...] Read more.

Cracks are the initial manifestation of various diseases on highways. Preventive maintenance of cracks can delay the degree of pavement damage and effectively extend the service life of highways. However, existing crack detection methods have poor performance in identifying small cracks and are unable to calculate crack width, leading to unsatisfactory preventive maintenance results. This article proposes an integrated method for crack detection, segmentation, and width calculation based on digital image processing technology. Firstly, based on convolutional neural network, a optimized crack detection network called CFSSE is proposed by fusing the fast spatial pyramid pooling structure with the squeeze-and-excitation attention mechanism, with an average detection accuracy of 97.10%, average recall rate of 98.00%, and average detection precision at 0.5 threshold of 98.90%; it outperforms the YOLOv5-mobileone network and YOLOv5-s network. Secondly, based on the U-Net network, an optimized crack segmentation network called CBU_Net is proposed by using the CNN-block structure in the encoder module and a bicubic interpolation algorithm in the decoder module, with an average segmentation accuracy of 99.10%, average intersection over union of 88.62%, and average pixel accuracy of 93.56%; it outperforms the U_Net network, DeepLab v3+ network, and optimized DeepLab v3 network. Finally, a laser spot center positioning method based on information entropy combination is proposed to provide an accurate benchmark for crack width calculation based on parallel lasers, with an average error in crack width calculation of less than 2.56%. Full article

17 pages, 546 KB

Open AccessArticle

AnomalyNLP: Noisy-Label Prompt Learning for Few-Shot Industrial Anomaly Detection

by Li Hua and Jin Qian

Electronics 2025, 14(20), 4016; https://doi.org/10.3390/electronics14204016 (registering DOI) - 13 Oct 2025

Abstract

Few-Shot Industrial Anomaly Detection (FSIAD) is an essential yet challenging problem in practical scenarios such as industrial quality inspection. Its objective is to identify previously unseen anomalous regions using only a limited number of normal support images from the same category. Recently, large [...] Read more.

Few-Shot Industrial Anomaly Detection (FSIAD) is an essential yet challenging problem in practical scenarios such as industrial quality inspection. Its objective is to identify previously unseen anomalous regions using only a limited number of normal support images from the same category. Recently, large pre-trained vision-language models (VLMs), such as CLIP, have exhibited remarkable few-shot image-text representation abilities across a range of visual tasks, including anomaly detection. Despite their promise, real-world industrial anomaly datasets often contain noisy labels, which can degrade prompt learning and detection performance. In this paper, we propose AnomalyNLP, a new Noisy-Label Prompt Learning approach designed to tackle the challenge of few-shot anomaly detection. This framework offers a simple and efficient approach that leverages the expressive representations and precise alignment capabilities of VLMs for industrial anomaly detection. First, we design a Noisy-Label Prompt Learning (NLPL) strategy. This strategy utilizes feature learning principles to suppress the influence of noisy samples via Mean Absolute Error (MAE) loss, thereby improving the signal-to-noise ratio and enhancing overall model robustness. Furthermore, we introduce a prompt-driven optimal transport feature purification method to accurately partition datasets into clean and noisy subsets. For both image-level and pixel-level anomaly detection, AnomalyNLP achieves state-of-the-art performance across various few-shot settings on the MVTecAD and VisA public datasets. Qualitative and quantitative results on two datasets demonstrate that our method achieves the largest average AUC improvement over baseline methods across 1-, 2-, and 4-shot settings, with gains of up to 10.60%, 10.11%, and 9.55% in practical anomaly detection scenarios. Full article

25 pages, 2636 KB

Open AccessFeature PaperArticle

Facial and Speech-Based Emotion Recognition Using Sequential Pattern Mining

by Younghun Song and Kyungyong Chung

Electronics 2025, 14(20), 4015; https://doi.org/10.3390/electronics14204015 (registering DOI) - 13 Oct 2025

Abstract

We propose a multimodal emotion recognition framework that integrates facial expressions and speech transcription (where text is derived from the transcribed speech), with a particular focus on effectively modeling the continuous changes and transitions of emotional states during conversation. Existing studies have primarily [...] Read more.

We propose a multimodal emotion recognition framework that integrates facial expressions and speech transcription (where text is derived from the transcribed speech), with a particular focus on effectively modeling the continuous changes and transitions of emotional states during conversation. Existing studies have primarily relied on single modalities (text or facial expressions). They often perform static emotion classification at specific time points. This approach limits their ability to capture abrupt emotional shifts or the structural patterns of emotional flow within dialogues. To address these limitations, this paper utilizes the MELD dataset to construct emotion sequences based on the order of utterances and introduces an analytical approach using Sequential Pattern Mining (SPM). Facial expressions are detected using DeepFace, while speech is transcribed with Whisper and passed through a BERT-based emotion classifier to infer emotions. The proposed method fuses multimodal results through a weighted voting scheme to generate emotion label sequences for each utterance. These sequences are then used to construct an emotion transition matrix, apply change-point detection, perform SPM, and train an LSTM-based classification model to predict the overall emotional flow of the dialogue. This approach goes beyond single-point judgments by capturing the contextual flow and dynamics of emotions and demonstrates superior performance compared to existing methods through experimental validation. Full article

(This article belongs to the Special Issue Application of Data Mining in Social Media)

9 pages, 1855 KB

Open AccessCommunication

Range Enhancement of a 60 GHz FMCW Heart Rate Radar Using Fabry–Perot Cavity Antenna

by Jae-Min Jeong, Hyun-Se Bae, Hong Ju Lee and Jae-Gon Lee

Electronics 2025, 14(20), 4014; https://doi.org/10.3390/electronics14204014 (registering DOI) - 13 Oct 2025

Abstract

This paper presents a bistatic 60 GHz frequency-modulated continuous-wave (FMCW) radar system for non-contact heart rate monitoring, utilizing high-gain Fabry–Perot cavity (FPC) antennas at both the transmitter and receiver. Each FPC antenna integrates a partially reflective surface (PRS) and a metallic ground plane [...] Read more.

