Condition monitoring approach based on dimensionality reduction techniques for detecting power quality disturbances in cogeneration systems

Gonzalez-Abreu, Artvin-Darien; Saucedo-Dorantes, Juan José; Osornio-Ríos, Roque Alfredo; Romero‐Troncoso, Rene J.; Delgado-Prieto, Miguel; Moriñigo-Sotelo, Daniel

doi:10.1109/etfa.2019.8869523

Cited by 3 publications

(5 citation statements)

References 10 publications

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“…In this sense, Table 2 summarizes the results obtained by the previous literature and also shows the conditions evaluated, as well as the used classification technique. Results reported in [19,20] present general values with high accuracy (99.7%); however, a classification percentage around 79% has been reported when the sag disturbance appears. This situation may be explained by the fact that the behavior of a signal with a sag is very similar to one that presents a healthy signal.…”

Section: Comparative Analysismentioning

confidence: 84%

“…This situation may be explained by the fact that the behavior of a signal with a sag is very similar to one that presents a healthy signal. Another important aspect is that the signals analyzed in [19,20] are free of noise, which is an important issue that significantly affects the performance of classification when real signals are under evaluation. For the remaining works, such as in [22][23][24], the overall percentages show variations within the range of 80% to 96.67%, which are lower than the accuracy reached by the proposed approach.…”

Section: Comparative Analysismentioning

confidence: 99%

“…These approaches are provided with the ability to learn; thus, they can be adapted to perform continuous monitoring that allows the automatic classification of different disturbances related to PQ. On the machine learning side, the work developed in [19] proposes an approach based on dimensionality reduction through a linear discriminant analysis (LDA); moreover, statistical features are used to characterize the behavior of different PQD, and finally, using a Neural Network as a classifier, it is possible to perform an automatic classification of the disturbance in the signal. Another example of this approach is presented in the work of [20].…”

Section: Introductionmentioning

confidence: 99%

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Methodology for the Detection and Classification of Power Quality Disturbances Using CWT and CNN

Perez-Anaya,

Jaen-Cuellar,

Elvira-Ortiz

et al. 2024

Energies

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Energy generation through renewable processes has represented a suitable option for power supply; nevertheless, wind generators and photovoltaic systems can suddenly operate under undesired conditions, leading to power quality problems. In this regard, the development of condition-monitoring strategies applied to the detection of power quality disturbances becomes mandatory to ensure proper working conditions of electrical machinery. Therefore, in this work we propose a diagnosis methodology for detecting power quality disturbances by means of the continuous wavelet transform (CWT) and convolutional neural network (CNN). The novelty of this work lies in the image processing that allows us to precisely highlight the discriminant patterns through spectrograms into 2D images; the images are cropped and reduced to a standard size of 128x128 pixels to retain the most relevant information. Subsequently, the identification of six power quality disturbances is automatically performed by a convolutional neural network. The effectiveness of the proposed method is validated under a set of synthetic data as well as a real data set; the obtained results make the proposal suitable for being incorporated into the monitoring of power quality disturbances in renewable energy systems.

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Section: Comparative Analysismentioning

confidence: 84%

Section: Comparative Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Methodology for the Detection and Classification of Power Quality Disturbances Using CWT and CNN

Perez-Anaya,

Jaen-Cuellar,

Elvira-Ortiz

et al. 2024

Energies

Self Cite

View full text Add to dashboard Cite

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“…If the Kullback-Leibler divergence is greater than the threshold, there is an abnormal event. In [162], different PQ events in power systems were identified based on statistical time-domain joint neural networks.…”

Section: Miscellaneous Detection or Classification Techniquesmentioning

confidence: 99%

A Review on Intelligent Detection and Classification of Power Quality Disturbances: Trends, Methodologies, and Prospects

Yan¹,

Chen²,

Geng³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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With increasing global concerns about clean energy in smart grids, the detection of power quality disturbances (PQDs) caused by energy instability is becoming more and more prominent. It is well acknowledged that the PQD effects on power grid equipment are destructive and hazardous, which causes irreversible damage to underlying electrical/electronic equipment of the concerned intelligent grids. In order to ensure safe and reliable equipment implementation, appropriate PQD detection technologies must be adopted to avoid such adverse effects. This paper summarizes the newly proposed and traditional PQD detection techniques in order to give a quick start to new researchers in the related field, where specific scenarios and events for which each technique is applicable are also clearly presented. Finally, comments on the future evolution of PQD detection techniques are given. Unlike the published review articles, this paper focuses on the new techniques from the last five years while providing a brief recap on traditional PQD detection techniques so as to supply researchers with a systematic and state-of-the-art review for PQD detection.

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“…To achieve meaningful clustering of a dataset in higher dimensions, dimensionality reduction is generally a prerequisite [11]. The primary aim of a good dimensionality reduction technique is to capture most information in lower dimension subspace so that the distance between 2 points carries more meaning in terms of the similarity.…”

Section: Clustering Frameworkmentioning

confidence: 99%

A Clustering Framework for Residential Electric Demand Profiles

Jain

AlSkaif

Dev

2020

2020 International Conference on Smart Energy Systems and Technologies (SEST)

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The availability of residential electric demand profiles data, enabled by the large-scale deployment of smart metering infrastructure, has made it possible to perform more accurate analysis of electricity consumption patterns. This paper analyses the electric demand profiles of individual households located in the city Amsterdam, the Netherlands. A comprehensive clustering framework is defined to classify households based on their electricity consumption pattern. This framework consists of two main steps, namely a dimensionality reduction step of input electricity consumption data, followed by an unsupervised clustering algorithm of the reduced subspace. While any algorithm, which has been used in the literature for the aforementioned clustering task, can be used for the corresponding step, the more important question is to deduce which particular combination of algorithms is the best for a given dataset and a clustering task. This question is addressed in this paper by proposing a novel objective validation strategy, whose recommendations are then cross-verified by performing subjective validation.

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Condition monitoring approach based on dimensionality reduction techniques for detecting power quality disturbances in cogeneration systems

Cited by 3 publications

References 10 publications

Methodology for the Detection and Classification of Power Quality Disturbances Using CWT and CNN

Methodology for the Detection and Classification of Power Quality Disturbances Using CWT and CNN

A Review on Intelligent Detection and Classification of Power Quality Disturbances: Trends, Methodologies, and Prospects

A Clustering Framework for Residential Electric Demand Profiles

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