Fault Detection and Diagnosis of Grid Connected PV Systems Using Bayesian Neural Network | Chapter 6 | Current Approaches in Engineering Research and Technology Vol. 6

 

At present, artificial neural networks are massively and successfully used for fault diagnosis of grid-connected PV (GCPV) systems thanks to their capabilities of learning and generalization. Solutions can be found with almost all types of Artificial Neural Networks in fault detection and diagnosis of PV systems. In fact, the use of ANN permits the development of intelligent algorithms that can learn all the PV systems working status and then be able to detect and identify possible malfunctions status and causes independently of the ANNs type. In this study, the architecture of both ANNs is multilayer perceptron (MLP), the transfer function used in this step is the sigmoid function. The approach required the Bayesian Regulation algorithm for ANN training. However, each type of ANN owns its proper characteristics from point of view of its accuracy, efficiency, reliability as well as its response of time in term of faults detection and diagnosis compared to others. In this article, fault detection and diagnosis of a small GCPV array based on an experimental setup using Bayesian Neural Network is presented. This type of Artificial Neural Network can secure the identification and localization of the greatest recurring cases in PV generators in particular short-circuit, open-circuit and healthy cases. This study requires four input data such as irradiation, cell temperature, current and voltage of maximum power point. Results show that this study proved an excellent level of accuracy displaying (99.88%) in terms of detection and localization of treated faults with a global response time equal to 16 min22. Fault detection and diagnosis of PV systems is necessary not only to increase system power system reliability but also to reduce the operating costs of PV plans.

 

Author(s) Details

C. Kara Mostefa Khelil
Electrical Engineering Department, Khemis Miliana University, Ain Defla, Algeria and SET Laboratory, Electronics Department, Blida 1 University, BP 270 Blida, Algeria.

 

B. Amrouche
SET Laboratory, Electronics Department, Blida 1 University, BP 270 Blida, Algeria and Renewable Energies Department, Blida 1 University, BP 270 Blida, Algeria.

 

K. Kara
SET Laboratory, Electronics Department, Blida 1 University, BP 270 Blida, Algeria.

 

Please see the book here:- https://doi.org/10.9734/bpi/caert/v6/1207

Coordinated Power Scheme to Mitigate Power Curtailment in the Application of a Microgrid | Chapter 2 | Research Developments in Science and Technology Vol. 8

In certain cases, while the percentage of renewable energy (RE) has grown, the output of RE has decreased. In the present energy market, wind power (WP) curtailments expand along with the percentage of photovoltaics (PV), adding to the challenges encountered by wind farm owners. In this paper, a method of supervisory power coordination is suggested. The main objectives are to prevent more RE electricity from entering the grid, which would cause curtailments. The suggested scheme is to build a grid-connected microgrid system to be run exclusively in response to loads and virtual power plant (VPP) requests such that the system is operated as an independent microgrid when there is no VPP request. This is done in order to make the proposed scheme practical. The efficacy of the proposed approach was examined using computer simulations in the MATLAB/Simulink environment. The verification was done using voltage criteria, including the AC voltage regulation between 6% of the rated AC voltage, the DC voltage regulation between 10% of the rated DC voltage, the power balance according to fluctuations in the loads, and the VPP demands for power. The simulation showed that the suggested technique is viable and warranted, not only for deploying different system configurations but also for minimising power curtailment.

Author(s) Details:

Heesang Ko,

Power System Research Team, Korea Institute of Energy Research, Jeju, Korea.

Gihoon Kim,

Power System Research Team, Korea Institute of Energy Research, Jeju, Korea.

Yanghyun Nam,

Power System Research Team, Korea Institute of Energy Research, Jeju, Korea.

Kyungsang Ryu,

Power System Research Team, Korea Institute of Energy Research, Jeju, Korea.

Please see the link here: https://stm.bookpi.org/RDST-V8/article/view/7395

Modeling and Control of Autonomous Photovoltaic System: Comparative Study the Fuzzy Logic and P&O Associated to PI Controller Algorithms Using SEPIC Converter for Maximum Power Point Tracking | Chapter 8 | Recent Developments in Engineering Research Vol. 10

In recent years, the rising demand for energy and emissions generated by the use of fossil fuels have led the general public to use renewable energy sources. There are excellent conditions for Morocco to benefit from renewable energy. Throughout the territory, the strength of insolation enables it to make these energy reserves a key factor in its socio-economic growth. Moreover, the rising energy needs of Morocco are mainly covered by imported coal and petroleum. This energy dependency would be minimised by the incorporation of renewable energies into electricity production, promising sustainable growth. It is becoming increasingly necessary to diversify and rationalise the means of electrical power production. However, as a function of light intensity and temperature, the output of this energy is non-linear and varies. Therefore, the photovoltaic PV panel's operating point does not always correspond with the maximum power point. A process is then used to scan for and monitor the maximum power (MPPT) in order to permanently produce the maximum power. This work deals with the intelligent control of an autonomous photovoltaic system (photovoltaic panel associated with the Single Ended Primary Inductor Converter 'SEPIC') using the fuzzy logic algorithm and PandO associated with the PI controller. The purpose of this research is to perform a comparative study between these two algorithms (fuzzy logic and P&O associated with the PI controller) and to demonstrate the effect of the proposed Intelligent MPPT Control in terms of response time, wave ripple and efficiency on the autonomous photovoltaic system.

Author (s) Details

Essaid Ait El Maati
Laboratory of Radiation - Matter & Instrumentation, University Hassan First, Faculty of Science and Technology, B.P.: 577, 26000 Settat, Morocco.

Abdelhadi Radouane
Laboratory of Radiation - Matter & Instrumentation, University Hassan First, Faculty of Science and Technology, B.P.: 577, 26000 Settat, Morocco.

Azeddine Mouhsen
Laboratory of Radiation - Matter & Instrumentation, University Hassan First, Faculty of Science and Technology, B.P.: 577, 26000 Settat, Morocco.

View Book :- https://bp.bookpi.org/index.php/bpi/catalog/book/370