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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

Author-ID : 2397517

Control & Systems Engineering Electrical & Electronics Engineering

Published : 7 Documents Claim Missing Document

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Articles

Mitigation of power quality problems using series active filter in a microgrid system Awais Farooqi; Muhammad Murtadha Othman; Ahmad Farid Abidin; Shahril Irwan Sulaiman; Mohd Amran Mohd Radzi
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 10, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Dynamic voltage restorer (DVR) is a series active filter device that is used to protect sensitive loads from power quality issues such as voltage sag, swell, harmonics or disturbances. This implies that the DVR is capable to mitigate power quality disturbances at load terminal. Harmonic is a major power quality problem polluting distribution network causing the end-user equipment to fail operating due to the occurrence of disturbances in voltage, current or frequency. This paper discusses on the DVR used as the proposed technique to mitigate the voltage sag and swell in a distribution network connected with energy storage system and mini-hydro turbine system.

Life-cycle assessment of residential-scale grid-connected photovoltaic system in Malaysia based on monocrystalline silicon modules Atiqah Hamizah Mohd Nordin; Shahril Irwan Sulaiman; Sulaiman Shaari; Rijalul Fahmi Mustapa
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 11, No 2: June 2020
Publisher : Institute of Advanced Engineering and Science

Even though PV systems have been promoted as a green form of electrification, such systems are still contributing to environmental impacts after considering life-cycle impact during material extraction, manufacturing processes of its components, installation, operation, and maintenance. This paper presents a life-cycle assessment to quantify the environmental impact of residential-scale grid-connected PV systems in Malaysia using monocrystalline silicon PV module. LCA had been carried out by using OpenLCA 1.8 software, Ecoinvent 3.5 database, and impact assessment method of IMPACT2002+ and CED. The influence of varying system capacity from 3 to 12 kWp, system lifetime of 21, 25 and 30 years, and solar irradiation of 1560.8, 1651.8, & 1935.5 kWh/m2/yr, were investigated. The results revealed that the greenhouse gas emissions rate, cumulative energy demand, and energy payback time of residential-scale grid-connected PV systems in Malaysia ranged from 37.97 to 67.26 g CO2-eq/kWh, 4387.10 to 4699.99 MJ/m2, and 6.37 to 7.90 years, respectively. This study also evaluated indicators of energy return on investment. The overall finding implies that the installation of residential-scale grid-connected PV systems in Malaysia offers significant potential for GHG emissions reduction in the country.

Performance comparison of artificial intelligence techniques in short term current forecasting for photovoltaic system Muhammad Murtadha Othman; Mohammad Fazrul Ashraf Mohd Fazil; Mohd Hafez Hilmi Harun; Ismail Musirin; Shahril Irwan Sulaiman
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 10, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

This paper presents artificial intelligence approach of artificial neural network (ANN) and random forest (RF) that used to perform short-term photovoltaic (PV) output current forecasting (STPCF) for the next 24-hours. The input data for ANN and RF is consists of multiple time lags of hourly solar irradiance, temperature, hour, power and current to determine the movement pattern of data that have been denoised by using wavelet decomposition. The Levenberg-Marquardt optimization technique is used as a back-propagation algorithm for ANN and the bagging based bootstrapping technique is used in the RF to improve the results of forecasting. The information of PV output current is obtained from Green Energy Research (GERC) University Technology Mara Shah Alam, Malaysia and is used as the case study in estimation of PV output current for the next 24-hours. The results have shown that both proposed techniques are able to perform forecasting of future hourly PV output current with less error.

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

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

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

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

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 Farid Abidin Ahmad Maliki Omar Atiqah Hamizah Mohd Nordin Awais Farooqi Halim Hassan Ismail Musirin Mohamad Khairuzzaman Mohamad Zamani Mohammad Fazrul Ashraf Mohd Fazil Mohd Amran Mohd Radzi Mohd Hafez Hilmi Harun Muhammad Murtadha Othman 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

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