A Laboratory for Power Quality Analysis

2001 ◽  
Vol 38 (3) ◽  
pp. 210-222 ◽  
Author(s):  
Julio Barros ◽  
Daniel Cando ◽  
Iker Durana
Keyword(s):  
Power Quality ◽  
Supply Voltage ◽  
Electrical Power ◽  
Quality Analysis ◽  
Voltage Supply ◽  
Different Types ◽  
Voltage Dips ◽  
Power Quality Analysis ◽  
Power Quality Disturbances

This paper describes a laboratory designed for electrical power quality analysis. Among the different types of disturbances in voltage supply that the laboratory allows us to generate are harmonics, voltage dips and short interruptions in voltage supply, voltage imbalance and frequency deviations. Using this laboratory we can test software for analysis, detection and classification of power quality disturbances and also study their effects on equipment.

Standardized Electrical Power Quality Analysis in Accordance with MIL-STD-704

2010 ◽  
Vol 3 (1) ◽  
pp. 124-136 ◽  
Author(s):  
Marco Amrhein ◽  
Brian Raczkowski ◽  
Grant Pitel ◽  
Jason Wells ◽  
Eric Walters ◽  
...  
Keyword(s):  
Power Quality ◽  
Electrical Power ◽  
Quality Analysis ◽  
Power Quality Analysis

scholarly journals POWER QUALITY ANALYSIS BY THE RIPPLE TECHNIQUE

2017 ◽  
Vol 2 (4) ◽  
pp. 227 ◽  
Author(s):  
Amam Hossain Bagdadee
Keyword(s):  
Power System ◽  
Power Quality ◽  
Electrical Power ◽  
Quality Monitoring ◽  
Quality Analysis ◽  
Electrical Power System ◽  
Power Quality Monitoring ◽  
Power Quality Analysis ◽  
Institute Of Technology

Characteristics of Power quality has been with us since the inception of the electrical Power system. However, the topic of power quality has attracted particular attention in recent years due to the increase of electronically controlled. Power quality problems caused disruptions to electrical or electronic equipment and the resulting consequences are very expensive. Ripple techniques will be studied in this paper for analysing power quality monitoring. In the case study based on the measurement of the site of the Asian Institute of Technology (AIT) and it was examined using the proposed ripple technique.

scholarly journals A Novel Deep Learning-Based Diagnosis Method Applied to Power Quality Disturbances

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2839
Author(s):  
Artvin-Darien Gonzalez-Abreu ◽  
Miguel Delgado-Prieto ◽  
Roque-Alfredo Osornio-Rios ◽  
Juan-Jose Saucedo-Dorantes ◽  
Rene-de-Jesus Romero-Troncoso
Keyword(s):  
Deep Learning ◽  
Power Quality ◽  
Electrical Power ◽  
Synthetic Data ◽  
Industrial Sector ◽  
Industrial Facilities ◽  
Diagnosis Method ◽  
Neural Network Structure ◽  
Power Quality Disturbances ◽  
Disturbance Detection

Monitoring electrical power quality has become a priority in the industrial sector background: avoiding unwanted effects that affect the whole performance at industrial facilities is an aim. The lack of commercial equipment capable of detecting them is a proven fact. Studies and research related to these types of grid behaviors are still a subject for which contributions are required. Although research has been conducted for disturbance detection, most methodologies consider only a few standardized disturbance combinations. This paper proposes an innovative deep learning-based diagnosis method to be applied on power quality disturbances, and it is based on three stages. Firstly, a domain fusion approach is considered in a feature extraction stage to characterize the electrical power grid. Secondly, an adaptive pattern characterization is carried out by considering a stacked autoencoder. Finally, a neural network structure is applied to identify disturbances. The proposed approach relies on the training and validation of the diagnosis system with synthetic data: single, double and triple disturbances combinations and different noise levels, also validated with available experimental measurements provided by IEEE 1159.2 Working Group. The proposed method achieves nearly a 100% hit rate allowing a far more practical application due to its capability of pattern characterization.

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