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Bulletin of Electrical Engineering and Informatics
Published by Universitas Ahmad Dahlan
ISSN : -     EISSN : -     DOI : -
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.
Arjuna Subject : -
Articles 2,901 Documents
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Power system contingency classification using machine learning technique Sandhya Rani Gongada; Muktevi Chakravarthy; Bhukya Mangu
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4031

Abstract

One of the most effective ways for estimating the impact and severity of line failures on the static security of the power system is contingency analysis. The contingency categorization approach uses the overall performance index to measure the system's severity (OPI). The newton raphson (NR) load flow technique is used to extract network variables in a contingency situation for each transmission line failure. Static security is categorised into five categories in this paper: secure (S), critically secure (CS), insecure (IS), highly insecure (HIS), and most insecure (MIS). The K closest neighbor machine learning strategy is presented to categorize these patterns. The proposed machine learning classifiers are trained on the IEEE 30 bus system before being evaluated on the IEEE 14, IEEE 57, and IEEE 118 bus systems. The suggested k-nearest neighbor (KNN) classifier increases the accuracy of power system security assessments categorization. A fuzzy logic approach was also investigated and implemented for the IEEE 14 bus test system to forecast the aforementioned five classifications.
Implementation and performance evaluation of multi level pseudo random sequence generator Hadeer Hussein Ali; Hadi T. Ziboon; Ashwaq Q. Hameed
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.3363

Abstract

In this paper, introduce a proposed multi-level pseudo-random sequence generator (MLPN). Characterized by its flexibility in changing generated pseudo noise (PN) sequence according to a key between transmitter and receiver. Also, introduce derive of the mathematical model for the MLPN generator. This method is called multi-level because it uses more than PN sequence arranged as levels to generation the pseudo-random sequence. This work introduces a graphical method describe the data processing through MLPN generation. This MLPN sequence can be changed according to changing the key between transmitter and receiver. The MLPN provides different pseudo-random sequence lengths. This work provides the ability to implement MLPN practically in more than one method such as microcontroller or field programmable gate array (FPGA). In this paper discusses MLPN performance using MATLAB as compared with golden PN sequence generator with different modulation schemes such as binary phase-shift keying (BPSK), quadrature phase-shift keying (QPSK), and quadrature amplitude modulation 16QAM. The simulation results show that MLPN performs almost likely golden PN sequence but sure with advantage its flexibility to change generated MLPN between transmitter and receiver. The MLPN sequence is applicable in the same field of PN sequence applications such as code division multiple access (CDMA), spread spectrum system (SSS), and data scrambling.
Fingerprint-based indoor positioning system using BLE: real deployment study Safwat, Rokaya; Shaaban, Eman; Al-Tabbakh, Shahinaz M.; Emara, Karim
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.3798

Abstract

There are a myriad of applications where the localization of interior surroundings is vital in the era of smart cities Bluetooth low energy (BLE) technology is designed for short-range wireless communication, low energy consumption, low cost hardware design and simple deployment with respect to other technologies. This paper presents a low cost BLE fingerprint-based indoor positioning system, where a minimum number of Beacons are deployed in different test bed subareas with different conditions. Collected measured received signal strength indicator (RSSI) signals received from all beacons in each grid cell of all areas of interest are stored. We experimented two deterministic matching algorithms: k-nearest neighbors (KNN) and weighted algorithm (WKNN), to match previously collected RSSI readings with the RSSI at mobile unknown location, to determine where the user is. Experiments results show that WKNN algorithm manages to obtain less mean and standard deviation positioning error for all subareas, that experiencing different conditions of obstructions, reflections, and interferences.
An automated approach for eggplant disease recognition using transfer learning Izazul Haque Saad; Md. Mazharul Islam; Isa Khan Himel; Md. Jueal Mia
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.3575

Abstract

In Bangladesh, eggplant is a widely grown crop that is vital to the country’s food security. The vegetable is consumed on a regular basis by the majority of people. Since Bangladesh’s economy is heavily reliant on agriculture, eggplant growing might help the country’s economy and productivity flourish more efficiently. It provides several health benefits, including reducing cancer risk, blood sugar control, heart health, eye health, and others. Although eggplant is a valuable crop, it is subject to severe diseases that reduce its productivity. It’s hard to detect those diseases manually and needs a lot of time and hard work. So, we introduce an agricultural and medical expert system based on machine vision that analyzes a picture acquired with a smartphone or portable device and classifies diseases to assist farmers in resolving the issue. We studied and used a convolutional neural network (CNN)-based transfer learning approach for identifying eggplant diseases in this paper. We have used various transfer learning models such as DenseNet201, Xception, and ResNet152V2. DenseNet201 had the highest accuracy of these models with 99.06%.
Cluster-based segmentation for tobacco plant detection and classification Thimmegowda, Thirthe Gowda Mallinathapura; Jayaramaiah, Chandrika
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4388

Abstract

Tobacco is one of the major economical crops in the agriculture sector. It is essential to detect tobacco plants using unmanned aerial vehicle (UAV) images for improved crop yield and plays an important role in the early treatment of tobacco plants. The proposed research work is carried out in three phases: In the first phase, we collect images from UAV’s and apply the French Commision Internationale de l'eclairage (CIE) L*a*b colour space model as pre-processing operations and segmentation. And then two prominent motion descriptors namely histogram of flow (HOF) and motion boundary histogram (MBH) are combined with the optimal histogram of oriented gradients (HOG) descriptor for exploring optimal motion trajectory and spatial measurements. And finally, the spatial variations with respect to the scale and illumination changes are incorporated using the optimal HOG descriptor. Here both dense motion patterns and HOG are refined using hierarchical feature selection using principal component analysis (PCA). The proposed model is trained and evaluated on different tobacco UAV image datasets and done a comparative analysis of different machine learning (ML) algorithms. The proposed model achieves good performance with 95% accuracy and 92% of sensitivity.
Embedded control unit design for energy management in smart homes Rawan Mazen Abusharia; Kasim Mousa Al-Aubidy
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.4103

