A rule-based S-Transform and AdaBoost based approach for power quality assessment

Reddy, Motakatla Venkateswara; Sodhi, Ranjana

doi:10.1016/j.epsr.2016.01.003

Cited by 43 publications

(25 citation statements)

References 22 publications

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“…In this category, the main objective is to achieve maximum detection accuracy. Different techniques such as Artificial Neural Networks (ANN) [21,23], Support Vector Machine (SVM) [22,24], K-nearest Neighbor (K-NN) [25], Fuzzy Logic (FL) [26], Adaboost Classifier [27], Decision Tree (DT) [28], etc. have been applied for detection and classification of PQ disturbances over the past years.…”

Section: Classification Approachesmentioning

confidence: 99%

“…The noise immune S-transform is combined with fuzzy expert system in [26] for assigning a certainty factor for every classification rule thereby to improve the robustness of the PQ detection system in the presence of noise. Another approach for power quality detection is proposed by considering the rule-based S-Transform as a feature extraction and an Adaptive Boost (AdaBoost) as a classifier in [27]. By considering the advantages of ANN and decision tree, a hybrid power quality detection framework is proposed in [28].…”

Section: Classification Approachesmentioning

confidence: 99%

See 1 more Smart Citation

Flexible Entropy Based Feature Selection and Multi Class SVM for Detection and Classification of Power Quality Disturbances

Shaik¹,

Reddy²

2018

IJIES

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Power quality disturbances (PQD) are becoming more and more complicated due to the vast growth of power quality oriented applications particularly the power electronic equipment's. Since the power with less power quality consequences to a huge loss in the electronic devices, there is a need to detect the power quality disturbances more effectively. This paper proposes a new method for PQD detection and classification based on the entropy characteristics of PQD signals and a novel support vector machine algorithm (SVM). The proposed approach develops a flexible entropy based feature selection (FEFS) mechanism to extract the unique characteristics of all types of PQDs such that the detection system can detect more effectively with in less time. Further to increase the accuracy of individual PQDs, this paper proposed Multi Class SVM (MC-SVM). Various experiments are conducted over the proposed work and at every experiment; the performance is measured through Classification accuracy, False Alarm Rate and computation time. Further a comparative analysis reveals the outstanding performance of proposed approach when compared with conventional approaches.

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Section: Classification Approachesmentioning

confidence: 99%

Section: Classification Approachesmentioning

confidence: 99%

Flexible Entropy Based Feature Selection and Multi Class SVM for Detection and Classification of Power Quality Disturbances

Shaik¹,

Reddy²

2018

IJIES

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“…A Wavelet transform (WT) has then been used to overcome the limitation of STFT which able to improve classification accuracy due to its fluctuating window size. However, WT method [10]- [13] is time consuming and can be easily affected by noises. From the literature, spectrogram has been proposed by researchers to analyze PQ signals.…”

Section: Introductionmentioning

confidence: 99%

Voltage Variation Signals Source Identification and Diagnosis Method

Tee¹,

Yusoff²,

Abdullah³

et al. 2019

IJACSA

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Power Quality (PQ) problem has become an important issue for generating bad impact to the users nowadays. It is important to detect and identify the source of the PQ problem. This paper presents a voltage variation signals source identification and diagnosis method by determining the average time frequency representation (TFR) phase power of the impedance. The signals focused in this study are the voltage variation signals, which include voltage sag, swell and interruption. The voltage variation signals from different source location (upstream, downstream as well as up and downstream) according to the IEEE Standard 1159 by using the mathematical models. The signals are first analyzed by using the Spectrograms which act as the feature producing tool. Then, the average power TFR of phase domain of each signal is calculated and tabulated. Finally, the performance of the method is identified by using support vector machine (SVM) and k-nearest neighbor (kNN). The results show that this method is an effective and suitable technique for identifying the source of voltage variation.

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“…Although characterization of PQDs has been achieved, research to reduce both the number of indices needed to perform this task and their complexity is required since the computational resources would be significantly reduced as well. Regarding intelligent algorithms, artificial neural networks [36], support vector machines [37], fuzzy systems oriented by particle swarm optimization [38], a decision tree and fuzzy C-means clustering classifiers [39], and the AdaBoost algorithm [40], among many others, have been presented. In these algorithms, the training dataset, training algorithm, topology or structure (size, complexity, and elements), and designer expertise are issues that have a direct impact on their performance.…”

mentioning

confidence: 99%

Homogeneity-PMU-Based Method for Detection and Classification of Power Quality Disturbances

et al. 2018

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Over the past few years, power quality (PQ) monitoring has become of paramount importance for utilities and users since poor PQ generates negative consequences. In monitoring, fast detection and accurate classification of PQ disturbances (PQDs) are desirable features. In this work, a new method to detect and classify PQDs is proposed. The proposal takes advantage of the low computational resources of both a phasor measurement unit (PMU)-based signal processing scheme and the homogeneity approach. To classify the PQDs, if–then–else rules are used. To validate and test the proposal, synthetic and real signals of sags, swells, interruptions, notching, spikes, harmonics, and oscillatory transients are considered. For the generation of real signals, a PQD generator based on a power inverter is used. In the proposed method, the PMU information is directly used to classify sags, swells, and interruptions, whereas the homogeneity index is used to distinguish among the remaining PQDs. Results show that the proposal is an effective and suitable tool for PQ monitoring.

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A rule-based S-Transform and AdaBoost based approach for power quality assessment

Cited by 43 publications

References 22 publications

Flexible Entropy Based Feature Selection and Multi Class SVM for Detection and Classification of Power Quality Disturbances

Flexible Entropy Based Feature Selection and Multi Class SVM for Detection and Classification of Power Quality Disturbances

Voltage Variation Signals Source Identification and Diagnosis Method

Homogeneity-PMU-Based Method for Detection and Classification of Power Quality Disturbances

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