IPQDA: A Software Tool for Intelligent Analysis of Power Quality Disturbances

2005 ◽  
pp. 1315-1318 ◽  
Author(s):  
Aini Hussain ◽  
Azah Mohamed ◽  
Mohd Hanif Md Saad ◽  
Mohd Haszuan Shukairi ◽  
Noor Sabathiah Sayuti
Keyword(s):  
Power Quality ◽  
Software Tool ◽  
Intelligent Analysis ◽  
Power Quality Disturbances

Classification and Identification of Power System Disturbances Using Wavalet and Artificial Neural Network Technique

2005 ◽  
Vol 2 (2) ◽  
pp. 25
Author(s):  
Noraliza Hamzah ◽  
Wan Nor Ainin Wan Abdullah ◽  
Pauziah Mohd Arsad
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Probabilistic Neural Network ◽  
Microsoft Excel ◽  
Waveform Data ◽  
The Neural Network ◽  
Harmonic Voltage ◽  
Power Quality Disturbances ◽  
Electronic Load ◽  
Wavelet Technique

Power Quality disturbances problems have gained widespread interest worldwide due to the proliferation of power electronic load such as adjustable speed drives, computer, industrial drives, communication and medical equipments. This paper presents a technique based on wavelet and probabilistic neural network to detect and classify power quality disturbances, which are harmonic, voltage sag, swell and oscillatory transient. The power quality disturbances are obtained from the waveform data collected from premises, which include the UiTM Sarawak, Faculty of Science Computer in Shah Alam, Jati College, Menara UiTM, PP Seksyen 18 and Putra LRT. Reliable Power Meter is used for data monitoring and the data is further processed using the Microsoft Excel software. From the processed data, power quality disturbances are detected using the wavelet technique. After the disturbances being detected, it is then classified using the Probabilistic Neural Network. Sixty data has been chosen for the training of the Probabilistic Neural Network and ten data has been used for the testing of the neural network. The results are further interfaced using matlab script code.  Results from the research have been very promising which proved that the wavelet technique and Probabilistic Neural Network is capable to be used for power quality disturbances detection and classification.

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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