Pub Date : 2025-10-01Epub Date: 2025-09-03DOI: 10.1016/j.gloei.2025.04.006
Hao Pan , Limin Jia
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.
{"title":"Coordinated control strategy for multi-DG DC microgrid based on two-layer fuzzy neural network","authors":"Hao Pan , Limin Jia","doi":"10.1016/j.gloei.2025.04.006","DOIUrl":"10.1016/j.gloei.2025.04.006","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":36174,"journal":{"name":"Global Energy Interconnection","volume":"8 5","pages":"Pages 732-746"},"PeriodicalIF":2.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145365811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-01Epub Date: 2025-09-04DOI: 10.1016/j.gloei.2025.08.001
Xizhou Du , Xing Lei , Ting Ye , Yingzhou Sun , Zewen Shang , Zhiqiang Liu , Tianyi Xu
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.
{"title":"A review of research on intelligent fault detection of power equipment based on infrared and voiceprint: methods, applications and challenges","authors":"Xizhou Du , Xing Lei , Ting Ye , Yingzhou Sun , Zewen Shang , Zhiqiang Liu , Tianyi Xu","doi":"10.1016/j.gloei.2025.08.001","DOIUrl":"10.1016/j.gloei.2025.08.001","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":36174,"journal":{"name":"Global Energy Interconnection","volume":"8 5","pages":"Pages 821-846"},"PeriodicalIF":2.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145365808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-01Epub Date: 2025-08-27DOI: 10.1016/j.gloei.2025.05.007
You Chen , Xingshuo Li , Xiaoyang Chen , Shuye Ding , Yizhi Chen , Wei Wang
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.
{"title":"Identifying time zones of power fluctuations method for photovoltaic power ramp rate optimization","authors":"You Chen , Xingshuo Li , Xiaoyang Chen , Shuye Ding , Yizhi Chen , Wei Wang","doi":"10.1016/j.gloei.2025.05.007","DOIUrl":"10.1016/j.gloei.2025.05.007","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":36174,"journal":{"name":"Global Energy Interconnection","volume":"8 5","pages":"Pages 778-789"},"PeriodicalIF":2.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145365813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-01Epub Date: 2025-07-08DOI: 10.1016/j.gloei.2025.03.005
Zuobin Zhu , Shumin Sun , Yueming Ding
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 multi-machine parallel PV ESS effectively suppresses system resonance under variations in power grid strength, enabling adaptive and stable grid-connected operations of distributed PV.
{"title":"Research on adaptive smooth switching control strategy for strong and weak power grids in multi-machine parallel PV energy storage VSG system","authors":"Zuobin Zhu , Shumin Sun , Yueming Ding","doi":"10.1016/j.gloei.2025.03.005","DOIUrl":"10.1016/j.gloei.2025.03.005","url":null,"abstract":"<div><div>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 multi-machine parallel PV ESS effectively suppresses system resonance under variations in power grid strength, enabling adaptive and stable grid-connected operations of distributed PV.</div></div>","PeriodicalId":36174,"journal":{"name":"Global Energy Interconnection","volume":"8 5","pages":"Pages 790-803"},"PeriodicalIF":2.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145365814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"A photovoltaic array DC arc fault location method integrating MKDANN and SPA","authors":"Chenye Huang , Wei Gao , Chenhao Huang , Liangshi Lin","doi":"10.1016/j.gloei.2025.07.004","DOIUrl":"10.1016/j.gloei.2025.07.004","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":36174,"journal":{"name":"Global Energy Interconnection","volume":"8 5","pages":"Pages 760-777"},"PeriodicalIF":2.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145365764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-01Epub Date: 2025-09-11DOI: 10.1016/j.gloei.2025.05.010
Yujia Gu , Jinpeng Guo , Hongqiang Li , Lei Zhou , Yuanchen Dong , Xin Ma , Xueping Pan
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.
