<?xml version="1.0" encoding="utf-8"?>
    <rss version="2.0">
      <channel xmlns:content="http://purl.org/rss/1.0/modules/content/">
        <title>Frontiers in Smart Grids | Smart Grid Control section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/smart-grids/sections/smart-grid-control</link>
        <description>RSS Feed for Smart Grid Control section in the Frontiers in Smart Grids journal | New and Recent Articles</description>
        <language>en-us</language>
        <generator>Frontiers Feed Generator,version:1</generator>
        <pubDate>2026-05-10T08:07:37.967+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsgr.2026.1652647</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsgr.2026.1652647</link>
        <title><![CDATA[From data to decision: a holistic data-driven method for HVDC commutation failure pre-judgement]]></title>
        <pubdate>2026-04-14T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Huanhuan Yang</author><author>Shuqing Zhang</author><author>Jianxin Zhang</author><author>Tongtong Zhang</author><author>Yong Mei</author><author>Weijie Zhang</author><author>Yanan Zhu</author><author>Tuo Jiang</author>
        <description><![CDATA[Commutation failure poses a significant operational risk on HVDC transmission systems, with its prediction facing challenges from complex underlying mechanisms and stringent real-time requirements. Data-driven techniques show promise, but practical implementation of fully data-driven solutions remains unresolved. This paper introduces a novel fully data-driven fast prediction framework for commutation failures, featuring three principal innovations: (i) A second-order determinant-based feature extraction algorithm that compresses data dimensionality while preserving critical disturbance characteristics; (ii) A cost-sensitive learning technique integrated with sample augmentation strategies to address sample bias problem; (iii) A classification performance evaluation protocol tailored for engineering applications. Experimental validation on the modified CIGRE benchmark system demonstrates 1.25 and 1% false alarm rate and miss rate respectively, with response time reduced to within 2 ms (at 2.5 kHz sampling rate). The proposed methodology significantly reduces dependency on extensive training datasets, offering a viable purely data-driven solution for real-time commutation failure pre-judgement.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsgr.2025.1617763</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsgr.2025.1617763</link>
        <title><![CDATA[CatBoost-enhanced convolutional neural network framework with explainable artificial intelligence for smart-grid stability forecasting]]></title>
        <pubdate>2025-11-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Stephanie Ness</author>
        <description><![CDATA[IntroductionMobile robots increasingly support inspection and emergency response in smart-grid infrastructure but require accurate, interpretable backend diagnostics. This work is proposing a hybrid model that integrates CatBoost (for tabular features) with a deep 1D-CNN (for spatial feature extraction) and integrates Local Interpretable Model-agnostic Explanations (LIME) to provide transparent, instance-level rationales.MethodsWe evaluate on a synthetic DSGC-based stability dataset (14 features) and externally on the IEEE PES 2018 fault-clearing corpus. The hybrid concatenates CatBoost output probabilities with a three-layer CNN feature vector, followed by dense layers (ReLU and Sigmoid). Models are trained using the Adam optimizer. Performance is reported via Accuracy, Precision, Recall, F1, confusion matrices, ROC-AUC, and LIME explanations.ResultsOn the generated synthetic data, the hybrid achieved 98.23% accuracy (F1 = 97.56%), outperforming ANN, DNN and CNN baselines. External validation on IEEE PES 2018 yielded F1 = 97.6%.DiscussionCombining gradient-boosted trees with deep convolutional features improves discrimination while and it is preserving local explainability. This way it can be supporting both grid operations and stability-aware robotic mission planning. Future work will extend to multiclass/regression settings and compare XAI methods (e.g., SHAP) alongside additional tabular learners (XGBoost/LightGBM).]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsgr.2024.1505351</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsgr.2024.1505351</link>
        <title><![CDATA[A multi-stage balancing scheduling method for flexible loads of large-scale electric vehicles]]></title>
        <pubdate>2024-12-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Songling Pang</author><author>Hailong Zhao</author><author>Meiyi Huo</author>
        <description><![CDATA[To address the degradation of grid quality and charging efficiency associated with the large-scale integration of electric vehicles (EVs), a multi-stage balanced flexible load scheduling method is proposed. This approach is designed to facilitate peak shaving and valley filling, balance intermittent energy fluctuations, and provide auxiliary services, thereby significantly altering system load characteristics, smoothing energy fluctuations, reducing operational costs, and enhancing the regulatory capabilities of power grid dispatching operations. A multi-objective optimization mathematical model is developed, focusing on key indicators that impact the scheduling process, including network loss, operational cost, and user satisfaction. A multi-stage flexible load scheduling framework is introduced within the competitive swarm optimization (CSO) algorithm, resulting in the design of an advanced CSO algorithm. This algorithm is distinguished from traditional methods by the implementation of advanced learning based on grouping after a random competitive learning phase, which enhances the efficiency of particle swarm learning while ensuring stable population convergence throughout the optimization process. Furthermore, the CSO framework is maintained to ensure effective population diversity, greatly improving the optimization performance. Simulation results indicate that the voltage fluctuation index of the proposed algorithm is 1.8% lower than that of the standard CSO algorithm, while network loss and operational costs are reduced by 2.83 and 5.81%, respectively, thereby validating the effectiveness and efficiency of the proposed approach.