Improved Evaluation Method of Flicker considering Disturbances of Power System

Jae-Chul Kim,Jong-Fil Moon,Seung-Bock Jung,Kyu-Ha Choe 한국조명·전기설비학회 2008 조명·전기설비학회논문지 Vol.22 No.3

This paper studies a more exact flicker evaluation method by detecting power quality disturbances and excluding the effects of power quality disturbances. Up to the present, power quality disturbances affect flicker evaluation index because power quality problems do not have been considered. However, flick index should represent only flicker without power quality disturbances. Thus, in this paper, we present the improved flicker evaluation method which removing the effects of power quality disturbances such as voltage sag and transient caused by fault and inverter/breaker switching. We detect voltage sag and transient using wavelet transform and remove the effects of power quality disturbances from flicker index.

Improved Evaluation Method of Flicker considering Disturbances of Power System

Kim, Jae-Chul,Moon, Jong-Fil,Jung, Seung-Bock,Choe, Kyu-Ha The Korean Institute of IIIuminating and Electrica 2008 조명·전기설비학회논문지 Vol.22 No.3

This paper studies a more exact flicker evaluation method by detecting power quality disturbances and excluding the effects of power quality disturbances. Up to the present, power quality disturbances affect flicker evaluation index because power quality problems do not have been considered. However, flick index should represent only flicker without power quality disturbances. Thus, in this paper, we present the improved flicker evaluation method which removing the effects of power quality disturbances such as voltage sag and transient caused by fault and inverter/breaker switching. We detect voltage sag and transient using wavelet transform and remove the effects of power quality disturbances from flicker index.

Empirical Wavelet Transform Based Approach for Extraction of Fundamental Component and Estimation of Time-Varying Power Quality Indices in Power Quality Disturbances

G. Ravi Shankar Reddy,Dr.Rameshwar Rao 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.11

In this paper Application of an Empirical Wavelet Transform based technique is proposed to estimate time-varying PQ indices for accurate assessment of Power Quality Disturbances. The EWT approach mainly aims to extract the actual fundamental frequency component and disturbance components from any distorted signal. The empirical wavelet transform consists of two major steps: detect the Fourier supports, and build the corresponding wavelet accordingly to those supports; filter the input signal with the obtained filter bank to get the fundamental component and disturbance components. Since the extracted components contain only one frequency component, Hilbert transform is utilized to estimate the instantaneous frequency and amplitude information, from this information we can estimate time-varying PQ indices. The proposed method is employed to assess successfully all sorts of Power Quality Disturbances such as voltage sag, swell, interruption, transients, harmonics, spikes, notches etc. From the results we can say that the proposed method detects disturbance start time, end time, duration of existence and its content more accurately.

Power Disturbance Classifier Using Wavelet-Based Neural Network

Jaeho Choi,Hongkyun Kim,Jinmok Lee,Gyo-Bum Chung 전력전자학회 2006 JOURNAL OF POWER ELECTRONICS Vol.6 No.4

This paper presents a wavelet and neural network based technology for the monitoring and classification of various types of power quality (PQ) disturbances. Simultaneous and automatic detection and classification of PQ transients, is recommended, however these processes have not been thoroughly investigated so far. In this paper, the hardware and software of a power quality data acquisition system (PQDAS) is described. In this system, an auto-classifying system combines the properties of the wavelet transform with the advantages of a neural network. Additionally, to improve recognition rate, extraction technology is considered.

Power Quality Disturbances Identification Method Based on Novel Hybrid Kernel Function

Zhao, Liquan,Gai, Meijiao Korea Information Processing Society 2019 Journal of information processing systems Vol.15 No.2

A hybrid kernel function of support vector machine is proposed to improve the classification performance of power quality disturbances. The kernel function mathematical model of support vector machine directly affects the classification performance. Different types of kernel functions have different generalization ability and learning ability. The single kernel function cannot have better ability both in learning and generalization. To overcome this problem, we propose a hybrid kernel function that is composed of two single kernel functions to improve both the ability in generation and learning. In simulations, we respectively used the single and multiple power quality disturbances to test classification performance of support vector machine algorithm with the proposed hybrid kernel function. Compared with other support vector machine algorithms, the improved support vector machine algorithm has better performance for the classification of power quality signals with single and multiple disturbances.

