Power Quality

Stones, John

doi:10.1016/b978-075064637-6/50043-5

Cited by 6 publications

(2 citation statements)

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“…Wavelet-based transform often being categorized into continuous wavelet transform (CWT) and discrete wavelet transform (DWT). It is said that CWT requires higher computing power than DWT [7] and that it needs an infinite number of inputs resulting in the redundancy of provided information [8]. Most of the time, researchers combine DWT with multi-resolution analysis (MRA) to increase their effectiveness in detecting PQDs [8].…”

Section: Introductionmentioning

confidence: 99%

Comparison analysis of different classification methods of power quality disturbances

Zakaria

Said

Rosmin

et al. 2022

IJECE

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<span lang="EN-US">Good power quality delivery has always been in high demand in power system utilities where different types of power quality disturbances are the main obstacles. As these disturbances have distinct characteristics and even unique mitigation techniques, their detection and classification should be correct and effective. In this study, eight different types of power quality disturbances were synthetically generated, by using a mathematical approach. Then, continuous wavelet transform (CWT) and discrete wavelet transform with multi-resolution analysis (DWT-MRA) were applied, which eight features were then extracted from the synthesized signals. Three classifiers namely, decision tree (DT), support vector machine (SVM) and k-nearest neighbors (KNN) were trained to classify these disturbances. The accuracy of the classifiers was evaluated and analyzed. The best classifier was then integrated with the full model, which the performance of the proposed model was observed with 50 random signals, with and without noise. This study found that wavelet-transform was effective to localize the disturbances at the instant of their occurrence. On the other hand, the SVM classifier is superior to other classifiers with an overall accuracy of 94%. Still, the need for these classifiers to be further optimized is crucial in ensuring a more effective detection and classification system.</span>

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Section: Introductionmentioning

confidence: 99%

Comparison analysis of different classification methods of power quality disturbances

Zakaria

Said

Rosmin

et al. 2022

IJECE

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“…Power quality (PQ) has been an acknowledged problem in power systems for decades. Power quality can have a large detrimental effect on industrial processes and the commercial sector [1]. While industrial processes typically differ in their requirements, from a power quality perspective, each specific industrial process has particular 'weaknesses' in terms of power quality attributes [2].…”

Section: Introductionmentioning

confidence: 99%

PMU-based metrics for Power Quality Assessment in Distributed Sensor Networks

Frigo¹,

Costa²,

Toro³

2022

Proceedings of the 25th IMEKO TC4 International Symposium and 23rd International Workshop on ADC and DAC Modelling and Testing

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In modern power systems, the measurement infrastructure represents the backbone of any monitoring and control application. Indeed, the everincreasing penetration of renewable energy sources and distributed generation has produced an operating scenario prone to instability and rapid variations. Power quality assessment procedures must evolve in order to address these challenges. In this regard, the use of phasor measurement units (PMUs), which measure the phasor values of current and voltage with high precision time stamp, presents a significant opportunity to evolve current power quality assessment procedures. This position paper suggests the inclusion of novel PMU-based metrics in order to extend quality power assessment procedures at each sensitive network node towards the further use of aggregated data at both local and/or central level. The proposed PMU-based metrics will provide a better description of the behavior of the system, allowing to take control actions as part of the extended quality power assessment procedures.

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