Papers by Keyword: State Estimation

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Authors: Gang Li, Chang Fu Zong, Qiang Zhang, Wei Hong
Abstract: According to the characteristics of the four independent drive (4WID) electric vehicles, the vehicle driving state estimation algorithm was designed based on the Unscented Kalman Filter (UKF). The algorithm used 3-DOF vehicle estimation model with the HSRI tire model. The 4WID EV longitudinal velocity, lateral velocity and side slip angle were estimated. The algorithm was verified through simulation experiment. The results showed that the algorithm could estimate the vehicle driving state more accurately.
Authors: Bing Jun Li, Su Quan Zhou, Xiao Xiang Lun
Abstract: It is of great importance to identify the location of the harmonic sources for the harmonic governance in the power system. Applied with optimal measurement placement (OMP) and harmonic state estimation (HSE), this paper presents a novel process based on PMU measurements to locate the harmonic sources in the distribution network. Considering the cost and the observability, the OMP can provide a scheme of the measurement placement with the minimum number of PMU measurements. In order to simplify the HSE equation, the measured data are converted to the form of voltage by the method proposed in this paper.By solving the HSE equation, the location and magnitude of the harmonic source are evaluated. The methodology is applied to the IEEE 33-bus system, and the obtained results are properly analyzed.
Authors: Zhao Kun, Yan Lei, Li Li, Qiang Li, Yan Sheng Lang, Pei Yu Jia
Abstract: Aiming at the problem that the current measurements provided by phasor measurement unit (PMU) is difficult to introduce to traditional state estimation, a new method to solve the compatibility problems of PMU and supervisory control and data acquisition (SCADA) is presented in this paper. By the rotated-pseudo-measurement (RPM) method, current measurements are transformed into the pseudo measurements and decoupled in polar coordinates, so that they could taken into nonlinear state estimation. Then the RPM method is simulated compared with the current transformation (CT) method. According to the result, it is showed that the RPM method has many advantages compared with the CT, such as independence of other measurements, small error in propagation and more significant in improving estimation accuracy. Moreover, the RPM method has the compatibility with traditional nonlinear state estimation and the fast calculating speed, which have good prospects of practice.
Authors: Qing Hua Gao, Jie Wang, Ming Lu Jin
Abstract: The particle filter (PF) algorithm provides an effective solution to the non-linear and non-Gaussian filtering problem. However, when the motion noises or observation noises are strong, the degenerate phenomena will occur, which leads to poor estimation. In this paper, we propose a modified particle filter (MPF) algorithm for improving the estimated precision through a particle optimization method. After calculating the coarse estimation with the traditional PF, we optimize the particles according to their weights and relative positions, then, move the particles toward the optimal probability distribution. The state estimation and target tracking experiments demonstrate the outstanding performance of the proposed algorithm.
Authors: Hui Li
Abstract: In this paper a novel method of meter placement for distribution systems is proposed. By analyzing the characters of the sensitive matrix of estimated errors of measurements, the sensitive genes are defined which are produced by measurements in the total variance of estimated errors of measurements. The scheme of meter placement is decided according to their orders in the sensitive gene table. The new approach can use lesser real-time meter placements and more pseudo measurements to satisfy the evaluated criterions on the base of branch-current-based state estimation. An analysis is done for the IEEE 33-bus systems and tested results present the advantage of new idea.
Authors: Xiu Hua Hu, Lei Guo, Hui Hui Li
Abstract: For multi-target tracking system, aiming at solving the problem of low precision of state estimation caused by the data correlation ambiguity, the paper presents a novel multi-sensor multi-target adaptive tracking algorithm based on fuzzy clustering theory. Based on the joint probability data association algorithm, the new approach takes account of the case that whether the measure is validated and its possibility of belong to false alarm, and improves the correlation criterion of effective measurement with existing track on the basis of fuzzy clustering theory, which all perfect the update equation of target state estimation and the covariance. Meanwhile, with the adaptive distributed fusion processing structure, it enhance the robustness of the system and without prejudice to the real-time tracking. With the simulation case studies of radar/infrared sensor fusion multi-target tracking system, it verifies the effectiveness of the proposed approach.
Authors: Bao Feng Wang
Abstract: In this paper, we discuss the data losses problem in state estimation process. In sensor network, due to the energy constrained and communication constrained, the system suffering varying data dropped problem, which include independent package dropped, related dropped in single data package transmission processing and partial package dropout in multiple data package transmission processing. Based on the proposed state estimation over sensor network model, the corresponding mathematical models are built and analyzed.
Authors: Hai Feng Liang, Xiao Lei Yu, Ding Hui Shen, Jing Zhang, Cheng Shan Wang
Abstract: This paper presents a branch-current distribution system state estimation algorithm considering zero-injection constraints. The algorithm takes the branch-current amplitude and phase angle as state variables, considering current-amplitude measurement easily without measurements conversion and makes full use of voltage amplitude measurements, power measurements and current measurements. In order to improve the robust performance of algorithm, exponential function is adopted as the objective function in this paper. The paper takes simulation test to verify the algorithm correctly and effectively on improved IEEE 34 nodes system.
Authors: Yu Bao Hou, Shu Yan Tang
Abstract: As the normal particle filter has an expensive computation and degeneracy problem, a propagation-prediction particle filter is proposed. In this scheme, particles after transfer are propagated under the distribution of state noise, and then the produced filial particles are used to predict the corresponding parent particle referring to measurement, in which step the newest measure information is added into estimation. Therefore predicted particle would be closer to the true state, which improves the precision of particle filter. Experiment results have proved the efficiency of the algorithm and the great predominance in little particles case.
Authors: Fu Zhong Wang, Xiao Ying Tian
Abstract: During robotic heading machine working, it often encounters the coal seam hardness changing tremendously. So the cutting mechanism will be easy to break down. For this problem, put forward a strategy, which using wavelet packet technology to study the vibration signal of cutting mechanism. Filtered the signal with wavelet packet; extracted vibration signal characteristics; established the energy eigenvector; used the Hilbert technology to extract frequency characteristics. According to the energy eigenvector and the frequency characteristics, estimated the cutting mechanism running status. The simulation in MATLAB proves that the control strategy can estimate the running state of cutting mechanism real-time, and lay an important foundation for the realization of coal mine roadway drivage unmanned working face.
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