Simple indicators for an effective Power Quality monitoring and analysis

Ignatova, Vanya; Villard, Dominique; Hypolite, Jean-Marie

doi:10.1109/eeeic.2015.7165321

Cited by 16 publications

(2 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ignatova et al. [24] summarized PQ monitoring at utility distribution substations, focusing on real‐time issues from a supply viewpoint. Three relays were installed to monitor PQ and voltage quality at three substations, providing field data for user evaluation.…”

Section: Literature Reviewmentioning

confidence: 99%

Initial condition based real time classification of power quality disturbance using deep convolution neural network with bidirectional long short‐term memory

Kandasamy,

Kumar,

Lakshmanan

et al. 2023

IET Generation Trans & Dist

View full text Add to dashboard Cite

The accurate classification of power quality disturbances (PQDs) is crucial for advancing real‐time monitoring and classification systems within the modern power grid. The proposed system must ensure dependable, safeguarded, and stable operating conditions amidst diverse power quality issues. This paper presents an approach to classifying power quality disturbances using a deep learning model that synergizes deep convolutional neural networks (DCNN) and Bidirectional Long Short‐Term Memory (BiLSTM). This amalgamation effectively extracts and classifies disturbance signals in real time, grounded on noise levels. The initial feature extraction from the signal is accomplished through a time‐frequency matrix. Subsequently, secondary extraction employs the BiLSTM layer to intricately and significantly classify disturbances in the power signal. This aids in transforming high‐dimensional matrices into a reduced set for enhanced performance. The detailed classification is facilitated by the softmax layer. The simulation results support the power quality evaluations under varied constraints and underscore the substantial classification of power quality disturbances through the DCNN‐BiLSTM algorithm, in comparison to alternative classification algorithms in terms of computational speed and accuracy.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Initial condition based real time classification of power quality disturbance using deep convolution neural network with bidirectional long short‐term memory

Kandasamy,

Kumar,

Lakshmanan

et al. 2023

IET Generation Trans & Dist

View full text Add to dashboard Cite

show abstract

“…The next step is the presentation of the matrix as a variable permanent function. Ignatova and Villard [44] define green-yellow-red indicators for all PQ problems. The proposed algorithm obtains the green-yellow-red indicators for both events and disturbances.…”

Section: Global Power Quality Indicesmentioning

confidence: 99%

Combined Cluster Analysis and Global Power Quality Indices for the Qualitative Assessment of the Time-Varying Condition of Power Quality in an Electrical Power Network with Distributed Generation

et al. 2020

View full text Add to dashboard Cite

This paper presents the idea of a combined analysis of long-term power quality data using cluster analysis (CA) and global power quality indices (GPQIs). The aim of the proposed method is to obtain a solution for the automatic identification and assessment of different power quality condition levels that may be caused by different working conditions of an observed electrical power network (EPN). CA is used for identifying the period when the power quality data represents a different level. GPQIs are proposed to calculate a simplified assessment of the power quality condition of the data collected using CA. Two proposed global power quality indices have been introduced for this purpose, one for 10-min aggregated data and the other for events—the aggregated data index (ADI) and the flagged data index (FDI), respectively. In order to investigate the advantages and disadvantages of the proposed method, several investigations were performed, using real measurements in an electrical power network with distributed generation (DG) supplying the copper mining industry. The investigations assessed the proposed method, examining whether it could identify the impact of DG and other network working conditions on power quality level conditions. The obtained results indicate that the proposed method is a suitable tool for quick comparison between data collected in the identified clusters. Additionally, the proposed method is implemented for the data collected from many measurement points belonging to the observed area of an EPN in a simultaneous and synchronous way. Thus, the proposed method can also be considered for power quality assessment and is an alternative approach to the classic multiparameter analysis of power quality data addressed to particular measurement points.

show abstract

Assessing the performance of smart buildings and smart retrofit interventions through key performance indicators: Defining minimum performance thresholds

Al Dakheel,

Del Pero,

Leonforte

et al. 2024

Energy and Buildings

View full text Add to dashboard Cite

Simple indicators for an effective Power Quality monitoring and analysis

Cited by 16 publications

References 4 publications

Initial condition based real time classification of power quality disturbance using deep convolution neural network with bidirectional long short‐term memory

Initial condition based real time classification of power quality disturbance using deep convolution neural network with bidirectional long short‐term memory

Combined Cluster Analysis and Global Power Quality Indices for the Qualitative Assessment of the Time-Varying Condition of Power Quality in an Electrical Power Network with Distributed Generation

Assessing the performance of smart buildings and smart retrofit interventions through key performance indicators: Defining minimum performance thresholds

Contact Info

Product

Resources

About