Magnetic measurement methods, which serve as a critical means for perceiving the operational status of transmission lines, enable the synchronous acquisition of both electrical parameters and spatial conditions of transmission lines. However, the coupling issue induced by current fluctuations and conductor galloping (CG) in magnetic field (MF) signatures cannot be effectively resolved by existing methods, rendering synchronous monitoring of CG and current significantly challenging. To address this problem, a synchronous online monitoring method for current and galloping is proposed in this paper, which employs a TMR sensor array deployed at a specific location above the conductor. Firstly, a spatial MF distribution model is established based on the CG equation, providing a theoretical foundation for sensor installation. Subsequently, a galloping discrimination index that is immune to current fluctuations is proposed, thereby enabling galloping monitoring even when current fluctuations are present. Then, the measurement accuracy of current during CG is effectively enhanced via algorithmic correction. The feasibility of the proposed method is validated using a scaled model constructed in the laboratory, and both the feasibility and uncertainty of its application in real-world scenarios are discussed.

Real-time and accurate measurement of current and sag in high-voltage overhead transmission lines ensures rapid adjustment and timely maintenance of smart grids. Magnetic field sensing technology offers high precision and low cost, and its integration with Kalman filtering provides a flexible and reliable solution for current and sag measurement. However, existing measurement methods still face two major challenges: firstly, the complex integral model of catenary overhead transmission lines incurs high computational costs; secondly, due to the lack of physical consistency in purely data-driven models, the results measured by sensor arrays in noisy environments deviate from the true values. To address these issues, this paper proposes a method for measuring current and sag in overhead transmission lines based on physics-constrained unscented Kalman filtering for an optimized model. First, the complex coupling model of magnetic field coefficient and sag is simplified through curve fitting to construct a computationally efficient overhead transmission line system. Second, physical constraints based on load variation and current thermal effects are explicitly embedded into the unscented Kalman filtering, enabling high-precision and physically consistent measurement of current and sag by sensor arrays in noisy environments. The effectiveness of the proposed method is verified through simulation experiments under different noise intensity scenarios and laboratory scaled-down model experiments.

Non-contact electric field sensing has emerged as a key research area due to its inherent advantages of safety, flexibility, and real-time capability. However, utilizing electric field data to inversely reconstruct conductor voltages in Multi-Circuit systems presents significant challenges, including high dimensionality, parameter coupling, and numerical instability. To address these challenges, this paper proposes a two-stage optimization method that decomposes the inverse problem into two sub-problems: voltage estimation and position estimation. The approach enhances solution accuracy and stability through the application of constraint conditions and exploitation of system structural characteristics. Additionally, a sensor array is employed for spatially distributed measurements to reduce parameter coupling and improve observability. The effectiveness of the proposed method is validated using double-circuit transmission lines as a demonstration case. Simulation and scaled experimental results demonstrate that the proposed method achieves voltage estimation errors within ±5% and position estimation errors within ±3% under various operating conditions, confirming its effectiveness for non-contact measurement applications.

With the increasing scale of centralized photovoltaic-plus-storage power stations, they are required not only to supply local load demands but also to provide power support to the upstream grid. The coupled interactions among renewable energy output, sharp fluctuations in local loads, and limited tie-line support capacity cause conventional dispatching methods to face issues in rare extreme scenarios, such as shortfalls in scheduled power delivery, increased imported power during peak support periods, load shedding, and voltage violations. To address these issues, this paper proposes a Wasserstein distributionally robust dispatch strategy for active distribution networks (ADNs) tailored for Upstream Power Support (UPS) tasks, incorporating tail risk assessment. An ADN operation model is established that integrates PV, energy storage, interruptible loads, and a bidirectional interface with the main grid, where the scheduled power delivery during UPS periods characterizes the support demand to the upper grid. A comprehensive risk loss function, encompassing load shedding, power shortfalls, peak-period power import, PV curtailment, and voltage violations, is constructed, and Conditional Value-at-Risk (CVaR) is employed to capture the tail risk caused by extreme scenarios. A Wasserstein ambiguity set is built around the empirical distribution of finite historical samples, and via dual reformulation, the worst-case distribution conditional risk model is transformed into a tractable mixed-integer second-order cone programming problem. Case studies on modified IEEE 33-bus and 69-bus ADN test systems demonstrate that the proposed method achieves a trade-off between routine operational costs and extreme-scenario security. By optimizing day-ahead charging/discharging schedules of energy storage, it reduces scheduled power shortfalls under low-PV and high-load conditions, as well as peak-period power import dependence, thereby enhancing the power support capability and supply resilience of the active distribution network.