{"title":"Scheme to Prevent Maloperation of Current Differential Protection Due to Non-Internal-Fault Events in Active Distribution Networks","authors":"Tong Yuan;Houlei Gao;Fang Peng;Lin Li","doi":"10.1109/TSG.2024.3525113","DOIUrl":"10.1109/TSG.2024.3525113","url":null,"abstract":"In active distribution networks (ADN), T-connected branches are commonly found in feeders, presenting a challenge for protection schemes. Current differential protection (CDP) is one of the solutions for such lines. Compared to a multi-terminal (<inline-formula> <tex-math>$geq 3$ </tex-math></inline-formula>) CDP, a two-terminal CDP is a more cost-effective alternative as it does not require instrument transformers and communication devices for each branch. However, implementing a two-terminal CDP for such a line requires careful handling of transient currents to prevent undesired tripping. This paper initially investigates the effects of transient current from T-connected transformers or induction motors (IMs) on two-terminal CDP and then proposes an anti-maloperation scheme based on current decaying and line voltage changing. In this scheme, the positive-sequence component of differential current (PSCDC) is calculated using the least error square (LES) method. Subsequently, the decaying ratio of PSCDC and the line voltage changing ratio are used to determine whether the two-terminal CDP should be blocked or unblocked. Finally, the effectiveness of the proposed scheme is verified through simulations using an ADN model based on PSCAD/EMTDC. Simulation results show that the proposed scheme can reliably identify transient current from T-connected transformer and IM under different conditions, significantly mitigating two-terminal CDP maloperation issues.","PeriodicalId":13331,"journal":{"name":"IEEE Transactions on Smart Grid","volume":"16 2","pages":"1019-1036"},"PeriodicalIF":8.6,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142917297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Carbon-Aware Scheduling of Thermostatically Controlled Loads: A Bilevel DRCC Approach","authors":"Yijie Yang;Jian Shi;Dan Wang;Chenye Wu;Zhu Han","doi":"10.1109/TSG.2024.3525134","DOIUrl":"10.1109/TSG.2024.3525134","url":null,"abstract":"Thermostatically controlled loads (TCLs), including air conditioners, heat pumps, water heaters, and refrigerators, play a pivotal role in demand response due to their thermal inertia and inherent flexibility. TCLs also substantially impact energy consumption and emissions within commercial and residential buildings, which makes them critical for the low-/zero-carbon transition that the building sector is undergoing to meet global climate objectives. To aid in this process, this paper proposes a carbon-aware robust scheduling approach for TCLs. The proposed approach precisely models carbon emissions attributed to TCLs, and formulates TCL scheduling as a distributionally robust chance-constrained (DRCC) optimization problem to ensure robust decision-making. We then develop a novel bilevel optimization reformulation strategy to address challenges such as over-conservatism and computational intractability that often arise from solving DRCC problems using conventional approaches. Real-world data evaluation demonstrates significant reductions in costs and carbon emissions compared to state-of-the-art methods, showcasing the effectiveness of our approach in potentially decarbonizing the building sector.","PeriodicalId":13331,"journal":{"name":"IEEE Transactions on Smart Grid","volume":"16 2","pages":"1233-1247"},"PeriodicalIF":8.6,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142917323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yihan Xia, Xinli Wang, Xiaohong Yin, Wanlin Bo, Lei Wang, Shaoyuan Li, Kang Li
{"title":"Federated Accelerated Deep Reinforcement Learning for Multi-Zone HVAC Control in Commercial Buildings","authors":"Yihan Xia, Xinli Wang, Xiaohong Yin, Wanlin Bo, Lei Wang, Shaoyuan Li, Kang Li","doi":"10.1109/tsg.2024.3524756","DOIUrl":"https://doi.org/10.1109/tsg.2024.3524756","url":null,"abstract":"","PeriodicalId":13331,"journal":{"name":"IEEE Transactions on Smart Grid","volume":"81 1","pages":""},"PeriodicalIF":9.