Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
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Artificial Intelligence-Based Optimal PID Controller Design for BLDC Motor with Phase Advance
Get PDFThis paper proposes the artificial intelligence (AI)-based optimal PID controller design optimization of brushless direct current (BLDC) motor speed control with phase advance approach. The proposed control system allows the speed adjustment of the BLDC motor by phase advance technique. In this paper, two selected AI algorithms, i.e., the adaptive tabu search (ATS) and the intensified current search (ICS) are conducted as the optimizer for the PID controller design. The proposed control system is simulated by MATLAB/SIMULINK. Results obtained by the ATS and ICS will be compared with those obtained by the Ziegler-Nichols (ZN) tuning rule and the genetic algorithm (GA). It shows that the speed response of the BLDC motor by phase advance with the PID controller optimized by the ICS outperforms better than the ZN, GA and ATS
Sensorless Predictive Direct Power Control PDPC_SVM For PWM Converter Under Different Input Voltage Conditions
Get PDFIn this paper, a new virtual flux (VF) based predictive direct power control (VF_PDPC) applied for three-phase pulse width modulation (PWM) rectifier is proposed. The virtual flux estimation is performed using a pure integrator in series with a new adaptive algorithm in order to cancel dc offset and harmonic distortions in the estimated VF. The introduced structure is able to produce two virtual flux positive sequence components orthogonal output signals under unbalanced and distorted voltage conditions. The main features of the proposed virtual flux estimator are, it's simple structure, accuracy, and fast VF estimation over the excited integrators. Therefore, the estimated VF is then used for robust sensorless VF-PDPC with a constant switching frequency using space vector modulation (SVM) and tested through numerical simulations. The instantaneous active and reactive powers provided by orthogonal (VF) positive sequence components are directly controlled. More importantly, this configuration gives quasi-sinusoidal and balanced current under different input voltage conditions without using the power compensation methods. The results of the simulation confirmed the validity of the proposed virtual flux algorithm and demonstrated excellent performance under different input voltage conditions, complete rejection of disturbances
Qualified Two-Hybrid Techniques by DWT Output to Predict Fault Location
Get PDFThe power transmission system is essential for the power scheme to transfer the energy from generators to consumers. The short circuit problem repeatedly occurs in the transmission system, and the main problem is to separate the sources from users. This research has applied two hybrid techniques to predict fault location. The first hybrid technique has involved the Discrete Wavelet Transformation (DWT) and Adaptive Neuro-Fuzzy Inference System (ANFIS), while the second hybrid technique is for DWT grouping and Support Vector Machine (SVM). These hybrid techniques are intended to estimate the fault location of each fault category in a transmission system. The DWT was applied to both D8 and D9 level at the 50 kHz sample frequency. The root mean square (RMS) values of the D8 and D9 coefficients were used for training using ANFIS and SVM techniques. After that, ANFIS and SVM were utilised to detect faults in the phase and ground lines. Several types of fault have been simulated, i.e. fault location, fault resistance, and original point of view. The RMS results from the two hybrid techniques were compared to find the best results. The tests of error estimation were performed for the three bus systems. The comparison of error estimation of the two methods shows that both hybrid techniques can be applied to predict fault locations
Evaluation and Applying Feature Extraction Techniques for Face Detection and Recognition
Get PDFDetecting the image and identifying the face has become important in the field of computer vision for recognizing and analyzing, reconstructing into 3D, and labelling the image. Feature extraction is usually the first stage in detection and recognition of the image processing and computer vision. It supports the conversion of the image into a quantitative data. Later, this converted data can be used for labelling, classifying and recognizing a model. In this paper, performance of such feature extraction techniques viz. Local Binary Pattern (LBP), Histogram of Oriented Gradients (HOG) and Convolutional Neural Network (CNN) technique is applied to detect and recognize the face. The experiments conducted with a data set addressing the issues like pose variation, facial expression and intensity of light. The efficiency of the algorithms were evaluated based on the computational time and accuracy rate
A Novel Design and Implementation of FBMC Transceiver for Low Power Applications
Get PDFThe complex structure of the Filter Bank Multicarrier (FBMC) communication system is the main drawback affecting the performance of the system and causes a high-power consumption. The complexity arises from using a polyphase filter bank, which consists of fast Fourier Transform/ Inverse Fast Fourier Transform (FFT/IFFT) processors and a filter bank of Finite Impulse Response (FIR) filters. This paper presents the analysis and the implementation of a new design model for FBMC transceiver in which the polyphase filter is removed completely in both transmitter and receiver and uses instead of it, a multi-level cascaded structure of FIR subfilters. The coefficients of each subfilter selected using an optimization algorithm to minimize the amplitude of sidelobes compared to the amplitude of the main lobe in the frequency response of the subfilter. The proposed design reduces the number of multiplications compared to the conventional design by 65%. The field-programmable gate array (FPGA) implementation results indicate that the proposed architecture saves 24% of resources of the FPGA board, works faster, and saves 27% of power consumption compared to conventional FBMC transceiver