This paper presents a bistatic 60 GHz frequency-modulated continuous-wave (FMCW) radar system for non-contact heart rate monitoring, utilizing high-gain Fabry–Perot cavity (FPC) antennas at both the transmitter and receiver. Each FPC antenna integrates a partially reflective surface (PRS) and a metallic ground plane to form a resonant cavity. Compared to conventional patch arrays of the same aperture, the FPC antenna improves the antenna gain from 4.1 dBi to 8.1 dBi at the transmitter and from 3.9 dBi to 7.8 dBi at the receiver, resulting in an overall link budget enhancement of approximately 7.9 dB. This dual high-gain configuration theoretically increases the maximum detection range by a factor of 2.48. The proposed radar system was implemented and experimentally validated under indoor conditions using both calibration targets and human participants. Active measurement results confirm that the bistatic radar equipped with FPC antennas extends the reliable heart rate detection distance by approximately 2.27 times compared to a conventional system, closely matching the theoretical prediction. These results confirm the practicality and effectiveness of FPC antennas in extending both the range and reliability of millimeter-wave vital sign detection systems. Full article

(This article belongs to the Special Issue Smart Antennas and Systems for 5G and Beyond: Latest Advances and Prospects)

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17 pages, 1106 KB

Open AccessArticle

Calibrated Global Logit Fusion (CGLF) for Fetal Health Classification Using Cardiotocographic Data

by Mehret Ephrem Abraha and Juntae Kim

Electronics 2025, 14(20), 4013; https://doi.org/10.3390/electronics14204013 (registering DOI) - 13 Oct 2025

Abstract

Accurate detection of fetal distress from cardiotocography (CTG) is clinically critical but remains subjective and error-prone. In this research, we present a leakage-safe Calibrated Global Logit Fusion (CGLF) framework that couples TabNet’s sparse, attention-based feature selection with XGBoost’s gradient-boosted rules and fuses their [...] Read more.

Accurate detection of fetal distress from cardiotocography (CTG) is clinically critical but remains subjective and error-prone. In this research, we present a leakage-safe Calibrated Global Logit Fusion (CGLF) framework that couples TabNet’s sparse, attention-based feature selection with XGBoost’s gradient-boosted rules and fuses their class probabilities through global logit blending followed by per-class vector temperature calibration. Class imbalance is addressed with SMOTE–Tomek for TabNet and one XGBoost stream (XGB–A), and class-weighted training for a second stream (XGB–B). To prevent information leakage, all preprocessing, resampling, and weighting are fitted only on the training split within each outer fold. Out-of-fold (OOF) predictions from the outer-train split are then used to optimize blend weights and fit calibration parameters, which are subsequently applied once to the corresponding held-out outer-test fold. Our calibration-guided logit fusion (CGLF) matches top-tier discrimination on the public Fetal Health dataset while producing more reliable probability estimates than strong standalone baselines. Under nested cross-validation, CGLF delivers comparable AUROC and overall accuracy to the best tree-based model, with visibly improved calibration and slightly lower balanced accuracy in some splits. We also provide interpretability and overfitting checks via TabNet sparsity, feature stability analysis, and sufficiency (k95) curves. Finally, threshold tuning under a balanced-accuracy floor preserves sensitivity to pathological cases, aligning operating points with risk-aware obstetric decision support. Overall, CGLF is a calibration-centric, leakage-controlled CTG pipeline that is interpretable and suited to threshold-based clinical deployment. Full article

(This article belongs to the Special Issue Advances in Algorithm Optimization and Computational Intelligence)

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16 pages, 2037 KB

Open AccessArticle

Risk Assessment of New Distribution Network Dispatching Operations Considering Multiple Uncertain Factors

by Lianrong Pan, Xiao Yang, Shangbing Yuan, Jiaan Li and Haowen Xue

Electronics 2025, 14(20), 4012; https://doi.org/10.3390/electronics14204012 (registering DOI) - 13 Oct 2025

Abstract

In traditional scheduling operations, dispatchers mainly rely on SCADA/EMS systems or personal experience. However, with access to a large number of new energy sources, the scale of the distribution network continues to expand, and its topology becomes increasingly complex, leading to potential security [...] Read more.

In traditional scheduling operations, dispatchers mainly rely on SCADA/EMS systems or personal experience. However, with access to a large number of new energy sources, the scale of the distribution network continues to expand, and its topology becomes increasingly complex, leading to potential security risks in scheduling operations. Therefore, it is very important to carry out risk assessments before scheduling operations. In this paper, risk theory is introduced into the field of distribution network scheduling operations, and a new risk assessment method is proposed considering various uncertain factors in the distribution network. In order to comprehensively analyze the influence of uncertainty factors in the operational process of a new distribution network, the output probability models of wind power, photovoltaic power, and load are first constructed in this study. Then, the improved Latin hypercube sampling method is used to extract the operating state of the distribution network system from the probability model, and the node voltage over-limit and line power flow overload are used as indicators to measure the severity of the consequences so as to establish a quantitative scheduling operation risk assessment system and analyze its framework in detail. Finally, simulation analysis is carried out in the improved IEEE-RTS79 test system: taking 15–25 lines from the operation state to the maintenance state as an example, this paper analyzes the influence of different locations and capacities of wind and solar access on the scheduling operation risk of distribution networks. The results can provide a reference for dispatchers to prevent risks before operation. Full article

(This article belongs to the Special Issue Digital Intelligence Technology and Applications, 2nd Edition)

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17 pages, 1307 KB

Open AccessArticle

Video Content Plagiarism Detection Using Region-Based Feature Learning

by Xun Jin, Su Yan, Rongchun Chen, Xuanyou Li, De Li and Yanwei Wang

Electronics 2025, 14(20), 4011; https://doi.org/10.3390/electronics14204011 (registering DOI) - 13 Oct 2025

Abstract

Due to the continuous increase in copyright infringement cases of video content, the economic losses of copyright owners continue to rise. To improve the efficiency of plagiarism detection in video content, in this paper, we propose region-based video feature learning. The first innovation [...] Read more.

Due to the continuous increase in copyright infringement cases of video content, the economic losses of copyright owners continue to rise. To improve the efficiency of plagiarism detection in video content, in this paper, we propose region-based video feature learning. The first innovation of this paper lies in the combination of temporal positional encoding and attention mechanisms to extract global features for weakly supervised model training. Self- and cross-attention mechanisms are combined to enhance similar features within and between videos by incorporating position coding to capture timing relationships between video frames. Global classification description is embedded for capturing global spatiotemporal information and combined with a weak supervised loss for model training. The second innovation is the frame sequence similarity calculation, which is composed of Chamfer similarity, coordinate attention mechanism, and residual connection, to aggregate similarity scores between videos. Experimental results show that the proposed method can achieve the mAP of 0.907 on the short video dataset from Douyin. The proposed method outperforms frame-level and video-level features in achieving higher detection accuracy, and further contributes to the improvement of video content plagiarism detection performance. Full article

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17 pages, 2558 KB

Open AccessArticle

Spatiotemporal Forecasting of Regional Electric Vehicles Charging Load: A Multi-Channel Attentional Graph Network Integrating Dynamic Electricity Price and Weather

by Hui Ding, Youyou Guo and Haibo Wang

Electronics 2025, 14(20), 4010; https://doi.org/10.3390/electronics14204010 (registering DOI) - 13 Oct 2025

Abstract

Accurate spatiotemporal forecasting of electric vehicle (EV) charging load is essential for smart grid management and efficient charging service operation. This paper introduced a novel spatiotemporal graph convolutional network with cross-attention (STGCN-Attention) for multi-factor charging load prediction. The model demonstrated a strong capability [...] Read more.

