冀中坳陷深层烃类相态预测

冀中坳陷位于渤海湾含油气盆地的西部,西临太行山,东界为沧县隆起,北至燕山褶皱带,南至邢衡隆起,呈北北东向延伸。面积为30260km^2。它是一个华北地台解体之后形成的中、新生代断陷坳陷,下第三系沉积厚达3000～9000米。坳陷中共有12个沉积凹陷,面积为19870km^2。它们分别组成两个凹陷带:东部凹陷带和西部凹陷带。前者由北向南为大厂、廊坊—固安、武清、坝县、饶阳、深县束鹿和晋县凹陷(晋县凹陷归属的问题,尚有不同的看法,作者暂将它列入东部凹陷带);后者为北京、徐水、保定和石家庄凹陷。两个凹陷带之间为雁列式的大兴、容城、高阳、刘村-深泽和宁晋中央凸起带所分隔(图1略)。

Time series prediction of mining subsidence based on a SVM

In order to study dynamic laws of surface movements over coal mines due to mining activities,a dynamic prediction model of surface movements was established,based on the theory of support vector machines(SVM) and times-series analysis.An engineering application was used to verify the correctness of the model.Measurements from observation stations were analyzed and processed to obtain equal-time interval surface movement data and subjected to tests of stationary,zero means and normality.Then the data were used to train the SVM model.A time series model was established to predict mining subsidence by rational choices of embedding dimensions and SVM parameters.MAPE and WIA were used as indicators to evaluate the accuracy of the model and for generalization performance.In the end,the model was used to predict future surface movements.Data from observation stations in Huaibei coal mining area were used as an example.The results show that the maximum absolute error of subsidence is 9 mm,the maximum relative error 1.5%,the maximum absolute error of displacement 7 mm and the maximum relative error 1.8%.The accuracy and reliability of the model meet the requirements of on-site engineering.The results of the study provide a new approach to investigate the dynamics of surface movements.

Support Vector Machines(SVM)-Markov Chain Prediction Model of Mining Water Inflow

This study was conducted to establish a Support Vector Machines(SVM)-Markov Chain prediction model for prediction of mining water inflow. According to the raw data sequence, the Support Vector Machines(SVM) model was built, and then revised by means of a Markov state change probability matrix. Through dividing the state and analyzing absolute errors and relative errors and other indexes of the measured value and the fitted value of SVM, the prediction results were improved. Finally,the model was used to calculate relative errors. Through predicting and analyzing mining water inflow, the prediction results of the model were satisfactory. The results of this study enlarge the application scope of the Support Vector Machines(SVM) prediction model and provide a new method for scientific forecasting water inflow in coal mining.

Research on the control method for voltage-current source hybrid-HVDC system

The hybrid-HVDC topology,which consists of line-commutated-converter(LCC)and voltage source converter(VSC)and combines their advantages,has extensive application prospects.A hybrid-HVDC system,adopting VSC on rectifier side and LCC on inverter side,is investigated,and its mathematic model is deduced.The commutation failure issue of the LCC converter in the hybrid-HVDC system is considered,and a novel coordinated control method is proposed to enhance the system commutation failure immunity.A voltage dependent voltage order limiter(VDVOL)is designed based on the constant DC voltage control on the rectifier side,and constant extinction angle backup control is introduced based on the constant DC current control with voltage dependent current order limiter(VDCOL)on the inverter side.The hybrid-HVDC system performances under normal operation state and fault state are simulated in the PSCAD/EMTDC.Then,system transient state performances with or without the proposed control methods under fault condition are further compared and analyzed.It is concluded that the proposed control method has the ability to effectively reduce the probability of commutation failure and improve the fault recovery performance of the hybrid-HVDC system.