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Bulletin of Electrical Engineering and Informatics

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Published by Universitas Ahmad Dahlan

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Core Subject : Engineering,

Electrical & Electronics Engineering

Bulletin of Electrical Engineering and Informatics (Buletin Teknik Elektro dan Informatika) ISSN: 2089-3191, e-ISSN: 2302-9285 is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world. The journal publishes original papers in the field of electrical, computer and informatics engineering.

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Articles 1,870 Documents

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Fabrication and studying the dielectric properties of (polystyrene-copper oxide) nanocomposites for piezoelectric application Dalal Hassan; Ahmed Hashim Ah-yasari
Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

The preparation of (polystyrene-copper oxide) nanocomposites have been investigated for piezoelectric application. The copper oxide nanoparticles were added to polystyrene by different concentrations are (0, 4, 8 and 12) wt.%. The structural and A.C electrical properties of (PS-CuO) nanocomposites were studied. The results showed that the dielectric constant and dielectric loss of (PS-CuO) nanocomposites decrease with increase in frequency. The A.C electrical conductivity increases with increase in frequency. The dielectric constant, dielectric loss and A.C electrical conductivity of polystyrene increase with increase in copper oxide nanoparticles concentrations. The results of piezoelectric application showed that the electrical resistance of (PS-CuO) nanocomposites decreases with increase in pressure.

Signal-to-noise Ratio Study on Pipelined Fast Fourier Transform Processor S. L. M. Hassan; N. Sulaiman; S. S. Shariffudin; T. N. T. Yaakub
Bulletin of Electrical Engineering and Informatics Vol 7, No 2: June 2018
Publisher : Institute of Advanced Engineering and Science

Fast Fourier transform (FFT) processor is a prevailing tool in converting signal in time domain to frequency domain. This paper provides signal-to-noise ratio (SNR) study on 16-point pipelined FFT processor implemented on field-programable gate array (FPGA). This processor can be used in vast digital signal applications such as wireless sensor network, digital video broadcasting and many more. These applications require accuracy in their data communication part, that is why SNR is an important analysis. SNR is a measure of signal strength relative to noise. The measurement is usually in decibles (dB). Previously, SNR studies have been carried out in software simulation, for example in Matlab. However, in this paper, pipelined FFT and SNR modules are developed in hardware form. SNR module is designed in Modelsim using Verilog code before implemented on FPGA board. The SNR module is connected directly to the output of the pipelined FFT module. Three different pipelined FFT with different architectures were studied. The result shows that SNR for radix-8 and R4SDC FFT architecture design are above 40dB, which represent a very excellent signal. SNR module on the FPGA and the SNR results of different pipelined FFT architecture can be consider as the novelty of this paper.

Maximum Loadability Enhancement with a Hybrid Optimization Method E. E. Hassan; T. K. A. Rahman; Z. Zakaria; N. Bahaman; M. H. Jifri
Bulletin of Electrical Engineering and Informatics Vol 7, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

Nowadays, a power system is operating in a stressed condition due to the increase in demand in addition to constraint in building new power plants. The economics and environmental constraints to build new power plants and transmission lines have led the system to operate very close to its stability limits. Hence, more researches are required to study the important requirements to maintain stable voltage condition and hence develop new techniques in order to address the voltage stability problem. As an action, most Reactive Power Planning (RPP) objective is to minimize the cost of new reactive resources while satisfying the voltage stability constraints and labeled as Secured Reactive Power Planning (SCRPP). The new alternative optimization technique called Adaptive Tumbling Bacterial Foraging (ATBFO) was introduced to solve the RPP problems in the IEEE 57 bus system. The comparison common optimization Meta-Heuristic Evolutionary Programming and original Bacterial Foraging techniques were chosen to verify the performance using the proposed ATBFO method. As a result, the ATBFO method is confirmed as the best suitable solution in solving the identified RPP objective functions.

