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Analysis and Optimization Control of Active Distribution Networks and Smart...
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Analysis and Optimization Control of Active Distribution Networks and Smart Grids

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 6748

Special Issue Editors

Dr. Jingrui Zhang

E-Mail Website
Guest Editor
Department of Instrumental and Electrical Engineering, Xiamen University, Xiamen 361005, China
Interests: power system dispatch; energy management of microgrid/active distribution networks; swarm intelligence algorithm
Special Issues, Collections and Topics in MDPI journals
Dr. Jian Wang

E-Mail Website
Guest Editor
College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
Interests: active distribution network; convex optimization; distributied generation
Special Issues, Collections and Topics in MDPI journals
Dr. Po Li

E-Mail Website
Guest Editor
Department of Instrumental and Electrical Engineering, Xiamen University, Xiamen 361005, China
Interests: power electronics; hybrid electric propulsion system
Dr. Tengpeng Chen

E-Mail Website
Guest Editor
Department of Instrumental and Electrical Engineering, Xiamen University, Xiamen 361102, China
Interests: smart grid; stochastic optimization; robust optimization; state estimation; energy management system; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

A safe, efficient, and green power grid is a key support for reducing carbon emissions and achieving carbon neutrality. An active distribution network is a main carrier of renewables and power load, and is also an important part of smart grids. However, in recent years, the increasing integration of intermittent renewables (e.g., wind power and photovoltaic) and new loads (e.g., electric vehicles) have had a huge impact on the form structure and operation characteristics of active distribution networks, including the network flexible interconnection, grid connections to new distribution equipment, multiple flexible control modes, and power imbalances of source and load. These not only increase the complexity of the analysis of the active distribution network, but also make the control of the distribution network more difficult. To address these issues, some new methods are needed to analyze the morphological evolution, flexibility, stability, and security of active distribution networks, and to realize their safe and stable operation through advanced control technology.

This Special Issue on ‘Analysis and Optimization Control of Active Distribution Networks and Smart Grids’ calls for state-of-the-art research works on this promising research area. This Special Issue will gather high-quality research articles with original contributions to studies on the analysis and optimization control of active distribution networks and smart grids. Topics of interest include, but are not limited to:

  • Forecasting of renewable generations and active loads in active distribution and smart grid;
  • Modeling and optimizing techniques in optimal operation of active distribution networks and smart grid;
  • Morphological evolution of active distribution networks and smart grid;
  • Hosting capacity assessment for renewables in active distribution networks and smart grid;
  • Flexibility and resilience quantification for active distribution networks and smart grid;
  • Security risk analysis and defense of cyber-physical system of active distribution networks and smart grids;
  • Stability analysis and control of active distribution networks and smart grids;
  • Power quality analysis and control of active distribution networks and smart grids;
  • Volt/var control and energy management for active distribution networks and smart grid under uncertainties;
  • Advanced learning-based modeling and optimization control of active distribution networks and smart grid with high penetration of renewables;
  • Power electronics converters control and optimization in hybrid propulsion microgrids

Dr. Jingrui Zhang
Dr. Jian Wang
Dr. Po Li
Dr. Tengpeng Chen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • active distribution network
  • smart grid
  • renewables
  • analysis and control
  • uncertainty
  • forecasting

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Published Papers (10 papers)

