A Neural Network based power quality signal classification system using wavelet energy distribution

Sebastian, Praveen; D'Sa, Pramod Antony

doi:10.1109/tapenergy.2015.7229617

Cited by 3 publications

(1 citation statement)

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“…Hence there is a requirement of efficient and powerful techniques for analyzing non-stationary signals (3)(4)(5)(6). Many researchers have recommended Wavelet transform for the analysis of PQDs to overcome the fixed window width problem of STFT (7). This approach automatically adapts to give correct time and frequency resolutions.…”

Section: Introductionmentioning

confidence: 99%

Classification of Power Quality Disturbances in Emerging Power System Using Discrete Wavelet Transform and K-Nearest Neighbor

Kankale¹,

Paraskar²,

Jadhao³

2022

ECS Trans.

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This paper presents a wavelet and machine learning-based approach for the classification of power quality disturbances (PQDs) in emerging power systems. Renewable energy resources-based distributed generation (DG) is rapidly being used in the emerging power system to address the ever-increasing energy demand. PQDs are thought to be caused by the power electronic converters used in DG systems, DG operating conditions, and other common factors such as faults, switching activities, and non-linear loads. These PQDs must be detected and classified since they can create a variety of difficulties in end-user equipment. The proposed algorithm comprises the simulation of the emerging power system with a solar PV system, creating PQDs cases such as voltage sag, voltage swell, and voltage interruptions, capturing voltage signals which will be further processed using a discrete wavelet transform for feature extraction. The features extracted from DWT analysis are further used to develop the machine learning-based classifier for classification of PQDs. The proposed algorithm has been tested on a variety of PQDs. The simulation result shows that the proposed algorithm is efficient and it outperforms in the classification of PQDs.

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