A scheme based on PMU data for power quality disturbances monitoring

Mejia-Barron, Arturo; Amezquita-Sanchez, Juan P.; Domínguez-González, Aurelio; Valtierra-Rodríguez, Martín; Razo-Hernández, José R.; Granados-Lieberman, David

doi:10.1109/iecon.2017.8216553

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…In ( 6), the variables are defined as follows: Since the duration of each event data is 40 s, frequency component f i are multiples of 1/40 (0.025) Hz as can be shown in (7),…”

Section: Generating Synthetic Event Data For Pmumentioning

confidence: 99%

“…For the classification of transients, several methods have been studied in the literature such as Decision Trees (DT) [3][4][5], Support Vector Machines (SVM) [4,5], Discrete Wavelet Transform [5,7] and Artificial Neural Networks [8]. Moreover, since the success of deep learning (DL)-based studies in automatic classification has been demonstrated, DL-based algorithms have recently been used frequently for classification of PQ Events and transients [9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

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Transient event classification using pmu data with deep learning techniques and synthetically supported training‐set

Gok

Salor

Taplamacıoğlu

2023

IET Generation Trans & Dist

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This paper presents a research work which focuses on generating synthetic data to enrich the training-set of a deep learning (DL) based classification system to classify power system transient events using PMU frequency measurements. The synthetically improved trainingset is shown to increase the classification performance compared to the case when only the actual-data training-set is used. The proposed classification system helps to reveal high-frequency transient variation information out of PMU measurements collected at a relatively much lower rate, especially when a small set of training-data exists. Synthetic PMU frequency data is generated based on the DFT analysis statistics on the limitedsize PMU frequency data. Generation of the synthetic data is achieved by re-synthesis of the PMU frequency data using inverse DFT, which imitates the DFT frequency and phase behaviour for each event type separately. Then the DL structure is trained to classify the power system transients using the synthetically enriched train set. The proposed method of generating synthetically supported training-set has lower computational complexity compared to the existing methods in the literature and helps to obtain improved classification results. It can be used to increase the classification performances of power quality devices performing transient event analysis, especially for those with access to a limited set of training-set.

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“…In ( 6), the variables are defined as follows: Since the duration of each event data is 40 s, frequency component f i are multiples of 1/40 (0.025) Hz as can be shown in (7),…”

Section: Generating Synthetic Event Data For Pmumentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Transient event classification using pmu data with deep learning techniques and synthetically supported training‐set

Gok

Salor

Taplamacıoğlu

2023

IET Generation Trans & Dist

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“…There have been many other combinations of approaches developed to detect and localize PQDs, including ST and PNN approaches [7,12], ST and decision trees (DT) [3,10], and DWT multi-resolution analysis (MRA) with decision-making architecture [5]. Researchers have also explored thresholdbased approaches using a feedforward neural network (FFNN) classifier with synthetic synchrophasor data [20], showing good overall classification accuracy (OCA) of standard PQDs. However, it is difficult to obtain real field data of µPMUs, since it is a new technology that has been recently embraced.…”

Section: Introductionmentioning

confidence: 99%

Distribution Network Power Quality Insights With Optimally Placed Micro-PMUs Incorporating Synthetic and Real Field Data

Anguswamy,

Datta,

Meegahapola

et al. 2023

IEEE Access

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Widespread deployments of optimally placed real-time power quality (PQ) monitoring tools such as distribution level micro-phasor measurement units (D-PMUs or µPMU), digital fault recorders, and PQ analyzers are expected to play a critical role in improving the stability and reliability of the smart grid. In this paper, an improved PQ disturbance (PQD) classification method using discrete wavelet transform (DWT) with a cubic multi-class support vector machine (CMSVM) classifier is proposed, which incorporates a decade's worth of high-quality continuous waveform PQ data from the Australian power network. This research also introduces misclassification cost (MC) and cost-sensitive classification theory into the area of PQD classifiers to build improved and more robust network models for the future. The method is evaluated using four case studies of synthetic and real-world PQD field data combinations and five application case studies using optimally placed µPMUs. The results indicate similar classification performance for standard PQDs than previous literature, alongside improved MC for complex PQD classes. Comparative analysis with

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“…After measuring the phasor and applying the P filter, they set the defect classification by determining the limit values with ANN and rule-based software (Mejia-Barron et al, 2017). This study uses the Box-Behnken response surface as analysis method, one of the experimental design methods to facilitate the ANN design phase.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Approach to Classify Power Quality Problems in Distribution Systems

Özgönenel

Akpinar

2021

JER is an international, peer-reviewed journal that publishes f

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Electrical power systems are expected to transmit continuously nominal rated sinusoidal voltage and current to consumers. However, the widespread use of power electronics has brought power quality problems. This study performs classification of power quality disturbances using an artificial neural network (ANN). The most appropriate ANN structure was determined using the Box-Behnken experimental design method. Nine types of disturbance (no fault, voltage sag, voltage, swell, flicker, harmonics, transient, DC component, electromagnetic interference, instant interruption) were investigated in computer simulations. The feature vectors used in the identification of the different types of disturbances were produced using the discrete wavelet transform and principal component analysis. Our results show that the optimized feed forward multilayer ANN structure successfully distinguishes power quality disturbances in simulation data and was also able to identify these disturbances in real time data from substations.

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A scheme based on PMU data for power quality disturbances monitoring

Cited by 5 publications

References 15 publications

Transient event classification using pmu data with deep learning techniques and synthetically supported training‐set

Transient event classification using pmu data with deep learning techniques and synthetically supported training‐set

Distribution Network Power Quality Insights With Optimally Placed Micro-PMUs Incorporating Synthetic and Real Field Data

A Hybrid Approach to Classify Power Quality Problems in Distribution Systems

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