• Journal of the Institute of Convergence Signal Processing
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• v.13 no.2
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• pp.91-98
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• 2012

Power Quality (PQ) signal such as sag, swell, harmonics, and impulsive transients are the major issues in the operations of the power electronics based devices and microprocessor based equipments. The effectiveness of wavelet based denoising techniques and recognizing different power quality events with noise has been presented in this paper. The algorithms involved in the noisy PQ recognition system are the wavelet based denoising and the back propagation neural networks. Also, in order to verify the real-time performances of the noisy PQ recognition systems under the noisy environments, SIL(Software In the Loop) and PIL(Processor In the Loop) were carried out, resulting in the excellent recognition performances.

• Proceedings of the KIEE Conference
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• 2001.07b
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• pp.1070-1072
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• 2001

In recent years, customers and power supplies are interested in power quality. Demands of customers are change from standard quality of distribution power system to various high quality of distribution power system. so, it is necessary to apply power quality compensator, in our project, we develop the power quality compensator of 150kVA which compensates power factor and voltage sag, interruption. it is very frequently occurred power qualify problems[1,2]. As a series and shunt compensator, power quality compensator consists of two inverters with common do link capacitor bank. It compensates the current quality in the shunt part and the voltage qualify in the series part. In this paper we present the design and control algorithm of power quality compensator. As a control algorithm is implemented by digital controller, we consider sample-and-hold of signals. In this simulation, we use EMTDC/PSCAD V3.0 software which can simulate instantaneous voltage and current.

• Proceedings of the KIPE Conference
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• 2005.07a
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• pp.90-92
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• 2005

As the developing of industry, the nonlinear equipments as inverter, converter, SMPS(Switching Mode Power Supply) and motor have been increased. But the sensitive electronic loads to the power quality, such as computer's and other electronic equipments, have been spreaded out very fast. Thus, the power quality (PQ) problems and the real time power quality monitoring (PQM) are one of the important issues industry and building area for the intelligent control of the systems. One of them, PQM using PLC(Power Line Communication) have good merits that can send and receive the PQ information without any new network line. This paper presents the PQM hardware and software to monitor the PQ information by using PLC that meets the categories of IEEE Std. 1159.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.5
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• pp.330-339
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• 2015

Power quality (PQ) problems are becoming a big issue, since delicate complex electronic devices are widely used. We present a new denoising technique using discrete wavelet transform (DWT), where a modified correlation thresholding is used in order to reliably detect the PQ disturbances. We consider various PQ disturbances on the basis of IEEE-1159 standard over noisy environments, including voltage swell, voltage sag, transient, harmonics, interrupt, and their combinations. These event signals are decomposed using DWT for the detection of disturbances. We then evaluate the PQ disturbance detection ratio of the proposed denoising scheme over Gaussian noise channels. Simulation results also show that the proposed scheme has an improved signal-to-noise ratio (SNR) over existing scheme.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.8
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• pp.1351-1359
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• 2007

In this paper, we have studied the power quality(PQ) diagnosis system with the two methods for PQ diagnosis. One to Apply a regulation value in compliance with mathematics calculation, and the other Automatic identification using Neural network algorithm. Neural network algorithm is used for an automatic diagnosis of the PQ. The regulation proposed by IEEE 1159 Working group is applied for the precision of the diagnosis. In order to divide accurate segmentation, the algorithm for a computer training used the back propagation out of several neural network algorithms. We have configured the proto-type sample by using Labview and a programmed Neural Networks Algorithm using with C. And arbitrary electric Signal generated by OMICRON Company's CMC 256-6 for an efficiency test.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.50-52
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• 2005

Research interests in PQ(Power Quality) measurement and monitoring system have been increased. This paper describes an experimental result for monitoring system that monitors power quality and undesirable accidents when distributed generations are connected to the power grid. Prior to develop a physical monitoring system for distributed generation applications, we constitute a mesuring system for the general PQ factors. In this paper, an approach to how to measure the PQ elements is presented by using TMS320C6701 processor. Simulation results using the PSCAD and the power system simulation equipment Dobel have verified the proposed measurement system.