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142911612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Frequency Constrained Proactive Scheduling for Secure Microgrid Formation in Wind Power Penetrated Distribution Systems","authors":"Sheng Cai;Yunyun Xie;Yuping Zhang;Weiyu Bao;Qiuwei Wu;Chen Chen;Jian Guo","doi":"10.1109/TSG.2024.3524557","DOIUrl":"10.1109/TSG.2024.3524557","url":null,"abstract":"Microgrid formation (MF) is a core solution for increasing the resilience of distribution systems in extreme situations. However, a significant power imbalance at the MF onset will result in the violation of dynamic frequency constraints, especially in low-inertia wind power penetrated distribution systems (WPP-DSs). To ensure the secure MF after emergencies, this paper proposes a frequency constrained proactive scheduling method for WPP-DSs. The primary frequency response (PFR) model is proposed to describe the microgrid frequency dynamics after islanding, where wind turbines are deloaded to provide a primary reserve. Then the PFR model is incorporated into the scheduling model to ensure frequency security during MF process. The proposed method proactively dispatches controllable units to mitigate the power imbalance and to reserve power for frequency regulation. After emergencies, the primary reserve is released, and adaptive microgrids are securely formed to sustain critical services. The PFR model is formulated as algebraic differential equations (ADEs), which makes it difficult to solve the model directly. Thus, the PFR model is discretized into difference equations and further linearized to facilitate solution. Simulation results validate the merits of the proposed method in reducing the conservatism of proactive scheduling strategies and improving the microgrid security during MF process.","PeriodicalId":13331,"journal":{"name":"IEEE Transactions on Smart Grid","volume":"16 2","pages":"989-1002"},"PeriodicalIF":8.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142911614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Resilient Logical-Based Localization of Faulted Section Considering Multiple Loss of Information","authors":"Ekta Purwar;Bhavesh R. Bhalja","doi":"10.1109/TSG.2024.3524552","DOIUrl":"10.1109/TSG.2024.3524552","url":null,"abstract":"Localizing faults (LoF) using online data from fault-indicating devices (FIDs) poses a significant challenge during the unavailability of information due to hidden relay failures or communication issues. Addressing this challenge, this paper introduces (i) Back-and-Forth Search algorithm and (ii) Clockwise-Counter-clockwise algorithm, which effectively leverages current direction data from network FIDs to locate faulted zones in distribution networks, even when facing multiple information losses. The proposed algorithms stand out due to their adaptable design, functioning seamlessly across various network configurations (radial or meshed), voltage ratings (medium or low), connection modes (grid-connected or islanded), distribution types (AC or DC), and integration of Distributed Generations (DGs). The performance of the proposed approach is verified by considering multiple scenarios of loss of information (LoI) along with extensive validation on the IEEE 37-bus distribution network within the MATLAB and coding environment. Results, comparisons, and discussions demonstrate the proposed method’s rapid detection, robustness, resilience, and reliability attributes.","PeriodicalId":13331,"journal":{"name":"IEEE Transactions on Smart Grid","volume":"16 2","pages":"1003-1018"},"PeriodicalIF":8.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142911615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Detection of FDIA in Power Grid Based on Hypergraph and Attention Mechanism","authors":"Xueping Li;Wanzhong Jiao;Qi Han;Zhigang Lu","doi":"10.1109/TSG.2024.3524629","DOIUrl":"10.1109/TSG.2024.3524629","url":null,"abstract":"False data injection attack (FDIA) is posing a threat to the security of power grids. Detection technology is an effective means to defend against FDIA, but the existing mainstream methods have insufficient detection capabilities for large-scale power grids. This study proposes a novel method that combines subgraph partitioning strategy and hypergraph model to detect FDIA. According to the principle the attack principle, the power grid is partitioned into subgraphs. Each subgraph is constructed as the hypergraph and then input into the hypergraph convolutional neural network (HGCNN). The hypergraph attention mechanism (HGAT) is adopted to pay attention to the hyperedge, where the attention score is calculated through the similarity between the node and the hyperedge. Simulations were conducted on IEEE 14-, 118-, and 300-bus systems. At the 10% attack intensity, the proposed method achieved 1.62%, 2.05%, and 2.18% higher accuracy than the optimal results of the comparison methods on three test systems, respectively.","PeriodicalId":13331,"journal":{"name":"IEEE Transactions on Smart Grid","volume":"16 2","pages":"1862-1871"},"PeriodicalIF":8.