Accurate spatiotemporal forecasting of electric vehicle (EV) charging load is essential for smart grid management and efficient charging service operation. This paper introduced a novel spatiotemporal graph convolutional network with cross-attention (STGCN-Attention) for multi-factor charging load prediction. The model demonstrated a strong capability to capture cross-scale spatiotemporal correlations by adaptively integrating historical charging load, charging pile occupancy, dynamic electricity prices, and meteorological data. Evaluations in real-world charging scenarios showed that the proposed model achieved superior performance in hour forecasting, reducing Mean Absolute Error (MAE) by 9% and 16% compared to traditional STGCN and LSTM models, respectively. It also attained approximately 30% higher accuracy than 24 h prediction. Furthermore, the study identified an optimal 1-2-1 multi-scale temporal window strategy (hour–day–week) and revealed key driver factors. The combined input of load, occupancy, and electricity price yielded the best results (RMSE = 37.21, MAE = 27.34), while introducing temperature and precipitation raised errors by 2–8%, highlighting challenges in fine-grained weather integration. These findings provided actionable insights for real-time and intraday charging scheduling. Full article

(This article belongs to the Special Issue Applications of Data Analytics and Artificial Intelligence in Electric Vehicles)

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16 pages, 1761 KB

Open AccessArticle

Data Driven Analytics for Distribution Network Power Supply Reliability Assessment Method Considering Frequency Regulating Scenario

by Yu Zhang, Jinyue Shi, Shicheng Huang, Liang Geng, Zexiong Wang, Hao Sun, Qingguang Yu, Xin Yao, Ding Liu, Weihua Zuo, Min Guo and Xiaoyu Che

Electronics 2025, 14(20), 4009; https://doi.org/10.3390/electronics14204009 (registering DOI) - 13 Oct 2025

Abstract

Islanded microgrids face significant frequency stability challenges due to limited system capacity, low inertia levels, and the strong variability in renewable energy sources. Traditional reliability assessment methods, often based on static power balance, struggle to comprehensively reflect frequency dynamic characteristics and their impact [...] Read more.

Islanded microgrids face significant frequency stability challenges due to limited system capacity, low inertia levels, and the strong variability in renewable energy sources. Traditional reliability assessment methods, often based on static power balance, struggle to comprehensively reflect frequency dynamic characteristics and their impact on power supply reliability. To address this issue, this paper proposes a sequential Monte Carlo reliability assessment method integrated with a system frequency response model. First, an SFR model for the isolated microgrid, incorporating diesel generators, gas turbines, energy storage, and wind turbines, is established. For synchronous units, a frequency deviation-based failure rate correction mechanism is introduced to characterize the impact of frequency fluctuations on equipment reliability. State transitions are achieved by integrating failure and repair rates to reach threshold values. Second, sequential Monte Carlo simulation is employed to conduct time-series simulations of annual operation. Random sampling of unit failure and repair times is used to calculate reliability metrics. MATLAB/Simulink simulation results demonstrate that system frequency fluctuations caused by power imbalance worsen unit failure rates, leading to microgrid reliability values lower than static calculations. This provides reference for planning, design, and operational scheduling of isolated microgrids. Full article

(This article belongs to the Special Issue Future Technologies for Data Management, Processing and Application)

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19 pages, 4859 KB

Open AccessArticle

A Dual-Mode Adaptive Bandwidth PLL for Improved Lock Performance

by Thi Viet Ha Nguyen and Cong-Kha Pham

Electronics 2025, 14(20), 4008; https://doi.org/10.3390/electronics14204008 (registering DOI) - 13 Oct 2025

Abstract

This paper proposed an adaptive bandwidth Phase-Locked Loop (PLL) that integrates integer-N and fractional-N switching for energy-efficient RF synthesis in IoT and mobile applications. The architecture exploits wide-bandwidth integer-N mode for rapid lock acquisition, then seamlessly transitions to narrow-bandwidth fractional-N mode for high-resolution [...] Read more.

This paper proposed an adaptive bandwidth Phase-Locked Loop (PLL) that integrates integer-N and fractional-N switching for energy-efficient RF synthesis in IoT and mobile applications. The architecture exploits wide-bandwidth integer-N mode for rapid lock acquisition, then seamlessly transitions to narrow-bandwidth fractional-N mode for high-resolution synthesis and noise optimization. The architecture features a bandwidth-reconfigurable loop filter with intelligent switching control that monitors phase error dynamics. A novel adaptive digital noise filter mitigates

Δ Σ

quantization noise, replacing conventional synchronous delay lines. The multi-loop structure incorporates a high-resolution digital phase detector to enhance frequency accuracy and minimize jitter across both operating modes. With 180 nm CMOS technology, the PLL consumes 13.2 mW, while achieving

- 119

dBc/Hz in-band phase noise and 1

p s_{r m s}

integrated jitter. With an operating frequency range at 2.9–3.2 GHz from a 1.8 V supply, the circuit achieves a worst case fractional spur of −62.7 dBc, which corresponds to a figure of merit (FOM) of −228.8 dB. Lock time improvements of 70% are demonstrated compared to single-mode implementations, making it suitable for high-precision, low-power wireless communication systems requiring agile frequency synthesis. Full article

(This article belongs to the Special Issue Emerging Techniques and Applications for Analog and Mixed-Signal Integrated Circuits)

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3 pages, 142 KB

Open AccessEditorial

Machine Learning and Cybersecurity—Trends and Future Challenges

by Wajeb Gharibi

Electronics 2025, 14(20), 4007; https://doi.org/10.3390/electronics14204007 (registering DOI) - 13 Oct 2025

Abstract

We are pleased to present this Special Issue of Electronics, which explores the dynamic intersection of machine learning (ML) and cybersecurity [...] Full article

(This article belongs to the Special Issue Machine Learning and Cybersecurity—Trends and Future Challenges)

17 pages, 1278 KB

Open AccessArticle

KG-FLoc: Knowledge Graph-Enhanced Fault Localization in Secondary Circuits via Relation-Aware Graph Neural Networks

by Xiaofan Song, Chen Chen, Xiangyang Yan, Jingbo Song, Huanruo Qi, Wenjie Xue and Shunran Wang

Electronics 2025, 14(20), 4006; https://doi.org/10.3390/electronics14204006 (registering DOI) - 13 Oct 2025

Abstract

This paper introduces KG-FLoc, a knowledge graph-enhanced framework for secondary circuit fault localization in intelligent substations. The proposed KG-FLoc innovatively formalizes secondary components (e.g., circuit breakers, disconnectors) as graph nodes and their multi-dimensional relationships (e.g., electrical connections, control logic) as edges, constructing the [...] Read more.

