Classification of power quality disturbances using quantum neural network and DS evidence fusion

He, Zhengyou; Zhang, Haiping; Zhao, Jing; Qing-quan, Qian

doi:10.1002/etep.584

Cited by 17 publications

(9 citation statements)

References 19 publications

(26 reference statements)

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“…On the other hand, the involvement of uncertainties in fault information has caused some problems in the operation of fault diagnosis methods [7], [8]. These uncertainties are mainly classified into three categories: 1) different faults cases exhibit a complex set of features and diverse behaviors, challenging the implementation of protection schemes based on a single fault feature [9], [10]; 2) the instability of the operating conditions can cause problems for protection schemes adjusted with a pre-defined setting value [11]; 3) due to high fault impedance, the currents produced by the faults are not usually sufficient to be detected by common protection devices [12], [13]. In traditional protection systems, the protection relay trips the system once the operating parameter deviates from a preset threshold.…”

Section: Introductionmentioning

confidence: 99%

High Impedance Single-Phase Faults Diagnosis in Transmission Lines via Deep Reinforcement Learning of Transfer Functions

et al. 2021

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Accurate and fast fault detection in transmission lines is of high importance to maintain the reliability of power systems. Most of the existing methods suffer from false detection of high-impedance faults. In this paper, the transfer function (TF) method is introduced to evaluate the effect of impedance and location of faults by analyzing the voltage and current signals in the frequency domain. Interpretation of the results of the TF method is considered as a weakness of this method. In order to alleviate this problem, a convolutional neural network (CNN) and the hybrid model of deep reinforcement learning (DRL) are utilized to identify and locate single-phase to ground short circuit faults in transmission lines. Single-phase to ground short circuit faults with various fault impedances are applied on an IEEE standard transmission line system. Then, the TF traces are calculated and are collected as input datasets for the proposed models. The fault location results for each network are evaluated via various statistical performance metrics such as correlation coefficient (R), mean squared error (MSE), and root mean squared error (RMSE). The R-value of the CNN and DRL models in fault identification is presented as 96.12% and 98.04%, respectively. Finally, in the early detection of single-phase to ground short circuit fault location (high impedance), the results revealed the efficiency of the DRL model with R=96.61% compared to CNN with R=95.21%. INDEX TERMSTransmission line faults, single-phase to ground short circuit, transfer function, deep reinforcement learning, convolutional neural network.

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Section: Introductionmentioning

confidence: 99%

High Impedance Single-Phase Faults Diagnosis in Transmission Lines via Deep Reinforcement Learning of Transfer Functions

et al. 2021

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“…The specific disturbance type is determined in the pattern recognition phase. Artificial intelligence methods such as the decision tree, rule‐based system, artificial neural network, fuzzy logic, expert system, support vector machine (SVM), or relevance vector machine are typically used as classifiers for PQ analysis. There are 2 important requirements the classifier must meet.…”

Section: Introductionmentioning

confidence: 99%

Classification of power quality disturbances using dual strong tracking filters and rule-based extreme learning machine

Chen

Xiao

2018

Int Trans Electr Energ Syst

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SummaryThe classification of single and simultaneous power quality disturbances (PQDs) has become an issue of concern in the power system field. This paper proposes a novel approach based on dual strong tracking filters (STFs) and the rule-based extreme learning machine (ELM) for detecting and classifying single and simultaneous PQDs. Dual STFs are a hybrid structure of a low-order STF and high-order STF. The fading factor of the low-order STF is used to detect sudden changes in PQDs; the fundamental amplitude variation is tracked by the high-order STF. Six distinctive features extracted from the dual STFs serve as the input to the ELM classifier for PQD classification. The rule-based ELM technique, which is equipped with certain decision rules, can improve the ELM classification accuracy when the number of hidden nodes is insufficient. In consideration of special structures of matrices, the real-time computation of the proposed method can be realized. A PQD dataset is generated in MATLAB for simulation experiments; the results show that 20 types of PQDs, including single and simultaneous disturbances, can be accurately classified under the different levels of noise via the proposed method. The method is also tested on a real recorded waveform to verify its effectiveness in PQD classification. | INTRODUCTIONIn recent years, power quality (PQ) has become a critical issue for researchers and developers concerned with power system stability and the propagation of PQ-related effects in the grid. Poor PQ can lead to maloperations or failures of sensitive loads such as embedded systems or electronic devices, ultimately incurring substantial cost for end users. The increased use of unbalanced and nonlinear loads, line faults, switching devices, and power electric converters represents a wide variety of disturbances. Faults among lines to ground, unbalanced, or nonlinear loads can lead to amplitude (AM) variations of the fundamental frequency such as sags, swells, and interruptions. Flickers andThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Data-based maintenance decision-making method is difficult to build for the same reason. The traditional reliability-based method, such as D-S evidence theory [ 33 , 34 , 35 , 36 ], Bayes theory [ 37 , 38 ], and fuzzy set theory [ 39 , 40 , 41 , 42 , 43 ], will face severe challenges with the uncertainty of information and variety of data types. When provided with conflicting evidence, the D-S evidence theory results tend to deviate from the understanding of the user.…”

Section: Introductionmentioning

confidence: 99%

Health Management Decision of Sensor System Based on Health Reliability Degree and Grey Group Decision-Making

Song

Wei

et al. 2018

Sensors

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Metal Oxide Semiconductor (MOS) gas sensor has been widely used in sensor systems for the advantages of fast response, high sensitivity, low cost, and so on. But, limited to the properties of materials, the phenomenon, such as aging, poisoning, and damage of the gas sensitive material will affect the measurement quality of MOS gas sensor array. To ensure the stability of the system, a health management decision strategy for the prognostics and health management (PHM) of a sensor system that is based on health reliability degree (HRD) and grey group decision-making (GGD) is proposed in this paper. The health management decision-making model is presented to choose the best health management strategy. Specially, GGD is utilized to provide health management suggestions for the sensor system. To evaluate the status of the sensor system, a joint HRD-GGD framework is declared as the health management decision-making. In this method, HRD of sensor system is obtained by fusing the output data of each sensor. The optimal decision-making recommendations for health management of the system is proposed by combining historical health reliability degree, maintenance probability, and overhaul rate. Experimental results on four different kinds of health levels demonstrate that the HRD-GGD method outperforms other methods in decision-making accuracy of sensor system. Particularly, the proposed HRD-GGD decision-making method achieves the best decision accuracy of 98.25%.

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Classification of power quality disturbances using quantum neural network and DS evidence fusion

Cited by 17 publications

References 19 publications

High Impedance Single-Phase Faults Diagnosis in Transmission Lines via Deep Reinforcement Learning of Transfer Functions

High Impedance Single-Phase Faults Diagnosis in Transmission Lines via Deep Reinforcement Learning of Transfer Functions

Classification of power quality disturbances using dual strong tracking filters and rule-based extreme learning machine

Health Management Decision of Sensor System Based on Health Reliability Degree and Grey Group Decision-Making

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