A new neural networks MPPT controller for PV systems

The model of the photovoltaic power generation system is complex, and it requires multiple iterations in calculation, which is not available for simulation analysis and calculation when a large number of PV systems connected to the grid. This paper presents a simplified model for PV system based on functional equivalence. DC part consisting of PV array and MPPT controller is simplified into a maximum output model; and inverter with its control strategy, of which the function is to achieve decoupling control of active and reactive power, are simplified to controlled voltage source with control module. Last, simulation tests are conducted based on Matlab/Simulink platform. The results show that the simplified model can not only ensure accuracy, but also improve the simulation efficiency, which is of a good practical value.

Get full-text (via PubEx)

Recent Approaches of Forecasting and Optimal Economic Dispatch to Overcome Intermittency of Wind and Photovoltaic (PV) Systems: A Review

Energies ◽

10.3390/en12224392 ◽

2019 ◽

Vol 12 (22) ◽

pp. 4392 ◽

Cited By ~ 9

Author(s):

Manzoor Ellahi ◽

Ghulam Abbas ◽

Irfan Khan ◽

Paul Mario Koola ◽

Mashood Nasir ◽

...

Keyword(s):

Neural Networks ◽

Particle Swarm Optimization ◽

Power Systems ◽

Fossil Fuels ◽

Renewable Energy Sources ◽

Economic Dispatch ◽

Energy Sources ◽

Swarm Optimization ◽

Pv Systems ◽

High Penetration

Renewable energy sources (RESs) are the replacement of fast depleting, environment polluting, costly, and unsustainable fossil fuels. RESs themselves have various issues such as variable supply towards the load during different periods, and mostly they are available at distant locations from load centers. This paper inspects forecasting techniques, employed to predict the RESs availability during different periods and considers the dispatch mechanisms for the supply, extracted from these resources. Firstly, we analyze the application of stochastic distributions especially the Weibull distribution (WD), for forecasting both wind and PV power potential, with and without incorporating neural networks (NN). Secondly, a review of the optimal economic dispatch (OED) of RES using particle swarm optimization (PSO) is presented. The reviewed techniques will be of great significance for system operators that require to gauge and pre-plan flexibility competence for their power systems to ensure practical and economical operation under high penetration of RESs.

Get full-text (via PubEx)

Robust Method for Diagnosis and Detection of Faults in Photovoltaic Systems Using Artificial Neural Networks

Periodica Polytechnica Electrical Engineering and Computer Science ◽

10.3311/ppee.14828 ◽

2020 ◽

Author(s):

Aicha Amani Djalab ◽

Mohamed Mounir Rezaoui ◽

Lakhdar Mazouz ◽

Ali Teta ◽

Nassim Sabri

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Good Accuracy ◽

Photovoltaic System ◽

Solar Panels ◽

Pv Systems ◽

Pv Module ◽

Sophisticated Method ◽

Artificial Neural ◽

And Performance

During their operation, PV systems can be subject of various faults and anomalies that could lead to a reduction in the effectiveness and the profitability of the PV systems. These faults can crash, cause a fire or stop the whole system. The main objective of this work is to present a sophisticated method based on artificial neural networks ANN for diagnosing; detecting and precisely classifying the fault in the solar panels in order to avoid a fall in the production and performance of the photovoltaic system. The work established in this paper intends in first place to propose a method to detect possible various faults in PV module using the Multilayer Perceptron (MLP) ANN network. The developed artificial neural network requires a large database and periodic training to evaluate the output parameters with good accuracy. To evaluate the accuracy and the performance of the proposed approach, a comparison is carried out with the classic method (the method of thresholding). To test the effectiveness of the proposed approach in detecting and classifying different faults, an extensive simulation is carried out using Matlab SIMULINK.

Get full-text (via PubEx)