• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.56 no.1
• /
• pp.81-87
• /
• 2007

This paper proposes a new capacitance estimation scheme for a DC-link capacitor in a three-phase AC/DC/AC PWM converter. A controlled AC voltage with a lower frequency than the line frequency is injected into the DC-link voltage, which then causes AC power ripples at the DC side. By extracting the AC voltage and power components on the DC output side using digital filters, the capacitance can then be calculated using the Support Vector Regression (SVR). By training of SVR, a function which relates a given input (capacitor's power) and its corresponding output (capacitance value) can be derived. This function is used to predict outputs for given inputs that are not included in the training set. The proposed method does not require the information of DC-link current and can be simply implemented with only software and no additional hardware. Experimental results confirm that the estimation error is less than 0.16%.

• Journal of the Korean Data and Information Science Society
• /
• v.16 no.4
• /
• pp.1159-1165
• /
• 2005

Basu et al. (1998) proposed a new density-based estimator, called the minimum density power divergence estimator (MDPDE), which avoid the use of nonparametric density estimation and associated complication such as bandwidth selection. Woodward et al. (1995) examined the minimum Hellinger distance estimator (MHDE), proposed by Beran (1977), in the case of estimation of the mixture proportion in the mixture of two normals. In this article, we introduce the MDPDE for a mixture proportion, and show that both the MDPDE and the MHDE have the same asymptotic distribution at a model. Simulation study identifies some cases where the MHDE is consistently better than the MDPDE in terms of bias.

• Proceedings of the KIEE Conference
• /
• 2008.07a
• /
• pp.197-198
• /
• 2008

Even if the DFT calculation is one of the general method to do phasor estimation, it can't adapt to alteration of the frequency. The Frequency is fastened to 60Hz in the ideal power system. However the frequency is not constant in real power system and fluctuates more followed by conditions of the power system. In these cases, the accurate phasor estimation is impossible by using a common DFT calculation, so that a frequency - adaptive phasor estimation method based on the fourier transform is proposed in this paper.

• Proceedings of the KIEE Conference
• /
• 2008.07a
• /
• pp.303-304
• /
• 2008

For the state estimation problem, the weighted least squares (WLS) method and the fast decoupled method are widely used at present. However, these algorithms can converge to local optimal solutions. Recently, modern heuristic optimization methods such as Particle Swarm Optimization (PSO) have been introduced to overcome the disadvantage of the classical optimization problem. However, heuristic optimization methods based on populations require a lengthy computing time to find an optimal solution. In this paper, we used PSO to search for the optimal solution of state estimation in power systems. To overcome the shortcoming of heuristic optimization methods, we proposed parallel processing of the PSO algorithm based on the PC cluster system. the proposed approach was tested with the IEEE-118 bus systems. From the simulation results, we found that the parallel PSO based on the PC cluster system can be applicable for power system state estimation.

• Journal of Electrical Engineering and Technology
• /
• v.8 no.1
• /
• pp.70-79
• /
• 2013

In this paper, a comparative study on the frequency estimation methods using IRDWT (Improved Recursive Discrete Wavelet Transform), FRDWT(Fast Recursive Discrete Wavelet Transform), and GCDFT(Gain Compensator Discrete Fourier Transform) is presented. The 345[kV] power system modeling data of the Republic of Korea by EMTP-RV is used to evaluate the performance of the proposed two kinds of RDWT(IRDWT and FRDWT) and GCDFT. The simulation results show that the frequency estimation technique based on FRDWT could be the optimal frequency measurement method, and thus can be applied to FDR(Fault Disturbance Recorder) for wide-area blackout protection or frequency measurement apparatus.

• Journal of Power Electronics
• /
• v.18 no.3
• /
• pp.910-922
• /
• 2018

Conventional battery state-of-charge (SoC) estimation methods either involve sophisticated models or consume considerable computational resource. This study constructs an enhanced coulomb counting method (Ah method) for the SoC estimation of lithium-ion batteries (LiBs) by expanding the Peukert equation for the discharging process and incorporating the Coulombic efficiency for the charging process. Both the rate- and temperature-dependence of battery capacity are encompassed. An SoC mapping approach is also devised for initial SoC determination and Ah method correction. The charge counting performance at different sampling frequencies is analyzed experimentally and theoretically. To achieve a favorable compromise between sampling frequency and accumulation accuracy, a frequency-adjustable current sampling solution is developed. Experiments under the augmented urban dynamometer driving schedule cycles at different temperatures are conducted on two LiBs of different chemistries. Results verify the effectiveness and generalization ability of the proposed SoC estimation method.

• Proceedings of the KIEE Conference
• /
• 2007.07a
• /
• pp.425-426
• /
• 2007

In power systems operation, state estimation takes an important role in security control. For the state estimation problem, conventional optimization algorithm, such as weighted least squares (WLS) method, has been widely used. But these algorithms have disadvantages of converging local optimal solution. In these days, a modern heuristic optimization methods such as Particle Swarm Optimization (PSO), are introducing to overcome the problems of classical optimization. In this paper, we suggested parallel particle swarm optimization (PPSO) to search an optimal solution of state estimation in power systems. To show the usefulness of the proposed method over the conventional PSO, proposed method is applied on the IEEE-57 bus system.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.37 no.7
• /
• pp.427-433
• /
• 1988

This paper presents a partitioned state estimation algorithm on the basis of network reduction by using a estimation technique of boundary line flows. The network is partitioned into several subnetworks, which generates boundary lines. The accurate estimation of boundary line flows enables us to perform state estimation on each sub-system independently. A precise method to estimate boundary line flows is presented for the partitioned state estimation. The proposed algorithm redices computation time and memory requirements remarkably. The proposed algorithm have been tested for IEEE sample system and verified to be applicable to practical power systems.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.24 no.1
• /
• pp.33-39
• /
• 2019

We propose a mechanical parameter estimation algorithm for surface-mounted permanent magnet synchronous motors (SPMSMs) using a sliding-mode observer (SMO) and an adaptive filter. The SMO estimates system disturbances in real time, which contain the information on mechanical parameters. A desirable feature that distinguishes the proposed estimation algorithm from other existing mechanical parameter estimators is that the adaptive filter estimates electromagnetic torque to improve the estimation performance. Moreover, the SMO acts as a low-pass filter to suppress the chattering effect, which enables the smooth output signals of the SMO. We verify the mechanical parameter estimation performance for SPMSM by conducting extensive experiments for the proposed algorithm.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.27 no.4
• /
• pp.332-338
• /
• 2022

This study proposes a battery state-of-health estimation method by applying a feature extraction technique. The technique that can improve estimation performance is the process of identifying and extracting meaningful data. To apply a data-driven-based aging state estimation method to batteries, health indicators are used as training data. However, limitations occur in extracting health indicators from charge/discharge cycles. This study proposes a deep-learning-based battery state-of-health estimation method that applies feature extraction techniques to compensate for this problem. According to the performance evaluation result of the proposed method, it has a low estimation error of 0.3887% based on an absolute error evaluation method.