This paper introduces KG-FLoc, a knowledge graph-enhanced framework for secondary circuit fault localization in intelligent substations. The proposed KG-FLoc innovatively formalizes secondary components (e.g., circuit breakers, disconnectors) as graph nodes and their multi-dimensional relationships (e.g., electrical connections, control logic) as edges, constructing the first comprehensive knowledge graph (KG) to structurally and operationally model secondary circuits. By reframing fault localization as a knowledge graph link prediction task, KG-FLoc identifies missing or abnormal connections (edges) as fault indicators. To address dynamic topologies and sparse fault samples, KG-FLoc integrates two core innovations: (1) a relation-aware gated unit (RGU) that dynamically regulates information flow through adaptive gating mechanisms, and (2) a hierarchical graph isomorphism network (GIN) architecture for multi-scale feature extraction. Evaluated on real-world datasets from 110 kV/220 kV substations, KG-FLoc achieves 97.2% accuracy in single-fault scenarios and 93.9% accuracy in triple-fault scenarios, surpassing SVM, RF, MLP, and standard GNN baselines by 12.4–31.6%. Beyond enhancing substation reliability, KG-FLoc establishes a knowledge-aware paradigm for fault diagnosis in industrial systems, enabling precise reasoning over complex interdependencies. Full article

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26 pages, 9097 KB

Open AccessArticle

Nonlinear Dynamics and Hybrid Synchronization of DC Biased Colpitts Chaotic Oscillators

by Darja Cirjulina, Ruslans Babajans, Sergejs Tjukovs, Elisabetta Spinazzola, Jacopo Secco, Dmytro Vovchuk and Dmitrijs Pikulins

Electronics 2025, 14(20), 4005; https://doi.org/10.3390/electronics14204005 (registering DOI) - 13 Oct 2025

Abstract

Chaos-based wireless communication systems can enhance the physical-layer security of IoT devices, but their reliability depends on stable chaotic behavior under real conditions. We investigate a modified Colpitts oscillator with a tunable base bias voltage, introduced as an independent control parameter to flexibly [...] Read more.

Chaos-based wireless communication systems can enhance the physical-layer security of IoT devices, but their reliability depends on stable chaotic behavior under real conditions. We investigate a modified Colpitts oscillator with a tunable base bias voltage, introduced as an independent control parameter to flexibly adjust nonlinear regimes. Using numerical studies, SPICE simulations, and hardware experiments, we show that simplified numerical models predict only a DC offset shift, whereas realistic implementations reveal qualitative changes in the dynamics, highlighting the need for experimental validation. We further demonstrate hybrid synchronization between the analog oscillator and an FPGA-based digital model. Despite model simplifications and non-idealities, synchronization is successfully achieved using the Pecora–Carroll method, showing that preserving the core dynamic structure is more critical than exact waveform replication. These results clarify the constraints of idealized models for predicting dynamical patterns while confirming the robustness of hybrid synchronization for secure, resource-constrained communication systems. Full article

(This article belongs to the Special Issue Design and Applications of Nonlinear Circuits and Systems, 2nd Edition)

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20 pages, 1084 KB

Open AccessArticle

Exploring the Role of AI and Software Solutions in Shaping Tourism Outcomes: A Factor, Neural Network, and Cluster Analysis Across Europe

by Anca Antoaneta Vărzaru, Claudiu George Bocean, Sorin Tudor, Răducu-Ștefan Bratu and Silviu Cârstina

Electronics 2025, 14(20), 4004; https://doi.org/10.3390/electronics14204004 (registering DOI) - 13 Oct 2025

Abstract

Tourism and digitalization have become increasingly interconnected, yet the complex, nonlinear relationships between technological adoption and tourism performance remain underexplored. This study aims to examine how enterprise software solutions influence tourism indicators across European countries. Using a triangulated methodological approach, we employed factor [...] Read more.

Tourism and digitalization have become increasingly interconnected, yet the complex, nonlinear relationships between technological adoption and tourism performance remain underexplored. This study aims to examine how enterprise software solutions influence tourism indicators across European countries. Using a triangulated methodological approach, we employed factor analysis to identify underlying dimensions, neural network modeling to detect nonlinear relationships, and hierarchical clustering to group countries based on digital and tourism profiles. The results consistently highlight CRM (Customer Relationship Management) as the most influential technological factor linked to both the net occupancy rate of beds and the number of nights spent at tourist accommodations. While AI (artificial intelligence) technologies currently have less impact, their importance is growing, as seen in emerging patterns. Cluster analysis further confirms that countries with higher CRM adoption tend to cluster together and show better tourism performance, indicating a clear connection between digital maturity and sector competitiveness. These findings emphasize the strategic importance of CRM as a transformative tool in hospitality and tourism management, while also recognizing the potential of AI to shape future trends. The study offers empirical support for tailored digital policies across European regions to promote inclusive and sustainable tourism growth. Full article

(This article belongs to the Topic Recent Applications of Artificial Intelligence in Economy and Society)

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31 pages, 670 KB

Open AccessArticle

A Traffic Forecasting Framework for Cellular Networks Based on a Dynamic Component Management Mechanism

by Xiangyu Liu, Yuxuan Li, Shibing Zhu, Qi Su, Jianmei Dai, Changqing Li, Jiao Zhu and Jingyu Zhang

Electronics 2025, 14(20), 4003; https://doi.org/10.3390/electronics14204003 (registering DOI) - 13 Oct 2025

Abstract

Accurate forecasting of cellular traffic in non-stationary environments remains a formidable challenge, as real-world traffic patterns dynamically evolve, emerge, and vanish over time. To tackle this, we propose a novel meta-learning framework, GMM-SCM-DCM, which features a Dynamic Component Management (DCM) mechanism. This framework [...] Read more.

Accurate forecasting of cellular traffic in non-stationary environments remains a formidable challenge, as real-world traffic patterns dynamically evolve, emerge, and vanish over time. To tackle this, we propose a novel meta-learning framework, GMM-SCM-DCM, which features a Dynamic Component Management (DCM) mechanism. This framework employs a Gaussian Mixture Model (GMM) for probabilistic meta-feature representation. The core innovation, the DCM mechanism, enables online structural evolution of the meta-learner by dynamically splitting, merging, or pruning Gaussian components based on a bimodal similarity metric, ensuring sustained alignment with shifting data distributions. A Single-Component Mechanism (SCM) is utilized for precise base learner initialisation. To ensure a rigorous and realistic validation, we reconstructed the Telecom Italia Milan dataset by applying unsupervised clustering and meta-feature engineering to identify and label four distinct functional zones: residential, commercial, mixed use, and crucially, non-stationary areas. This curated dataset provides a critical testbed for non-stationary forecasting. Comprehensive experiments demonstrate that our model significantly outperforms traditional methods and meta-learning baselines, achieving a 9.3% reduction in MAE and approximately 70% faster convergence. The model’s superiority is further confirmed through extensive ablation studies, robustness tests across base learners and data scales, and successful cross-dataset validation on the Shanghai Telecom dataset, showcasing its exceptional generalization capability and practical utility for real-world network management. Full article

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14 pages, 2805 KB

Open AccessArticle

Optimization of 6T-SRAM Cell Based on CNN-Informed NSGA-II with Consideration of Parasitic Resistance

by Qiwen Zheng, Ye Wu, Chun Zhao and Jiafeng Zhou

Electronics 2025, 14(20), 4002; https://doi.org/10.3390/electronics14204002 (registering DOI) - 13 Oct 2025

Abstract

Optimizing static random-access memory (SRAM) cells requires considering parasitic effects, as their impact on circuits in advanced nodes becomes increasingly complex. In this paper, Convolutional Neural Network-Informed Non-dominated Sorting Genetic Algorithms-II (CNN-Informed NSGA-II) was proposed to optimize 7 nm FinFET 6T-SRAM cells taking [...] Read more.

