Sigmoidal and Gaussian functions based neural neuron technique for grid interactive solar energy system enabling power quality improvement

Pal, Kanwar; Kumar, Shailendra; Singh, Bhim; Kandpal, Tara C.

doi:10.1049/iet-gtd.2020.0790

Cited by 2 publications

(2 citation statements)

References 31 publications

(31 reference statements)

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“…The TanH activation function has similar characteristics to sigmoid [47]. The difference between these functions is that the hyperbolic tangent has a range of variation from –1 to 1 as an output, while the sigmoid results only in positive values, calculated from the equation:

\begin{equation} f (x) = \frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}.

…”

Section: Classifier Model Architecturementioning

confidence: 99%

Classification of insulators using neural network based on computer vision

Stefenon

Corso

Nied

et al. 2021

IET Generation Trans & Dist

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Insulators of the electrical power grid are usually installed outdoors, so they suffer from environmental stresses, such as the presence of contamination. Contamination can increase surface conductivity, which can lead to system failures, reducing the reliability of the network. The identification of insulators that have their properties compromised is important so that there are no discharges through its insulating body. To perform the classification of contaminated insulators, this paper presents computer vision techniques for the extraction of contamination characteristics, and a neural network (NN) model for the classification of this condition. Specifically, the Sobel edge detector, Canny edge detection, binarization with threshold, adaptive binarization with threshold, threshold with Otsu and Riddler–Calvard techniques will be evaluated. The results show that it is possible to have an accuracy of up to 97.50% for the classification of contaminated insulators from the extraction of characteristics with computer vision using the NN for the classification. The proposed model is more accurate than well‐established models such as support‐vector machine (SVM), k‐nearest neighbor (k‐NN), and ensemble learning methods. This showed that optimizing the model's parameters can make it superior to solve the problem in question.

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\begin{equation} f (x) = \frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}.

…”

Section: Classifier Model Architecturementioning

confidence: 99%

Classification of insulators using neural network based on computer vision

Stefenon

Corso

Nied

et al. 2021

IET Generation Trans & Dist

View full text Add to dashboard Cite

show abstract

“…Grid-interfacing converters (GIC) [1][2][3][4][5][6][7][8][9][10][11][12][13] are almost ubiquitous in today's modern power system. GICs are used to integrate distributed energy sources, generate DC voltage through active rectification of the grid voltage, improve the power quality through active power filtering, maintain constant voltage at the sensitive load, emulate the grid voltage for power converter testing etc.…”

Section: Introductionmentioning

confidence: 99%

A Quasi open‐loop robust three‐phase grid‐synchronization technique for non‐ideal grid

Ahmed

Biricik

Benbouzid

2021

IET Generation Trans & Dist

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Development of advanced grid-synchronization technique for unbalanced and distorted grid is considered in this paper. In this context, self-tuning filter (STF) is considered to extract the fundamental component from the measured unbalanced and distorted voltages. Standard STF considers balanced grid voltages which is not always possible to ensure in the actual power grid. To mitigate this issue, an extended STF is proposed by analyzing the standard STF in the state-space framework. To make the proposed ESTF grid-following, a robust open-loop frequency estimator is also applied. The closed-loop system enjoys excellent filtering benefit from the extended STF side while taking advantage of the fast convergence speed property of the open-loop frequency estimation technique. Numerical simulation and experimental results with double second-order generalized integrator phase-locked loop are provided to validate the theoretical developments.

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Sigmoidal and Gaussian functions based neural neuron technique for grid interactive solar energy system enabling power quality improvement

Cited by 2 publications

References 31 publications

Classification of insulators using neural network based on computer vision

Classification of insulators using neural network based on computer vision

A Quasi open‐loop robust three‐phase grid‐synchronization technique for non‐ideal grid

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