PAPR Reduction Using Huffman and Arithmetic Coding Techniques in F-OFDM System Azlina Idris; Nur Atiqah Md Deros; Idris Taib; Murizah Kassim; Mohd Danial Rozaini; Darmawaty Mohd Ali
Bulletin of Electrical Engineering and Informatics Vol 7, No 2: June 2018
Publisher : Institute of Advanced Engineering and Science

Filtered orthogonal frequency division multiplexing (F-OFDM) was introduced to overcome the high side lobes in the OFDM system. Filtering is implemented in the system to reduce the out-of-band emission (OOBE) for the spectrum utilization and to meet the diversified expectation of the upcoming 5G networks. The main drawback in the system is the high peak to average ratio (PAPR). This paper investigates the method used in reducing the PAPR in the F-OFDM system. The proposed method using the block coding technique to overcome the problem of high PAPR are the Arithmetic coding and Huffman coding. This research evaluates the performance of F-OFDM system based on the PAPR values. From the simulation results, the PAPR reduction of the Arithmetic coding is 8.9% lower, while the Huffman Coding is 6.7% lower in the F-OFDM system. The results prove that the Arithmetic Coding will out-perform the Huffman coding in the F-OFDM system.

Hybrid Stand-alone Photovoltaic Systems Sizing Optimization Based on Load Profile Zulkifli Othman; Shahril Irwan Sulaiman; Ismail Musirin; Ahmad Maliki Omar; Sulaiman Shaari
Bulletin of Electrical Engineering and Informatics Vol 7, No 2: June 2018
Publisher : Institute of Advanced Engineering and Science

This paper presents a sizing optimization technique for Hybrid Stand-Alone Photovoltaic (HSAPV). In this research, three optimization techniques have been developed, namely Dolphin Echolocation Algorithm (DEA), Fast Evolutionary Programming (FEP), and Classical Evolutionary Programming (CEP). These techniques have been incorporated into the sizing process to maximize the technical performance of the SAPV system. The components of PV modules, charge controllers, inverters, and batteries are used to determine the optimum value. These components are used as the control parameters to maximize the expected performance ratio (PR) of the SAPV system. The Iterative Sizing Algorithm (ISA) is the benchmarking technique to conduct the optimization technique achieving maximum PR value and minimal computation time. Results obtained from the research show that DE overcomes FEP and CEP. In addition, the optimization techniques also demonstrated comparatively fast with respect to ISA as the benchmark technique."

Load Management for Voltage Control Study Using Parallel Immunized-computational Intelligence Technique Amirul Izzat Abu Bakar; Mohamad Khairuzzaman Mohamad Zamani; Ismail Musirin; Nor Azura Md Ghani
Bulletin of Electrical Engineering and Informatics Vol 7, No 2: June 2018
Publisher : Institute of Advanced Engineering and Science

The increase of power demand is a crucial issue in the power system community in many parts of the world. Malaysia has also witnessed the familiar scenario due to the current development throughout the country has invited the urgency of increase in the power supply. Since Malaysia practices vertical system; where the electricity is supplied by only one utility, load management is an important issue so that the delivery of electricity is implemented without discrimination. Parallel Computational Intelligence will be developed which can alleviate and avoid all the unsolved issues, highlighting the weakness of current schemes. Parallel Computational Intelligence is developed to manage the optimal load in making sure the system maintains the stability condition, within the voltage limits. This paper presents evolutionary programming (EP) technique for optimizing the voltage profile. In this study, 3 algorithms which are Gaussian, Cauchy and Parallel EP were developed to solve optimal load management problem on IEEE 26-bus Reliability Test System (RTS). Results obtained from the study revealed that the application of Parallel EP has significantly reduced the time for the optimization process to complete.

A Review of Low Power Wide Area Technology in Licensed and Unlicensed Spectrum for IoT Use Cases Noor Laili Ismail; Murizah Kassim; Mahamod Ismail; Roslina Mohamad
Bulletin of Electrical Engineering and Informatics Vol 7, No 2: June 2018
Publisher : Institute of Advanced Engineering and Science

There are many platforms in licensed and license free spectrum that support LPWA (low power wide area) technology in the current markets. However, lack of standardization of the different platforms can be a challenge for an interoperable IoT environment. Therefore understanding the features of each technology platform is essential to be able to differentiate how the technology can be matched to a specific IoT application profile. This paper provides an analysis of LPWA underlying technology in licensed and unlicensed spectrum by means of literature review and comparative assessment of Sigfox, LoRa, NB-IoT and LTE-M. We review their technical aspect and discussed the pros and cons in terms of their technical and other deployment features. General IoT application requirements is also presented and linked to the deployment factors to give an insight of how different applications profiles is associated to the right technology platform, thus provide a simple guideline on how to match a specific application profile with the best fit connectivity features.