Optimizing static random-access memory (SRAM) cells requires considering parasitic effects, as their impact on circuits in advanced nodes becomes increasingly complex. In this paper, Convolutional Neural Network-Informed Non-dominated Sorting Genetic Algorithms-II (CNN-Informed NSGA-II) was proposed to optimize 7 nm FinFET 6T-SRAM cells taking into account parasitic resistance. CNN-Informed NSGA-II uses a trained CNN model integrated into the conventional NSGA-II, thereby reducing its computational complexity. This approach provides a generally applicable solution that significantly improves the efficiency of circuits while balancing competitive performance metrics. Compared to the ideal (parasitic-free) 6T-SRAM cell design, the optimized 6T-SRAM cell design (considering parasitic effects) achieves a reduction of 81.60% in Write Dynamic Power and 64.65% in Write Time; HSNM and RSNM are improved by 11.92% and 6.42%, respectively. The optimized 7 nm FinFET 6T-SRAM cell structure in this paper outperforms the parasitic-free structure in terms of the performance parameters above, even when taking into account parasitic effects. Full article

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19 pages, 3266 KB

Open AccessArticle

Empirically Informed Multi-Agent Simulation of Distributed Energy Resource Adoption and Grid Overload Dynamics in Energy Communities

by Lu Cong, Kristoffer Christensen, Magnus Værbak, Bo Nørregaard Jørgensen and Zheng Grace Ma

Electronics 2025, 14(20), 4001; https://doi.org/10.3390/electronics14204001 (registering DOI) - 13 Oct 2025

Abstract

The rapid proliferation of residential electric vehicles (EVs), rooftop photovoltaics (PVs), and behind-the-meter batteries is transforming energy communities while introducing new operational stresses to local distribution grids. Short-duration transformer overloads, often overlooked in conventional hourly or optimization-based planning models, can accelerate asset aging [...] Read more.

The rapid proliferation of residential electric vehicles (EVs), rooftop photovoltaics (PVs), and behind-the-meter batteries is transforming energy communities while introducing new operational stresses to local distribution grids. Short-duration transformer overloads, often overlooked in conventional hourly or optimization-based planning models, can accelerate asset aging before voltage limits are reached. This study introduces a second-by-second, multi-agent-based simulation (MABS) framework that couples empirically calibrated Distributed Energy Resource (DER) adoption trajectories with real-time-price (RTP)–driven household charging decisions. Using a real 160-household feeder in Denmark (2024–2025), five progressively integrated DER scenarios are evaluated, ranging from EV-only adoption to fully synchronized EV–PV–battery coupling. Results reveal that uncoordinated EV charging under RTP shifts demand to early-morning hours, causing the first transformer overload within four months. PV deployment alone offers limited relief, while adding batteries delays overload onset by 55 days. Only fully coordinated EV–PV–battery adoption postponed the first overload by three months and reduced total overload hours in 2025 by 39%. The core novelty of this work lies in combining empirically grounded adoption behavior, second-level temporal fidelity, and agent-based grid dynamics to expose transient overload mechanisms invisible to coarser models. The framework provides a diagnostic and planning tool for distribution system operators to evaluate tariff designs, bundled incentives, and coordinated DER deployment strategies that enhance transformer longevity and grid resilience in future energy communities. Full article

(This article belongs to the Special Issue Wind and Renewable Energy Generation and Integration)

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31 pages, 6524 KB

Open AccessArticle

Comprehensive Assessment of Wind Energy Potential with a Hybrid GRU–Weibull Prediction Model

by Asiye Aslan, Mustafa Tasci and Selahattin Kosunalp

Electronics 2025, 14(20), 4000; https://doi.org/10.3390/electronics14204000 (registering DOI) - 12 Oct 2025

Abstract

Wind energy is a critical renewable resource in the global effort toward sustainable development and climate change mitigation. This paper introduces a hybrid forecasting framework that integrates multistep gated recurrent unit (GRU) modeling with Weibull distribution analysis to assess wind energy potential and [...] Read more.

Wind energy is a critical renewable resource in the global effort toward sustainable development and climate change mitigation. This paper introduces a hybrid forecasting framework that integrates multistep gated recurrent unit (GRU) modeling with Weibull distribution analysis to assess wind energy potential and predict long-term wind speed dynamics. The approach combines deterministic and probabilistic components, improving robustness against seasonal variability and uncertainties. To demonstrate its effectiveness, the framework was applied to hourly wind data collected from multiple stations across diverse geographical regions in Turkey. Weibull parameters, wind power density, capacity factor, and annual energy production were estimated, while five machine learning models were compared for forecasting accuracy. The GRU model outperformed alternative methods, and the hybrid GRU–Weibull approach produced highly consistent forecasts aligned with historical patterns. Results highlight that the proposed framework offers a reliable and transferable methodology for evaluating wind energy resources, with applicability beyond the case study region. Full article

(This article belongs to the Special Issue Wind and Renewable Energy Generation and Integration)

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18 pages, 680 KB

Open AccessArticle

Wide-Area Backup Protection Area Division Method Considering Communication Constraints

by Wei Han, Baojiang Tian, Gaofeng Hao, Zhen Liu, Fengqing Cui, Xiaoyu Li, Miao Chen and Yikai Wang

Electronics 2025, 14(20), 3999; https://doi.org/10.3390/electronics14203999 (registering DOI) - 12 Oct 2025

Abstract

Aiming at the partition requirements of regional centralized wide-area backup protection systems, this paper proposes a grid partition method considering communication constraints. Firstly, a mathematical model is constructed by combining the communication delay threshold and the channel bandwidth utilization rate, and the optimal [...] Read more.