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23 pages, 3055 KiB  
Article
Integrated Coordinated Control of Source–Grid–Load–Storage in Active Distribution Network with Electric Vehicle Integration
by Shunjiang Wang, Yiming Luo, Peng Yu and Ruijia Yu
Processes 2025, 13(5), 1285; https://doi.org/10.3390/pr13051285 - 23 Apr 2025
Viewed by 101
Abstract
In line with the strategic plan for emerging industries in China, renewable energy sources like wind power and photovoltaic power are experiencing vigorous growth, and the number of electric vehicles in use is on a continuous upward trend. Alongside the optimization of the [...] Read more.
In line with the strategic plan for emerging industries in China, renewable energy sources like wind power and photovoltaic power are experiencing vigorous growth, and the number of electric vehicles in use is on a continuous upward trend. Alongside the optimization of the distribution network structure and the extensive application of energy storage technology, the active distribution network has evolved into a more flexible and interactive “source–grid–load–storage” diversified structure. When electric vehicles are plugged into charging piles for charging and discharging, it inevitably exerts a significant impact on the control and operation of the power grid. Therefore, in the context of the extensive integration of electric vehicles, delving into the charging and discharging behaviors of electric vehicle clusters and integrating them into the optimization of the active distribution network holds great significance for ensuring the safe and economic operation of the power grid. This paper adopts the two-stage “constant-current and constant-voltage” charging mode, which has the least impact on battery life, and classifies the electric vehicle cluster into basic EV load and controllable EV load. The controllable EV load is regarded as a special “energy storage” resource, and a corresponding model is established to enable its participation in the coordinated control of the active distribution network. Based on the optimization and control of the output behaviors of gas turbines, flexible loads, energy storage, and electric vehicle clusters, this paper proposes a two-layer coordinated control model for the scheduling layer and network layer of the active distribution network and employs the improved multi-target beetle antennae search optimization algorithm (MTTA) in conjunction with the Cplex solver for solution. Through case analysis, the results demonstrate that the “source–grid–load–storage” coordinated control of the active distribution network can fully tap the potential of resources such as flexible loads on the “load” side, traditional energy storage, and controllable EV clusters; realize the economic operation of the active distribution network; reduce load and voltage fluctuations; and enhance power quality. Full article
(This article belongs to the Special Issue Analysis and Optimization Control of Active Distribution Networks and Smart Grids)
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18 pages, 3723 KiB  
Article
Ultra-Short-Term Load Forecasting for Extreme Scenarios Based on DBSCAN-RSBO-BiGRU-KNN-Attention with Fine-Tuning Strategy
by Leibao Wang, Jifeng Liang, Jiawen Li, Yonghui Sun, Hongzhu Tao, Qiang Wang and Tengkai Yu
Processes 2025, 13(4), 1161; https://doi.org/10.3390/pr13041161 - 11 Apr 2025
Viewed by 227
Abstract
Extreme scenarios involving abnormal load fluctuations pose serious challenges to the safe and stable operation of power systems. To address these challenges, an ultra-short-term load forecasting model is proposed, specifically designed for extreme conditions. The model combines density-based spatial clustering of applications with [...] Read more.
Extreme scenarios involving abnormal load fluctuations pose serious challenges to the safe and stable operation of power systems. To address these challenges, an ultra-short-term load forecasting model is proposed, specifically designed for extreme conditions. The model combines density-based spatial clustering of applications with noise (DBSCAN), random search Bayesian optimization (RSBO), bidirectional gated recurrent units (BiGRUs), k-nearest neighbor (KNN), and an attention mechanism, enhanced by a fine-tuning strategy to improve forecasting accuracy. Firstly, the original load data are reconstructed weekly, and extreme scenarios are identified using the DBSCAN. Secondly, the RSBO is employed to optimize model parameters within the high-dimensional search space. To further refine performance, the final fully connected layer is fine-tuned to adapt to extreme conditions. Finally, case studies demonstrate that the proposed approach reduces the root mean square error (RMSE) by 12.37% and the mean absolute error (MAE) by 6.73% compared to benchmark models, achieving superior accuracy under all tested extreme scenarios. Full article
(This article belongs to the Special Issue Analysis and Optimization Control of Active Distribution Networks and Smart Grids)
