For distribution networks, topology and line parameter information is the pre-requisite of further functions such as network analysis, operation and control. To identify the topology or estimate the parameters, a lot of existing literature relies on the knowledge of feasible topologies or full-scale deployment of advanced measuring devices, such as phase measurement units (PMUs), which is unrealistic in practice. Therefore, we propose a data-driven topology and parameter joint estimation method for non-PMU distribution networks, only using historical measurement data from smart meters. First, the topology label matrix, as a unique label of the network topology and corresponding parameters, is created and calculated based on historical data. The numeric characteristics of the topology label matrix is further analyzed to extract the topology and parameter information. Subsequently, a clustering based topology identification and parameter estimation methodology is proposed as a data-driven linear regression model. To reduce the coupling during regression, a power supply path matrix is utilized to improve the accuracy. Moreover, to improve the credibility of the method, an error-correction mechanism is proposed to indicate whether the inaccurate identification exists and how to correct it. Finally, the proposed method is tested in IEEE 33-node, 123-node networks and a large-scale system. The results demonstrate that the proposed method can provide an accurate estimation of the topology and line parameters based on samples of measurement with noise and is also effective in a large-scale system.

Hybrid AC/DC distribution networks with the high-efficiency consumption and high-proportion access of new energy have become a crucial trend for future modern distribution networks. High penetration of renewable energy brings various controllable resources along with uncertainties to hybrid AC/DC distribution networks, making conventional single objective optimization fail to meet optimal operation requirements of flexibility and reliability. In this paper, considering uncertainties of distribution generations and loads, a multi-objective robust optimization model based on various controllable devices is proposed. To increase resource utilization, the proposed method comprehensively and properly models the full variety of possible control means (i.e., flexible distribution switch, voltage source converter, energy storages, et al). Utilizing the abundant control means, a multi-objective optimization which minimizes network losses, voltage deviations and operation cost simultaneously is modeled. Then, based on the multi-objective model, a two-stage robust optimization based on second order cone method and column constraint generation algorithm is proposed to achieve the solution, which can deal with the fluctuation of loads and renewable energy quickly and effectively. Outdoing traditional single objective robust optimization, our multi-objective robust optimization based on various controllable resources can decease the network losses, increase the profits and improve voltage profiles significantly, which obviously improves the safety, flexibility and economy simultaneously. Finally, compared with existing robust optimization, the proposed method is tested in numerous case studies to verify its effectiveness and advantages in a modified IEEE 33-node system.

The current lead insulation of high-temperature superconductivity equipment is under the combined action of large temperature gradient field and strong electric field. Compared with a uniform temperature field, its electric field distortion is more serious, and it is easy to induce surface discharge to generate high-energy particles, destroy the insulation surface structure and accelerate insulation degradation. In this paper, the degradation reaction process of bisphenol F epoxy resin under the impact of high-energy particles, such as O₃⁻, HO^–, H₃O⁺ and NO⁺, is calculated based on ReaxFF simulation. According to the different types of high-energy particles under different voltage polarities, the micro-degradation mechanism, pyrolysis degree and pyrolysis products of epoxy resin are analyzed. The results show that in addition to the chemical reaction of high-energy particles with epoxy resin, their kinetic energy will also destroy the molecular structure of the material, causing the cross-linked epoxy resin to pyrolyze, and the impact of positive particles has a more obvious impact on the pyrolysis of epoxy resin.

Superconducting power lead equipment for epoxy insulation, such as high-temperature superconducting DC power or liquefied natural gas energy pipelines, as well as high-temperature superconducting cables, has long been used in extreme environments, from liquid nitrogen temperatures to normal temperatures. It is easy to induce surface discharge and flashover under the action of strong electric field, which accelerates the insulation failure of current leads. In this paper, two-dimensional nano-material GO was used to control the electrical properties of epoxy resins. The DC surface discharge and flashover characteristics of the prepared epoxy resin–GO composite insulation materials were tested at room temperature with liquid nitrogen. The surface discharge mechanism of the epoxy resin–GO composite insulation materials was analyzed. The experimental results show that the insulation properties of epoxy composites doped with GO changed. Among them, the surface flashover voltage of 0.05 wt% material is the best, which can inhibit the discharge phenomenon and improve its insulation properties in extreme environments, from room temperature to liquid nitrogen temperature. It is found that the development process of surface discharge of composite insulating materials under liquid nitrogen is quite different from that under room temperature. Before critical flashover, the repetition rate and amplitude of surface discharge remain at a low level until critical flashover. Furthermore, the voltage of the first flashover is significantly higher than that of the subsequent flashover under the action of the desorption gas on the surface of the composite insulating material and the gasification layer produced by the discharge. Given that the surface flashover voltage of 0.05 wt% epoxy composite is the best, the research and analysis of 0.05 wt% composite is emphasized. In the future design of superconducting power lead insulation, the modification method of adding GO to epoxy resin can be considered in order to improve its insulation performance.

Accurate fault localization is critical for ensuring reliable power supply in active distribution networks, yet conventional state estimation (SE)-based methods fail to differentiate authentic fault responses from measurement distortions due to uncertainties in fault parameters. To overcome this limitation, a robust three-phase SE-driven fault localization methodology is proposed. First, a measurement transformation-based SE model is built for fault conditions, leveraging real-time voltage phasor measurements and pseudo-measurements derived from pre-fault SE results. Then, a robust fault SE model is built using the quadratic-constant-based generalized maximum likelihood estimation, solved through the iteratively reweighted least squares algorithm that postpones phasor measurement weight updates until after initial iterations to prevent residual contamination. Furthermore, a fault localization algorithm is proposed through the systematic traversal of candidate buses, where each potential fault localization is assessed by performing robust fault SE with the fault current injected into this bus. The matching index is designed, accounting for the weight disparity of different types of measurements and measurement placement. Extensive simulations on a 33-bus unbalanced distribution network validate the method’s effectiveness under various measurement noise levels, fault resistances and incorrect data severity. The approach maintains comparable accuracy to conventional SE under normal operating conditions, while it exhibits superior robustness against measurement anomalies and effectively preserves fault localization reliability when confronted with incorrect data.