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This paper sets the thermal power plant security management system as the research object. First of all, with the application of the Interpretative Structural Modeling (ISM), we establish a multi-level hierarchical structure model on analysis of the influencing factors of thermal power plant security management. Furthermore, based on the structure model, we use the Analytic Hierarchy Process (AHP) to determine weights of all the influencing factors, in order to identify the main factors. Finally, according to the identified main factors, we provide several suggestions for security management of thermal power plant.

This paper’s primary mission is to predict the cost of power transmission and transformation projects of a certain China’s province based on GA-RBF and PSO-RBF neural network. The projects’ data is divided into two main categories-power transformation projects and power line construction projects, with the cost per capacity (RMB/kVA) and cost per unit length (RMB/km) as the indicators of each category. After filtering out main influencing factors and initialization processing for the data, the obtained normalized data can be put into GA-RBF and PSO-RBF predicting model. The empirical analysis is carried on by Matlab. The prediction accuracy can be compared intuitively based on the output of neural network, and from the results we can conclude that GA-RBF is more precise than PSO-RBF when applied to project cost prediction.

With the improvement of utilization technology of fly ash, the fly ash is gradually changing from waste to important resources. Therefore, the forecast of regional fly ash production and sales is becoming more and more important to power plant operation. This paper selects Beijing-Tianjin-Tangshan area, Zhangjiakou area, Southeastern Coastal area, Western area this four typical region of China, using the 2010-2013 quarter production and sales data of fly ash as the original data sequence in the four region to build a BP neural network model for network for 2014-2015 prediction analysis. From the prediction results we can conclude that prediction accuracy conforms to the required standard, indicating that the prediction model is valid.

The protection provided by filter I for the corewall constitutes an important safety guarantee for the impervious bodies of corewall dams. In particular, the fine particle content (the content of particles < 0.075 mm in size) of filter I provides a filter protection effect for the corewall. Furthermore, this also determines its own drainage characteristics as an important gradation requirement for filter design. In practical engineering, due to the nature of the aggregate source, the crushing and screening process, and rolling construction, the fine particle content may exceed relevant standards. Relying on the field measured gradation envelope of the filter of a gravelly earth core rockfill dam project, this study prepared two classes of filter I (i.e., filter I of class I with a fine particle content of 8%, and filter I of class II with a fine particle content of 12%). Permeability property test, large-scale compression test, large-scale static triaxial test, and large-scale dynamic triaxial test were conducted. Furthermore, dynamic finite-element calculation of the dam under seismic action was performed, and the anti-seismic liquefaction property of the filter was analyzed. According to the permeability property test, the permeability coefficients of the two classes of filter I were both 1∼5×10−3 cm/s. Lower content of particles < 0.075 mm in size increased the permeability coefficient. According to the mechanical test, the stress-strain test curves of both classes of filter I presented nonlinearity, compressive hardening, elastoplasticity, and other general laws. The strength index and deformation resistance both increased with increasing relative density. Under the same relative density, both strength index and deformation resistance declined with increasing fine particle content. According to the dynamic finite-element anti-seismic liquefaction analysis on filter I of class II (with a content of particles < 0.075 mm in size of 12%) under seismic action, the liquefaction degree had a maximum value of 0.58 (less than 0.8), and the anti-liquefaction safety coefficient had a minimum value of 1.72, which would not trigger liquefaction of filter I. This experimental study and computational analysis offers references for the exploration of the gradation design of filter I of earth core rockfill dams and investigates the related engineering influence.