To bolster the resilience of power systems against typhoon disasters, this paper develops a holistic framework of wind disaster warning for transmission lines. This paper proposes a hybrid prediction model to quantify the transmission line damage probability under typhoon disaster based on extreme value type I probability distribution, Monte Carlo method, and Random Forest. Specifically, this paper uses the extreme value type I probability distribution and the Monte Carlo method to simulate the random wind field, and predict the damage probability of transmission lines under each wind field using the Random Forest method. This paper takes typhoon “Mangkhut” in 2018 as a case study, and compare the performance of the hybrid model based on random wind field with the Random Forest method under predicted and measured wind field. The results demonstrate that the hybrid model can effectively utilize wind speed data to obtain a more reliable prediction and achieves the best synthetic similarity to the actual damage situations.

For power system disaster prevention and mitigation, risk assessment and visualization under typhoon disaster have important scientific significance and engineering value. However, current studies have problems such as incomplete factors, strong subjectivity, complicated calculations, and so on. Therefore, a novel risk assessment and its visualization system consisting of a data layer, knowledge extraction layer, and visualization layer on power towers under typhoon disaster are proposed. On the data layer, a spatial multi-source heterogeneous information database is built based on equipment operation information, meteorological information, and geographic information. On the knowledge extraction layer, six intelligent risk prediction models are established based on machine learning algorithms by hyperparameter optimization. Then the relative optimal model is selected by comparing five evaluation indicators, and the combined model consisting of five relatively superior models is established by goodness of fit method with unequal weight. On the visualization layer, the predicted results are visualized with accuracy of $1\mathrm{km}\times 1\mathrm{km}$ by ArcGIS 10.4. In results, the power tower damage risk assessment is carried out in a Chinese coastal city under the typhoon ‘Mujigae’. By comparing predicted distribution and similarity indicator of the combined model with those of the other models, it is shown that the combined model is superior not only in quality but also in quantity.

Under the typhoon disaster, the power grid often has serious accidents caused by falling power towers and breaking lines. It is of great significance to analyze and predict the damage probability of a transmission line-tower system for disaster prevention and reduction. However, some problems existing in current models, such as complicated calculation, few factors, and so on, affect the accuracy of the prediction. Therefore, a damage probability assessment method of a transmission line-tower system under a typhoon disaster is proposed. Firstly, considering the actual wind load and the design wind load, physical models for calculating the damage probability of the transmission line and power tower are established, respectively based on model-driven thought. Then, the damage probability of the transmission line-tower system is obtained, combining the transmission line and power tower damage probability. Secondly, in order to improve prediction accuracy, this paper analyzes the historical sample data containing multiple influencing factors, such as geographic information, meteorological information, and power grid information, and then obtains the correction coefficient based on data-driven thought. Thirdly, the comprehensive damage probability of the transmission line-tower system is calculated considering the results of model-driven and data-driven thought. Ultimately, the proposed method is verified to be effective, taking typhoon ‘Mangkhut’ in 2018 as a case study.

With the anticipated integration of numerous hybrid photovoltaic (PV) plants into subtransmission and distribution grids, managing a mix of inverter-based energy resources such as PV systems and battery energy storage systems (BESS) becomes crucial. These resources are required to effectively coordinate for primary frequency (f) and voltage (V) control and participate in power sharing, particularly in weaker grids. Currently, inverter-based energy resources are predominantly coordinated by droop-based control, which proves inadequate for hybrid PV plants in more resistive subtransmission and distribution grids due to the tightly coupled active power (P) and reactive power (Q). To overcome this challenge, this paper proposes an innovative coordinating systematic primary control strategy for grid-forming inverters in hybrid PV plants based on the multiple-input and multiple-output (MIMO) decoupling control. This method adaptively decouples the connected subtransmission or distribution grids during operation, with the aim of achieving effective, coordinated, and independent primary f and V regulation and accurate power sharing. For verification, comparative case studies are conducted in Simulink between the proposed control strategy and a conventional droop control scheme. The findings indicate that our proposed control method facilitates autonomous and independent primary f and V control, along with precise power sharing without relying on communication links. This results in markedly enhanced steady-state and dynamic performance. The decentralized primary controller offers simplicity, robustness, and cost-effectiveness, contributing to the stability and resilience of utility grids.

Existing grid-forming inverter control schemes for distributed energy resources (DERs) primarily rely on active power (P)-frequency (f) and reactive power (Q)-voltage (V) droop mechanisms that are tailored for highly inductive transmission grids. However, in weak distribution grids where P and Q are highly coupled due to their resistive network characteristics, these control schemes cannot provide independent and accurate f and V regulation. This will further deteriorate the dynamic and stability performance, potentially resulting in inverter and load tripping during disturbances. To address this challenge, this paper proposes an innovative decoupling grid-forming control scheme, which is designed based on a systematic perspective that considers the inherent coupling characteristic of the entire distribution grid. The small-signal stability of the proposed controller is analyzed by varying controller parameters and the grid strength. The effectiveness of this controller is comprehensively verified using both MATLAB and OPAL-RT platforms by comparing it with existing grid-forming control strategies. The results show that the proposed controller can effectively decouple P and Q regulation in weak distribution grids. It enables DERs to provide independent, accurate, and autonomous f and V regulation, thus improving grid stability and dynamics. The proposed control strategy is cost-effective, communication-free, and can be easily commercialized due to its straightforward and robust circuit design.

In recent years, great progress has been made in Chinese corpus processing. A fifty-million-word Chinese National Corpus project has been put into effect, and many automatic corpus processing programs have also been developed. In this paper, we will briefly introduce our work on constructing a large scale annotated corpus for Chinese grammatical research and developing a Chinese Corpus Multilevel Processing system—CCMP. First, we present our annotation scheme. Second, we discuss some basic methodologies for Chinese corpus analysis and propose a man-machine mutually dependent corpus processing model. Finally, we introduce the survey of our CCMP. We hope our work will give impetus to further research in Chinese corpus linguistics.