• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1802-1811
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• 2016

A novel battery SOH estimation algorithm based on outlier detection has been presented. The Battery state of health (SOH) is one of the most important parameters that describes the usability state of the power battery system. Firstly, a battery system model with lifetime fading characteristic was established, and the battery characteristic parameters were acquired from the lifetime fading process. Then, the outlier detection method based on angular distribution was used to identify the outliers among the battery behaviors. Lastly, the functional relationship between battery SOH and the outlier distribution was obtained by polynomial fitting method. The experimental results show that the algorithm can identify the outliers accurately, and the absolute error between the SOH estimation value and true value is less than 3%.

• Proceedings of the KIEE Conference
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• 1989.11a
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• pp.186-190
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• 1989

A Study for Bad Data Processing in state estimation by a Neural Network is presented. State estimation is the process of assigning a value to an unknown system state variable based on measurement from that system according to some criteria. In this case, the ability to detect and identify bad measurements is extremely valuable, and much time in oder to achieve the state estimation is needed. This paper proposed new bad data processing using Neural Network in order to settle it. The concept of neural net is a parallel distributed processing. In this paper, EBP (Error Back Propagation) algorithm based on three layered feed forward network is used.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.182-185
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• 1996

The purpose of slate estimation in power system is to estimate the best-fit Slate variables from the measurements contaminated by various kind of noise. But because the majority of state estimation modules in EMS lack the convergence characteristics, sometimes the desirable outputs can't be obtained. So, in this paper, the new algorithm using the load now output as initial values in the state estimation calculation is proposed to guarantee the convergence. And if the load now outputs were used as the initial values in the calculation, the change in each step would be small compared to the original method using the flat start point. And the Inverse Lemma is used in the algorithm to calculate the new stale in each iteration step for reducing the calculation time. The proposed algorithm was tested on the IEEE 14, 30, 118 bus systems. Eventually, we were able to verity that the differences between the results obtained by the original method and proposed method were relatively small, and the effectiveness of the proposed algorithm increased when applied to the bigger systems.

• International Journal of Control, Automation, and Systems
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• v.4 no.2
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• pp.165-171
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• 2006

This paper proposes a data fusion algorithm of nonlinear multi sensor dynamic systems of synchronous sampling based on filtering step by step. Firstly, the object state variable at the next time index can be predicted by the previous global information with the systems, then the predicted estimation can be updated in turn by use of the extended Kalman filter when all of the observations aiming at the target state variable arrive. Finally a fusion estimation of the object state variable is obtained based on the system global information. Synchronously, we formulate the new algorithm and compare its performances with those of the traditional nonlinear centralized and distributed data fusion algorithms by the indexes that include the computational complexity, data communicational burden, time delay and estimation accuracy, etc.. These compared results indicate that the performance from the new algorithm is superior to the performances from the two traditional nonlinear data fusion algorithms.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.192-192
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• 2000

In this paper, a platoon merging is considered as a remote-controlled system with the state represented by a stochastic process. In this system, it becomes to encounter situations where a single decision maker controls a large number of subsystems, and observation and control signals are sent over a communication channel with finite capacity and significant transmission delays. Unlike classical estimation problem in which the observation is a continuous process corrupted by additive noise, there is a constraint that the observation must be coded and transmitted over a digital communication channel with finite capaci쇼. A recursive coder-estimator sequence is a state estimation scheme based on observations transmitted with finite communication capacity constraint. Using the coder-estimator sequence, the remote control station designs a feedback controller. In this paper, we introduce a stochastic model for the lead vehicle in a platoon of vehicles considering the angle between a road surface and a horizontal plane as a stochastic process. The simulation results show that the inter-vehicle distance and the deviation from the desired inter-vehicle distance are well regulated.