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16 pages, 5879 KiB  
Article
Partial Discharge Pattern Recognition Based on Swin Transformer for Power Cable Fault Diagnosis in Modern Distribution Systems
by Yifei Li, Cheng Gong, Tun Deng, Zihao Jia, Fang Wang, Qiao Zhao and Jingrui Zhang
Processes 2025, 13(3), 852; https://doi.org/10.3390/pr13030852 - 14 Mar 2025
Viewed by 415
Abstract
As critical infrastructure in modern distribution systems, power cables face progressive insulation degradation from partial discharge (PD), while conventional recognition methods struggle with feature extraction and model generalizability. This study develops an integrated experimental platform for PD pattern recognition in power cable systems, [...] Read more.
As critical infrastructure in modern distribution systems, power cables face progressive insulation degradation from partial discharge (PD), while conventional recognition methods struggle with feature extraction and model generalizability. This study develops an integrated experimental platform for PD pattern recognition in power cable systems, comprising a control console, high-voltage transformer, high-frequency current transformer, and ultra-high-frequency (UHF) signal acquisition equipment. Four distinct types of discharge-defective models are constructed and tested through this dedicated high-voltage platform, generating a dataset of phase-resolved partial discharge (PRPD) spectra. Based on this experimental foundation, an improved Swin Transformer-based framework with adaptive learning rate optimization is developed to address the limitations of conventional methods. The proposed architecture demonstrates superior performance, achieving 94.68% classification accuracy with 20 training epochs while reaching 97.52% at the final 200th epoch. Comparisons with the original tiny version of the Swin Transformer model show that the proposed Swin Transformer with an adaptive learning rate attains a maximum improvement of 6.89% over the baseline model in recognition accuracy for different types of PD defect detection. Comparisons with other deeper Convolutional Neural Networks illustrate that the proposed lightweight Swin Transformer can achieve comparable accuracy with significantly lower computational demands, making it more promising for application in real-time PD defect diagnostics. Full article
(This article belongs to the Special Issue Analysis and Optimization Control of Active Distribution Networks and Smart Grids)
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29 pages, 5353 KiB  
Article
LSTM Model Combined with Rolling Empirical Mode Decomposition and Sample Entropy Reconstruction for Short-Term Wind Speed Forecasting
by Sen Yao, Hong Zhu, Xin Zhou, Tingxin Peng and Jingrui Zhang
Processes 2025, 13(3), 819; https://doi.org/10.3390/pr13030819 - 11 Mar 2025
Viewed by 581
Abstract
This research introduces a new hybrid forecasting approach based on a rolling decomposition–merging–prediction framework integrating Empirical Mode Decomposition (EMD), sample entropy, and Long Short-Term Memory (LSTM) to further enhance the accuracy of wind speed predictions. To avoid the information leakage issue caused by [...] Read more.
This research introduces a new hybrid forecasting approach based on a rolling decomposition–merging–prediction framework integrating Empirical Mode Decomposition (EMD), sample entropy, and Long Short-Term Memory (LSTM) to further enhance the accuracy of wind speed predictions. To avoid the information leakage issue caused by decomposing wind speed data, a rolling EMD method is applied to the framework to ensure that the data points to be predicted are excluded from the decomposition process. The input speed data of the prediction model are then decomposed into a series of Intrinsic Mode Functions (IMFs) and a residual component, capturing the local variation characteristics of the wind speed data. Next, the sample entropy method is employed to calculate the entropy values of these components, which are then reclassified and aggregated into three components based on their calculated entropy values, corresponding to high, medium, and low frequencies. The three reconstructed components are then employed as input features in an LSTM model for wind speed prediction. To demonstrate the effectiveness of the proposed model, experiments using three different datasets were conducted with wind speed data collected from a wind farm. The statistical experimental results indicate that the proposed EMD-LSTM achieves improvements in metrics of MAE, RMSE, and MAPE by at least 3.64%, 7.25%, and 5.02%, respectively, compared to other methods across the evaluated test datasets. Furthermore, the Wilcoxon test results provide additional evidence, confirming that the EMD-LSTM model exhibits a statistically significant advantage in prediction performance over the ARIMA, GRU, and SVM models. Full article
(This article belongs to the Special Issue Analysis and Optimization Control of Active Distribution Networks and Smart Grids)