{"title":"Total inertia evaluation of multiple PV power stations with virtual inertia control using a small number of measurements","authors":"Yujia Gu , Jinpeng Guo , Hongqiang Li , Lei Zhou , Yuanchen Dong , Xin Ma , Xueping Pan","doi":"10.1016/j.gloei.2025.05.010","DOIUrl":"10.1016/j.gloei.2025.05.010","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":36174,"journal":{"name":"Global Energy Interconnection","volume":"8 5","pages":"Pages 747-759"},"PeriodicalIF":2.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145365816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01Epub Date: 2025-06-18DOI: 10.1016/j.gloei.2025.03.004
Abdulbari Talib Naser , Nur Fadilah Ab Aziz , Karam Khairullah Mohammed , Karmila binti Kamil , Saad Mekhilef
Weather variations present a major challenge for photovoltaic (PV) systems in obtaining the optimal output during maximum power point tracking (MPPT), particularly under partial shadowing conditions (PSCs). Bypass diodes are typically installed across the series-connected PV modules to avoid the occurrence of the hotspots. Consequently, the power curve exhibits several local peaks (LPs) and one global peak (GP). The conventional MPPTs typically become stuck in one of these LPs, presenting a significant decrease in both the power output and overall efficiency of the PV system. A major constraint of several optimization techniques is their inability to differentiate between the irradiance fluctuations and load alterations. In this study, we analyze seven different methods for MPPT. These include: the team game algorithm (TGA), social ki driver algorithm (SSD), differential evolution (DE), grey wolf optimization (GWO), particle swarm optimization (PSO), cuckoo search (CS), and the perturb and observe (P&O) method. These algorithms were applied in practice, and their effectiveness was experimentally demonstrated under different amounts of solar irradiation while maintaining a constant temperature. The results indicate that the CS and TGA approaches can accurately track the MPPT across various positions on the P-V curve. These methods achieve average efficiencies of 99.59% and 99.54%, respectively. Additionally, the TGA achieves superior performance with the shortest average tracking time of 0.92 s, outperforming the existing MPPT algorithms.
{"title":"Performance assessment of meta-heuristic MPPT strategies for solar panels under complex partial shading conditions and load variation","authors":"Abdulbari Talib Naser , Nur Fadilah Ab Aziz , Karam Khairullah Mohammed , Karmila binti Kamil , Saad Mekhilef","doi":"10.1016/j.gloei.2025.03.004","DOIUrl":"10.1016/j.gloei.2025.03.004","url":null,"abstract":"<div><div>Weather variations present a major challenge for photovoltaic (PV) systems in obtaining the optimal output during maximum power point tracking (MPPT), particularly under partial shadowing conditions (PSCs). Bypass diodes are typically installed across the series-connected PV modules to avoid the occurrence of the hotspots. Consequently, the power curve exhibits several local peaks (LPs) and one global peak (GP). The conventional MPPTs typically become stuck in one of these LPs, presenting a significant decrease in both the power output and overall efficiency of the PV system. A major constraint of several optimization techniques is their inability to differentiate between the irradiance fluctuations and load alterations. In this study, we analyze seven different methods for MPPT. These include: the team game algorithm (TGA), social ki driver algorithm (SSD), differential evolution (DE), grey wolf optimization (GWO), particle swarm optimization (PSO), cuckoo search (CS), and the perturb and observe (P&O) method. These algorithms were applied in practice, and their effectiveness was experimentally demonstrated under different amounts of solar irradiation while maintaining a constant temperature. The results indicate that the CS and TGA approaches can accurately track the MPPT across various positions on the P-V curve. These methods achieve average efficiencies of 99.59% and 99.54%, respectively. Additionally, the TGA achieves superior performance with the shortest average tracking time of 0.92 s, outperforming the existing MPPT algorithms.</div></div>","PeriodicalId":36174,"journal":{"name":"Global Energy Interconnection","volume":"8 4","pages":"Pages 554-571"},"PeriodicalIF":2.6,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144902454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The utilization of unused rooftops is a promising solution to meet the growing energy needs of urban areas. This study identifies the strategic locations for installing photovoltaic (PV) systems and assesses the energy production in Nador, Morocco, comparing various PV modules based on sunlight, while also integrating an economic analysis. A key innovation of this study lies in the novel application of LiDAR (Light Detection and Ranging) point clouds combined with photogrammetric restitution, enabling the construction of a 3D model of buildings. A Boolean multicriteria analysis was implemented to determine the effective surface area of each roof, considering parameters, such as slope, orientation, shadow, and accessibility, while excluding unsuitable buildings. A substantial area of 336 ha suitable for solar systems was identified, representing 61% of the total area of existing structures, with an average annual irradiation of 1,413.71 kWh/m2. The CIS (copper/indium/selenium) PV module stands out as an attractive option, offering an energy capacity of 168.56 MWp and significant energy production of 311.35 GWh. Their moderate initial cost of 376.95 million USD makes them financially appealing with a feasible return on investment within 10 years. Environmentally, the CIS module contributes notably to reduced CO2 emissions, thereby mitigating its environmental impact. By implementing the CIS module, solar energy production is expected to significantly exceed the estimated demand of the urban population. The data were integrated into a Geographic Information System to target roofs suitable for solar panels, forming the basis of an accurate solar cadastre. This study actively contributes to shaping a sustainable energy landscape by promoting environment-friendly solutions, thereby playing a role in transitioning to a more sustainable energy future in Nador.