• Journal of Power Electronics
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• v.9 no.1
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• pp.109-116
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• 2009

This paper addresses the output control of a utility-connected double-fed induction machine (DFIM) for wind power generation systems (WPGS). DFIM has a back-to-back converter to control outputs of DFIM driven by the wind turbine for WPGS. To supply commercially the power of WPGS to the grid without any problems related to power quality, the real and reactive powers (PQ) at the stator side of DFIM are strictly controlled at the required level, which in this paper is realized with the Fuzzy PI controller based on the field orientation control. For the Sinusoidal Pulse Width Modulation (SPWM) converter connected to the rotor side of DFIG to maintain the controllability of PQ at the state side of DFIM, the DC voltage of the DC link capacitor is also controlled at a certain level with the conventional Proportion-Integral (PI) controller of the real power. In addition, the power quality at the grid connected to the rotor side of DFIM through the back-to-back converter is maintained in a certain level with a PI controller of the reactive power. The controllers for the PQ at the stator side of DFIM, the DC link voltage of the back-to-back inverter and the reactive power at the grid connected to the rotor side of DFIM are designed and simulated in the PSIM program, of which the result verifies the performance of the proposed controllers.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.1
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• pp.116-124
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• 2009

This paper proposes a new Artificial-Intelligent(AI) Power Quality(PQ) diagnosis algorithm using Discrete Wavelet Transform(DWT), Fast Fourier Transform(FFT), Root-Mean-Square(RMS) value. The developed algorithm is able to detect and classify the PQ problems such as the transient, the voltage sag, the voltage swell, the voltage interruption and the total harmonics distortion. The 15.36[kHz] sampling frequency is used to measure the voltages in a power system. The measured signals are used for DWT, FFT, RMS calculation. For AI diagnosis of the PQ problems, a simple multi-layered Artificial Neural Network(ANN) with the back-propagation algorithm is adopted, programmed in C++ and tested in PSIM simulation studies. Finally, the algorithm, which is installed in MP PQ+256 with TI DSP320C6713, is proved to diagnose the PQ problems efficiently.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.2
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• pp.169-176
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• 2010

Nonstationary power quality(PQ) signals which the Sag, Swell, Impulsive Transients, and Harmonics make sometimes the operations of the industrial power electronics equipment, speed and motion controller, plant process control systems in the undesired environments. So, this PQ problem might be critical issues between power suppliers and consumers. Therefore, We have studied the PQ recognition system in order to acquire, analyze, and recognize the PQ signals using the software, i.e, MATLAB, Simulink, and CCS, and the hardware. i.e., TMS320C6713DSK(TI), The algorithms of the PQ recognition system in the Wavelet transforms and Backpropagation algorithms of the neural networks. Also, in order to verify the real-time performances of the PQ recognition system under the environments of software and hardware systems, SIL(Software In the Loop) and PIL(Processor In the Loop) were carried out, resulting in the excellent recognition performances of average 99%.

• Journal of Power Electronics
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• v.15 no.6
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• pp.1601-1608
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• 2015

This paper presents a PQ control strategy for micro grid inverters with axial voltage regulators. The inverter works in the voltage-controlled mode and can help improve the terminal power quality. The inverter has two axial voltage regulators. The 1st regulator involves the output voltage amplitude and output impedance, while the 2nd regulator controls the output frequency. The inverter system is equivalent to a controllable voltage source with a controllable inner output impedance. The basic PQ control for micro grid inverters is easy to accomplish. The output active and reactive powers can be decoupled well by controlling the two axial voltages. The 1st axial voltage regulator controls the reactive power, while the 2nd regulator controls the active power. The paper analyses the axial voltage regulation mechanism, and evaluates the PQ decoupling effect mathematically. The effectiveness of the proposed control strategy is validated by simulation and experimental results.