• Proceedings of the KIEE Conference
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• summer
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• pp.286-288
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• 2004

The design of a measurement system to perform Harmonic State Estimation (HSE) is a very complex problem. In particular, the number of harmonic instruments available is always limited. Therefore, a systematic procedure is needed to design the optimal placement of measurement points. This paper presents a new HSE algorithm which is based on an optimal placement of measurement points using Genetic Algorithms (GAs). This HSE has been applied to the Simulation Test Power System for the validation of the new HSE algorithm. The study results have indicated an economical and effective method for optimal placement of measurement points using Genetic Algorithms (GAs) in the Harmonic State Estimation (HSE).

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.11
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• pp.483-490
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• 2006

When the nonlinearities, such as friction and backlash, are not considered in the controller design, undesirable oscillations can occur in the steady-state response of a control system. This paper deals with a method to reduce oscillations that often appear in the steady-state response of a pendulum system, which is controlled by a state feedback controller based on the linearized system model. With an assumption that the oscillations shown in the steady-state are caused by the Coulomb friction, we improve the performance of stabilization and tracking by estimating and compensating for the Coulomb friction in the pendulum system. Experimental results show that the control performance can be improved sufficiently by the proposed method, when it is applied to an inverted cart pendulum which is a multi-variable unstable system. Furthermore, we could see that the Coulomb friction model used in the estimation of the friction is valid in applying the suggested method.

• Journal of Advanced Navigation Technology
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• v.16 no.6
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• pp.1123-1133
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• 2012

This study configuration Vector Control System which is stable and has outstanding Dynamic Characteristics in Very Low Speed Region and Low Speed Region, and proposes Instantaneous Speed Observer and Very Low Speed Control method and vector control system of the speed estimation a using Reduced-Dimensional State Observer. The Observer proposed in this system, by appling Reduced-Dimensional State Observer to Load-Torque estimation and using for speed estimation, implements system composition simply and is capable of accurate Instantaneous Speed estimation in Very Low Speed Region. Also, this study reduces influence by System Noise and suggests an induction motor speed control system which is effective in Load Disturbance, modeling error, estimation noise and so on without changing pole of an Observer.

• Journal of IKEEE
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• v.22 no.3
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• pp.754-758
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• 2018

Analysis methods in the power system are static analysis, dynamic analysis and online analysis, offline analysis. The static analysis is used for the existing power system analysis method and the static analysis is mainly used for PSS / E. However, in the real system where the value changes in real time which we are using, dynamic analysis is required which can be analyzed in real time for accurate analysis. Therefore, attention is focused on EMS (Energy Management System) and importance is increasing. Among the various EMS systems, we will cover state estimation, which is a static on-line analysis that can receive and interpret data from the acquisition point in real time. DC systems are spreading in various fields such as DC load, DC distribution, renewable energy. As such, much attention and attention are focused on the DC system. In this paper, we have studied the feasibility through the case study and the interpretation of the state estimation that can be applied to the DC system.

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.14-19
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• 2021

This paper presents LiDAR static obstacle map based vehicle dynamic state estimation algorithm for urban autonomous driving. In an autonomous driving, state estimation of host vehicle is important for accurate prediction of ego motion and perceived object. Therefore, in a situation in which noise exists in the control input of the vehicle, state estimation using sensor such as LiDAR and vision is required. However, it is difficult to obtain a measurement for the vehicle state because the recognition sensor of autonomous vehicle perceives including a dynamic object. The proposed algorithm consists of two parts. First, a Bayesian rule-based static obstacle map is constructed using continuous LiDAR point cloud input. Second, vehicle odometry during the time interval is calculated by matching the static obstacle map using Normal Distribution Transformation (NDT) method. And the velocity and yaw rate of vehicle are estimated based on the Extended Kalman Filter (EKF) using vehicle odometry as measurement. The proposed algorithm is implemented in the Linux Robot Operating System (ROS) environment, and is verified with data obtained from actual driving on urban roads. The test results show a more robust and accurate dynamic state estimation result when there is a bias in the chassis IMU sensor.