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Study On Quality Risk Management Of The Power Grid Project

Posted on:2018-11-22Degree:MasterType:Thesis
Country:ChinaCandidate:P ZhengFull Text:PDF
GTID:2322330512979534Subject:Management Science and Engineering
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With the continuous development of China’s social and economic development,the Electric Grid Project has become an important infrastructure for the development of national economy,the quality of Electric Grid Projects directly affects the efficiency of power grid.In order to ensure the quality of Electric Grid Projects,the State Grid Corporation of China attaches great importance to the application of project management theory in the lifecycle of project,under the premise of the implementation of the relevant national quality laws and regulations,comes up with a series of regulations and documents,and make sure the primay position of quality management.However,the quality control system of Electric Grid Project is not yet mature,safety and quality accidents have been an important problem in the Electric Grid Project.This paper fuses the qualitative and quantitative analysis,the combination of static and dynamic analysis,try to identify the quality risk factors of the Electric Grid Project,in-depth analysis of the complex relationship between the quality risk factors,mines deep reasons of quality risks,establishes the power grid project construction quality risk system dynamics model,uses mathematical method to predict the construction process quality risk level in the work of construction,guides prevention of safety accidents in construction of grid project,so as to guarantee the Electric Grid Project quality.In this paper,we fuse literature analysis,case analysis,and expert investigation method,identify the quality risk factors in the Electric Grid Project,determines five subsystems about personnel,technology,management,equipment and materials,and environmental risk system.In combination with the quality risk occurrence regularity.we construct the quality risk causal relationship structure of project construction and the hierarchical relationship of quality risk factors in the Electric Grid Project with the help of analysis the interpretation method of structure model,which reveals the surface,middle and deep reasons of the quality risk in the Electric Grid Project,and use MICMAC to make sure the importance of risk factor from a another point view.On the basis of the above analysis,system dynamic,integrated weighting method,multiple linear regression method are applied to the analysis of quality risk in the Electric Grid Project,constructs quality risk management system dynamic model,using Vensim_PLE software to simulate the risk level of whole quality and every subsystem,concludes the complex interaction between different risk factors in complex systems.From the policy optimization,makes different schemes about the proportion of quality control investment,investment strategy and investment conversion,compromises the influence of different strategies on system quality risk level,quantificationally observes the effect of different control measures on system risk level in the complex Electric Grid Project,reveals quality risk management and optimization measures in the Electric Grid Project,provides the basis for managers to develop the quality risk management,promotes the establishment of long-term mechanism of quality risk management of the Electric Grid Project.According to the above results,this article puts forward a dynamic and interactive quality risk management mechanism that takes the institutional documents as a guide,uses means of information technology,brings the life cycle as the process of management,takes eleven security measures as points,focuses on people,technology,management,environment,equipment and materials for the five quality elements to carry out quality risk management work,hopes to give some guidance to quality risk management of the Electric Grid Project.
Keywords/Search Tags:Electric Grid Project, Quality Risk Management, Interpretive Structural Model, MICMAC Analysis, System Dynamic Model, Control Strategies
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