Aiming at the partition requirements of regional centralized wide-area backup protection systems, this paper proposes a grid partition method considering communication constraints. Firstly, a mathematical model is constructed by combining the communication delay threshold and the channel bandwidth utilization rate, and the optimal number of master stations is dynamically determined. The communication delay threshold strictly meets the backup protection requirements of 10 to 15 ms, and the bandwidth utilization rate is optimized to a 50% balance point. Secondly, a weighted evaluation function is established based on the shortest communication distance and site connectivity, and the master station is selected according to a geographical dispersion constraint. Furthermore, the dual factors of data transmission delay and propagation delay are combined, in which the minimum bandwidth of the transmission delay correlation path and the length of the propagation delay correlation path are used to realize the optimal division of sub-stations using a graph theory algorithm. Finally, a standby master station with a physical isolation distance of not less than 50 km is selected in each sub-area to effectively deal with failure problems caused by a master station failure. The simulation results show that the proposed method can reduce the propagation delay and significantly improve the reliability of the protection system, while maintaining a balanced distribution of the number of substations. Full article

(This article belongs to the Special Issue Advanced Online Monitoring and Fault Diagnosis of Power Equipment)

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19 pages, 2621 KB

Open AccessArticle

ISANet: A Real-Time Semantic Segmentation Network Based on Information Supplementary Aggregation Network

by Fuxiang Li, Hexiao Li, Dongsheng He and Xiangyue Zhang

Electronics 2025, 14(20), 3998; https://doi.org/10.3390/electronics14203998 (registering DOI) - 12 Oct 2025

Abstract

In autonomous-driving real-time semantic segmentation, simultaneously maximizing accuracy, minimizing model size, and sustaining high inference speed remains challenging. This tripartite demand poses significant constraints on the design of lightweight neural networks, as conventional frameworks often suffer from a trade-off between computational efficiency and [...] Read more.

In autonomous-driving real-time semantic segmentation, simultaneously maximizing accuracy, minimizing model size, and sustaining high inference speed remains challenging. This tripartite demand poses significant constraints on the design of lightweight neural networks, as conventional frameworks often suffer from a trade-off between computational efficiency and feature representation capability, thereby limiting their practical deployment in resource-constrained autonomous driving systems. We introduce ISANet, an information supplementary aggregation framework that markedly elevates segmentation accuracy without sacrificing frame rate. ISANet integrates three key components: (i) the Spatial-Supplementary Lightweight Bottleneck Unit (SLBU) that splits channels and employs compensatory branches to extract highly expressive features with minimal parameters; (ii) the Missing Spatial Information Recovery Branch (MSIRB) that recovers spatial details lost during feature extraction; and (iii) the Object Boundary Feature Attention Module (OBFAM) that fuses multi-stage features and strengthens inter-layer information interaction. Evaluated on Cityscapes and CamVid, ISANet attains 76.7% and 73.8% mIoU, respectively, while delivering 58 FPS and 90 FPS with only 1.37 million parameters. Full article

(This article belongs to the Section Artificial Intelligence)

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22 pages, 1817 KB

Open AccessArticle

Switchgear Health Monitoring Based on Ripplenet and Knowledge Graph

by Xudong Ouyang, Shaoyang He, Yilin Cui, Zhongchao Zhang, Xiaofeng Yu and Donglian Qi

Electronics 2025, 14(20), 3997; https://doi.org/10.3390/electronics14203997 (registering DOI) - 12 Oct 2025

Abstract

High-voltage switchgear is an important component of the power system, and its operation safety will directly affect the reliability of the power supply of the power system. At present, the operation and maintenance decision-making of the switchgear mainly relies on manual work, which [...] Read more.

High-voltage switchgear is an important component of the power system, and its operation safety will directly affect the reliability of the power supply of the power system. At present, the operation and maintenance decision-making of the switchgear mainly relies on manual work, which has problems such as low efficiency and poor reliability of judgment results. Therefore, this paper proposes an intelligent operation and maintenance auxiliary method for high-voltage switchgear based on the combination of the Ripplenet algorithm and knowledge graph, which ensures high efficiency while improving the reliability of the results. Among them, the knowledge graph is mainly based on the Bidirectional Encoder Representations from Transformers-Whole Word Masking (BERT-wwm) algorithm, and it is constructed in a bottom-up and top-down manner. It consists of 240 nodes and 960 relationships. Based on this knowledge graph, the intelligent operation and maintenance auxiliary method of high-voltage switchgear based on Ripplenet is studied. Based on textual information such as on-site information and fault reports, the judgment reasoning of the fault type of the high-voltage switchgear and recommendations for operation and maintenance solutions are realized. The diagnostic accuracy of this method for high-voltage switchgear faults can reach 95.96%. Full article

(This article belongs to the Special Issue Advances in Condition Monitoring and Fault Diagnosis)

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35 pages, 2135 KB

Open AccessReview

Hybrid Molecular–Electronic Computing Systems and Their Perspectives in Real-Time Medical Diagnosis and Treatment

by David J. Herzog and Nitsa J. Herzog

Electronics 2025, 14(20), 3996; https://doi.org/10.3390/electronics14203996 (registering DOI) - 12 Oct 2025

Abstract

Advantages in CMOS MOSFET-based electronics served as a basis for modern ubiquitous computerization. At the same time, theoretical and practical developments in material science, analytical chemistry and molecular biology have presented the possibility of applying Boolean logic and information theory findings on a [...] Read more.

Advantages in CMOS MOSFET-based electronics served as a basis for modern ubiquitous computerization. At the same time, theoretical and practical developments in material science, analytical chemistry and molecular biology have presented the possibility of applying Boolean logic and information theory findings on a molecular basis. Molecular computing, both organic and inorganic, has the advantages of high computational density, scalability, energy efficiency and parallel computing. Carbon-based and carbohydrate molecular machines are potentially biocompatible and well-suited for biomedical tasks. Molecular computing-enabled sensors, medication-delivery molecular machines, and diagnostic and therapeutic nanobots are at the cutting edge of medical research. Highly focused diagnostics, precision medicine, and personalized treatment can be achieved with molecular computing tools and machinery. At the same time, traditional electronics and AI advancements create a highly effective computerized environment for analyzing big data, assist in diagnostics with sophisticated pattern recognition and step in as a medical routine aid. The combination of the advantages of MOSFET-based electronics and molecular computing creates an opportunity for next-generation healthcare. Full article

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30 pages, 2868 KB

Open AccessArticle

2²⁴-CPSK–CSS–WCDMA FPGA-Based Reconfigurable Chaotic Modulation for Multiuser Communications in the 2.45 GHz Band

by Jose-Cruz Nuñez-Perez, Miguel-Angel Estudillo-Valdez, José-Ricardo Cárdenas-Valdez, Gabriela-Elizabeth Martinez-Mendivil and Yuma Sandoval-Ibarra

Electronics 2025, 14(20), 3995; https://doi.org/10.3390/electronics14203995 (registering DOI) - 12 Oct 2025

Abstract

This article presents an innovative chaotic communication scheme that integrates the multiuser access technique known as Wideband Code Division Multiple Access (W-CDMA) with the chaos-based selective strategy Chaos-Based Selective Symbol (CSS) and the unconventional modulation Chaos Parameter Shift Keying (CPSK). The system is [...] Read more.