{"title":"Assessing the technical and economic potential of rooftop solar panels in Nador, Morocco, using advanced GIS methodology and remote sensing data","authors":"Rachid Lambarki , Elmostafa Achbab , Mehdi Maanan , Hassan Rhinane","doi":"10.1016/j.gloei.2025.01.010","DOIUrl":"10.1016/j.gloei.2025.01.010","url":null,"abstract":"<div><div>The utilization of unused rooftops is a promising solution to meet the growing energy needs of urban areas. This study identifies the strategic locations for installing photovoltaic (PV) systems and assesses the energy production in Nador, Morocco, comparing various PV modules based on sunlight, while also integrating an economic analysis. A key innovation of this study lies in the novel application of LiDAR (Light Detection and Ranging) point clouds combined with photogrammetric restitution, enabling the construction of a 3D model of buildings. A Boolean multicriteria analysis was implemented to determine the effective surface area of each roof, considering parameters, such as slope, orientation, shadow, and accessibility, while excluding unsuitable buildings. A substantial area of 336 ha suitable for solar systems was identified, representing 61% of the total area of existing structures, with an average annual irradiation of 1,413.71 kWh/m<sup>2</sup>. The CIS (copper/indium/selenium) PV module stands out as an attractive option, offering an energy capacity of 168.56 MWp and significant energy production of 311.35 GWh. Their moderate initial cost of 376.95 million USD makes them financially appealing with a feasible return on investment within 10 years. Environmentally, the CIS module contributes notably to reduced CO<sub>2</sub> emissions, thereby mitigating its environmental impact. By implementing the CIS module, solar energy production is expected to significantly exceed the estimated demand of the urban population. The data were integrated into a Geographic Information System to target roofs suitable for solar panels, forming the basis of an accurate solar cadastre. This study actively contributes to shaping a sustainable energy landscape by promoting environment-friendly solutions, thereby playing a role in transitioning to a more sustainable energy future in Nador.</div></div>","PeriodicalId":36174,"journal":{"name":"Global Energy Interconnection","volume":"8 4","pages":"Pages 625-639"},"PeriodicalIF":2.6,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144902450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01Epub Date: 2025-07-09DOI: 10.1016/j.gloei.2025.05.004
Youssef Akarne , Ahmed Essadki , Tamou Nasser , Maha Annoukoubi , Ssadik Charadi
The integration of renewable energy sources into modern power systems necessitates efficient and robust control strategies to address challenges such as power quality, stability, and dynamic environmental variations. This paper presents a novel sparrow search algorithm (SSA)-tuned proportional-integral (PI) controller for grid-connected photovoltaic (PV) systems, designed to optimize dynamic performance, energy extraction, and power quality. Key contributions include the development of a systematic SSA-based optimization framework for real-time PI parameter tuning, ensuring precise voltage and current regulation, improved maximum power point tracking (MPPT) efficiency, and minimized total harmonic distortion (THD). The proposed approach is evaluated against conventional PSO-based and P&O controllers through comprehensive simulations, demonstrating its superior performance across key metrics: a 39.47% faster response time compared to PSO, a 12.06% increase in peak active power relative to P&O, and a 52.38% reduction in THD, ensuring compliance with IEEE grid standards. Moreover, the SSA-tuned PI controller exhibits enhanced adaptability to dynamic irradiance fluctuations, rapid response time, and robust grid integration under varying conditions, making it highly suitable for real-time smart grid applications. This work establishes the SSA-tuned PI controller as a reliable and efficient solution for improving PV system performance in grid-connected scenarios, while also setting the foundation for future research into multi-objective optimization, experimental validation, and hybrid renewable energy systems.
