Abstract Floating photovoltaic (FPV) technology is emerging as a highly promising approach to accelerate decarbonization of the global economy, due to its higher power generation efficiency and lower land occupation.With the rapid development of FPV technology, the mechanical performance degradation of key components caused by the harsh marine environment has become a pressing issue, as it significantly contributes to failure behavior observed in FPV systems.A comprehensive compilation of the mechanical performance of key components in FPV systems is also currently unavailable.Here, the mechanical behavior of each structural component in FPV systems under harsh marine environments is systematically reviewed.It further emphasizes the synergistic effects of mechanical performance degradation among different components on the overall system.The drop-off rate (v) of normalized elongation at break(EAB) of polymer under the synergistic effect of various environmental factors increases from 7.5 10 4 h 1 to 21.8 10 4 h 1 compared with the single environmental stress.Moreover,the development of novel materials and innovative mechanical structures applied in FPV systems to enhance mechanical performance is discussed.The novel flexible PV modules applied in FPV systems minimize the loads acting on the mooring lines by 80% and increase power generation by 5%.Notably, this paper provides a theoretical foundation for developing standards of FPV systems, especially the establishment of standards related to the synergistic effects of the mechanical performance degradation of different key components on FPV systems.

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Abstract Conventional coordinated control strategies for DC bus voltage signal (DBS) in islanded DC microgrids (IDCMGs) struggle with coordinating multiple distributed generators (DGs) and cannot effectively incorporate state of charge (SOC) information of the energy storage system, thereby reducing the system flexibility.In this study, we propose an adaptive coordinated control strategy that employs a two-layer fuzzy neural network controller (FNNC) to adapt to varying operating conditions in an IDCMG with multiple PV and battery energy storage system (BESS) units.The first-layer FNNC generates optimal operating mode commands for each DG, thereby avoiding the requirement for complex operating modes based on SOC segmentation.An optimal switching sequence logic prioritizes the most appropriate units during mode transitions.The second-layer FNNC dynamically adjusts the droop power to overcome power distribution challenges among DG groups.This helps in preventing the PV power from exceeding the limits and mitigating the risk of BESS overcharging or over-discharging.The simulation results indicate that the proposed strategy enhances the coordinated operation of multi-DG IDCMGs, thereby ensuring the efficient and safe utilization of PV and BESS.

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Abstract The increasing penetration of PV power generation inevitably leads to the decline of system inertia, posing challenges to frequency stability.To this end, virtual inertia control has been proposed; however, it causes more fluctuations of system inertia.To address this issue, a novel equivalent inertia evaluation method for multiple PV power generation under virtual inertia control is proposed.The total system inertia is first estimated based on historical or injected disturbance.Then, the total inertia of multiple PV power generation is directly calculated by subtracting the inertia of synchronous generators from the estimated system inertia.To improve practicality, a partition-based strategy is introduced, which divides the system into regions characterized by homogeneous frequency response behaviors.After partitioning, only the synchronous generator data within the region and inter-area transmission line power are required for evaluation,reducing the demand for PMU data compared to traditional methods requiring measurements at each PV connection point.Comprehensive simulation results in a 10-machine 39-bus system penetrated with multiple PV power generation validated the effectiveness of the proposed method.

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Abstract This paper proposes a fingerprint matching method integrating transfer learning and online learning to tackle the challenges of environmental adaptability and dynamic interference resistance in photovoltaic (PV) array DC arc fault location methods based on electromagnetic radiation (EMR)signals.Initially, a comprehensive analysis of the time-frequency characteristics of series arc EMR signals is carried out to pinpoint effective data sources that reflect fault features.Subsequently,a multi-kernel domain-adversarial neural network(MKDANN)is introduced to extract domain-invariant features,and a feature extractor designed specifically for fingerprint matching is devised.To reduce inter-domain distribution differences, a multi-kernel maximum mean discrepancy (MK-MMD) is integrated into the adaptation layer.Moreover, to deal with dynamic environmental changes in real-world situations, the support-class passive aggressive(SPA) algorithm is utilized to adjust model parameters in real time.Finally, MKDANN and SPA technologies are smoothly combined to build a fully operational fault location model.Experimental results indicate that the proposed method attains an overall fault location accuracy of at least 95%, showing strong adaptability to environmental changes and robust interference resistance while maintaining excellent online learning capabilities during model migration.

