Advanced metering infrastructure and energy storage for location and mitigation of power quality disturbances in the utility grid with high penetration of renewables

2022 ◽  
Vol 157 ◽  
pp. 111988
Author(s):  
Robert Smolenski ◽  
Pawel Szczesniak ◽  
Wojciech Drozdz ◽  
Lukasz Kasperski
Keyword(s):  
Energy Storage ◽  
Power Quality ◽  
Advanced Metering Infrastructure ◽  
High Penetration ◽  
Power Quality Disturbances ◽  
Utility Grid ◽  
Advanced Metering

scholarly journals Comprehensive Review on Detection and Classification of Power Quality Disturbances in Utility Grid With Renewable Energy Penetration

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 146807-146830 ◽  
Author(s):  
Gajendra Singh Chawda ◽  
Abdul Gafoor Shaik ◽  
Mahmood Shaik ◽  
Sanjeevikumar Padmanaban ◽  
Jens Bo Holm-Nielsen ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Power Quality ◽  
Comprehensive Review ◽  
Power Quality Disturbances ◽  
Utility Grid

Power Quality Enhancement using STATCOM with Energy Storage

2014 ◽  
pp. 349-354 ◽  
Author(s):  
A. Yanushkevich ◽  
Z. Müller ◽  
J. Švec ◽  
J. Tlustý ◽  
V. Valouch
Keyword(s):  
Energy Storage ◽  
Power Quality ◽  
Quality Enhancement

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.

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