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsgr.2024.1476695</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsgr.2024.1476695</link>
        <title><![CDATA[Study on improved control strategy of virtual synchronous generator]]></title>
        <pubdate>2024-11-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ji Li</author><author>Fan Yang</author><author>Huanmin Wang</author><author>Bingqing Chu</author>
        <description><![CDATA[Virtual synchronous generator (VSG) control technology for photovoltaic, energy storage, wind power, and other new energy to provide flexibility in the grid interface characteristics, is conducive to improving the stability of the power system, and has been widely considered by many scholars. Firstly, an improved VSG control method is proposed through simulation and analysis, which realizes the complete decoupling of the frequency response time constant and the inertia quantity of the active power control loop and reduces the complexity of the parameter design of the VSG system. Secondly, to avoid the frequent action of the VSG system caused by small-scale frequency change perturbation, this study proposes a VSG frequency optimization control method for VSG frequency control with rated angular velocity ωset feedforward composed of multivariate factors when considering a primary frequency regulation dead zone. Thirdly, the impact of VSG parameter design on the system is investigated through the system response characteristics of power scheduling, primary frequency regulation at the grid connection, and the small-signal dynamic characterization of the improved VSG. Finally, Simulation and experimental verification yielded an active power overshoot of 7% and a maximum frequency deviation of 0.17 Hz for the improved system. The improved control method resulted in an improvement of 0.1 s in the frequency response time and a reduction of 0.15 Hz in the oscillation amplitude. The response speed of the improved control method is much better, while the oscillation amplitude is reduced to meet the grid's regulation requirements. The simulation and experimental analysis verify the feasibility of the improved VSG control method.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsgr.2024.1449152</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsgr.2024.1449152</link>
        <title><![CDATA[Smooth control strategy for emergency switching of multi-port flexible interconnected distribution system modes]]></title>
        <pubdate>2024-08-20T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Dan Pang</author><author>Yu Yin</author><author>Zhipeng Wang</author><author>Jinming Ge</author><author>Wei Wang</author><author>Zhenhao Wang</author><author>Hongyin Yi</author><author>Yan Zhuang</author>
        <description><![CDATA[IntroductionWith the rise of distributed energy resources, the interconnection of distribution networks and Flexible Multi-State Switch (FMSS) has become a key technology in the construction of new distribution networks. FMSS plays a significant role in enhancing the reliability and flexibility of the system.MethodsThis paper investigates the impact of mode-switching in FMSSs on voltage shocks, current shocks, and power fluctuations in the event of a feeder fault in a multi-port flexible interconnected distribution system. Firstly, an improved state-tracking control method is proposed to effectively mitigate these impacts. Secondly, for feeder faults connected to the fixed DC bus voltage (Udc-Q) control port, a reselection method for the Udc-Q control port at the main station is introduced, aiming to select the optimal port to maintain the stability of the DC bus voltage.ResultsSimulation experiments have validated the effectiveness of the proposed control method in reducing voltage and current shocks as well as power fluctuations. Additionally, the proposed control strategy has demonstrated an enhancement in the safe and stable operation of the system under emergency fault conditions.DiscussionThe control strategy presented in this paper is capable of addressing the challenges posed by feeder faults and ensuring the stability of the DC bus voltage and reliable power supply to feeder loads, thereby enhancing the overall performance and reliability of the flexible interconnected distribution system. The research findings have been verified on the MATLAB/Simulink simulation platform.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsgr.2024.1397380</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsgr.2024.1397380</link>
        <title><![CDATA[Cyber resilience methods for smart grids against false data injection attacks: categorization, review and future directions]]></title>
        <pubdate>2024-05-03T00:00:00Z</pubdate>
        <category>Mini Review</category>
        <author>Andrew D. Syrmakesis</author><author>Nikos D. Hatziargyriou</author>