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Abstract Photovoltaic(PV)systems are being increasingly implemented in the grid,and their intermittent output fluctuations threaten the stability of the grid,thereby requiring effective power ramp control(PRRC)strategies.In this study,we proposed a power fluctuation identification method to optimize the PRRC strategy.The K-means++ cluster based on DTW used in this method, which clusters the historical PV power generation data into power curves corresponding to a specific weather type(sunny,cloudy,and rainy)in a time zone.Subsequently, wavelet decomposition is applied to discretize the power curves with extreme RR overrun to accurately identify the extreme fluctuation time zones.We conducted an analysis using minute-level data from a 100 kW PV plant in Arizona, which demonstrates that the proposed method can effectively identify high-risk periods.Weather patterns within the time zones were quantitatively identified using a weather probability model.A hardware-in-the-loop experimental platform was employed to validate two days of actual power data in Arizona, demonstrating the weather zoning accuracy of the method and the reasonableness of the control.The proposed methodology contributes significantly to PRRC strategy selection and parameter optimization(e.g.,ESS capacity storage allocation and APC power reserve ΔP) in different time zones and weather conditions.

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Abstract To enable distributed PV to adapt to variations in power grid strength and achieve stable grid connection while enhancing operational flexibility,it is essential to configure grid-connected inverters with an integrated grid-following control mode,allowing smooth switching between GFL and GFM modes.First,impedance models of GFL and GFM PV energy storage VSG systems were established,and grid stability was analyzed.Second, an online impedance identification method based on voltage fluctuation data screening was proposed to enhance the accuracy of impedance identification.Additionally, a PV energy storage GFM/GFL VSG smooth switching method based on current inner loop compensation was introduced to achieve stable grid-connected operation of distributed photovoltaics under changes in strong and weak power grids.Finally, a grid stability analysis was conducted on the multi-machine parallel PV ESS, and a simulation model of a multi-machine parallel PV ESS based on current inner loop compensation was established for testing.Results showed that, compared to using a single GFM or single GFL control for the PV VSG system, the smooth switching method of multimachine parallel PV ESS effectively suppresses system resonance under variations in power grid strength, enabling adaptive and stable grid-connected operations of distributed PV.

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Abstract New electric power systems characterized by a high proportion of renewable energy and power electronics equipment face significant challenges due to high-frequency (HF) electromagnetic interference from the high-speed switching of power converters.To address this situation, this paper offers an in-depth review of HF interference problems and challenges originating from power electronic devices.First, the root cause of HF electromagnetic interference, i.e., the resonant response of the parasitic parameters of the system to highspeed switching transients, is analyzed, and various scenarios of HF interference in power systems are highlighted.Next, the types of HF interference are summarized, with a focus on common-mode interference in grounding systems.This paper thoroughly reviews and compares various suppression methods for conducted HF interference.Finally,the challenges involved and suggestions for addressing emerging HF interference problems from the perspective of both power electronics equipment and power systems are discussed.This review aims to offer a structured understanding of HF interference problems and their suppression techniques for researchers and practitioners.

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Abstract As modern power systems grow in complexity, accurate and efficient fault detection has become increasingly important.While many existing reviews focus on a single modality, this paper presents a comprehensive survey from a dual-modality perspective-infrared imaging and voiceprint analysis-two complementary, non-contact techniques that capture different fault characteristics.Infrared imaging excels at detecting thermal anomalies, while voiceprint signals provide insight into mechanical vibrations and internal discharge phenomena.We review both traditional signal processing and deep learning-based approaches for each modality, categorized by key processing stages such as feature extraction and classification.The paper highlights how these modalities address distinct fault types and how they may be fused to improve robustness and accuracy.Representative datasets are summarized,and practical challenges such as noise interference,limited fault samples,and deployment constraints are discussed.By offering a cross-modal,comparative analysis,this work aims to bridge fragmented research and guide future development in intelligent fault detection systems.The review concludes with research trends including multimodal fusion, lightweight models, and self-supervised learning.

