Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
To adapt to the rapid development of power transmission and transformation projects, improve their emergency response capability level, and reduce the losses caused by accidents, the projection pursuit method was introduced into the emergency response capability evaluation of power transmission and transformation projects. The emergency response capability evaluation system of power transmission and transformation projects has been established mostly from each composition and structure of power transmission and transformation engineering systems, and highly subjective evaluation methods have been adopted to assess the models established. In this study, a total of 19 concrete indexes were selected from 4 aspects-monitoring and early warning capability, emergency control capability, emergency rescue and disposal capability, and emergency support capability-to establish an emergency response capability evaluation index system of power transmission and transformation projects. Then, an emergency response capability evaluation model for power transmission and transformation projects was constructed based on the projection pursuit model, followed by optimization using real code accelerated genetic algorithm (RAGA); for high-dimensional data, this model could directly find the structure and features of data itself, avoiding the limitations of subjective judgment and contributing to more truthful and reliable evaluation results; finally, this model was used to evaluate and analyze the emergency response capability of six power transmission and transformation projects: GZXS S00kV, JXXYS00kV, QHXN 750kV, YNZT S00kV, JSNJ S00kV, and SXXA 750kV. The results show that the six power transmission and transformation projects are different in the emergency response capability level; the emergency response capability level of power transmission and transformation projects is greatly affected by the early warning personnel deployment capability, daily emergency drill capability, emergency technology implementation capability, emergency training and education capability, and risk response capability.
Czasopismo
Rocznik
Tom
Strony
116--129
Opis fizyczny
Bibliogr. 29 poz.
Twórcy
autor
- Wuhan Textile University, College of Art and Design, No.I Textile Road, Hongshan District, Wuhan, China
autor
- Wuhan Textile University, College of Art and Design, No.I Textile Road, Hongshan District, Wuhan, China
autor
- Wuhan Textile University, College of Art and Design, No.I Textile Road, Hongshan District, Wuhan, China
Bibliografia
- [1] Pan H, Zheng F, Li YK. Research on safety management of power transmission and distribution projects based on social network analysis, Science and Technology Management Research, (3) (2017)174-178
- [2] An L, Wang MB, Tan ZF. Risk assessment model of power transmission and transformation project based on set pair fault tree method, East China Power, 39(1) (2011)12-18.
- [3] Chai QF, Xiao ZD, Gao JQ, et al. Research on pipeline risk assessment based on cone network analysis, Chinese Journal of Safety Sciences, 27(7) (2017)88-93.
- [4] Lu WG. Construction of evaluation index system for urban power public emergency response capability, Science and Technology Management Research, (8) (2010)50-54.
- [5] Men YS, Zhu CY, Yu Z, et al. Construction and evaluation of index system for emergency response capability of power grid infrastructure, Journal of Safety and Environment, 6(13) (2014)84-87.
- [6] Zhang GH, Wu Y, Zhang M. Evaluation of enterprise emergency management capability based on grey clustering analysis, Economic Mathematics, (3) (2011)94-99.
- [7] Chang Y, Liu C, Liu M, et al., Differentiation degree combination weighting method for investment decision-making risk assessment in power grid construction projects. Global Energy Interconnection 2019;2(5):465-477.
- [8] Hasanipanah M, Naderi R, Kashir J, Noorani SA, Zeynali Aaq Qaleh A, Prediction of blastproduced ground vibration using particle swarm optimization, Engineering with Computers, 33(2) (2017)173-179.
- [9] Ansari OA, Gong Y, Liu W, Chung CY. Data-driven operation risk assessment of wind-integrated power systems via mixture models and importance sampling. Journal of Modern Power Systems and Clean Energy. 2020;8(3):437-445.
- [10] Ronco P, Gallina V, Torresan S, Zabeo A, Semenzin E, Critto A, Marcomini A. The KULTURisk regional risk assessment methodology for water-related natural hazards-Part 1: Physicalenvironmental assessment, Hydrology and Earth System Sciences, 18(12) (2014)5399-5414.