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20 pages, 5424 KiB  
Article
The Multi-Point Cooperative Control Strategy for Electrode Boilers Supporting Grid Frequency Regulation
by Tao Shi, Chunlei Wang and Zhiqiang Chen
Processes 2025, 13(3), 785; https://doi.org/10.3390/pr13030785 - 8 Mar 2025
Viewed by 463
Abstract
With the large-scale integration of wind power, photovoltaic, and other renewable energy sources into the power grid, their inherent randomness and variability present significant challenges to the frequency stability of power systems. Conventional thermal power units with limited frequency regulation capabilities face further [...] Read more.
With the large-scale integration of wind power, photovoltaic, and other renewable energy sources into the power grid, their inherent randomness and variability present significant challenges to the frequency stability of power systems. Conventional thermal power units with limited frequency regulation capabilities face further strain, as frequent power fluctuations accelerate wear and tear, thereby shortening their operational lifespans. This makes it increasingly difficult to meet the demands for frequency regulation. Electrode boilers, as flexible electrical loads, can be retrofitted to enhance their flexibility and participate in grid frequency regulation alongside renewable energy units. This not only improves frequency stability but also reduces wear on generating units. However, the frequency regulation process involves balancing multiple objectives, such as maintaining system frequency stability, ensuring economic efficiency, and optimizing operational effectiveness. Traditional control strategies often struggle to address these competing objectives effectively. To address these challenges, this paper proposes a multi-objective collaborative optimization control decision model for electrode boilers to assist in grid frequency regulation. The model not only meets the frequency regulation requirements but also considers additional constraints, including the operational efficiency of electrode boilers, economic benefits, and equipment degradation. A genetic algorithm is employed to solve the model, and simulation analysis is conducted using the IEEE 14-node system. The results demonstrate that this strategy significantly enhances frequency stability, improves boiler operational efficiency, and boosts economic benefits, offering a viable solution for integrating electrode boilers into grid frequency regulation. Full article
(This article belongs to the Special Issue Analysis and Optimization Control of Active Distribution Networks and Smart Grids)
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36 pages, 12581 KiB  
Article
Data Clustering-Driven Fuzzy Inference System-Based Optimal Power Flow Analysis in Electric Networks Integrating Wind Energy
by Gheorghe Grigoras, Bogdan Livadariu and Bogdan-Constantin Neagu
Processes 2025, 13(3), 676; https://doi.org/10.3390/pr13030676 - 27 Feb 2025
Viewed by 381
Abstract
The development of smart grids has led to an increased focus by transmission and distribution network operators on the Optimal Power Flow (OPF) problem. The solutions identified for an OPF problem are vital to ensure the real-time optimal control and operation of electric [...] Read more.
The development of smart grids has led to an increased focus by transmission and distribution network operators on the Optimal Power Flow (OPF) problem. The solutions identified for an OPF problem are vital to ensure the real-time optimal control and operation of electric networks and can help enhance their efficiency. In this context, this paper proposed an original solution to the OPF problem, represented by optimal voltage control in electric networks integrating wind farms. Based on a fuzzy inference system (FIS) built in the Fuzzy Logic Designer of the Matlab environment, where the fuzzification process was improved through fuzzy K-means clustering, two approaches were developed, representing novel tools for OPF analysis. The decision-maker can use these two approaches only successively. The FIS-based first approach considers the load requested at the PQ-type buses and the powers injected by the wind farms as the fuzzy input variables. Based on the fuzzy inference rules, the FIS determines the suitable tap positions for power transformers to minimise active power losses. The second approach (I-FIS), representing an improved variant of FIS, calculates the steady-state regime to determine power losses based on the suitable tap positions for power transformers, as determined with FIS. A real 10-bus network integrating two wind farms was used to test the two proposed approaches, considering comprehensive characteristic three-day tests to thoroughly highlight the performance under different injection active power profiles of the wind farms. The results obtained were compared with those of the best methods in constrained nonlinear mathematical programming used in OPF analysis, specifically sequential quadratic programming (SQP). The errors calculated throughout the analysis interval between the SQP-based approach, considered as the reference, and the FIS and I-FIS-based approaches were 5.72% and 2.41% for the first day, 1.07% and 1.19% for the second day, and 1.61% and 1.33% for the third day. The impact of the OPF, assessed by calculating the efficiency of the electric network, revealed average percentage errors between 0.04% and 0.06% for the FIS-based approach and 0.01% for the I-FIS-based approach. Full article