0.18µm-CMOS Rectifier with Boost-converter and Duty-cycle-control for Energy Harvesting Roskhatijah Radzuan; Mohd Khairul Mohd Salleh; Nuha A. Rhaffor; Shukri Korakkottil Kunhi Mohd
Bulletin of Electrical Engineering and Informatics Vol 7, No 2: June 2018
Publisher : Institute of Advanced Engineering and Science

Existing works on battery-less of energy harvesting systems often assume as a high efficiency of rectifier circuit for power management system. In practice, rectifier circuit often varies with output power and circuit complexity. In this paper, based on a review of existing rectifier circuits for the energy harvesters in the literature, an integrated rectifier with boost converter for output power enhancement and complexity reduction of power management system is implemented through 0.18-micron CMOS process. Based on this topology and technology, low threshold-voltage of MOSFETs is used instead of diodes in order to reduce the power losses of the integrated rectifier circuit. Besides, a single switch with the duty-cycle control is introduced to reduce the complexities of the integrated boost converter. Measurement results show that the realistic performances of the rectifier circuit could be considerably improved based on the performances showed by the existing study.

Results of Fitted Neural Network Models on Malaysian Aggregate Dataset Nor Azura Md Ghani; Saadi Bin Ahmad Kamaruddin; Ismail Musirin; Hishamuddin Hashim
Bulletin of Electrical Engineering and Informatics Vol 7, No 2: June 2018
Publisher : Institute of Advanced Engineering and Science

This result-based paper presents the best results of both fitted BPNN-NAR and BPNN-NARMA on MCCI Aggregate dataset with respect to different error measures. This section discusses on the results in terms of the performance of the fitted forecasting models by each set of input lags and error lags used, the performance of the fitted forecasting models by the different hidden nodes used, the performance of the fitted forecasting models when combining both inputs and hidden nodes, the consistency of error measures used for the fitted forecasting models, as well as the overall best fitted forecasting models for Malaysian aggregate cost indices dataset.

Comparison of Solar Radiation Intensity Forecasting Using ANFIS and Multiple Linear Regression Methods Hadi Suyono; Rini Nur Hasanah; R. A. Setyawan; Panca Mudjirahardjo; Anthony Wijoyo; Ismail Musirin
Bulletin of Electrical Engineering and Informatics Vol 7, No 2: June 2018
Publisher : Institute of Advanced Engineering and Science

Solar radiation forecasting is important in solar energy power plants (SEPPs) development. The electrical energy generated from the sunlight depends on the weather and climate conditions in the area where the SEPPs are installed. The condition of solar irradiation will indirectly affect the electrical grid system into which the SEPPs are injected, i.e. the amount and direction of the power flow, voltage, frequency, and also the dynamic state of the system. Therefore, the prediction of solar radiation condition is very crucial to identify its impact into the system. There are many methods in determining the prediction of solar radiation, either by mathematical approach or by heuristic approach such as artificial intelligent method. This paper analyzes the comparison of two methods, Adaptive Neuro Fuzzy Inference (ANFIS) method, which belongs into the heuristic methods, and Multiple Linear Regression (MLP) method, which uses a mathematical approach. The performance of both methods is measured using the root mean square error (RMSE) and the mean absolute error (MAE) values. The data of the Swiss Basel city from Meteoblue are used to test the performance of the two methods being compared. The data are divided into four cases, being classified as the training data and the data used as predictions. The solar radiation prediction using the ANFIS method indicates the results which are closer to the real measurement results, being compared to the the use MLP method. The average values of RMSE and MAE achieved are 123.27 W/m2 and 90.91 W/m2 using the ANFIS method, being compared to 138.70 W/m2 and 101.56 W/m2 respectively using the MLP method. The ANFIS method gives better prediction performance of 12.51% for RMSE and 11.71% for MAE with respect to the use of the MLP method.

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