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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) Indonesian Journal of Electrical Engineering and Computer Science
Shahril Irwan Sulaiman
Universiti Teknologi Mara
Control & Systems Engineering Electrical & Electronics Engineering

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Journal : Indonesian Journal of Electrical Engineering and Computer Science

 1 
Performance simulation of the integration of hybrid stand-alone photovoltaic system at Tuba Island Shahril Irwan Sulaiman; Nur Amira Ali
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 1: October 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i1.pp107-115

Abstract

Hybrid photovoltaic-diesel generator power system is important for rural electrification with the diesel generator supplying electricity when battery bank fails to meet the load demand. However, the operation of diesel generator could also be very costly due to high operation and maintenance cost when compared to photovoltaic-battery system. As a result, proper sizing must be conducted to determine the economic indicators of the hybrid photovoltaic-diesel generator system throughout its lifetime. This paper presents the design of such system for an island resort in Langkawi, Kedah, Malaysia. HOMER software was used to simulate the design parameters and economic performance of the system as compared to the existing diesel generator system. Apart from that, different capacities of PV array, battery bank and inverter were investigated in this study to determine the optimum configuration of these components such that the total cost of supplying the load demand at the resort could be minimized. The results showed that the hybrid photovoltaic-diesel generator system is more economically feasible than the existing diesel generator system used at the resort.
Active and Reactive Power Scheduling Optimization using Firefly Algorithm to Improve Voltage Stability under Load Demand Variation Mohamad Khairuzzaman Mohamad Zamani; Ismail Musirin; Halim Hassan; Sharifah Azwa Shaaya; Shahril Irwan Sulaiman; Nor Azura Md. Ghani; Saiful Izwan Suliman
Indonesian Journal of Electrical Engineering and Computer Science Vol 9, No 2: February 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v9.i2.pp365-372

Abstract

This paper presents active and reactive power scheduling using firefly algorithm (FA) to improve voltage stability under load demand variation. The study involves the development of firefly optimization engine for power scheduling process involving the active and reactive power for wind generator. The scheduling optimization of wind generator is tested by using IEEE 30-Bus Reliability Test System (RTS). Voltage stability of the system is assessed based in a pre-developed voltage stability indicator termed as fast voltage stability index (FVSI). This study also considers the effects on the loss and voltage profile of the system resulted from the optimization, where the FVSI value at the observed line, minimum voltage of the system and loss were monitored during the load increment. Results obtained from the study are convincing in addressing the scheduling of power in wind generator. Implementation of FA approach to solve power scheduling revealed its flexibility and feasible for solving larger system within different objective functions.This paper presents active and reactive power scheduling using firefly algorithm (FA) to improve voltage stability under load demand variation. The study involves the development of firefly optimization engine for power scheduling process involving the active and reactive power for wind generator. The scheduling optimization of wind generator is tested by using IEEE 30-Bus Reliability Test System (RTS). Voltage stability of the system is assessed based in a pre-developed voltage stability indicator termed as fast voltage stability index (FVSI). This study also considers the effects on the loss and voltage profile of the system resulted from the optimization, where the FVSI value at the observed line, minimum voltage of the system and loss were monitored during the load increment. Results obtained from the study are convincing in addressing the scheduling of power in wind generator. Implementation of FA approach to solve power scheduling revealed its flexibility and feasible for solving larger system within different objective functions.
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 Ahmad Maliki Omar Atiqah Hamizah Mohd Nordin Halim Hassan Ismail Musirin Mohamad Khairuzzaman Mohamad Zamani Nor Azura Md. Ghani Norfarizani Nordin Nur Amira Ali Puteri Nor Ashikin Megat Yunus Rijalul Fahmi Mustapa Saiful Izwan Suliman Sharifah Azwa Shaaya Sulaiman Shaari