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Lyapunov Method-Based Coherent Aggregation of Grid-Forming Converters for Transient Stability Equivalents","authors":"Chao Shen;Wei Gu;Xia Shen;Yijun Xu","doi":"10.1109/TSG.2024.3524727","DOIUrl":"10.1109/TSG.2024.3524727","url":null,"abstract":"Paralleled grid-forming converters (GFMs) system suffers from transient instability issues while accurate model is complicated and unsuitable to give stability analysis. Existing aggregation method requires either extensive computation or linearized model assumption to realize coherent identification, which might not distinguish unstable units from stable clusters. In this paper, a two-step algorithm is proposed to realize coherent recognition for multi-GFMs system based on Lyapunov energy function: 1) unstable GFMs are distinguished from stable clusters based on Lyapunov’s function, 2) stable GFMs are further divided into different clusters using stored potential energy as a criterion. First, large-signal model considering transient interactions and virtual impedance-based fault ride through (VI-FRT) control is derived in transient stability time-scale. Then, Lyapunov energy function (LEF) is constructed for multi-GFMs system taking virtual damping coefficients and voltage dynamics into account. Compared with existing methods, the constructed LEF presents higher stability prediction accuracy and lower computational burden. Moreover, it is found that the relative potential energy among different GFMs can be adopted to identify coherent clusters, which is proved to be mathematically equivalent to coherency recognition using power angle deviation as the indicator. Finally, parameters aggregation is realized using the concept of center of inertia (COI). Based on the proposed method, equivalent reduced model has good accuracy in transient stability prediction compared with full-order model. Both numerical simulations and hardware-in-the-loop (HIL) experiments are provided to validate the feasibility of the proposed method.","PeriodicalId":13331,"journal":{"name":"IEEE Transactions on Smart Grid","volume":"16 2","pages":"1462-1479"},"PeriodicalIF":8.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142911613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhuoli Zhao, Qinggang Yang, Zehan Zhang, Yuewu Wang, Hanyuan Tan, Junhua Wu, Loi Lei Lai
{"title":"Hierarchical Distributed Model Predictive Stabilization Control of Multi-Scale Oscillations in Wind-Solar Hybrid Multi-Microgrids","authors":"Zhuoli Zhao, Qinggang Yang, Zehan Zhang, Yuewu Wang, Hanyuan Tan, Junhua Wu, Loi Lei Lai","doi":"10.1109/tsg.2024.3524400","DOIUrl":"https://doi.org/10.1109/tsg.2024.3524400","url":null,"abstract":"","PeriodicalId":13331,"journal":{"name":"IEEE Transactions on Smart Grid","volume":"197 1","pages":""},"PeriodicalIF":9.6,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142908373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Probabilistic-Based Approach for Detecting Simultaneous Load Redistribution Attacks Through Entropy Analysis and Deep Learning","authors":"Ali Khaleghi;Hadis Karimipour","doi":"10.1109/TSG.2024.3524455","DOIUrl":"10.1109/TSG.2024.3524455","url":null,"abstract":"Load redistribution attacks (LRAs) are one of the most sneaky and realistic types of false data injection attacks (FDIAs), in which the attacker manipulates the measurements in a way that depicts a false image of the system situation for the operator. Due to the uncertainty in the system’s parameters, system modeling (AC or DC), and so on, detection LRAs have a lot of challenges. To overcome the difficulty of devising a general mechanism for LRA detection based on deterministic methods, we propose a probabilistic approach based on entropy analysis and deep learning. The ratio of cyber loads to real loads (RCLRLs) is the major input of the proposed detection algorithm to make the presented method applicable for different load levels in the system. By extracting the entropy of RCLRLs under LRAs, our method reduces dependency on system modeling and the system’s parameters. We employ the bias correction method on forecasted loads to approximate the real load in the system, enhancing our approach’s accuracy. The framework is a decentralized algorithm that detects simultaneous LRAs in different areas and ensures scalability for large systems. Simulations on the IEEE 118-bus systems demonstrate the proposed method’s high accuracy and rapid response.","PeriodicalId":13331,"journal":{"name":"IEEE Transactions on Smart Grid","volume":"16 2","pages":"1851-1861"},"PeriodicalIF":8.6,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142908371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}