中國南方電網有限責任公司

To promote sustainable 3D concrete printing (3DCP), this study develops an extrusion-based 3D printable geopolymer concrete and evaluates the effectiveness of four chemical retarders, namely tartaric acid, sucrose, sodium tripolyphosphate, and barium chloride, in improving its printability. Based on a comparative assessment of time-dependent flowability and compressive strength of the mixture, the barium chloride demonstrated the most favorable overall performance among the four retarders and was therefore selected for further investigation. When the barium chloride dosage exceeded 2.5%, the mixtures satisfied the early-age strength requirements for printing and demonstrated stable extrudability and good buildability. For mixtures containing 2.5% and 3.5% barium chloride, the open time reached approximately 30 minutes and 60 minutes, respectively. A higher dosage (3.5%) shows better printing quality, resulting in printed structures with compressive and tensile strengths surpassing those of mold-cast specimens and exhibiting reduced mechanical anisotropy. Furthermore, the printed concrete showed the highest compressive strength along with the printing direction, whereas its tensile strength in this direction was lower due to the influence of interlayer interfaces. Overall, a dosage of 3.5% barium chloride provided superior flowability and extended open time, achieving an optimal balance between printability and mechanical performance. This formulation offers a promising retarder strategy for extrusion-based geopolymer concrete 3D printing.

The traditional power system dominated by synchronous generators is gradually evolving into a modern power system featured by high-penetrated renewable energy. As a key technology for high-penetrated renewable energy, the grid-forming voltage source converter (GFM-VSC) has received increasing attention. However, the large-disturbance stability analysis of power systems with multiple GFM-VSCs is still a challenging problem due to various limitations of existing methods, including huge computational burden and difficulty in considering network losses. This paper is intended to address these issues from the perspective of reduced-order modeling and domain of attraction (DA) estimation. The innovations involve three aspects. First, the reduced-order modeling method for power systems with multiple GFM-VSCs is proposed using the standard dual-time-scale model in singular perturbation theory. Second, an expanding annular domain (EAD) algorithm is developed to estimate the DA with an entire boundary to analyze the large-disturbance stability of power systems. Third, the conditions of using the reduced-order modeling method based on singular perturbation theory have been clarified. The validity of the reduced-order modeling method is illustrated on a modified 39-bus system with 10 GFM-VSCs.

In the active landscape of 6G wi-fi systems, combining knowledge graph technology and advanced data analysis strategies presents a transformative approach to monitoring and managing relay protection status in intelligent substations. This paper implements a risk identification framework for relay protection equipment operation in smart substations. Based on this, the paper proposes an innovative Knowledge Graph (KG)-based risk identification model that effectively combines the remote tracking abilities of KG with advanced predictive and analytical ability to ensure efficient, accurate and reliable control over substation equipment. Our model facilitates real-time data collection and exchange by combining interconnected devices with sensors, actuators and network connectivity, improving substations' operational performance and risk management. The LSTM component was also introduced, which is well-suited for analyzing the time series data and also applied to monitor operational patterns, detect anomalies and predict the future risk of failures. Simultaneously, the KG-based system is effectively used for local fault cases to identify risky equipment and become aware of fault paths within the substation's network. This approach effectively minimizes human intervention by automating the monitoring process and significantly reducing the dependence on traditional, costly solutions. The proposed design consists of three key modules - KG-LSTM-FD for fault detection, KG-LSTM-RI for risk identification and KG-LSTM-FL for fault location estimation. Our model proved exceptional sensitivity and reliability, achieving a 99.98% successful rate throughout diverse fault scenarios with location estimation errors within 1%. The KG-LSTM-based risk identification model, examined through a prototype system, marks a tremendous advancement in combining the physical world with computer-based systems, providing a cost-effective, highly efficient and scalable solution for the next generation of smart substations.

The rapid expansion of the Industrial Internet of Things (IIoT) enables fine-grained sensing and distributed actuation for demand-side flexibility, but also introduces large-scale heterogeneous load networks that must coordinate in real time under time-varying connectivity and coupled constraints. Under the Industry 5.0 paradigm, such coordination requires not only efficiency but also resilience and transparency under unreliable communications. To address these challenges, this paper proposes a Graph Attention Diffusion-based Flexible Response Optimization (GAD-FRO) algorithm, which integrates attention-guided diffusion operators into a distributed primal-dual framework. GAD-FRO achieves topology-aware and communication-robust coordination without centralized control by adaptively prioritizing reliable neighbors and smoothing information propagation over dynamic graphs. Experiments on four benchmark datasets (Pecan Street, Building Data Genome 2, UK-DALE, and ACN-Data) demonstrate that GAD-FRO achieves faster convergence, lower objective cost, and stronger robustness to packet loss than representative distributed baselines, while approaching centralized oracle performance with low runtime and communication overhead.

The proliferation of power electronic equipment in distribution networks has exacerbated harmonic pollution, posing significant challenges to power quality. Notably, photovoltaic (PV) inverters share a homologous topology with active power filters and possess inherent capabilities for harmonic mitigation. However, harnessing PV inverters for harmonic governance involves multiple stakeholders with strong pricing autonomy, and a viable market transaction mechanism remains absent. To address this gap, this paper first introduces the concept of a Harmonic Governance Aggregator (HA) as a third-party intermediary to coordinate transactions and impose unified constraints on the otherwise arbitrary pricing behavior of distributed PV users. Subsequently, a two-layer master-slave game-theoretic trading model is proposed. The upper layer models the price negotiation between the HA and the harmonic source user, while the lower layer captures the strategic pricing interactions between the HA and individual PV users. The proposed framework is validated using the IEEE 13-node test system. Simulation results demonstrate that the model achieves effective harmonic mitigation while simultaneously delivering significant economic benefits to all participants, thereby effectively incentivizing their active engagement in the harmonic governance market.