This article presents an innovative chaotic communication scheme that integrates the multiuser access technique known as Wideband Code Division Multiple Access (W-CDMA) with the chaos-based selective strategy Chaos-Based Selective Symbol (CSS) and the unconventional modulation Chaos Parameter Shift Keying (CPSK). The system is designed to operate in the 2.45 GHz band and provides a robust and efficient alternative to conventional schemes such as Quadrature Amplitude Modulation (QAM). The proposed CPSK modulation enables the encoding of information for multiple users by regulating the 36 parameters of a Reconfigurable Chaotic Oscillator (RCO), theoretically allowing the simultaneous transmission of up to

2^{24}

independent users over the same channel. The CSS technique encodes each user’s information using a unique chaotic segment configuration generated by the RCO; this serves as a reference for binary symbol encoding. W-CDMA further supports the concurrent transmission of data from multiple users through orthogonal sequences, minimizing inter-user interference. The system was digitally implemented on the Artix-7 AC701 FPGA (XC7A200TFBG676-2) to evaluate logic-resource requirements, while RF validation was carried out using a ZedBoard FPGA equipped with an AD9361 transceiver. Experimental results demonstrate optimal performance in the 2.45 GHz band, confirming the effectiveness of the chaos-based W-CDMA approach as a multiuser access technique for high-spectral-density environments and its potential for use in 5G applications. Full article

(This article belongs to the Special Issue Feature Papers in 'Microwave and Wireless Communications' Section, 2nd Edition)

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33 pages, 3538 KB

Open AccessReview

A Comprehensive Review of AI Methods in Agri-Food Engineering: Applications, Challenges, and Future Directions

by Kaichen Wu, Zhenyang Ji, Hanyue Wang, Xiaoyan Shao, Haohan Li, Wence Zhang, Wa Kong, Jing Xia and Xu Bao

Electronics 2025, 14(20), 3994; https://doi.org/10.3390/electronics14203994 (registering DOI) - 12 Oct 2025

Abstract

The deep integration of artificial intelligence (AI) is a core driver for digitalization and intelligence in agricultural and food engineering, boosting production efficiency, resource optimization, and product quality. This review systematically analyzes AI’s application scenarios, technical pathways, and challenges across the agricultural value [...] Read more.

The deep integration of artificial intelligence (AI) is a core driver for digitalization and intelligence in agricultural and food engineering, boosting production efficiency, resource optimization, and product quality. This review systematically analyzes AI’s application scenarios, technical pathways, and challenges across the agricultural value chain. It aims to develop a structured taxonomy of AI-driven technical application mechanisms in agriculture, highlighting their roles in optimizing core agricultural processes. A systematic literature review was conducted using reputable databases, including Google Scholar, IEEE Xplore, ScienceDirect, Web of Science, SpringerLink, and Scopus, focusing on peer-reviewed articles from the last decade. Findings show that AI-enhanced techniques improve product quality and safety inspection efficiency. However, challenges like multi-source data synchronization barriers, high intelligent equipment costs, and model adaptability limitations in complex agricultural environments remain. This review contributes to the field by providing a unified framework for understanding AI applications in agri-food engineering, identifying key research gaps, and highlighting pathways for sustainable technology adoption that can benefit diverse agricultural stakeholders. Full article

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40 pages, 4388 KB

Open AccessArticle

Optimized Implementation of YOLOv3-Tiny for Real-Time Image and Video Recognition on FPGA

by Riccardo Calì, Laura Falaschetti and Giorgio Biagetti

Electronics 2025, 14(20), 3993; https://doi.org/10.3390/electronics14203993 (registering DOI) - 12 Oct 2025

Abstract

In recent years, the demand for efficient neural networks in embedded contexts has grown, driven by the need for real-time inference with limited resources. While GPUs offer high performance, their size, power consumption, and cost often make them unsuitable for constrained or large-scale [...] Read more.

In recent years, the demand for efficient neural networks in embedded contexts has grown, driven by the need for real-time inference with limited resources. While GPUs offer high performance, their size, power consumption, and cost often make them unsuitable for constrained or large-scale applications. FPGAs have therefore emerged as a promising alternative, combining reconfigurability, parallelism, and increasingly favorable cost–performance ratios. They are especially relevant in domains such as robotics, IoT, and autonomous drones, where rapid sensor fusion and low power consumption are critical. This work presents the full implementation of a neural network on a low-cost FPGA, targeting real-time image and video recognition for drone applications. The workflow included training and quantizing a YOLOv3-Tiny model with Brevitas and PyTorch, converting it into hardware logic using the FINN framework, and optimizing the hardware design to maximize use of the reprogrammable silicon area and inference time. A custom driver was also developed to allow the device to operate as a TPU. The resulting accelerator, deployed on a Xilinx Zynq-7020, could recognize 208 frames per second (FPS) when running at a 200 MHz clock frequency, while consuming only 2.55 W. Compared to Google’s Coral Edge TPU, the system offers similar inference speed with greater flexibility, and outperforms other FPGA-based approaches in the literature by a factor of three to seven in terms of FPS/W. Full article

(This article belongs to the Special Issue Machine Learning in Electronic and Biomedical Engineering, 3rd Edition)

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15 pages, 4919 KB

Open AccessArticle

A Novel Multi-Mode Resonator-Based Ultra-Wideband Bandpass Filter Topology

by Rathod Rajender, Rusan Kumar Barik, Gabriele Ciarpi, Slawomir Koziel, Simone Genovesi and Daniele Rossi

Electronics 2025, 14(20), 3992; https://doi.org/10.3390/electronics14203992 (registering DOI) - 12 Oct 2025

Abstract

In this paper, a novel multi-mode resonator-based ultra-wideband bandpass filter topology is proposed, analyzed, and experimentally validated. The filter comprises a short shunt-stepped impedance resonator and shunt-open stubs. Thus, it can be easily implemented using microstrip technology, offering a simple and cost-effective alternative [...] Read more.

