scholarly journals Single-phase grid-connected PV system with golden section search-based MPPT algorithm

2021 ◽  
Vol 7 (4) ◽  
pp. 25-36
Author(s):  
Shuang Xu ◽  
Riming Shao ◽  
Bo Cao ◽  
Liuchen Chang
Keyword(s):  
Single Phase ◽  
Golden Section ◽  
Pv System ◽  
Golden Section Search

scholarly journals Design and Implementation of Three-Level DC-DC Converter with Golden Section Search Based MPPT for the Photovoltaic Applications

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Chouki Balakishan ◽  
N. Sandeep ◽  
M. V. Aware
Keyword(s):  
High Voltage ◽  
High Performance ◽  
High Efficiency ◽  
Low Cost ◽  
Golden Section ◽  
Boost Converter ◽  
Pv System ◽  
Point Tracking ◽  
Golden Section Search ◽  
Energy Conversion Systems

In many photovoltaic (PV) energy conversion systems, nonisolated DC-DC converters with high voltage gain are desired. The PV exhibits a nonlinear power characteristic which greatly depends on the environmental conditions. Hence in order to draw maximum available power various algorithms are used with PV voltage/current or both as an input for the maximum power point tracking (MPPT) controller. In this paper, golden section search (GSS) based MPPT control and its application with three-level DC-DC boost converter for MPPT are demonstrated. The three-level boost converter provides the high voltage transfer which enables the high power PV system to work with low size inductors with high efficiency. The balancing of the voltage across the two capacitors of the converter and MPPT is achieved using a simple duty cycle based voltage controller. Detailed simulation of three-level DC-DC converter topology with GSS algorithm is carried out in MATLAB/SIMULINK platform. The validation of the proposed system is done by the experiments carried out on hardware prototype of 100 W converter with low cost AT’mega328 controller as a core controller. From the results, the proposed system suits as one of the solutions for PV based generation system and the experimental results show high performance, such as a conversion efficiency of 94%.

Quantification of thermal energy generation in annular hyperbolic porous-finned heat sinks using inverse optimization

2021 ◽  
pp. 095440892110243
Author(s):  
Sagnik Pal ◽  
Ranjan Das
Keyword(s):  
Heat Transfer ◽  
Surface Temperature ◽  
Heat Generation ◽  
Thermal Energy ◽  
Inverse Method ◽  
Golden Section ◽  
Energy Generation ◽  
Heat Sinks ◽  
Search Technique ◽  
Golden Section Search

The present paper introduces an accurate numerical procedure to assess the internal thermal energy generation in an annular porous-finned heat sink from the sole assessment of surface temperature profile using the golden section search technique. All possible heat transfer modes and temperature dependence of all thermal parameters are accounted for in the present nonlinear model. At first, the direct problem is numerically solved using the Runge–Kutta method, whereas for predicting the prevailing heat generation within a given generalized fin domain an inverse method is used with the aid of the golden section search technique. After simplifications, the proposed scheme is credibly verified with other methodologies reported in the existing literature. Numerical predictions are performed under different levels of Gaussian noise from which accurate reconstructions are observed for measurement error up to 20%. The sensitivity study deciphers that the surface temperature field in itself is a strong function of the surface porosity, and the same is controlled through a joint trade-off among heat generation and other thermo-geometrical parameters. The present results acquired from the golden section search technique-assisted inverse method are proposed to be suitable for designing effective and robust porous fin heat sinks in order to deliver safe and enhanced heat transfer along with significant weight reduction with respect to the conventionally used systems. The present inverse estimation technique is proposed to be robust as it can be easily tailored to analyse all possible geometries manufactured from any material in a more accurate manner by taking into account all feasible heat transfer modes.

scholarly journals GOLDEN EXPONENTIAL SMOOTHING: A SELF-ADJUSTED METHOD FOR IDENTIFYING OPTIMUM ALPHA

2020 ◽  
Vol 5 (2) ◽  
pp. 587
Author(s):  
Fong Yeng Foo ◽  
Azrina Suhaimi ◽  
Soo Kum Yoke
Keyword(s):  
Golden Section ◽  
Exponential Smoothing ◽  
Series Data ◽  
Training Process ◽  
Times Series ◽  
Golden Section Search ◽  
Training Stage ◽  
Forecasting Method ◽  
Double Exponential Smoothing ◽  
Accuracy Of Prediction

The conventional double exponential smoothing is a forecasting method that troubles the forecaster with a tremendous choice of its parameter, alpha. The choice of alpha would greatly influence the accuracy of prediction. In this paper, an integrated forecasting method named Golden Exponential Smoothing (GES) was proposed to solve the problem. The conventional method was reformed and interposed with golden section search such that an optimum alpha which minimizes the errors of forecasting could be identified in the algorithm training process.  Numerical simulations of four sets of times series data were employed to test the efficiency of GES model. The findings show that the GES model was self-adjusted according to the situation and converged fast in the algorithm training process. The optimum alpha, which was identified from the algorithm training stage, demonstrated good performance in the stage of Model Testing and Usage.

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