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Bulletin of Electrical Engineering and Informatics

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Published by Institute of Advanced Engineering and Science

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Core Subject : Science, Engineering,

Computer Science & IT Electrical & Electronics Engineering Engineering

Bulletin of Electrical Engineering and Informatics ISSN: 2302-9285 is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication, computer engineering, computer science, information technology and informatics from the global world. The journal publishes original papers in the field of electrical (power), electronics, instrumentation & control, telecommunication and computer engineering; computer science; information technology and informatics. Authors must strictly follow the guide for authors. Please read these instructions carefully and follow them strictly. In this way you will help ensure that the review and publication of your paper is as efficient and quick as possible. The editors reserve the right to reject manuscripts that are not in accordance with these instructions.

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Articles 539 Documents

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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.

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.

Harmonic Contribution Analysis of Electric Arc Furnace by Using Spectrogram M. H. Jopri; A. R. Abdullah; M. Manap; T. Sutikno; M. R. Ab Ghani
Bulletin of Electrical Engineering and Informatics Vol 7, No 2: June 2018
Publisher : Institute of Advanced Engineering and Science

In this paper, spectrogram, a fast and accurate technique is introduced for the analysis of the contribution. Based on a rule-based classifier and the threshold settings that referred to the IEEE Standard 1159 2009, the analysis of the harmonic and interharmonic contribution of EAF are carried out successfully. Moreover, the impact of contribution is measured using total harmonic distortion (THD) and total non-harmonic distortion (TnHD). In addition, spectrogram also gives 100 percent correct detection and able to analyze the contribution impact. It is proven that the proposed method is accurate, fast and cost efficient for analyzing the impact of harmonic and interharmonic of EAF.

An Identification of Multiple Harmonic Sources in a Distribution System by Using Spectrogram M. H. Jopri; A. R. Abdullah; M. Manap; T. Sutikno; M. R. Ab Ghani
Bulletin of Electrical Engineering and Informatics Vol 7, No 2: June 2018
Publisher : Institute of Advanced Engineering and Science

The identification of multiple harmonic sources (MHS) is vital to identify the root causes and the mitigation technique for a harmonic disturbance. This paper introduces an identification technique of MHS in a power distribution system by using a time-frequency distribution (TFD) analysis known as a spectrogram. The spectrogram has advantages in term of its accuracy, a less complex algorithm, and use of low memory size compared to previous methods such as probabilistic and harmonic power flow direction. The identification of MHS is based on the significant relationship of spectral impedances, which are the fundamental impedance (Z1) and harmonic impedance (Zh) that estimate the time-frequency representation (TFR). To verify the performance of the proposed method, an IEEE test feeder with several different harmonic producing loads is simulated. It is shown that the suggested method is excellent with 100% correct identification of MHS. The method is accurate, fast and cost-efficient in the identification of MHS in power distribution arrangement.

Performance Comparison of Artificial Intelligence Techniques for Non-intrusive Electrical Load Monitoring Khairuddin Khalid; Azah Mohamed; Ramizi Mohamed; Hussain Shareef
Bulletin of Electrical Engineering and Informatics Vol 7, No 2: June 2018
Publisher : Institute of Advanced Engineering and Science

The increased awareness in reducing energy consumption and encouraging response from the use of smart meters have triggered the idea of non-intrusive load monitoring (NILM). The purpose of NILM is to obtain useful information about the usage of electrical appliances usually measured at the main entrance of electricity to obtain aggregate power signal by using a smart meter. The load operating states based on the on/off loads can be detected by analysing the aggregate power signals. This paper presents a comparative study for evaluating the performance of artificial intelligence techniques in classifying the type and operating states of three load types that are usually available in commercial buildings, such as fluorescent light, air-conditioner and personal computer. In this NILM study, experiments were carried out to collect information of the load usage pattern by using a commercial smart meter. From the power parameters captured by the smart meter, effective signal analysis has been done using the time time (TT)-transform to achieve accurate load disaggregation. Load feature selection is also considered by using three power parameters which are real power, reactive power and the TT-transform parameters. These three parameters are used as inputs for training the artificial intelligence techniques in classifying the type and operating states of the loads. The load classification results showed that the proposed extreme learning machine (ELM) technique has successfully achieved high accuracy and fast learning compared with artificial neural network and support vector machine. Based on validation results, ELM achieved the highest load classification with 100% accuracy for data sampled at 1 minute time interval.

Optimal Configuration of Wind Farms in Radial Distribution System Using Particle Swarm Optimization Technique Mahesh Kumar; Bhagwan Das; Perumal Nallagownden; Irraivan Elamvazuthi; Sadia Ali Khan
Bulletin of Electrical Engineering and Informatics Vol 7, No 2: June 2018
Publisher : Institute of Advanced Engineering and Science

Recently, a wide range of wind farm based distributed generations (DGs) are being integrated into distribution systems to fulfill energy demands and to reduce the burden on transmission corridors. The non-optimal configuration of DGs could severely affect the distribution system operations and control. Hence, the aim of this paper is to analyze the wind data in order to build a mathematical model for power output and pinpoint the optimal location. The overall objective is minimization of power loss reduction in distribution system. The five years of wind data was taken from 24o 44’ 29” North, 67o 35’ 9” East coordinates in Pakistan. The optimal location for these wind farms were pinpointed via particle swarm optimization (PSO) algorithm using standard IEEE 33 radial distribution system. The result reveals that the proposed method helps in improving renewable energy near to load centers, reduce power losses and improve voltage profile of the system. Moreover, the validity and performance of the proposed model were also compared with other optimization algorithms.

Efficient Implementation of Mean, Variance and Skewness Statistic Formula for Image Processing Using FPGA Device Aqwam Rosadi Kardian; Sunny Arief Sudiro; Sarifuddin Madenda
Bulletin of Electrical Engineering and Informatics Vol 7, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v7i3.687

Processing statistic formula in image processing and accessing data from memory is easy in software, the other hand for hardware implementation is more dificult considering a lot of constraint. This article proposes an implementation of optimum mean, variance and skewness formula in FGPA Device. The proposed circuit design for all formulas only need three additions component (in three accumulators) and two divisions using two shift-right-registers, two subtractors, one adder and six multipliers. For 8x8 image size need 64 clock cycles to finish the mean, variance and skewness calculations, comparing other approach that need more than 1024 additions component without skewness calculation. Implementation into FPGA needs 68 slices of flip-flops and 121 of 4 input LUTs.

Page 31 of 54 | Total Record : 539

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