The COVID-19 epidemic has disrupted the normal teaching and learning in universities, which poses significant challenges to college education. The traditional face-to-face learning mode has been switched to online (distance) learning, causing various influences on students' academic performance. As higher education plays a central role in technology innovation and society development, it is of great importance to investigate and improve online education in the context of COVID-19. This study distributed online questionnaires to college students from 30 provinces or municipalities in China to evaluates the SWOT (Strengths, Weaknesses, Opportunities, and Threats) factors of shifting from traditional learning to online learning during COVID-19 Pandemic. The SWOT analysis has been employed to construct 16 kind of internal and external evaluation factors and 4 kind of improvement strategies for assess online education. The basic data of subjective weight method -AHP comes from the questionnaire survey, and the weight value of SWOT factors is determined through the questionnaire survey results. The fuzzy MARCOS approach is used to select the most suitable strategies for its effective implementation. Several coping strategies are suggested to improve the online education in post-pandemic era, which is essential for higher education and promoting a civilized and sustainable society. "By reforming and innovating the teacher led teaching mode, stimulate students' interest in learning, get rid of the boring learning state, create a good learning atmosphere and improve the teaching quality" is the most effective strategy to enhance the online learning experience and increase students' satisfaction. This methodology is applicable with a case study concerning the students' online education in pandemic and the validity of this approach is presented through comparative analysis and sensitivity analysis. Through example verification, it is found that SWOT method is suitable for online education evaluation research no matter how the research object changes.

Using a high proportion of new energy is becoming the development trend of the modern power industry, with broad application prospects and potential threats to power system operation safety. This paper proposes a hybrid adaptive velocity update relaxation particle swarm optimization algorithm (AVURPSO) and recursive least square (RLS) method to quickly estimate the DSSR boundary using hyper-plane expression. Firstly, the operating point data in the high-dimension nodal injection space are analyzed using the AVURPSO algorithm to identify the key generators, equivalent search space, and critical points, which have relatively great effects on transient angle stability. The hyper-plane expression of the DSSR boundary, which matches the critical points best, is finally fitted by the RLS approach. Hence, the adopted algorithm is applied to rapidly approximate the DSSR boundary by hyper-plane expression in power injection spaces. Finally, the proposed algorithm is validated using a simulation case study on three wind farm regions of the actual Hami Power Grid of China using the DIgSILENT/Power Factory software. Consequently, the mentioned method effectively captures the security stability boundary of the new energy power system and realizes the three-dimensional visualization space of DSSR. By leveraging the DSSR, the state analysis can be conducted rapidly on several parameters, including security and stability assessments in relation to various energy supply capabilities. Meanwhile, these indices are calculated offline and applied online. The findings of this investigation confirm the efficacy and accuracy of the suggested modeling used in the analyzed system, offering technical assistance ensuring the stability of the new energy power system. The DSSR allows the rapid analysis of several parameters, including security and stability assessments with various energy supply capabilities.

With the rapid expansion of new energy in China, the large-scale grid connection of new energy is increasing, and the operating safety of the new energy power system is being put to the test. The static security and stability region (SSSR) with hyper-plane expression is an effective instrument for situational awareness and the stability-constrained operation of power systems. This paper proposes a hybrid improved particle swarm optimization (IPSO) and recursive least square (RLS) approach for rapidly approximating the SSSR boundary. Initially, the operating point data in the high-dimensional nodal injection space is examined using the IPSO algorithm to find the key generators, equivalent search space, and crucial points, which have a relatively large impact on static stability. The RLS method is ultimately utilized to fit the SSSR border that best suits the crucial spots. Consequently, the adopted algorithm technique was used to rapidly approximate the SSSR border in power injection spaces. Finally, the suggested algorithm is confirmed by simulating three kinds of generators of the new energy 118 bus system using the DIgSILENT/Power Factory. As a result, this method accurately characterized the stability border of the new energy power system and created the visualization space of the SSSR. Using the SSSR, a rapid state analysis could be undertaken on a variety of parameters, such as security evaluation with diverse energy supply capacities. This study’s findings confirmed the accuracy and efficacy of the suggested modeling for the considered system and may thus give technical support for the new energy power system’s stability.

With the rapid development of new energy in China, the large-scale grid connection of new energy continues to rise, and the operation safety of new energy power systems is facing a severe test. Therefore, correct identification and assessment of security risks become an important prerequisite to effectively improve the operation safety level of the new energy power systems. In order to accurately and effectively complete the security and stability assessment of the new energy power system operation, a new energy power system operation security assessment model based on the fuzzy DEMATEL-AEW cloud model was built. Firstly, the paper collects the index set that affects the safe and stable operation of the new energy power system, and selects 15 indexes that have a great impact on the safe and stable operation of the system through the association rules mining algorithm (Apriori), and establishes the evaluation index system for the safe and stable operation of the new energy power system. Then, the fuzzy decision-making laboratory analysis method (DEMATEL) is used to determine the interaction between the evaluation indicators, draw the network structure diagram between the evaluation indicators, and determine the subjective weight of each evaluation indicator. The objective weight of each evaluation index is determined by using the anti-entropy weight (AEW) method, and the comprehensive weight of the evaluation index is calculated by using the dynamic weighting of cooperative game theory; Finally, the weight results are combined with the evaluation layer of cloud model to evaluate the safety of all levels of safety assessment indicators, and the simulation analysis of the example is completed through MATLAB. The results show that the operation security status level of the power system is between average and good, not only taken some effective measures to control some risk factors, but also normal inspection and monitoring are arranged; At the same time, carried out the comparative analysis of the simulation results in the security assessment of power system operation to verify the feasibility and accuracy of evaluation methods, and has guiding significance for the security assessment of power system.