A Classification Method for Power-Quality Disturbances Using Hilbert–Huang Transform and LSTM Recurrent Neural Networks

Miguel Angel Rodriguez,John Felipe Sotomonte,Jenny Cifuentes,Maximiliano Bueno-López 대한전기학회 2021 Journal of Electrical Engineering & Technology Vol.16 No.1

Power quality disturbances are one of the main problems in an electric power system, where deviations in the voltage and current signals can be evidenced. These sudden changes are potential causes of malfunctions and could aff ect equipment performance at diff erent demand locations. For this reason, a classifi cation strategy is essential to provide relevant information related to the occurrence of the disturbance. Nevertheless, traditional data extraction and detection methods have failed to carry out the classifi cation process with the performance required, in terms of accuracy and effi ciency, due to the presence of a non-stationary and non-linear dynamics, specifi c of these signals. This paper proposes a hybrid approach that involves the implementation of the Hilbert–Huang Transform (HHT) and long short-term memory (LSTM), recurrent neural networks (RNN) to detect and classify power quality disturbances. Nine types of synthetic signals were reproduced and pre-processed taking into account the mathematical models and their specifi cations established in the IEEE 1159 standard. In order to eliminate the presence of mode mixing, the ensemble empirical decomposition (EEMD) and masking signal methods were implemented. Additionally, based on the successful benefi ts of LSTM RNNs reported in the literature, associated to the high accuracy rates achieved at learning long short-term dependencies, this classifi cation technique is implemented to analyze the sequences obtained from the HHT. Based on the experimental results, it is possible to show that the ensemble recognition approach using the EEMD yields a better classifi cation accuracy rate (98.85%) compared with the masking signal and the traditional HHT approach

Power Quality Improvement for Grid Connected Inverters under Distorted and Unbalanced Grids

Hyun-Sou Kim,Jung-Su Kim,Kyeong-Hwa Kim 전력전자학회 2016 JOURNAL OF POWER ELECTRONICS Vol.16 No.4

A power quality improvement scheme for grid connected inverters, even in the presence of the disturbances in grid voltages due to harmonic distortions and three-phase imbalance, is presented for distributed generation (DG) power systems. The control objective is to force the inverter currents to follow their references with robustness even under external disturbances in grid voltages. The proposed scheme is realized by a disturbance observer (DOB) based current control scheme. Since the uncertainty in a system can be effectively canceled out using an estimated disturbance by the DOB, the resultant system behaves like a closed-loop system consisting of a disturbance-free nominal model. For experimental verification, a 2 kVA laboratory prototype of a grid connected inverter has been built using a digital signal processor (DSP) TMS320F28335. Through comparative simulations and experimental results under grid disturbances such as harmonic distortion and imbalance, the effectiveness of the proposed DOB based current control scheme is demonstrated.

Power Quality Improvement for Grid Connected Inverters under Distorted and Unbalanced Grids

Kim, Hyun-Sou,Kim, Jung-Su,Kim, Kyeong-Hwa The Korean Institute of Power Electronics 2016 JOURNAL OF POWER ELECTRONICS Vol.16 No.4

A power quality improvement scheme for grid connected inverters, even in the presence of the disturbances in grid voltages due to harmonic distortions and three-phase imbalance, is presented for distributed generation (DG) power systems. The control objective is to force the inverter currents to follow their references with robustness even under external disturbances in grid voltages. The proposed scheme is realized by a disturbance observer (DOB) based current control scheme. Since the uncertainty in a system can be effectively canceled out using an estimated disturbance by the DOB, the resultant system behaves like a closed-loop system consisting of a disturbance-free nominal model. For experimental verification, a 2 kVA laboratory prototype of a grid connected inverter has been built using a digital signal processor (DSP) TMS320F28335. Through comparative simulations and experimental results under grid disturbances such as harmonic distortion and imbalance, the effectiveness of the proposed DOB based current control scheme is demonstrated.

필터뱅크와 적응필터를 이용한 전력품질 외란 검출기법

윤재준(Jae-Jun Yun),이정규(Jeong-kyu Lee),손상욱(Sang-Wook Sohn),배현덕(Hyeon-Deok Bae) 대한전기학회 2012 전기학회논문지 Vol.61 No.1

In power quality monitoring, it is very important to detect disturbances (sag, swell, transient, and interruption) accurately. In this paper, a detection method for power quality disturbances by combining the filter bank system and adaptive filter is proposed. To decompose power signal, binary tree structured filter bank system is designed. In the filter bank system, the fundamental filter bank(QMF bank) is used as a module in each decomposing level. An adaptive filter is used to improve the detection accuracy of disturbances for each subband signal. In the adaptive filter, the measure of estimated error change is used to detect singular points of power quality disturbances. Computer simulations were performed on synthetic signals which have disturbances to assess the performance of the proposed method.

Extraction of Fundamental Component in Power Quality Application using Tunable-Q Wavelet Transform

G.Ravi Shankar Reddy,Rameshwar Rao 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.8

Application of a Tunable-Q Wavelet Transform based technique is proposed in this paper for the extraction of Fundamental frequency component in Power Quality Disturbances. The TQWT filters are designed to extract the fundamental frequency component from the complete voltage (or) current signal. This is achieved by tuning the Q-factor and redundancy of the wavelet by primarily investigating the presence of interharmonics near the fundamental frequency. To test the effectiveness of the proposed scheme, the system is verified with various Power Quality Disturbances as per IEEE standards encountered in power system are considered here.