Modified PDPWM control with MPPT algorithm for equal power sharing in cascaded multilevel inverter for standalone PV system under partial shading

Yahya, Mawadah Glaa; Yahya, Massara Glaa

doi:10.11591/ijpeds.v14.i1.pp533-545

Cited by 3 publications

(2 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Apart from the studies of MPPT with the proposed converter mentioned before, the MPPT algorithms are proposed in [53]- [54]. The new maximum power point tracking approach using a genetic algorithm is presented in [53] and uses a particle swarm optimization (MPSO) [54].…”

Section: B Significant Of Maximum Power Point Trackingmentioning

confidence: 99%

Local-Stability Analysis of Cascaded Control for a Switching Power Converter

Afkar,

Gavagsaz-Ghoachani,

Phattanasak

et al. 2024

Journal of Robotics and Control

View full text Add to dashboard Cite

Switching power converters are integral in various applications like transportation and renewable energy. After their design, ensuring stable closed-loop poles is critical to maintain safe operating conditions. This study focuses on a switching DC-DC boost converter with a cascade control approach using an energy controller for the outer loop and indirect-sliding mode control for the inner loop. The research objective involves investigating stability through eigenvalue evaluation at different operating points. A large-signal average model is applied to make controlled performance independent of the operating point by fixing system poles. Nonlinear controllers, specifically indirect-sliding mode control, are chosen for their robustness, constant switching frequency, and implementation ease. Results indicate that insufficient decoupling leads to eigenvalue displacement, impacting control parameter choices. The research contribution is investigating the local stability of cascaded control, considering its advantageous implications for both performance and design. This study contributes to the understanding of switching power converters' stability, emphasizing the proposed methodology's broader applicability to diverse converter structures. The proposed approach, applicable to various switching power converters, sheds light on the importance of proper decoupling between outer and inner loop dynamics.

show abstract

Section: B Significant Of Maximum Power Point Trackingmentioning

confidence: 99%

Local-Stability Analysis of Cascaded Control for a Switching Power Converter

Afkar,

Gavagsaz-Ghoachani,

Phattanasak

et al. 2024

Journal of Robotics and Control

View full text Add to dashboard Cite

show abstract

“…In (Yahya and Yahya, 2023), DC-DC boost converter technology to track the maximum power of a photovoltaic (PV) system using a maximum power point tracking (MPPT) controller based on a modified version of particle swarm optimization (MPSO). A DC-DC boost converter was utilized to increase the input DC voltage of the PV module.…”

Section: To DC Boost Converter Configurationmentioning

confidence: 99%

Fault detection in a distribution network using a combination of a discrete wavelet transform and a neural Network’s radial basis function algorithm to detect high-impedance faults

Gogula

Edward²

2023

Front. Energy Res.

View full text Add to dashboard Cite

High Impedance Fault detection in a solar photovoltaic (PV) and wind generator integrated power system is described in this paper using discrete wavelet transform and a neural network with radial basis function (NNRBF). For this paper, the integration of solar photovoltaic and wind systems was modelled in a MATLAB/Simulink environment to create an IEEE 13-bus system. Microgrids (MG’s) are mostly powered by renewable energy. Uncertainty about renewables has shifted attention to ensuring a steady supply and long-term viability. It has been addressed in the paper whether or not a small-scale distant end source connection may be made at the terminal of a radial distribution feeder. Some typical power system problems compromise the reliability of the grid’s power supply. To solve this problem, this study suggests a criterion algorithm based on the neural network with radial basis function (NNRBF), and a defect detection method based on the discrete wavelet transform (DWT). The MATLAB/Simulink model of the system is then used to produce fault and travelling wave signals. The db4 wavelet is used to deconstruct the travelling wave signals into detail and approximate signals, which are then combined with the data from the two-terminal travelling wave localization approach for fault detection. After that, the optimal maximum coefficients of the wavelets are extracted and fed into the proposed radial basis function neural network (NNRBF). The results show that both the criterion algorithm and the fault detection algorithm are reliable in their assessments of whether or not faults exist in the power system, and that neither algorithm is particularly sensitive to variations in fault type, fault detection, fault initial angle, or transition resistance. After that, the optimal maximum coefficients of the wavelets are extracted and fed into the proposed radial basis function neural network (NNRBF). Overhead distribution system faults are simulated in Matlab/Simulink, and the technique is rigorously validated across a wide range of system situations. It has been shown through simulations that the proposed method can be relied upon to successfully and dependably protect high impedance fault (Hi-Z).

show abstract

Improving the Performance of Hybrid System-Based Renewable Energy by Artificial Intelligence

Kechida,

Gozim,

Toual

2024

Power Electronics and Drives

View full text Add to dashboard Cite

Artificial intelligence (AI) has emerged as a critical indicator of technological progress in recent years. The present study uses AI to enhance the efficiency of a hybrid system that operates on renewable energy sources. The hybrid system we propose consists of a wind energy conversion system (WECS), a photovoltaic system (PVS), a battery storage system (BSS) and electronic power converters. AI manages these converters cleverly. We use the maximum power point tracking (MPPT)-based fuzzy logic controller (FLC) to regulate the boost converter in the PVS and the WECS. We propose an adaptive neuro fuzzy inference system (ANFIS)-based controller to control the bidirectional converter of the storage system. The design of this module intends to maintain voltage stability on the direct current (DC) bus and improve energy quality. We study and simulate this system using MATLAB/SIMULINK. The results of this research show that the FLC-MPPT technique outperforms the Perturb and Observe (P&O) algorithm in terms of efficiency in power production. The console we propose also shows good results in maintaining the voltage stability in the DC bus in comparison with the proportional integral (PI) controller. This paper has the potential to contribute to the development of environmentally friendly resource performance.

show abstract

Modified PDPWM control with MPPT algorithm for equal power sharing in cascaded multilevel inverter for standalone PV system under partial shading

Cited by 3 publications

References 24 publications

Local-Stability Analysis of Cascaded Control for a Switching Power Converter

Local-Stability Analysis of Cascaded Control for a Switching Power Converter

Fault detection in a distribution network using a combination of a discrete wavelet transform and a neural Network’s radial basis function algorithm to detect high-impedance faults

Improving the Performance of Hybrid System-Based Renewable Energy by Artificial Intelligence

Contact Info

Product

Resources

About