- [11] Ghose T, Pandey HW, Gadham KR. Risk assessment of microgrid aggregators considering demand response and uncertain renewable energy sources, Journal of Modern Power Systems and Clean Energy, 7(6) (2019)1619-1631.
- [12] Cui P, Li D. Measuring the disaster resilience of an urban community using ANP FCE method from the perspective of capitals, Social Science Quarterly, 100(6) (2019)2059-2077.
- [13] Dormishi AR, Ataei M, Khaloo Kakaie R, Mikaeil R, Shaffiee Haghshenas S. Performance evaluation of gang saw using hybrid ANFIS-DE and hybrid ANFIS-PSO algorithms, Journal of Mining and Environment, 10(2) (2019)543-557.
- [14] Toutounchian S, Abbaspour M, Dana T, Abedi Z. Design of a safety cost estimation parametric model in oil and gas engineering, procurement and construction contracts, Safety Science, 106(2018)35-46.
- [15] Zhou Y, Li N, Wu W, Wu J. Assessment of provincial social vulnerability to natural disasters in China, Natural Hazards, 71(2014)2165-2186.
- [16] Liu X, Chen H. Integrated assessment of ecological risk for multi-hazards in Guangdong province in southeastern China. Geomatics, Natural Hazards and Risk, 10(1) (2019)2069-2093.
- [17] Xie M, Jia T, Dai Y. Hybrid photovoltaic/solar chimney power plant combined with agriculture: The transformation of a decommissioned coal-fired power plant, Renewable Energy, 1(2022)191:1-6.
- [18] Guo E, Zhang J, Wang Y, Si H, Zhang F. Dynamic risk assessment of waterlogging disaster for maize based on CERES-Maize model in Midwest of Jilin Province, China, Natural Hazards, 83(2016)1747-1761.
- [19] Zhao J, Jin J, Guo Q, Liu L, Chen Y, Pan M. Dynamic risk assessment model for flood disaster on a projection pursuit cluster and its application, Stochastic Environmental Research and Risk Assessment, 28(2014)2175-2183.
- [20] Huang X, Bai H. Risk prediction of rural public security environmental carrying capacity based on the risk entropy, Natural Hazards, 90(2018)157-171.
- [2l]Ma M, Wang H, Jia P, Liu R, Hong Z, Labriola LG, Hong Y, Miao L. Investigation of inducements and defenses of flash floods and urban waterlogging in Fuzhou, China, from 1950 to 2010, Natural Hazards, 91(2018)803-818.
- [22] Grynbaum J. Alliance contracting eliminates the risks of EPC contracts, Power Engineering, 108(7) (2004)56-59.
- [23] Lan Z, Huang M. Safety assessment for seawall based on constrained maximum entropy projection pursuit model, Natural Hazards, 91(2018)1165-1178.
- [24] Mukilan K, Rameshbabu C, Velumani P. A modified particle swarm optimization for risk assessment and claim management in engineering procurement construction projects, Materials Today: Proceedings, 42(2021)786-794.
- [25] Qi C, Fourie A, Chen Q. Neural network and particle swarm optimization for predicting the unconfined compressive strength of cemented paste backfill, Construction and Building Materials, 159(2018)473-478.
- [26] Rusu AD. The use of triangular fuzzy numbers in fuzzy analysis of critical paths in project planning, International Journal of Construction The Machine, 64(68) (2018)17-24.
- [27] Lynn N, Suganthan PN. Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation, Swarm and Evolutionary Computation, 24(2015)11-24.
- [28] Pal R, Wang P, Liang X. The critical factors in managing relationships in international engineering, procurement, and construction (IEPC) projects of Chinese organizations, International Journal of Project Management, 35(7) (2017)1225-1237.
- [29] Berra A, Larabi Marie-Sainte S, Ruiz-Gazen A. Genetic algorithms and particle swarm optimization for exploratory projection pursuit, Annals of Mathematics and Artificial Intelligence, 60(2010)153-178.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-83310036-822b-4105-8b94-62a26f753534
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