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Supplemental material, sj-docx-1-anr-10.1177_20530196231167019 for The Sihailongwan Maar Lake, northeastern China as a candidate Global Boundary Stratotype Section and Point for the Anthropocene Series by Yongming Han, An Zhisheng, Dewen Lei, Weijian Zhou, Luyuan Zhang, Xue Zhao, Dongna Yan, Richard Arimoto, Neil L Rose, Sarah L Roberts, Li Li, Yalan Tang, Xingqi Liu, Xuewu Fu, Tobias Schneider, Xiaolin Hou, Jianghu Lan, Liangcheng Tan, Xingxing Liu, Jing Hu, Yunning Cao, Weiguo Liu, Feng Wu, Tianli Wang, Xiaoke Qiang, Ning Chen, Peng Cheng, Yifei Hao, Qiyuan Wang, Guoqiang Chu, Meiling Guo, Mei Han, Zhihai Tan, Chong Wei and Ulrike Dusek in The Anthropocene Review

Sedimentary charcoal records are widely used to reconstruct regional changes in fire regimes through time in the geological past. Existing global compilations are not geographically comprehensive and do not provide consistent metadata for all sites. Furthermore, the age models provided for these records are not harmonised and many are based on older calibrations of the radiocarbon ages. These issues limit the use of existing compilations for research into past fire regimes. Here, we present an expanded database of charcoal records, accompanied by new age models based on recalibration of radiocarbon ages using IntCal20 and Bayesian age-modelling software. We document the structure and contents of the database, the construction of the age models, and the quality control measures applied. We also record the expansion of geographical coverage relative to previous charcoal compilations and the expansion of metadata that can be used to inform analyses.

With the development of energy and information technologies, smart distribution system has become an emerging area for the development of smart grid. Self-healing and active distribution system are the hot research topics of smart distribution network. The Distribution Automation System (DAS) is the fundamental system for integration and realization of such technologies, which integrates computer science, modern communication technologies and distribution automation technologies. The reliability of DAS has an profound influence on the application of emerging smart distribution network. After rigorous testing, DAS itself has been certified and authorized for higher security, reliability. As the model of distribution system evolves frequently, the correctness of the network model will seriously affect the applicability and reliability of distribution automation system. The network model management and validation are the key technologies to improve the reliability of DAS and applicability of advanced applications for smart distribution system. At present, there are relative researches on model validation technologies, such as basic grammar examination, model name and topological relation validation. Although these approaches can improve the automation and reliability level of distribution network model management to a certain extent. However, there are still some shortcomings: lack of automatic verification ways for interactive validation based on both diagram and model for DAS, which is heavily dependent on the system operators and there may be remaining potential security risks; lack of automatic testing tools, which increases the workload for the systematic validation of network model, and reduces efficiency and reliability at the same time. Compared with traditional manual test approach, automatic testing technology utilities emerging test approach to realize fully automatic test or semi automatic testing, which could effective reduce manual intervention, accelerated testing cycle and enhance testing efficiency by reduction of the repetitive and redundant testing process. Besides, with carefully designed test cases, automatic testing technology could guarantee the reliability of complex control system. Automatic test has been applied in the development of modern complex automation system. This paper presents a model validation framework based on automatic testing technology for distribution system. Firstly, an automatic testing framework for network model validation is proposed. Then the procedure and test approach are developed based on the automatic testing framework. The approach proposed in this paper has been applied in a practical distribution automation system. Actual examples are presented to prove that the proposed approach can effectively improve the efficiency of model management and validation for distribution system and greatly reduce the potential model related risks which could otherwise downgrade the reliability and correctness of DAS. The proposed approach could be applied and provide foundation for the further development of smart distribution system.

In modern distribution system high efficient, clean, reliable and flexible electricity energy is required to supply to customers. Self-healing distribution network has been raised as one of the most emerging and significant characters for such smart distribution system to cope with all kinds of threatens from inside and outside of the system. Events and alarms are the bases for system status comprehend and decision making. Alarm management is a traditional feature of Supervisor control and data acquisition (SCADA) and distribution management systems (DMS), and key feature for smart self-healing distribution system as intelligent alarm. When events occur in distribution system, too many alarms rush to control center. The alarms are separated and maybe suspicious preventing the understanding of the whole event. In this paper, an alarm management framework is proposed for intelligent alarm processing. Firstly the events and alarms for self-healing smart distribution system are categorized. Then a complex event processing based intelligent alarm processing method is proposed for effective management alarms and events. Finally, example is shown based our framework. and the advantage of alarm management is illustrated. The alarm management framework can not only recognize the event sequence, providing effective intelligent alarm, but also reduce the amount of alarms to process by system operators. facilitating decision making.

As society and economic rely highly on power supply. The self-healing smart distribution system is the ideal solution to solve the conflicts between society requirement and development of power grid. In such system, there are a lot of distributed energy resources (DER), such as new energy resources (wind turbine, photovoltaic, storage device) and small client site generators, which play important roles by enhancing client site reliability, deferring property investment. However, as more information bursts into control center, the work for operators turns out complex. The conceptual operation cockpit which origins from aerospace is raised to help better understand the system running level and make effectively operational decision. In this article, we investigate on the key performance indicators (KPI) for self-healing systems and operation cockpit based on balanced scorecard (BSC). With the KPI system, operators can obtain the self-healing and smart distribution system performance and situation from a higher, system and business prospective, and they can get better understand and operational decision support. The work in this article is useful for evaluation of self-healing smart distribution and can direct the application of operation cockpit.

The distribution networks feature diverse range of equipments and wiring, and the modeling and techniques are essential to the construction of distribution automation. The modeling of power system are mature, and the modeling method is very useful for analysis of power grid. Thus, the modeling of power system are used in the transmission network, which is established based on study on the structure and characteristics of transmission network, and applied successfully in the transmission network. However, it is not much convenient for the distribution network, which will lead to complex model, large admittance matrix, huge storage space and enormous data processing workload. A simplified model of distribution network based on the minimum area is proposed in this paper. A region means power supply range of distribution network, consisting of following primary equipments: feeder segment, switch, bus segment, transformer and load etc. A minimum area means a minimum power supply area for controlling and operating of distribution network, excluding inside switches, and the operating status of the internal equipment is consistent (online or offline), so it's the smallest unit for controlling and operating of distribution network. Based on the model of minimum area, a fault diagnosis and location algorithm is developed for distribution network, especially for operating of loop distribution network or with distributed power supply. Moreover, an optimum recovery program for fault downstream power supply interrupted region is introduced. The simplified model of distribution network based on the minimum area and the fault diagnosis and location algorithm thus developed is very important for applicability and practicality of projects to promote the distribution feeder automation.