A Dual Monitoring Technique to Detect Power Quality Transients Based on the Fourth-Order Spectrogram

González-de-la-Rosa, Juan-José; Agüera-Pérez, Agustín; Palomares-Salas, José Carlos; Florencias-Oliveros, Olivia; Sierra-Fernández, José María

doi:10.3390/en11030503

Cited by 5 publications

(4 citation statements)

References 18 publications

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“…This does not exclude the possibility to apply higher-order statistics (HOS) and Kurtosis procedures. However, the HOS evaluation is more oriented to harmonic analysis (spectral kurtosis) [5][6][7]. Basically, the arithmetic average value per cycle and the previous average calculated during many cycles provide information about the voltage waveform integrity and the supply continuity (interruptions).…”

Section: Statistical Processing Techniquesmentioning

confidence: 99%

An Embedded Sensor Node for the Surveillance of Power Quality

et al. 2019

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The energy supply of office buildings and smart homes is a key issue in the global energy system. The growing use of microelectronics-based technology achieves new devices for a more comfortable life and wider use of electronic office equipment. On the one hand, these applications incorporate more and more sensitive electronic devices which are potentially affected by any external electrical transient. On the other hand, the existing electrical loads, which generally use electronic power systems (such as different types of battery chargers, ballasts, inverters, switching power supplies, etc.), generate different kinds of transients in their own electrical internal network. Moreover, improvements in the information of the state of the mains alternating current (AC) power line allows risk evaluation of any disturbance caused to permanently connected electronic equipment, such as computers, appliances, home security systems, phones, TVs, etc. For this reason, it is nowadays more important to introduce monitoring solutions into the electrical network to measure the level of power quality so that it can protect itself when necessary. This article describes a small and compact detector using a low-cost microcontroller and a very simple direct acquiring circuit. In addition; it analyzes different methods to implement various power quality (PQ) surveillance algorithms that can be implemented in this proposed minimum hardware platform. Hence; it is possible to achieve cheap and low-power monitoring devices that can become nodes of a wireless sensor network (WSN). The work shows that using a small computational effort; reasonable execution speed; and acceptable reliability; this solution can be used to detect a variety of large disturbance phenomena and spread the respective failure report through a 433 MHz or 2.4 GHz radio transmitter. Therefore, this work can easily be extended to the Internet of Things (IoT) paradigm. Simultaneously, a software application (PulsAC) has been developed to monitor the microcontroller’s real-time progress and detection capability. Moreover, this high-level code (C++ language), allows us to test and debug the different utilized algorithms that will be later run by the microcontroller unit. These tests have been performed with real signals introduced by a function generator and superimposed on the true AC sine wave

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Section: Statistical Processing Techniquesmentioning

confidence: 99%

An Embedded Sensor Node for the Surveillance of Power Quality

et al. 2019

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“…[3][4][5][6] Instruments incorporating new functions in the performance of PQD classification have been developed over the last decades. [7][8][9][10] Because statistical records of the distribution of power quality events are not available in most cases, non-parametric methods are believed to be more suitable for these applications. 11 There are several non-parametric methods that are used in the classification of PQDs, including artificial neural networks, 12 fuzzy logic, 13 and support vector machines, 14,15 as well as the combined use of such approaches like deep neural networks.…”

Section: Introductionmentioning

confidence: 99%

“…Technological developments, especially in the field of artificial intelligence, 2 have brought great advantages to researchers who focus on PQD classification 3‐6 . Instruments incorporating new functions in the performance of PQD classification have been developed over the last decades 7‐10 …”

Section: Introductionmentioning

confidence: 99%

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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“…In power systems, power quality (PQ) has been a significant issue that is disrupted by increasing uncertain, intermittent, renewable energy penetration on the generation side [1,2] and increasing uptake of electric vehicles (EVs) on the demand side [3][4][5]. In essence, PQ refers to multifarious electromagnetic phenomena that deviate voltage and current from ideal waveforms, which are known as PQ disturbances (PQD).…”

Section: Introductionmentioning

confidence: 99%

Hybrid Approach for Detecting and Classifying Power Quality Disturbances Based on the Variational Mode Decomposition and Deep Stochastic Configuration Network

et al. 2018

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This paper proposes a novel, two-stage and hybrid approach based on variational mode decomposition (VMD) and the deep stochastic configuration network (DSCN) for power quality (PQ) disturbances detection and classification in power systems. Firstly, a VMD technique is applied to discriminate between stationary and non-stationary PQ events. Secondly, the key parameters of VMD are determined as per different types of disturbance. Three statistical features (mean, variance, and kurtosis) are extracted from the instantaneous amplitude (IA) of the decomposed modes. The DSCN model is then developed to classify PQ disturbances based on these features. The proposed approach is validated by analytical results and actual measurements. Moreover, it is also compared with existing methods including wavelet network, fuzzy and S-transform (ST), adaptive linear neuron (ADALINE) and feedforward neural network (FFNN). Test results have proved that the proposed method is capable of providing necessary and accurate information for PQ disturbances in order to plan PQ remedy actions accordingly.

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A Dual Monitoring Technique to Detect Power Quality Transients Based on the Fourth-Order Spectrogram

Cited by 5 publications

References 18 publications

An Embedded Sensor Node for the Surveillance of Power Quality

An Embedded Sensor Node for the Surveillance of Power Quality

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

Hybrid Approach for Detecting and Classifying Power Quality Disturbances Based on the Variational Mode Decomposition and Deep Stochastic Configuration Network

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