This page has only limited features, please log in for full access.
This paper presents the design and analytical modeling of the proposed solar photovoltaic standalone system under varying environmental conditions. The proposed system consists of a unique structure of a solar PV-tree, maximum power point tracking (MPPT) technique, and DC–DC converter. The output voltage acquired from the solar PV tree is low. A DC–DC boost converter is utilized to step-up the required amount of voltage level. In this paper, the appropriate duty cycle is obtained for extracting the optimum power from the solar PV tree by using various MPPT mechanisms such as perturb and observe (P&O), incremental conductance (INC), and a radial basis function network (RBFN)-based neural network (NN). The proposed solar photovoltaic tree-based energy harvesting system is designed and validated by using MATLAB/SIMULINK software and real-time application. The simulation results of the above-mentioned three techniques are compared with each other in order to show the effectiveness of the proposed system with RBFN. The RBFN-MPPT provides a significant improvement in tracking efficiency of 6.0% and 5.72% as compared with the P&O method and the INC method at 1000 W/m2 irradiance condition. From the simulation and real-time results, it is concluded that the RBFN-based NN provides better tracking efficiency and less oscillation as compared with the other two algorithms.
Pitchai Pandiyan; Subramani Saravanan; Natarajan Prabaharan; Ramji Tiwari; Thangam Chinnadurai; Neelakandan Babu; Eklas Hossain. Implementation of Different MPPT Techniques in Solar PV Tree under Partial Shading Conditions. Sustainability 2021, 13, 7208 .
AMA StylePitchai Pandiyan, Subramani Saravanan, Natarajan Prabaharan, Ramji Tiwari, Thangam Chinnadurai, Neelakandan Babu, Eklas Hossain. Implementation of Different MPPT Techniques in Solar PV Tree under Partial Shading Conditions. Sustainability. 2021; 13 (13):7208.
Chicago/Turabian StylePitchai Pandiyan; Subramani Saravanan; Natarajan Prabaharan; Ramji Tiwari; Thangam Chinnadurai; Neelakandan Babu; Eklas Hossain. 2021. "Implementation of Different MPPT Techniques in Solar PV Tree under Partial Shading Conditions." Sustainability 13, no. 13: 7208.
The performance evaluation of 1.26 kW fuel cell fed electric vehicle system with reconfigured Quadratic Boost Converter along with the neural network based maximum power point tracking algorithm is presented in this paper. The acceptance of EV in modern society is relevant for the creation of pollution free environment. The main reason for creation of excessive pollution is transportation by the mode of roadways, with the own internal combustion engines by using crude oil as primary energy source. In this paper, a 1.26 kW Proton Exchange Membrane Fuel Cell (PEMFC) fed electric vehicle is designed in MATLAB/Simulink environment. To integrate PEMFC to brushless DC (BLDC) motor are configured Quadratic Boost Converter is designed for high static converter voltage gain. The performance of the proposed EV system is analysed with perturb and observer method and neural network based MPPT control techniques and obtained results are compared at different fuel cell input temperature conditions with respect to different time periods.
K. Kumar; Ramji Tiwari; P Venkata Varaprasad; Challa Babu; K Jyotheeswara Reddy. Performance evaluation of fuel cell fed electric vehicle system with reconfigured quadratic boost converter. International Journal of Hydrogen Energy 2020, 46, 8167 -8178.
AMA StyleK. Kumar, Ramji Tiwari, P Venkata Varaprasad, Challa Babu, K Jyotheeswara Reddy. Performance evaluation of fuel cell fed electric vehicle system with reconfigured quadratic boost converter. International Journal of Hydrogen Energy. 2020; 46 (11):8167-8178.
Chicago/Turabian StyleK. Kumar; Ramji Tiwari; P Venkata Varaprasad; Challa Babu; K Jyotheeswara Reddy. 2020. "Performance evaluation of fuel cell fed electric vehicle system with reconfigured quadratic boost converter." International Journal of Hydrogen Energy 46, no. 11: 8167-8178.
An artificial neural network (ANN) based maximum power point tracking (MPPT) technique for proton exchange membrane fuel cell (PEMFC) is analysed and proposed in this paper. The proposed ANN technique employs Radial basis function network (RBFN) based MPPT strategy to extract the maximum available power from fuel cell in different operating condition. In order to achieve high voltage rating, a novel high step up DC/DC converter is incorporated in the proposed configuration. To validate the performance of the proposed configuration, the result is compared with different DC/DC converter and MPPT control strategy. The proposed system is simulated in MATLAB/Simulink platform to analyse the performance of the system.
Suresh Srinivasan; Ramji Tiwari; Murugaperumal Krishnamoorthy; M.Padma Lalitha; K.Kalyan Raj. Neural network based MPPT control with reconfigured quadratic boost converter for fuel cell application. International Journal of Hydrogen Energy 2020, 46, 6709 -6719.
AMA StyleSuresh Srinivasan, Ramji Tiwari, Murugaperumal Krishnamoorthy, M.Padma Lalitha, K.Kalyan Raj. Neural network based MPPT control with reconfigured quadratic boost converter for fuel cell application. International Journal of Hydrogen Energy. 2020; 46 (9):6709-6719.
Chicago/Turabian StyleSuresh Srinivasan; Ramji Tiwari; Murugaperumal Krishnamoorthy; M.Padma Lalitha; K.Kalyan Raj. 2020. "Neural network based MPPT control with reconfigured quadratic boost converter for fuel cell application." International Journal of Hydrogen Energy 46, no. 9: 6709-6719.