Abstract

This paper deals with smart home energy management through load scheduling and optimal use of available energy sources. In this study, three energy sources were considered: the national electricity grid, photovoltaic (PV) energy, and the storage unit. The PV array can provide the maximum power to the load at a given operating point where the output power changes with temperature, radiation and load. Therefore, a real-time controller is proposed to track the maximum power. An energy management algorithm has been proposed in a smart home to achieve the main goal of making the electricity bill as low as possible. The algorithm involves scheduling loads by assigning a priority to each load. The loads are supplied with the required power according to their priorities and the available energy. The obtained results indicate that supplying the PV system with a fuzzy-based MPPT indicates an increase in system efficiency. The results also showed that the use of energy management based on load scheduling led to a significant reduction in the electricity bill.
Multimodal music emotion recognition in Indonesian songs based on CNN-LSTM, XLNet transformers Sams, Andrew Steven; Zahra, Amalia
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4231

Abstract

Music carries emotional information and allows the listener to feel the emotions contained in the music. This study proposes a multimodal music emotion recognition (MER) system using Indonesian song and lyrics data. In the proposed multimodal system, the audio data will use the mel spectrogram feature, and the lyrics feature will be extracted by going through the tokenizing process from XLNet. Convolutional long short term memory network (CNN-LSTM) performs the audio classification task, while XLNet transformers performs the lyrics classification task. The outputs of the two classification tasks are probability weight and actual prediction with the value of positive, neutral, and negative emotions, which are then combined using the stacking ensemble method. The combined output will be trained into an artificial neural network (ANN) model to get the best probability weight output. The multimodal system achieves the best performance with an accuracy of 80.56%. The results showed that the multimodal method of recognizing musical emotions gave better performance than the single modal method. In addition, hyperparameter tuning can affect the performance of multimodal systems.
Modeling and parameter estimation of solar photovoltaic based MPPT control using EKF to maximize efficiency Rachid Kerid; Younes Bounnah
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.3782

Abstract

In this paper, we focus on the design, modeling and implementation of a MPPT controller based maximum power tracking of photovoltaic system. The electrical characteristic of The PV system is non-linear and changes with the solar irradiation and the ambient temperature. Therefore, the incremental conductance (IC) method control is known for its stability and robustness, and is used to extract the maximum energy from the PV source using a boost converter topology. It provides a strong basis for the improvement and optimization of control parameters of a photovoltaic system. Implementing MPPT algorithm usually need the use of a lot of sensors if accuracy of the system has to be increased. However, IC method with an extended Kalman filter (EKF) can be utilized in order to estimate some parameters to reduce the number of Sensors. The EKF is deployed in the optimal position to estimate both current and the capacitor voltage, thus allowing to eliminate two sensors devise from the entire PV system, which increases the system efficiency and reliability, simplifies the control method and decreases the system cost. The performance of the proposed technique is validated by experimental and simulation results under different operating conditions and load changes.
Automated water quality monitoring and regression-based forecasting system for aquaculture Wei, Toh Yin; Tindik, Emmanuel Steward; Fui, Ching Fui; Haviluddin, Haviluddin; Hijazi, Mohd Hanafi Ahmad
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4464

Abstract

Water quality in fish tanks is essential to reduce fish mortality. Many factors affect the water quality, such as pH, dissolved oxygen, and temperature in fish tanks. Existing work has presented water quality monitoring systems for aquaculture, which are useful for automatic monitoring and notify any incidence of decline in water quality. It enables the fish farms to make interventions to reduce fish mortality. However, advanced monitoring through forecasting is necessary to ensure consistent optimum water quality. This paper presents a web-based water quality monitoring and forecasting system for aquaculture. First, a water quality forecasting model based on the long short-term memory is designed and developed. The model is evaluated and fine-tuned using the existing public dataset. Second, the prototype of the water quality monitoring and forecasting system is developed. An Arduino and Raspberry Pi based water quality data acquisition tool is built. A web-based application is then developed to present the monitoring data and forecasting. A notification module is included to send an alert message to the fish farmers when necessary. The system is tested and evaluated at the fish hatchery in Universiti Malaysia Sabah. The findings show that the proposed system provides better water quality management for fish farms.
Packet loss compensation over wireless networked using an optimized FOPI-FOPD controller for nonlinear system Muhannad Ali Hasan; Ahmed A. Oglah; Mehdi J. Marie
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4345

Abstract

Wireless networked control systems (WNCS) consist of an actuator, sensor, and controller communicating over wireless networks in place of traditional point-to-point wired connection. Due to their main advantages, a decrease in maintenance costs, more flexibility, and safety could be achieved. As a result, it attracted a great deal of interest, but packet losses and time delays in the wireless network through transmitting and receiving the data are considered very challenging issues, which impair the output accuracy of the WNCS and can affect the entire system stability. In this study, integer-order proportional integral-proportional derivative (PI-PD) and fractional-order PI-PD (FOPI-FOPD) controllers are proposed to reduce the effect of expected packet loss in a WNCS to improve system performance. At high packet loss percent, the PI controller is introduced to act as a compensator in the feed-forward loop to keep the system stable. MATLAB/Simulink and Truetime simulator are used to simulate the WNCS. The rotary inverted pendulum (RIP) is utilized as the object of the controllers. Grey wolf optimization (GWO) algorithm is used to find the optimal controllers and compensator parameters. The simulation results showed that the FOPI-FOPD is superior to PI-PD in the packet loss compensation.

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