(This article belongs to the Special Issue Analysis and Optimization Control of Active Distribution Networks and Smart Grids)
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26 pages, 12223 KiB  
Article
Integrating GIS and AHP for Photovoltaic Farm Site Selection: A Case Study of Ikorodu, Nigeria
by Hubert Onuoha, Iheanacho Denwigwe, Olubayo Babatunde, Khadeejah Adebisi Abdulsalam, John Adebisi, Michael Emezirinwune, Taiwo Okharedia, Akintade Akindayomi, Kolawole Adisa and Yskandar Hamam
Processes 2025, 13(1), 164; https://doi.org/10.3390/pr13010164 - 9 Jan 2025
Cited by 1 | Viewed by 1239
Abstract
Large-scale renewable energy plants such as solar photovoltaic (PV) farms are vital to the global transition to a green energy economy. They reduce greenhouse gas emissions, mitigate climate change, and promote sustainable and resilient energy. However, large-scale solar PV farms need adequate planning [...] Read more.
Large-scale renewable energy plants such as solar photovoltaic (PV) farms are vital to the global transition to a green energy economy. They reduce greenhouse gas emissions, mitigate climate change, and promote sustainable and resilient energy. However, large-scale solar PV farms need adequate planning and site selection for optimal performance. This study presents a geographic information system (GIS)-based multi-criteria decision-making (MCDM) framework utilizing the analytic hierarchy process (AHP) to identify optimal sites for utility-scale photovoltaic (PV) farms in Ikorodu, Lagos State, Nigeria. By integrating critical environmental, technical, economic, and social factors, the model evaluates land suitability for solar energy projects across the study area. The finding indicates that 68.77% of the land is unsuitable for development, with only 17.78% classified as highly suitable and 12.67% as moderately suitable. Marginally suitable and most appropriate areas are minimal, at 0.73% and 0.04%, respectively. This study provides a replicable approach for stakeholders and policymakers aiming to implement sustainable energy solutions, aligning with national renewable energy targets. Future research could integrate dynamic factors such as community engagement, land use changes, and evolving environmental policies to enhance decision-making models. This framework offers valuable insights into renewable energy planning and contributes to advancing Nigeria’s transition to sustainable energy systems. Full article
(This article belongs to the Special Issue Analysis and Optimization Control of Active Distribution Networks and Smart Grids)
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19 pages, 1478 KiB  
Article
Risk Analysis and Mitigation Strategy of Power System Cascading Failure Under the Background of Weather Disaster
by Ping Liu, Penghui Liu, Yang Yang, Jilong Wu, Guang Tian, Zitong Zhang and Longyue Chai
Processes 2025, 13(1), 45; https://doi.org/10.3390/pr13010045 - 27 Dec 2024
Viewed by 705
Abstract
In mountainous regions, forested areas, and other zones prone to natural disasters, power equipment faces heightened risks of shutdown. Such disruptions significantly elevate the risk of secondary cascading failures within the power grid. Consequently, devising cascading failure mitigation strategies from an operational perspective [...] Read more.
In mountainous regions, forested areas, and other zones prone to natural disasters, power equipment faces heightened risks of shutdown. Such disruptions significantly elevate the risk of secondary cascading failures within the power grid. Consequently, devising cascading failure mitigation strategies from an operational perspective is of paramount importance for containing the spread of cascading failures in the power system during disasters and minimizing the losses incurred from disaster incidents. Firstly, based on the severity of natural disaster accident risks, this paper establishes a risk index for power equipment for the first time, providing a new perspective for the refined analysis of the development model of cascading failures in power systems. Subsequently, a new collaborative mitigation strategy for system cascading failures is proposed at the operational control level. This strategy, in conjunction with proactive prevention and control measures, aims to promptly sever potential cascading failure paths upon the occurrence of a disaster, thereby ensuring that the area of power outage is minimized to the greatest extent possible. The effectiveness of the proposed strategy is verified through simulation cases. The results show that in the scenario set in this article, the risk of cascading failures under natural disasters is nearly five times higher than that without natural disasters. At the same time, the cascading failure control method proposed in this study can reduce the risk of cascading failure by about 80%. Full article
