• Journal of Power Electronics
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• v.16 no.5
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• pp.1950-1963
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• 2016

A novel open-circuit fault diagnosis method for 3-phase 4-wire 3-level active power filters based on voltage error correlation is proposed in this paper. This method is based on observing the output pole voltage error of the active power filter through two kinds of algorithms. One algorithm is a voltage error analytical algorithm, which derives four output voltage error analytic expressions through the pulse state, current value and dc bus voltage, respectively, assuming that all of the IGBTs of a certain phase come to an OC fault. The other algorithm is a current circuit equation algorithm, which calculates the real-time output voltage error through basic circuit theory. A correlation is introduced to measure the similarity of the output voltage errors between the two algorithms, and OC faults are located by the maximum of the correlations. A FPGA has been chosen to implement the proposed method due to its fast prototyping. Simulation and experimental results are presented to show the performance of the proposed OC fault diagnosis method.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.5
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• pp.611-616
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• 2008

Error correcting codes (ECCs) are commonly used to protect against the soft errors. Single error correcting and double error detecting (SEC-DED) codes are generally used for this purpose. The proposed approach in this paper selectively reduced power consumption, delay, and area in single-error correcting, double error-detecting checker circuits that perform memory error correction. The multi-objective genetic algorithm is employed to solve the non -linear optimization problem. The proposed method allows that user can choose one of different non-dominated solutions depending on which consideration is important among them. Because we use multi-objective genetic algorithm, we can find various dominated solutions. Therefore, we can choose the ECC according to the important factor of the power, delay and area. The method is applied to odd-column weight Hsiao code which is well- known ECC code and experiments were performed to show the performance of the proposed method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.5
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• pp.713-718
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• 2014

DC urban railway power system consists of DC power network and AC power network. The DC power network supplies electric power to railway vehicles and the AC power network supplies electric power to station electric equipment. Recently, because of power consumption reduction and peak load shaving, intelligent measurement of regenerative energy and renewable energy adapted on DC urban railway is required. For this reason, DC smart metering system for DC power network shall be developed. Therefore, in this paper, DC voltage sensor, current sensor, and DC smart meter were developed and evaluated by performance test. DC voltage sensor was developed for measuring standard voltage range of DC urban railway, and DC current sensor was developed as hall effect split core type in order to install in existing system. DC smart meter possesses function of general intelligent electric power meter, such as measuring electricity and wireless communication etc. And, DC voltage sensor showed average 0.17% of measuring error for 2,000V/50mA, and current sensor showed average 0.21% of measuring error for ${\pm}2,000V/{\pm}4V$ in performance test. Also DC smart meter showed maximum 0.92% of measuring error for output of voltage sensor and current sensor. In similar environment for real DC power network, measuring error rate was under 0.5%. In conclusion, accuracy of DC smart metering system was confirmed by performance test, and more detailed performance will be verified by further real operation DC urban railway line test.

• Nuclear Engineering and Technology
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• v.51 no.3
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• pp.709-718
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• 2019

The international nuclear industry has undergone a lot of changes since the Fukushima, Chernobyl and TMI nuclear power plant accidents. However, there are still large and small component deficiencies at nuclear power plants in the world. There are many causes of electrical equipment defects. There are also factors that cause component failures due to human errors. This paper analyzed the root causes of failure and types of human error in 300 cases of electrical component failures. We analyzed the operating experience of electrical components by methods of root causes in K-HPES (Korean-version of Human Performance Enhancement System) and by methods of human error types in HuRAM+ (Human error-Related event root cause Analysis Method Plus). As a result of analysis, the most electrical component failures appeared as circuit breakers and emergency generators. The major causes of failure showed deterioration and contact failure of electrical components by human error of operations management. The causes of direct failure were due to aged components. Types of human error affecting the causes of electrical equipment failure are as follows. The human error type group I showed that errors of commission (EOC) were 97%, the human error type group II showed that slip/lapse errors were 74%, and the human error type group III showed that latent errors were 95%. This paper is meaningful in that we have approached the causes of electrical equipment failures from a comprehensive human error perspective and found a countermeasure against the root cause. This study will help human performance enhancement in nuclear power plants. However, this paper has done a lot of research on improving human performance in the maintenance field rather than in the design and construction stages. In the future, continuous research on types of human error and prevention measures in the design and construction sector will be required.

