Power quality time series data mining using S-transform and fuzzy expert system

Behera, Himansu Sekhar; Dash, P.K.; Biswal, Birendra

doi:10.1016/j.asoc.2009.10.013

Cited by 99 publications

(69 citation statements)

References 20 publications

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“…The classification accuracy of [8,22] is 98% and 99%, respectively; whereas the classification accuracy of this study is 98.7% for the simulation. Although the classification accuracy in [22] is slightly better, the partial results are obtained without noise; differently, the results in this study are obtained under the condition of noise level. Furthermore, the classification of disturbances is completed only by four features by using the proposed algorithm, which is the minimum of the feature numbers in Table 4.…”

Section: Performance Comparison and Discussionmentioning

confidence: 99%

“…As seen in this table, the performance results of this method are desirable Table 3. DOF of the antecedent part using random data in fuzzy rule base (SNR = 20 dB) Patterns DOF1 DOF2 DOF3 DOF4 DOF5 DOF6 DOF7 DOF8 DOF9 DOF10 DOF11 C1 C2 C3 C4 C5 C6 The advantage of the new fuzzy classification described in this article is the consideration of the combined disturbances and instantaneous disturbances, as some of them are not recognized in [8,22]. The classification accuracy of [8,22] is 98% and 99%, respectively; whereas the classification accuracy of this study is 98.7% for the simulation.…”

Section: Performance Comparison and Discussionmentioning

confidence: 99%

“…DOF of the antecedent part using random data in fuzzy rule base (SNR = 20 dB) Patterns DOF1 DOF2 DOF3 DOF4 DOF5 DOF6 DOF7 DOF8 DOF9 DOF10 DOF11 C1 C2 C3 C4 C5 C6 The advantage of the new fuzzy classification described in this article is the consideration of the combined disturbances and instantaneous disturbances, as some of them are not recognized in [8,22]. The classification accuracy of [8,22] is 98% and 99%, respectively; whereas the classification accuracy of this study is 98.7% for the simulation. Although the classification accuracy in [22] is slightly better, the partial results are obtained without noise; differently, the results in this study are obtained under the condition of noise level.…”

Section: Performance Comparison and Discussionmentioning

confidence: 99%

“…A classifier of PQ disturbances based on a rule base using DT-fuzzy (DT-F) is proposed in [15], but the complexity of the rule base of DT-F may limit its applications. The classification of disturbances in [22] uses 5 features extracted by ST, and the simplified rule base is designed with 12 rules to reduce system complexity such as additional references. To ensure the effectiveness of simplified rules, fuzzy rules must closely relate to the other parts of the fuzzy reasoning process, particularly to the fuzzy set numbers of input fuzzy variables and the shape and position parameters of membership function.…”

Section: Introductionmentioning

confidence: 99%

“…However, because of the sensitivity of noise and insufficient data, the accuracy of disturbance patterns may be affected. Given the complexity and various types of PQ disturbance signals, the application of artificial neural network (ANN) [10][11][12][13] and fuzzy logic [14][15][16][17][18][19][20][21][22] has gotten increasing attention. ANN, which has strong adaptivity and fault tolerance, is a powerful tool for recognizing PQ disturbances and has already obtained remarkable research results.…”

Section: Introductionmentioning

confidence: 99%

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Recognition and Classification of Power Quality Disturbances on the basis of Pattern Linguistic Values

Liu¹,

Liu²,

Xu³

2016

Journal of Electrical Engineering and Technology

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-This paper presents a new recognition and classification method for power quality (PQ) disturbances on the basis of pattern linguistic values. This method solves the difficulty of recognizing disturbances rapidly and accurately by using fuzzy logic. This method uses classification disturbance patterns to define the linguistic values of fuzzy input variables and used the input variables of corresponding disturbance pattern to set membership functions. This method also sets the fuzzy rules by analyzing the distribution regularities of the input variable values. One characteristic of this method is that the linguistic values of fuzzy input variables and the setting of membership functions are not only related to the input variables but also to the character of classification disturbance and the classification results. Furthermore, the number of fuzzy rules is equal to the number of disturbance patterns. By using this method for disturbance classification, the membership function and design of fuzzy rules are directly related to the objective of classification, thus effectively reducing the complexity of the design process and yielding accurate classification results. The classification results of the simulation and measured data verify the feasibility and effectiveness of this method.

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Section: Performance Comparison and Discussionmentioning

confidence: 99%

Section: Performance Comparison and Discussionmentioning

confidence: 99%

Section: Performance Comparison and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Recognition and Classification of Power Quality Disturbances on the basis of Pattern Linguistic Values

Liu¹,

Liu²,

Xu³

2016

Journal of Electrical Engineering and Technology

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Power quality disturbance detection and classification using signal processing and soft computing techniques: A comprehensive review

Mishra

2019

Int Trans Electr Energ Syst

130

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Summary Power quality (PQ) studies have gained huge attention from the academics and the industry over the past three decades. The main objective of this article is to provide a comprehensive review on the state‐of‐the‐art techniques based on digital signal processing (DSP) and machine learning for automatic recognition of PQ events. It is aimed to present extensive information on the status of detection and classification of PQ events to the academics following a line of investigation on the similar domain. On the other hand, microgrid is one of the emerging architecture under the umbrella of smart grid infrastructure. In microgrid environment, the integration of renewable energy sources and distributed generators boosts the application of power electronic technology, which is the main cause of various PQ issues. Therefore, various PQ detection and classification (PQD&C) schemes for microgrid application using DSP and machine learning techniques have been reviewed in this article. Moreover, a comparative assessment on various PQD&C techniques has been carried out in details considering several criteria, such as type of data used (synthetic or real world), type of PQ disturbance studied (single or multiple), and performance in terms of accuracy (both noiseless and noisy environment). Hence, several types of research work in PQD&C area, such as the detection principles, benefits, and weaknesses of former works related to each PQD&C technique, are tinted in this manuscript. Therefore, from this review, the opportunities in PQD&C research domain in the power system can be explored further.

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CNN / Bi‐LSTM‐based deep learning algorithm for classification of power quality disturbances by using spectrogram images

Özer

Efe

Özbay

2021

Int Trans Electr Energ Syst

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This paper, using an inverse signal approach, presents a novel deep learning algorithm based on a convolutional neural network (CNN) and bidirectional long short‐term memory (Bi‐LSTM) together with spectrograms for the classification of power quality disturbances (PQDs). The proposed method focuses on the region where the PQD event occurs, using a deep learning‐based spectrogram with the aim of increasing classification success rates. In the proposed approach, the time shift of the signal relative to the pure sine signal was found and relative to this time shift, an inverse sine wave was generated according to the original signal. By collecting this sine wave together with the original signal, spectrograms of both signals were obtained and converted into red/green/blue (RGB) images that were then combined. Finally, classification was carried out via CNN/Bi‐LSTM. In this context, 29 different disturbance events in both single and combined structures were used. The proposed model was applied to the disturbance events and 99.33% classification accuracy was obtained.

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Power quality time series data mining using S-transform and fuzzy expert system

Cited by 99 publications

References 20 publications

Recognition and Classification of Power Quality Disturbances on the basis of Pattern Linguistic Values

Recognition and Classification of Power Quality Disturbances on the basis of Pattern Linguistic Values

Power quality disturbance detection and classification using signal processing and soft computing techniques: A comprehensive review

CNN / Bi‐LSTM‐based deep learning algorithm for classification of power quality disturbances by using spectrogram images

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