{"title":"Optimized control of grid-connected photovoltaic systems: Robust PI controller based on sparrow search algorithm for smart microgrid application","authors":"Youssef Akarne , Ahmed Essadki , Tamou Nasser , Maha Annoukoubi , Ssadik Charadi","doi":"10.1016/j.gloei.2025.05.004","DOIUrl":"10.1016/j.gloei.2025.05.004","url":null,"abstract":"<div><div>The integration of renewable energy sources into modern power systems necessitates efficient and robust control strategies to address challenges such as power quality, stability, and dynamic environmental variations. This paper presents a novel sparrow search algorithm (SSA)-tuned proportional-integral (PI) controller for grid-connected photovoltaic (PV) systems, designed to optimize dynamic performance, energy extraction, and power quality. Key contributions include the development of a systematic SSA-based optimization framework for real-time PI parameter tuning, ensuring precise voltage and current regulation, improved maximum power point tracking (MPPT) efficiency, and minimized total harmonic distortion (THD). The proposed approach is evaluated against conventional PSO-based and P&O controllers through comprehensive simulations, demonstrating its superior performance across key metrics: a 39.47% faster response time compared to PSO, a 12.06% increase in peak active power relative to P&O, and a 52.38% reduction in THD, ensuring compliance with IEEE grid standards. Moreover, the SSA-tuned PI controller exhibits enhanced adaptability to dynamic irradiance fluctuations, rapid response time, and robust grid integration under varying conditions, making it highly suitable for real-time smart grid applications. This work establishes the SSA-tuned PI controller as a reliable and efficient solution for improving PV system performance in grid-connected scenarios, while also setting the foundation for future research into multi-objective optimization, experimental validation, and hybrid renewable energy systems.</div></div>","PeriodicalId":36174,"journal":{"name":"Global Energy Interconnection","volume":"8 4","pages":"Pages 523-536"},"PeriodicalIF":2.6,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144902452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Low-voltage direct current (DC) microgrids have recently emerged as a promising and viable alternative to traditional alternating current (AC) microgrids, offering numerous advantages. Consequently, researchers are exploring the potential of DC microgrids across various configurations. However, despite the sustainability and accuracy offered by DC microgrids, they pose various challenges when integrated into modern power distribution systems. Among these challenges, fault diagnosis holds significant importance. Rapid fault detection in DC microgrids is essential to maintain stability and ensure an uninterrupted power supply to critical loads. A primary challenge is the lack of standards and guidelines for the protection and safety of DC microgrids, including fault detection, location, and clearing procedures for both grid-connected and islanded modes. In response, this study presents a brief overview of various approaches for protecting DC microgrids.
{"title":"Signal processing and machine learning techniques in DC microgrids: a review","authors":"Kanche Anjaiah , Jonnalagadda Divya , Eluri N.V.D.V. Prasad , Renu Sharma","doi":"10.1016/j.gloei.2025.05.002","DOIUrl":"10.1016/j.gloei.2025.05.002","url":null,"abstract":"<div><div>Low-voltage direct current (DC) microgrids have recently emerged as a promising and viable alternative to traditional alternating current (AC) microgrids, offering numerous advantages. Consequently, researchers are exploring the potential of DC microgrids across various configurations. However, despite the sustainability and accuracy offered by DC microgrids, they pose various challenges when integrated into modern power distribution systems. Among these challenges, fault diagnosis holds significant importance. Rapid fault detection in DC microgrids is essential to maintain stability and ensure an uninterrupted power supply to critical loads. A primary challenge is the lack of standards and guidelines for the protection and safety of DC microgrids, including fault detection, location, and clearing procedures for both grid-connected and islanded modes. In response, this study presents a brief overview of various approaches for protecting DC microgrids.</div></div>","PeriodicalId":36174,"journal":{"name":"Global Energy Interconnection","volume":"8 4","pages":"Pages 598-624"},"PeriodicalIF":2.6,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144902458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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