(This article belongs to the Special Issue Analysis and Optimization Control of Active Distribution Networks and Smart Grids)
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25 pages, 1471 KiB  
Article
Optimal Placement and Sizing of Modular Series Static Synchronous Compensators (M-SSSCs) for Enhanced Transmission Line Loadability, Loss Reduction, and Stability Improvement
by Cristian Urrea-Aguirre, Sergio D. Saldarriaga-Zuluaga, Santiago Bustamante-Mesa, Jesús M. López-Lezama and Nicolás Muñoz-Galeano
Processes 2025, 13(1), 34; https://doi.org/10.3390/pr13010034 - 27 Dec 2024
Cited by 1 | Viewed by 856
Abstract
This paper addresses the optimal placement and sizing of Modular Static Synchronous Series Compensators (M-SSSCs) to enhance power system performance. The proposed methodology optimizes four key objectives: reducing transmission line loadability, minimizing power losses, mitigating voltage deviations, and enhancing voltage stability using the [...] Read more.
This paper addresses the optimal placement and sizing of Modular Static Synchronous Series Compensators (M-SSSCs) to enhance power system performance. The proposed methodology optimizes four key objectives: reducing transmission line loadability, minimizing power losses, mitigating voltage deviations, and enhancing voltage stability using the L-index. The methodology is validated on two systems: the IEEE 14-bus test network and a sub-area of the Colombian power grid, characterized by aging infrastructure and operational challenges. The optimization process employs three metaheuristic algorithms—Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Teaching–Learning-Based Optimization (TLBO)—to identify optimal configurations. System performance is analyzed under both normal operating conditions and contingency scenarios (N − 1). The results demonstrate that M-SSSC deployment significantly reduces congestion, enhances voltage stability, and improves overall system efficiency. Furthermore, this work highlights the practical application of M-SSSC in modernizing real-world grids, aligning with sustainable energy transition goals. This study identifies the optimal M-SSSC configurations and placement alternatives for the analyzed systems. Specifically, for the Colombian sub-area, the most suitable solutions involve installing M-SSSC devices in capacitive mode on the Termocol–Guajira and Santa Marta–Guajira 220 kV transmission lines. Full article
(This article belongs to the Special Issue Analysis and Optimization Control of Active Distribution Networks and Smart Grids)
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18 pages, 4460 KiB  
Article
Novel Quasi-Z-Source Inverter with High-Frequency AC Link of High-Proportion Renewable-Energy Power System
by Wenjuan Dong, Xingang Wang, DeLiNuEr Azan, Yuwei Wang and Lei Li
Processes 2024, 12(12), 2842; https://doi.org/10.3390/pr12122842 - 11 Dec 2024
Viewed by 860
Abstract
Z-source/quasi-z-source inverters can make up for some limitations of traditional voltage-/current-source inverters. In recent years, more and more research has been carried on z-source/quasi-z-source inverters, but most of them are unable to realize input/output galvanic isolation. The proposal of high-frequency isolated z-source/quasi-z-source inverters [...] Read more.
Z-source/quasi-z-source inverters can make up for some limitations of traditional voltage-/current-source inverters. In recent years, more and more research has been carried on z-source/quasi-z-source inverters, but most of them are unable to realize input/output galvanic isolation. The proposal of high-frequency isolated z-source/quasi-z-source inverters greatly enriches the topological family of this type of converter but places relatively high voltage stress on the capacitors. In this paper, a novel circuit topology of a quasi-z-source inverter with a high-frequency AC link of a new high-proportion power system is proposed. The operating principle and abnormal operating states, such as discontinuous-conduction mode (DCM) operation and abnormal states caused by component failures, are analyzed. The double closed-loop control strategy is analyzed and designed, and a grid-connected photovoltaic system based on the inverter is designed. The experimental results verify that the presented inverter has advantages such as high-frequency electrical isolation, bi-directional power flow, lower voltage stress on the capacitors, etc. Full article
(This article belongs to the Special Issue Analysis and Optimization Control of Active Distribution Networks and Smart Grids)
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