        <description><![CDATA[For a more efficient monitoring and control of electrical energy, the physical components of conventional power systems are continuously integrated with information and communication technologies, converting them into smart grids. However, energy digitalization exposes power systems into a wide range of digital risks. The term cyber resilience for electrical grids expands the conventional resilience of power systems, which mainly refers to extreme weather phenomena. Since this is a relatively new term, there is a need for the establishment of a solid conceptual framework. This paper analyzes and classifies the state-of-the-art research methodologies proposed for strengthening the cyber resilience of smart grids. To this end, the proposed work categorizes the cyberattacks against smart grids, identifies the vulnerable spots of power system automation and establishes a common ground about the cyber resilience. The paper concludes with a discussion about the limitations of the proposed methods in order to extract useful suggestions for future directions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsgr.2023.1188074</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsgr.2023.1188074</link>
        <title><![CDATA[Model predictive control–based robust-control strategy of distribution control for a grid-connected AC microgrid]]></title>
        <pubdate>2023-12-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>James Xorse Halivor</author>
        <description><![CDATA[The challenge of ensuring the reliable running of power systems has gotten more difficult in recent years due to the rising complexity of power system networks. The decreasing accessibility of fossil fuels has necessitated a greater dependence on renewable energy sources, such as solar systems, wind power, and hydroelectric power, by the international community. As a result, there is an increasing demand for AC microgrids to offer an effective approach for distributing power. The power system networks that consist of microgrids frequently have a significant number of failures, surpassing 80%. These failures occur because microgrids are susceptible to unexpected changes in different distributed generating sources. The variations greatly impair the operating efficiency of the microgrid and have negative consequences for the distribution system. The microgrid consists of numerous dispersed generation units and local loads. The load in a microgrid exhibits parametric uncertainty, which adds to the fluctuation observed in its performance. The formulated control strategy is Model Predictive Control, which aims to achieve robust performance even in the presence of unaccounted-for loads, dynamic loads, harmonic loads, and both balanced and unbalanced loads. The authors of this paper have developed a control approach that utilizes model predictive control (MPC) and is characterized by its robustness and optimality. MPC has the ability to predict the future behavior of a certain system. The controller successfully mitigates and reduces any disruptions that may occur within the power distribution system by taking into account its healthy characteristics. The model is implemented in the MATLAB Simulink environment, where it produces an accurate and appropriate total harmonic distortion value. The model was compared to previous efforts and significantly improved by increasing some crucial parameters by up to 90%. The value functions as a measure of the controller's performance quality and the improved efficiency of the microgrid system.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsgr.2023.1129541</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsgr.2023.1129541</link>
        <title><![CDATA[Research on the flow characteristics identification of steam turbine valve based on FCM-LSSVM]]></title>
        <pubdate>2023-03-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xiaoguang Hao</author><author>Fei Jin</author><author>Bin Wang</author><author>Qinghao Zhang</author><author>Chuang Wu</author><author>Hao Sun</author>
        <description><![CDATA[Due to aging and deformation of the through-flow path and system modifications, the flow characteristics of the turbine inlet valve often deviate from the design value, which affects the unit load control accuracy and operational stability. In order to obtain the actual valve flow characteristics of the turbine and thus improve the FM performance, an FCMLSSVM model is proposed in this paper to identify the valve flow characteristics. First, FCM clustering is proposed to classify the historical operating data of the plant and obtain a wide range of variable operating conditions. Then, using least squares support vector machine (LSSVM), the relationship between turbine input and output variables was modeled in each condition cluster, with integrated valve position command, speed, and real power generated as input variables and actual steam inlet flow as output variables. Using a 330 MW turbine unit as an application example, the established FCM-LSSVM model was validated for the valve flow characteristics of the turbine. The results show that the model can obtain accurate valve flow characteristics without conducting tests on the turbine. The method can save a lot of labor and material resources in doing the characteristic test, and after comparison, the proposed method can identify the flow characteristics more accurately among the existing neural network identification methods, which can provide technical support to improve the unit frequency regulation characteristics and improve the accuracy of valve operation.]]></description>
      </item>
      </channel>
    </rss>