FPGA-Based Smart Sensor for Detection and Classification of Power Quality Disturbances Using Higher Order Statistics

Martínez-Figueroa, G. de J.; Moriñigo-Sotelo, Daniel; Zorita-Lamadrid, Ángel L.; Morales-Velazquez, Luis; Romero-Troncoso, René de Jesús

doi:10.1109/access.2017.2732726

Cited by 30 publications

(25 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Firstly, the present proposal does not require a space transformation unlike other works [15,[22][23][24][25][26][27][28]30,31]; as the proposal uses both a PMU scheme based on filters and a time-domain index, low computational resources are required. Secondly, the proposal only uses the H index to classify PQDs, unlike other approaches that compute more than two indices [32,34]. This fact simplifies the proposed methodology, reduces the computational burden, and shows the effectiveness of the H index for detection of PQDs.…”

Section: Discussionmentioning

confidence: 98%

“…For the Kalman filter, the measurement noise and a harmonic model have to be defined a priori; in practice, they cannot be known accurately. Unlike the Kalman filter, EMD-based methods are adaptive techniques, i.e., information of the input signal is not required; however, the mode-mixing effect in the EMD method and the high computational burden in the ensemble EMD method can compromise their performance and applicability for PQ monitoring.On the other hand, some indices and well-established parameters such as root-mean-square (RMS)-based parameters (RMS value, variation rate of the RMS values, oscillation number of the RMS values, total harmonic distortion factor, and lower harmonic distortion factor) [32], a PQ deviation index based on principal curves [33], higher order statistics (mean, variance, skewness, and kurtosis) [34], and a derivative factor with some threshold-based rules [35], have been reported to characterize PQDs. 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.…”

mentioning

confidence: 99%

“…On the other hand, some indices and well-established parameters such as root-mean-square (RMS)-based parameters (RMS value, variation rate of the RMS values, oscillation number of the RMS values, total harmonic distortion factor, and lower harmonic distortion factor) [32], a PQ deviation index based on principal curves [33], higher order statistics (mean, variance, skewness, and kurtosis) [34], and a derivative factor with some threshold-based rules [35], have been reported to characterize PQDs. 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.…”

mentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2018

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 98%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2018

View full text Add to dashboard Cite

show abstract

“…In this context, Sensors 4.0 has been coined colloquially to Industry 4.0 [9]. Smart sensors are sensing devices that are equipped with digital features for data processing, storage and efficient transformation of data [10][11][12]. Smart sensors automatized the zero correction, calibration, and scaling of the measured signals by using microprocessors, in contrast to meticulous design, testing, and debugging faced by the conventional analog sensors [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Gray-box Soft Sensors in Process Industry: Current Practice, and Future Prospects in Era of Big Data

et al. 2020

View full text Add to dashboard Cite

Virtual sensors, or soft sensors, have greatly contributed to the evolution of the sensing systems in industry. The soft sensors are process models having three fundamental categories, namely white-box (WB), black-box (BB) and gray-box (GB) models. WB models are based on process knowledge while the BB models are developed using data collected from the process. The GB models integrate the WB and BB models for addressing the concerns, i.e., accuracy and intuitiveness, of industrial operators. In this work, various design aspects of the GB models are discussed followed by their application in the process industry. In addition, the changes in the data-driven part of the GB models in the context of enormous amount of process data collected in Industry 4.0 are elaborated.

show abstract

“…With respect to waveform data analytics, the primary goal is to extract, from the voltage and current waveforms associated with devices and locations, the signatures of various equipment disturbances and abnormal conditions from which researchers can develop appropriate algorithms to identify equipment abnormalities [10]. However, acquired data in itself is generally insufficient to determine the exact nature of the equipment condition [11].…”

Section: Introductionmentioning

confidence: 99%

Multi-Labeled Recognition of Distribution System Conditions by a Waveform Feature Learning Model

Moon

Kim

2019

Energies

View full text Add to dashboard Cite

A waveform contains recognizable feature patterns. To extract the features of various equipment disturbance conditions from a waveform, this paper presents a practical model to estimate distribution line (DL) conditions by means of a multi-label extreme learning machine. The motivation for the waveform learning is to develop device-embedded models which are capable of detecting and classifying abnormal operations on the DLs. In waveform analysis, power quality waveform modeling criteria are adopted for pattern classification. Typical disturbance waveforms are applied as class models, and the formula-generated waveform features are compared with field-acquired waveforms for disturbance classification. In particular, filtered symmetrical components of the modified varying window scale are applied for feature extraction. The proposed model interacts suitably with the parameter update method in classifying the waveforms in real network situations. The classification result showed disturbance features on model with the real DL waveform data holds a potential for determining additional DL conditions and improving its classification performance through the update mechanism of the learning machine.

show abstract

FPGA-Based Smart Sensor for Detection and Classification of Power Quality Disturbances Using Higher Order Statistics

Cited by 30 publications

References 33 publications

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

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

Gray-box Soft Sensors in Process Industry: Current Practice, and Future Prospects in Era of Big Data

Multi-Labeled Recognition of Distribution System Conditions by a Waveform Feature Learning Model

Contact Info

Product

Resources

About