K-means Clustering In Knee Cartilage Classification: Data from the OAI
Get PDFKnee osteoarthritis is a degenerative joint disease which affects people mostly from elderly population. Knee cartilage segmentation is still a driving force in managing early symptoms of knee pain and its consequences of physical disability. However, manual delineation of the tissue of interest by single trained operator is very time consuming. This project utilized a fully-automated segmentation that combined a series of image processing methods to process sagittal knee images. MRI scans undergo Bi-Bezier curve contrast enhancement which increase the distinctiveness of cartilage tissue. Bone-cartilage complex is extracted with dilation of mask resulted from region growing at distal femoral bone. Later, the processed image is clustered with k = 2, into two groups, including coarse cartilage group and background. The thin layer of cartilage is successfully clustered with satisfactory accuracy of 0.987±0.004, sensitivity 0.685±0.065 of and specificity of 0.994±0.004. The results obtained are promising and potentially replace the manual labelling process of training set in convolutional neural network model
Impact of unbalanced harmonic loads towards winding temperature rise using FEM modeling
Get PDFThis paper investigates the hot spot temperature of transformer thermal model due to unbalanced harmonic loads from the network. The finite element method has been used to solve the coupling multiphysic for heat transfer in solid and fluid. All material properties in the model were been took into consideration such as copper as the coil material, iron as the core material and transformer oil as the coolant material for the transformer. The transient study on the model has been set for 1minutes using 30 degree celcius as the ambient temperature reference. The simulation hot spot temperature result has been compared for rated load (without harmonic) versus the unbalanced load (with harmonic) which shown in 2D regime. It can be clearly seen the significant increment of the hotspot temperature of the transformer from the rated load to the unbalanced harmonic load. The result has successfully shows the detection of the prospect failure of the transformer due to the harmonic current load in a form of winding loss that contributes to the hotspot temperature of the transformer
Design and Performance Analysis of a Wine-bridge RC Harmonic Oscillation Generator with an Operational Amplifier
Get PDFThis paper presents the special features of harmonic generators and their widespread use and in particular the design, simulation and experimental studies of a Wine-bridge RC generator with an operational amplifier. The results obtained are analyzed and compared. It can be deduced that the parameters of the output signal can be accurately realized - the necessary frequency and the amplitude by setting the parameters of the constituent components
An Analytical Approach for the Optimal Operation of Simultaneous AC-DC Power Transmission System
Get PDFSimultaneous AC-DC power transmission technique can improve both loadability and stability of a power system with long transmission line. But, there is a tradeoff between loadability and stability i.e. increase in the improvement of loadability causes the decrease in the improvement of stability and vice versa. Actually, it is a multi-objective optimization problem where the objective function depends on two decision variables with opposite in nature. Firstly, this paper presents an analytical expression for the objective function which is the function of two decision variables i.e. power flowing capacity and critical clearing time. Secondly, a mathematical model is developed for the optimal point of the objective function. Considering a typical system a numerical analysis is performed using the proposed expressions. Again, the impact of the line length and the voltage level of a transmission line on the objective function are also investigated. Finally, the developed model of the optimal point is applied to a real system to justify its ability to evaluate the combined benefit of loadability and stability of simultaneous AC-DC system
Implementation of Deep Learning Based Method for Optimizing Spatial Diversity MIMO Communication
Get PDFAs an alternative solution of the isuue trade-off phenomenon between performance and computational complexity always become the hugest dilemma suffered by researchers, this research proposes an optimization in spatial diversity MIMO communication system using end-to-end learning based model, specifically, it adapts autoencoder model. Two models are introduced in this research which each of them address a problem about data detection task and channel estimation task that has not been addressed in the previous research. The proposed models were evaluated in one of the most common channel impairment which is Rayleigh fading with additional Additive White Gaussian Noise (AWGN) and compared to the standard Alamouti scheme. The results show that these deep learning based models for MIMO communication system result in very promising results by outperforming the baseline methods. In perfect CSIR (Channel State Information in Receiver side) case, the proposed models achieve BER nearly at SNR 22.5 dB. While in channel estimation case, the proposed models can exceed the baseline performance even by only transmitting 2 pilots