UKF在INS/GPS直接法卡尔曼滤波中的应用

提出将Unscented卡尔曼滤波(UKF)用于INS/GPS组合导航系统的直接法卡尔曼滤波,避免了对非线性的系统状态方程进行线性化.以INS输出的导航参数及平台误差角等作为系统状态,惯导力学编排方程和姿态误差方程作为系统状态方程,GPS输出的导航参数作为量测,采用UKF方法对系统导航参数直接进行估计.仿真结果表明,UKF方法有效地解决了直接法卡尔曼滤波中系统状态方程的非线性问题,并使INS/GPS组合导航系统具有较高的导航定位精度.     

A Novel, Low Temperature Synthesis Method of Dimethyl Ether Over Cu–Zn Catalyst Based on Self-Catalysis Effect of Methanol

A new DME synthesis route from syngas at a relatively low temperature (443 K) has been developed for the first time by the combination of a conventional DME synthesis catalyst (Cu/ZnO:HZSM-5 catalyst) with methanol as a catalytic solvent. The addition of methanol to the reaction system is the key to the success of DME synthesis at this temperature. Indeed, a CO conversion of 29 and 43% with a DME selectivity of 69 and 68% were achieved at 443 or 453 K, respectively, and 4 MPa, when methanol was used as a catalytic solvent. Importantly, no other by-products including methanol and hydrocarbons were observed in the DME product attained, suggesting no significant subsequent purification stages. Assuming no scale up problems, this process potentially provides a high purity of DME with less energy consumption, and so offers an opportunity for the economically viable future sustainable production of DME.     

A discrete wavelet transform approach to discriminating among inrush current,external fault,and internal fault in power transformer using low‐frequency components differential current only

This paper proposes an algorithm based on discrete wavelet transform (DWT) for discriminating among inrush current, internal fault, and external fault in power transformers. Fault conditions are simulated using the Alternative Transients Program/Electromagnetic Transients Program (ATP/EMTP). Daubechies4 (db4) is employed as the mother wavelet to decompose low‐frequency components from fault signals. The ratio between per unit (p.u.) differential current and p.u. time is suggested as an index. The numerator of the ratio is the difference between the maximum differential current and the minimum differential current in terms of p.u. with a base value selected at the transformer‐rated current. The ratio is calculated for all three phases, and from a trial and error process the indices for the separation among the internal fault condition, the external fault condition, and inrush condition are defined. The results obtained from the proposed technique show good accuracy for discriminating faults in the considered system. In addition, the proposed algorithm uses data of the differential current with a time of quarter cycle under the analysis. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

A novel probabilistic neural network‐based algorithm for classifying internal fault in transformer windings

The major function of protective devices in a power system is to detect the occurrence of faults and to isolate the faulty sections from the rest of the system. Much progress has been made in the development algorithms for detecting faults in power transformers, which depend on transients‐based techniques. This paper presents an algorithm based on a combination of discrete wavelet transforms and probabilistic neural networks (PNNs) for classifying internal faults in a two‐winding three‐phase transformer. Fault conditions of the transformer are simulated using alternative transients program/electromagnetic transients program (ATP/EMTP) in order to obtain current signals. The mother wavelet Daubechies4 is employed to decompose the high‐frequency components from these signals. All three phases of the differential current signals are used in the fault detection decision algorithm. The variations of first‐scale high‐frequency component that detects fault are used as an input for the training pattern. The training process for the neural network and fault diagnosis decision is implemented using toolboxes on MATLAB/Simulink. Various cases and fault types based on the Thailand electricity transmission and distribution systems are studied to verify the validity of the algorithm. Backpropagation neural network is also compared with the PNN in this paper. It is found that the proposed method gives satisfactory accuracy with less training time, and will be particularly useful in the development of a modern differential relay for a transformer protection scheme. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.