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Montesquieu was the first to classify the powers of government into the modern trinity of legislative, executive and judicial.
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This relationship was one of the reasons to classify the power metric as another enrichment-type metric.
This paper presents a novel approach to detect and classify the power quality disturbance (PQD) signals based on singular spectrum analysis (SSA), curvelet transform (CT) and deep convolutional neural networks (DCNNs).
In this paper, variational mode decomposition (VMD) and a newly developed weighted online sequential extreme learning machine (WOSELM) are integrated to detect and classify the power quality events (PQEs) in real-time.
GMM is mainly used describe the wave distribution situation according to the current information, so as to find the power similarity; while SVM is used to classify the power features of different electric appliances, so as to summarize the classification properties of different electric appliances and establish a classification model.
We use cluster analysis in order to classify the power spectrum profiles after a log2 transformation.
However, this way of speaking is for the purposes of classifying the powers.
The proposed type-2 fuzzy logic method classifies the power quality disturbances even in the presence of higher level of uncertainty hence it is very useful in real time scenario.
To identify open challenges in the area and facilitate future advancements, it is essential to synthesize and classify the research on power- and energy-efficient design conducted to date.
This paper proposes MPMU based ASWABP scheme, that can detect the transmission line fault, identifies the parent bus, faulty branch & relays to be supervised and classify the type of fault during power system disturbances.
The technical specifications of the fuel cell systems (FCSs) these buses are equipped with will differ based on the powertrain configurations and vehicle control strategies, but can generally be classified into the power-follow and soft-run modes.
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