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Ahmad Maliki Omar
Universiti Teknologi MARA

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Hybrid Artificial Neural Network with Meta-heuristics for Grid-Connected Photovoltaic System Output Prediction Norfarizani Nordin; Shahril Irwan Sulaiman; Ahmad Maliki Omar
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 1: July 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v11.i1.pp121-128

Abstract

This paper presents the performance evaluation of hybrid Artificial Neural Network (ANN) model with selected meta-heuristics for predicting the AC output power fof a Grid-Connected Photovoltaic (GCPV). The ANN has been hybridized with three meta-heuristics, i.e. Cuckoo Search Algorithm (CSA), Evolutionary Programming (EP) and Firefly Algorithm (FA) separately. These meta-heuristics were used to optimize the number of neurons, learning rate and momentum rate such that the Root Mean Square Error (RMSE) of the prediction was minimized during the ANN training process. The results showed that CSA had outperformed EP and FA in producing the lowest RMSE. Later, Mutated Cuckoo Search Algorithm (MCSA) was introduced by incorporating Gaussian mutation operator in the conventional CSA. Further investigation showed that MSCA performed better prediction when compared with the conventional CSA in terms of RMSE and computation time.
Online Performance Monitoring of Grid-Connected Photovoltaic System using Hybrid Improved Fast Evolutionary Programming and Artificial Neural Network Puteri Nor Ashikin Megat Yunus; Shahril Irwan Sulaiman; Ahmad Maliki Omar
Indonesian Journal of Electrical Engineering and Computer Science Vol 8, No 2: November 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v8.i2.pp399-406

Abstract

This paper presents the development of online performance monitoring methods for grid-connected photovoltaic (GCPV) system based on hybrid Improved Fast Evolutionary Programming and Artificial Neural Network (IFEP-ANN). The approach has been developed and validated using previous predicted data measurement. Solar radiation (SR), module temperature (MT) and ambient temperature (AT) has been employed as the inputs, and AC output power (PAC) as the sole output to the neural network model. The actual data from the server has been called and uploaded every five minute interval into Matlab by using FTP (File Transfer Protocol) and the predicted AC output power has been produced based on the prediction developed in the training stages. It is then compared with the actual AC output power by using Average Test Ratio, AR. Any predicted AC output power less than the threshold set up, indicates an error has been occurred in the system. The obtained results show that the hybrid IFEP-ANN gives good performance by producing a sufficiently high correlation coefficient, R value of 0.9885. Besides, the proposed technique can analyse and monitor the system in online mode. 
Co-Authors Norfarizani Nordin Puteri Nor Ashikin Megat Yunus Shahril Irwan Sulaiman