• Proceedings of the KIEE Conference
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• 2002.07a
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• pp.12-15
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• 2002

Korea Power System Protection Setting Rule was used from the rectify 1990's. Thereafter transmission voltage is raised the voltage into 765kV, and introduction to new technology of Power System, and was many of variation but, it is using. The present is using Digital type distance relay for 765kV transmission line protection. If impedance value of transmission line were to value lower than setting, this would be operating and relay setting rule is for 85% into Zone 1 self section, and Zone 2 is a 125%, Zone 3 is a 225%. Which's $15{\sim}25%$ include current transformer error 5%, potential transformer 5%, relay calculation error 5% and margin factor from the field experience. This paper is discussed transmission protective relay and relay setting rule of high voltage power system and we verify the correctness relay setting rule with distance relay using Matlab simulation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.13 no.4
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• pp.285-301
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• 1988

The symbol error rate equations of multi-level quadrature PRS(QPRS) system have been derived in the individual and composite environment of Gaussian/impulsive noise, cochannel CW interference, carrier offset, phase jitter and fading. And using the derived error rate equations, the probability of error has been evaluated and shown in graphs as functions of carrier to noise power ratio, carrier to interference power ratio, phase error, impulsive index, the ration of Gaussian noise to impulsive noise power component, signal to noise power ration in phase locked loop(PLL), and fading figures. The rseults show that the error rate performances are generally more more degraded by impulsive noise than by Gaussian noise. But on the contrary the erors occurred more frequently by Gaussian noise than impulsive noise in a fading environment.

• Journal of Power Electronics
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• v.9 no.5
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• pp.748-756
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• 2009

This paper presents a compensation algorithm for position error due to an amplitude imbalance between resolver output signals. Resolvers are typically used to obtain absolute position information for motor drive systems in severe environments. Position error is caused by an amplitude imbalance of the resolver output signals. As a result, the d- and q-axis currents of synchronous reference frame have periodic ripples in the stator fundamental frequency in permanent magnet synchronous motor (PMSM) drive systems. Therefore, this paper proposes a compensation algorithm to reduce the position error generated by the amplitude imbalance. The proposed method does not require any additional hardware, and reduces computation time with a simple integral operation according to rotor position. In addition, the position error can be directly compensated for by the estimated position error. The effectiveness of the proposed compensation algorithm is verified through several simulations and experiments.

• Journal of Power System Engineering
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• v.17 no.5
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• pp.86-93
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• 2013

This paper proposes the used of genetic algorithm for optimizing PI controller and describes the dynamic modeling simulation for the permanent magnet synchronous motor driven by simplified vector control with the aid of MATLAB-Simulink environment. Furthermore, three kinds of error criterion minimization, integral absolute error, integral square error, and integral time absolute error, are used as objective function in the genetic algorithm. The modeling procedures and simulation results are described and presented in this paper. Computer simulation results indicate that the genetic algorithm was able to optimize the PI controller and gives good control performance of the system. Moreover, simplified vector control on permanent magnet synchronous motor does not need to regulate the direct axis component current. This makes simplified vector control of the permanent magnet synchronous motor very useful for some special applications that need simple control structure and low cost performance.

• Journal of IKEEE
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• v.23 no.1
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• pp.120-126
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• 2019

Recently, accurate prediction of power consumption based on machine learning techniques in Internet of Energy (IoE) has been actively studied using the large amount of electricity data acquired from advanced metering infrastructure (AMI). In this paper, we propose a deep learning model based on Gated Recurrent Unit (GRU) as an artificial intelligence (AI) network that can effectively perform pattern recognition of time series data such as the power consumption, and analyze performance of the prediction based on real household power usage data. In the performance analysis, performance comparison between the proposed GRU-based learning model and the conventional learning model of Long Short Term Memory (LSTM) is described. In the simulation results, mean squared error (MSE), mean absolute error (MAE), forecast skill score, normalized root mean square error (RMSE), and normalized mean bias error (NMBE) are used as performance evaluation indexes, and we confirm that the performance of the prediction of the proposed GRU-based learning model is greatly improved.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.2
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• pp.566-578
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• 2018

In this paper, we design a segment training based individual channel estimation (STICE) scheme for the classical two-way relay network (TWRN) with multi-pair sources (MPS) and amplify-and-forward (AF). We adopt the linear minimum mean square error (LMMSE) channel estimator to minimize the mean square error (MSE) without channel estimation error, where the optimal power allocation strategy from the relay for different sources is obtained. Then the MSE gains are given with different source pairs among the proposed power allocation scheme and the existing power allocation schemes. Numerical results show that the proposed method outperforms the existing ones.