In this paper, a novel multi-mode resonator-based ultra-wideband bandpass filter topology is proposed, analyzed, and experimentally validated. The filter comprises a short shunt-stepped impedance resonator and shunt-open stubs. Thus, it can be easily implemented using microstrip technology, offering a simple and cost-effective alternative to multilayer and high-temperature superconductor thin-film-based bandpass filters. S-parameter expressions for the proposed filter are derived using even- and odd-mode methods. To validate theoretical results, a filter prototype operating at the center frequency (

f_{o}

) of 6.85 GHz is designed, fabricated, and experimentally tested. The measured 3 dB fractional bandwidth (FBW) of the filter exceeds 176%, and the selectivity factor (SF) reaches 0.87. Additionally, the filter outperforms most existing designs in the literature in terms of insertion loss (IL) and return loss (RL). Finally, a figure of merit (FoM) is proposed to measure the trade-off among key performance parameters (i.e., FBW, IL, RL, SF,

f_{o}

, and group delay flatness), and confirms that the proposed bandpass filter exhibits the best FoM compared to the state of the art. Full article

(This article belongs to the Special Issue Microwave Circuits and Microwave Engineering)

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19 pages, 8882 KB

Open AccessArticle

A Robust Design Strategy for Resonant Controllers Tuned Beyond the LCL-Filter Resonance Frequency

by Xin Zhao, Chuan Xie, Josep M. Guerrero and Xiaohua Wu

Electronics 2025, 14(20), 3991; https://doi.org/10.3390/electronics14203991 (registering DOI) - 12 Oct 2025

Abstract

Compared to the L-filter, the LCL-filter provides superior high-frequency harmonic attenuation for a given inductance. However, it also introduces resonance issues that can compromise system stability. Consequently, the bandwidth of the inner current loop must be maintained well below the resonant frequency [...] Read more.

Compared to the L-filter, the LCL-filter provides superior high-frequency harmonic attenuation for a given inductance. However, it also introduces resonance issues that can compromise system stability. Consequently, the bandwidth of the inner current loop must be maintained well below the resonant frequency of the filter. This paper proposes a robust controller design strategy for LCL-filtered converters to extend the harmonic control range under wide variations in grid impedance. An analysis of the resonant controller phase-frequency characteristics reveals its capability to provide phase compensation up to 2π. Building on this finding, the damping ratio and phase leading angle are systematically optimized through a joint analysis of the phase characteristics introduced by the resonant controller and active damping, thereby enhancing system robustness. With these optimized parameters, the center frequency of the resonant controller can be tuned above the LCL-filter resonance frequency without inducing instability. In contrast to conventional methods, the proposed approach allows the LCL-filter to be designed with a lower resonance frequency. This enables improved attenuation of switching-frequency harmonics without compromising the tracking performance for higher-order harmonics. Such a capability is particularly beneficial in high-power and weak-grid scenarios, where the filter resonance frequency may fall to just a few hundred hertz. Experimental results validate the effectiveness of the proposed design strategy. Full article

(This article belongs to the Special Issue New Trends in Power Electronics for Renewable Energy and Power Systems)

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28 pages, 33417 KB

Open AccessArticle

Self-Synchronized Common-Mode Current Control Strategy for Power Rebalancing in CPS-PWM Modulated Energy-Storage Modular Multilevel Converters

by Biyang Liu, Cheng Jin, Gong Chen, Kangli Liu and Jianfeng Zhao

Electronics 2025, 14(20), 3990; https://doi.org/10.3390/electronics14203990 (registering DOI) - 12 Oct 2025

Abstract

Capacitor voltage imbalance among submodules in energy storage modular multilevel converters (MMCs) can lead to current distortion, power oscillations, and even system instability. Traditional voltage control strategies, inherited from non-storage MMCs, offer limited regulation capabilities and are insufficient to address the complex balancing [...] Read more.

Capacitor voltage imbalance among submodules in energy storage modular multilevel converters (MMCs) can lead to current distortion, power oscillations, and even system instability. Traditional voltage control strategies, inherited from non-storage MMCs, offer limited regulation capabilities and are insufficient to address the complex balancing requirements across phases, arms, and submodules in distributed Energy-Storage MMCs (ES-MMC). This paper proposes a self-synchronized common-mode current strategy to achieve capacitor voltage rebalancing in Carrier Phase-Shifted PWM (CPS-PWM) modulated ES-MMCs. The proposed method establishes both phase-level and arm-level power rebalancing pathways by utilizing the common-mode current in the upper and lower arms. Specifically, the DC component of the common-mode current is employed to regulate common-mode power between the arms, while the fundamental-frequency component, through its interaction with the fundamental modulation voltage, is used to adjust differential-mode power. By coordinating these two power components within each phase, the method enables effective capacitor voltage rebalancing among submodules in the presence of power imbalance caused by a nonuniform distributed energy storage converter. A comprehensive analysis of differential- and common-mode voltage regulation under CPS-PWM is presented. The corresponding control algorithm is developed to inject adaptive common-mode voltage based on capacitor voltage deviations, thereby inducing self-synchronized balancing currents. Simulation and experimental results verify that the proposed strategy significantly improves power distribution uniformity and reduces capacitor voltage deviations under various load and disturbance conditions. Full article

(This article belongs to the Special Issue Enhancing Power System Resilience: Advanced Algorithm, Control Strategies, and Topologies for a Sustainable Energy Future)

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48 pages, 7374 KB

Open AccessArticle

Optimal Control for On-Load Tap-Changers and Inverters in Photovoltaic Plants Applying Teaching Learning Based Optimization

by Rolando A. Silva-Quiñonez, Higinio Sánchez-Sainz, Pablo Garcia-Triviño, Raúl Sarrias-Mena, David Carrasco-González and Luis M. Fernández-Ramírez

Electronics 2025, 14(20), 3989; https://doi.org/10.3390/electronics14203989 (registering DOI) - 12 Oct 2025

Abstract

This research presents an optimized control strategy for the coordinated operation of parallel grid connected photovoltaic (PV) plants and an On Load Tap Changer (OLTC) transformer. The proposed framework integrates inverter-level active and reactive power dispatch with OLTC tap control through an Energy [...] Read more.

This research presents an optimized control strategy for the coordinated operation of parallel grid connected photovoltaic (PV) plants and an On Load Tap Changer (OLTC) transformer. The proposed framework integrates inverter-level active and reactive power dispatch with OLTC tap control through an Energy Management System (EMS) based on an improved Teaching Learning Based Optimization (TLBO) algorithm. The EMS minimizes operational costs while maintaining voltage stability and respecting electrical and mechanical constraints. Comparative analyses with Monte Carlo, fmincon, and conventional TLBO methods demonstrate that the optimized TLBO achieves up to two orders of magnitude faster convergence and higher robustness, enabling more reliable performance under variable irradiance and load conditions. Simulation and Hardware-in-the-Loop (HIL) results confirm that the coordinated OLTC inverter control significantly enhances reactive power capability and voltage regulation. The proposed optimized TLBO based EMS offers an effective and computationally efficient solution for dynamic energy management in medium scale PV systems, supporting grid reliability and maximizing renewable energy utilization. Full article

(This article belongs to the Special Issue Section Collection Series: New Horizons and Recent Advances in Power Electronics)

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Electronics, Volume 14, Issue 20 (October-2 2025) – 47 articles

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