Radial Basis Function Neural Network Technique for Efficient Maximum Power Point Tracking in Solar Photo-Voltaic System

Chandra, Surabhi; Gaur, Prerna

doi:10.1016/j.procs.2020.03.288

Cited by 14 publications

(8 citation statements)

References 18 publications

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“…The L 1 and L 2 values are a function of the maximum input current ripple. Hence, the magnitudes of inductors L 1 , L 2 , and L M and capacitors C 1 , C 2 , and C S are evaluated from Equations ( 33) to (36):…”

Section: Converter Design Equationsmentioning

confidence: 99%

“…19,[32][33][34] The P&O MPPT technique is a simple and most commonly used method to attain maximum power position of the PV system. [35][36][37] The solar PV voltage ripple (V pvr ), current ripple (I pvr ), MPPT efficiency (η pv ), maximum power point (MPP) settling time (t sMPP ), and voltage stress (V sw ) across the semiconductor switch are investigated. The application of the MPPT control algorithm is relatively simple due to the existence of a single switch in the proposed HGS.…”

Section: Simulation Analysis Of the Proposed Hgs Connected To Pv Arraymentioning

confidence: 99%

“…The P&O MPPT control is used for governing the maximum power voltage in each configuration 19,32–34 . The P&O MPPT technique is a simple and most commonly used method to attain maximum power position of the PV system 35–37 . The solar PV voltage ripple (V pvr ), current ripple (I pvr ), MPPT efficiency (η pv ), maximum power point (MPP) settling time (t sMPP ), and voltage stress (V sw ) across the semiconductor switch are investigated.…”

Section: Simulation Analysis Of the Proposed Hgs Connected To Pv Arraymentioning

confidence: 99%

See 2 more Smart Citations

An efficient switched inductor–capacitor‐based novel non‐isolated high gain SEPIC for solar energy applications

Chandra

Gaur

2022

Circuit Theory & Apps

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The solar photovoltaic (PV) sources have low voltage output and hence cannot be used directly in micro‐grid applications. Therefore, to boost the low voltage output, a novel high gain single‐ended primary inductor converter (SEPIC) is proposed in this manuscript. The proposed high gain SEPIC (HGS) has an advantage of obtaining high voltage gain at a low duty ratio and continuous input current, which makes it viable for PV applications. The proposed converter offers simple control, and the high gain is obtained without the use of any transformer or coupled inductor so the semiconductor switch will not experience a voltage overshoot during the turn‐off operation. Hence, the conduction losses of the converter switch are reduced, and the performance of the converter is enhanced. Additionally, this manuscript also investigates the effectiveness of the proposed HGS in integration with the PV array. An extensive performance evaluation of the proposed HGS is investigated and compared with different SEPIC topologies in irradiance change conditions. Experimental implementation of the proposed HGS confirms the theoretical analysis and verifies the performance. Also, the real time (RT) analysis of the proposed HGS with PV array is conducted to show the validation of high gain operation for the PV application.

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Section: Converter Design Equationsmentioning

confidence: 99%

Section: Simulation Analysis Of the Proposed Hgs Connected To Pv Arraymentioning

confidence: 99%

Section: Simulation Analysis Of the Proposed Hgs Connected To Pv Arraymentioning

confidence: 99%

See 1 more Smart Citation

An efficient switched inductor–capacitor‐based novel non‐isolated high gain SEPIC for solar energy applications

Chandra

Gaur

2022

Circuit Theory & Apps

Self Cite

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“…e fuzzy classification of input signals was performed in the output unit. e radial basis kernel function was assumed to be an exponential sigmoid with fuzzy membership [28]. e output of the j th FRBN is then given by…”

Section: A Fuzzy Radial Basis Neuralmentioning

confidence: 99%

“…The radial basis kernel function was assumed to be an exponential sigmoid with fuzzy membership [ 28 ]. The output of the j th FRBN is then given by

where

is the kernel center vector in the j th FRBN.…”

Section: A Dynamic Fuzzy Radial Basis Adaptive Inference Networkmentioning

confidence: 99%

A Fuzzy Radial Basis Adaptive Inference Network and Its Application to Time-Varying Signal Classification

Huang

Liu

et al. 2021

Computational Intelligence and Neuroscience

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A fuzzy radial basis adaptive inference network (FRBAIN) is proposed for multichannel time-varying signal fusion analysis and feature knowledge embedding. The model which combines the prior signal feature embedding mechanism of the radial basis kernel function with the rule-based logic inference ability of fuzzy system is composed of a multichannel time-varying signal input layer, a radial basis fuzzification layer, a rule layer, a regularization layer, and a T-S fuzzy classifier layer. The dynamic fuzzy clustering algorithm was used to divide the sample set pattern class into several subclasses with similar features. The fuzzy radial basis neurons (FRBNs) were defined and used as parameterized membership functions, and typical feature samples of each pattern subclass were used as kernel centers of the FRBN to realize the embedding of the diverse prior feature knowledge and the fuzzification of the input signals. According to the signal categories of FRBN kernel centers, nodes in the rule layer were selectively connected with nodes in the FRBN layer. A fuzzy multiplication operation was used to achieve synthesis of pattern class membership information and establishment of fuzzy inference rules. The excitation intensity of each rule was used as the input of T-S fuzzy classifier to classify the input signals. The FRBAIN can adaptively establish fuzzy set membership functions, fuzzy inference, and classification rules based on the learning of sample set, realize structural and data constraints of the model, and improve the modeling properties of imbalanced datasets. In this paper, the properties of FRBAIN were analyzed and a comprehensive learning algorithm was established. Experimental validation was performed with classification diagnoses from four complex cardiovascular diseases based on 12-lead ECG signals. Results demonstrated that, in the case of small-scale imbalanced datasets, the proposed method significantly improved both classification accuracy and generalizability comparing with other methods in the experiment.

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The Survey of MPPT under non-uniform atmospheric conditions for the Photovoltaic Generation Systems

Manisha

Gaur

2021

Int. j. inf. tecnol.

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Radial Basis Function Neural Network Technique for Efficient Maximum Power Point Tracking in Solar Photo-Voltaic System

Cited by 14 publications

References 18 publications

An efficient switched inductor–capacitor‐based novel non‐isolated high gain SEPIC for solar energy applications

An efficient switched inductor–capacitor‐based novel non‐isolated high gain SEPIC for solar energy applications

A Fuzzy Radial Basis Adaptive Inference Network and Its Application to Time-Varying Signal Classification

The Survey of MPPT under non-uniform atmospheric conditions for the Photovoltaic Generation Systems

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