Study On The External Environment Early-Warning System Of Power Grid Asset Life Cycle Management

Since2008, the electricity demand growth fell back quickly under the multiple influences of economic crisis and world economy. It made the asset profit of the Grid Corporation been seriously affected. Therefore, the asset management mode of Grid Corporation had to been changed. Life Cycle Asset Management (LCAM) is an important means to change the asset management mode, enhance the management level, improve operational efficiency and asset quality, prolong equipment life, and optimize asset cost-effectiveness. Furthermore, LCAM is also the inevitable choice to the Smart Grid development. But it is a systematic project to carry out LCAM. It will be faced with many difficulties and challenges to achieve the overall goal of LCAM. One is the lack of risk awareness. In other words, it is inadequate to study the external environment. And there were many passive decision-making situations. For the reason, it is an inevitable requirement to carry out the LCAM to correctly understand and grasp the development trend of the external environment and improve the ability to respond to the external environment.Facing with the complex dynamic and uncertain external environment, what ways should be to grasp the changing trends of external environment and take appropriate and effective measures to quickly adapt to the change of external environment with the Grid Corporate. Possibly, which means should be to adjust and adapt the changes of external environment and make the external environment conducive to achieve the goals of LCAM. This paper builds an early-warning model with the external environment of LCAM to be designed to address the problem that how to grasp the trend of the external environment. The followings are the main elements involved in the thesis.This thesis firstly described the concept, the overall model framework, the evaluation index system and features of LCAM. Then it outlined the concept, characteristics, the general research methods and response strategies of external environments. On this basis, the concept and main classifications of the external environments with LCAM were proposed. The necessity was proposed to research the external environment of LCAM and to build a suitable model for the external environment early-warning.On the basis of understanding the overall model and evaluation indicators of LCAM, it was proposed the identification process of the key external environments with LCAM. The questionnaire with the external environments of LCAM was designed. Simultaneously, the external environments identification model based on the transfer entropy and TOPSIS was constructed. The Matlab toolbox corresponding with the identification model was built to identify the key external environments Influence LCAM. The external environment Influence mechanisms were deeply analyzed separately in Planning and Design Phase, in Procurement and Construction Phase, in Operation and Maintenance Phase, in Retried and Disposal Phase.After the key external environments had been identified, the substitute means by objective indicators was used to transfer the qualitative external environments into the quantitative indicators. The external environment alternative index system of LCAM was received. And it contained22categories external environments and56indicators. Then, the correlation analysis between alternative indicators and management objectives was conducted to select the lager correlation indicators. And all the lager correlation indicators were constituted the external environment early-warning index system of LCAM.After the early-warning index system was presented, the early-warning models based on the principle component analysis and based on the PSO-BPNN were separately constructed. But the model based on the principle component analysis (PCA) can't make the external environment link with the management goal of LCAM. And the particle dimensions of PSO-BPNN algorithm were too numerous. Meanwhile, the accuracy of PSO-BPNN algorithm was too vulnerable to be affected by the sample data. Therefore, the thesis presented the external environment early-warning model based on PCA-PSO-BPNN algorithm by integrated the above two models. The new model can link the external environment and the management goal indicator of SEC (Security-Efficiency-Cost). And the new model complied the early warning with the external environment by the self-learning mechanism of the BPNN algorithm. It can help the decision-makers timely capture the changing information of the external environment and the risks and opportunities from the external environments changes.The above external environment early-warning models were simulated by the Matlab software and the quarter data from2008to2013. The validity of the PCA-PSO-BPNN model was verified by comparing the predictions and errors of the above models. On the basis, the external environment status in the third quarter of2013was predicted by the trained model. The result showed that the current external environment be classified in the slight alert status. And the appropriate measures should be taken to avoid its adverse effect.Respectively, the corresponding strategies to avoid risks and take advantage of opportunities was presented separately in Planning and Design Phase, in Procurement and Construction Phase, in Operation and Maintenance Phase, in Retried and Disposal Phase. The purpose was to help to make the decision-makers to achieve to the management goal of LCAM.Finally, the shortcomings and the future work direction of this thesis were summarized.