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All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS)
Musirin, Ismail
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Dynamic voltage restorer (DVR) in a complex voltage disturbance compensation Mansor, Muhammad Alif; Othman, Muhammad Murtadha; Musirin, Ismail; Noor, Siti Zaliha Mohammad
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 10, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1103.199 KB) | DOI: 10.11591/ijpeds.v10.i4.pp2222-2230

Abstract

Nowadays, a distribution network is operating in a stressful manner because of a complex voltage disturbance stirred by its nonlinear, intensified, sensitive and complex loading condition with vast proliferation of electronic equipment required for the integration of renewable energy. A distribution network that mostly inflicted by the complex voltage disturbance can be referred to as the merge of stationary voltage disturbances with a short duration voltage disturbance under a nonlinear loading condition. Therefore, the dynamic voltage restorer (DVR) integrating with the battery bank will have enough energy storage to overcome long and deep complex voltage disturbance that occurs in a distribution network installed with the photovoltaic (PV) system. The results are obtained with satisfactorily findings in compensating the complex voltage disturbance using DVR.
Performance comparison of artificial intelligence techniques in short term current forecasting for photovoltaic system Othman, Muhammad Murtadha; Fazil, Mohammad Fazrul Ashraf Mohd; Harun, Mohd Hafez Hilmi; Musirin, Ismail; Sulaiman, Shahril Irwan
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 10, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (292.241 KB) | DOI: 10.11591/ijpeds.v10.i4.pp2148-2156

Abstract

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.
Significant implication of unified power quality conditioner in power quality problems mitigation Hasan, Kamrul; Othman, Muhammad Murtadha; Rahman, Nor Farahaida Abdul; Hannan, M. A.; Musirin, Ismail
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 10, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (454.548 KB) | DOI: 10.11591/ijpeds.v10.i4.pp2231-2237

Abstract

This paper presents an analysis of a three-phase unified power quality conditioner (UPQC) in terms of design and performance. A back to back connection of a series compensator and a shunt compensator with a common DC-bus is utilized to build the UPQC model. The series compensator compensates the power quality problems such as grid voltage sags/swells for the grid side. During sag and swell condition, the compensated voltage is injected by the series compensator in phase with the point of common coupling (PCC) or out of phase with PCC. The load current harmonics is compensated by using the shunt compensator. The dynamic performance and  the steady state of the designed model are analyzed by using MATLAB-Simulink under several disturbances such as PCC voltage harmonics, voltage sags/swells and load unbalancing using a nonlinear load. 
Optimal population size of particle swarm optimization for photovoltaic systems under partial shading condition Hashim, Norazlan; Nik Ismail, Nik Fasdi; Johari, Dalina; Musirin, Ismail; Rahman, Azhan Ab.
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp4599-4613

Abstract

Particle swarm optimization (PSO) is the most widely used soft computing algorithm in photovoltaic systems to address partial shading conditions (PSC). The success of the PSO run heavily depends on the initial population size (NP). A higher NP increases the probability of a global peak (GP) solution, but at the expense of a longer convergence time. To find the optimal value of NP, a trade-off is typically made between a high success rate and a reasonable convergence time. The most used trade-off method is a trial-and-error approach that lacks explicit guidelines and empirical evidence from detailed analysis, which can affect data reproducibility when different systems are used. Hence, this study proposes an empirical trade-off method based on the performance index (PI) indicator, which takes into account the weighted importance of success rate and convergence time. Furthermore, the impact of NP on achieving a successful PSO was empirically investigated, with the PSO tested with 16 different NPs ranging from 3 to 50, and 10,000 independent runs on various PSC problems. Overall, this study found that the best NP to use was 25, which had the best average PI value of 0.9373 for solving all PSC problems under consideration.
Co-Authors Fazil, Mohammad Fazrul Ashraf Mohd Hannan, M. A. Harun, Mohd Hafez Hilmi Hasan, Kamrul Hashim, Norazlan Johari, Dalina Mansor, Muhammad Alif Nik Ismail, Nik Fasdi Noor, Siti Zaliha Mohammad Othman, Muhammad Murtadha Rahman, Azhan Ab. Rahman, Nor Farahaida Abdul Sulaiman, Shahril Irwan