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Abstract Transient stability assessment (TSA) based on artificial intelligence typically has two distinct model management approaches: a unified management approach for all faulted lines and a separate management approach for each faulted line.To address the shortcomings of the aforementioned approaches, namely accuracy, training time, and model management complexity, a multi-model management approach for power system TSA based on multi-moment feature clustering has been proposed.First, the steady-state and transient features present under fault conditions were obtained through a transient simulation of line faults.The input sample set was then constructed using the aforementioned multi-moment electrical features and the embedded faulty line numbers.Subsequently, K-means clustering was conducted on each line based on the similarity of their electrical features, employing t-SNE dimensionality reduction.The PSO-CNN model was trained separately for each cluster to generate several independent TSA models.Finally, a model effectiveness evaluation system consisting of five metrics was established,and the effect of the sample imbalance ratio on the model effectiveness was investigated.The model effectiveness was evaluated using the IEEE 39-bus system algorithm.The results showed that the multi-model management strategy based on multi-moment feature clustering can effectively combine the two advantages of superior evaluation performance and streamlined model management by fully extracting system features.Moreover, this approach allows for more flexible adjustments to line topology changes.

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Abstract In this study,we analyzed the untapped energy potential of remote mountainous regions in eastern Morocco,thereby addressing the research gap on sustainable electrification in such areas.We proposed a hybrid energy system corresponding to the local conditions and integrated the solar,wind,and biomass energy using batteries and green hydrogen as storage systems,considering the grid as a backup.Simulations conducted using HOMER Pro indicate an annual energy output of 5.6 GWh from solar, 6.9 GWh from wind,and 1 GWh from biomass,thereby ensuring 100%renewable self-sufficiency.The system is highly cost-effective and achieves a levelized cost of energy of 0.024$/kWh while significantly reducing the greenhouse gas emissions by over 99%for CO2 and 100%for SO2.This study presents a sustainable, reliable, and economically viable solution for rural electrification, which concurs with SDG 7.

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Abstract Accurate forecasting of electricity spot prices is crucial for market participants in formulating bidding strategies.However, the extreme volatility of electricity spot prices,influenced by various factors,poses significant challenges for forecasting.To address the data uncertainty of electricity prices and effectively mitigate gradient issues,overfitting,and computational challenges associated with using a single model during forecasting, this paper proposes a framework for forecasting spot market electricity prices by integrating wavelet packet decomposition (WPD) with a hybrid deep neural network.By ensuring accurate data decomposition, the WPD algorithm aids in detecting fluctuating patterns and isolating random noise.The hybrid model integrates temporal convolutional networks (TCN)and long short-term memory(LSTM)networks to enhance feature extraction and improve forecasting performance.Compared to other techniques,it significantly reduces average errors,decreasing mean absolute error(MAE)by 27.3%,root mean square error(RMSE)by 66.9%, and mean absolute percentage error (MAPE) by 22.8%.This framework effectively captures the intricate fluctuations present in the time series, resulting in more accurate and reliable predictions.

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Abstract The state of health SoH of lithium ion batteries plays a predominant role in ensuring the safe and reliable operation of electric vehicles.In this, a novel SoH estimation approach using support vector regression with a Gaussian kernel optimized using the Bayesian optimization technique (BO-SVR with a Gaussian kernel) was proposed.Unlike, traditional approaches that use the internal resistance, and battery capacity as input parameters, this study utilized the equivalent discharging voltage difference interval and equivalent charging voltage difference interval, as they capture the dynamic voltage characteristics associated with the battery degradation.The model was simulated using MATLAB 2023a.The mean absolute error,R2,root mean squared error,and mean squared error were considered as performance indicators.The simulation results indicated that the proposed BO-SVR with a Gaussian kernel model had superior performance to other kernel SVR and Gaussian Process Regression models, with a reduced RMSE of 0.0082, thus demonstrating its potential to predict the SoH more accurately.

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