Dynamic response improvement of hybrid system by implementing ANN-GA for fast variation of photovoltaic irradiation and FLC for wind turbine

Izadbakhsh, Maziar; Rezvani, Alireza; Gandomkar, Majid

doi:10.1515/aee-2015-0024

Cited by 26 publications

(14 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In short, the MEA has the following advantages, which is compared with the GA . (1) The convergence and alienation of the MEA can be parallel, which is conducive to improving the operational efficiency.…”

Section: Research On the Mppt Methods Based On The Bp Neural Network mentioning

confidence: 99%

“…In short, the MEA has the following advantages, which is compared with the GA. [47][48][49][50][51] (1) The convergence and alienation of the MEA can be parallel, which is conducive to improving the operational efficiency. (2) The MEA algorithm has good memory ability and can remember multiple generations of evolutionary information.…”

Section: Bp Neural Network With Mea Optimizedmentioning

confidence: 99%

See 1 more Smart Citation

Research on the voltage prediction of unmanned aerial vehicle photovoltaic modules based on new combination optimization algorithm

Wang

Shen

2019

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary Solar energy is a new energy source that is not only renewable but also available everywhere in the world. Thus, this new energy source is used on solar unmanned aerial vehicles. The use of solar photovoltaic systems (PVS) involves the conversion of solar energy into electricity. A photovoltaic cell is the core part of solar unmanned aerial vehicle (UAV) for providing energy. The solar panels are laid on the wings of solar unmanned aerial vehicles. This paper proposes a new MPPT controller for predicting the voltage to obtain the maximum power from the solar panel. In this paper, on the basis of studying the characteristics of photovoltaic cells, combined with the advantages of the mind evolutionary algorithm (MEA) and back propagation network, this article proposes a new type of BP network modeling structure based on the MEA, which is used for the modeling of photovoltaic cells. First, the MEA is constructed based on topology of a back propagation network. Then, this algorithm is used to obtain the optimal solutions, which are regarded as the initial weights and threshold values of the BP neural network. Finally, the simulation experiment is performed MATLAB software; comparing the different prediction results of the MEA optimization BP Network and genetic algorithm (GA) optimization BP neural network with the simple use of the BP neural network. The simulation results indicate that the optimization of the BP neural network by the MEA decreased the mean absolute percent error (MAPE) and root mean square error (RMSE) evaluation indicators by approximately 64% and 0.0524, respectively, compared with the back propagation neural network (BPNN). Overall, the MEA optimization BP neural network has high precision, small error, and a short training time.

show abstract

Section: Research On the Mppt Methods Based On The Bp Neural Network mentioning

confidence: 99%

Section: Bp Neural Network With Mea Optimizedmentioning

confidence: 99%

Research on the voltage prediction of unmanned aerial vehicle photovoltaic modules based on new combination optimization algorithm

Wang

Shen

2019

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

show abstract

“…In [16], to command the MPP of a hybrid system, Izadbakhsh et al proposed an ANN genetic controller with three layers for various PV irradiation, as well as a fuzzy logic controller with three trapezoidal memberships, a min-max method, and a centroid mechanism for a wind turbine.…”

Section: Related Workmentioning

confidence: 99%

Sizing Control and Hardware Implementation of a Hybrid Wind-Solar Power System, Based on an ANN Approach, for Pumping Water

Zarrad

Hajjaji

Aymen

et al. 2019

International Journal of Photoenergy

View full text Add to dashboard Cite

In our day, solar energy and wind energy are becoming more and more used as renewable sources by various countries for different uses such as in an isolated home. These energies admit a unique limitation related to the characteristic of energy instability. For this, the objective of this manuscript is to command and synchronize the power flow of a hybrid system using two sources of energy (solar and wind). The first contribution of our work is the utilization of an artificial neural network controller to command, at fixed atmospheric conditions, the maximum power point. The second contribution is the optimization of the system respecting real-time constraints to increase a generating system performance. As a matter of fact, the proposed system and the controller are modeled using MATLAB/Simulink and a Xilinx System Generator is utilized for hardware implementation. The simulation results, compared with other works in the literature, present high performance, efficiency, and precision. The suggested system and its control strategy give the opportunity of optimizing the hybrid power system performance, which is utilized in rural pumping or other smart house applications.

show abstract

“…Many classification methods were developed in the literature [29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45]. Author selected Bayes classifier [6].…”

Section: Bayes Classifiermentioning

confidence: 99%

Recognition of Acoustic Signals of Induction Motors with the Use of MSAF10 and Bayes Classfier

Głowacz¹

2016

Archives of Metallurgy and Materials

View full text Add to dashboard Cite

Condition monitoring of deterioration in the metallurgical equipment is essential for faultless operation of the metallurgical processes. These processes use various metallurgical equipment, such as induction motors or industrial furnaces. These devices operate continuously. Correct diagnosis and early detection of incipient faults allow to avoid accidents and help reducing financial loss. This paper deals with monitoring of rotor electrical faults of induction motor. A technique of recognition of acoustic signals of induction motors is presented. Three states of induction motor were analyzed. Studies were carried out for methods of data processing: Method of Selection of Amplitudes of Frequencies (MSAF10) and Bayes classifier. Condition monitoring is helpful to protect induction motors and metallurgical equipment. Further researches will allow to analyze other metallurgical equipment.

show abstract

Dynamic response improvement of hybrid system by implementing ANN-GA for fast variation of photovoltaic irradiation and FLC for wind turbine

Cited by 26 publications

References 37 publications

Research on the voltage prediction of unmanned aerial vehicle photovoltaic modules based on new combination optimization algorithm

Research on the voltage prediction of unmanned aerial vehicle photovoltaic modules based on new combination optimization algorithm

Sizing Control and Hardware Implementation of a Hybrid Wind-Solar Power System, Based on an ANN Approach, for Pumping Water

Recognition of Acoustic Signals of Induction Motors with the Use of MSAF10 and Bayes Classfier

Contact Info

Product

Resources

About