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Performance evaluation of three-leg voltage source inverter fed unsymmetrical two-phase induction motor based on genetic algorithm for parameter estimation
(2019-12-01)
Kongsuk, Prayad
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Kinnares, Vijit
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Phumiphak, Phunyaphat
This paper presents an evaluation method of performance characteristics in terms of loss, currents and electromagnetic torque of an unsymmetrical two-phase induction motor driven by a three-leg Voltage Source Inverter (VSI) providing unbalanced two-phase voltages. The model parameters for consideration of loss and dynamic performance of the motor are estimated by using Genetic Algorithm (GA). Also, synthesized winding current waveforms based on a super-position method and frequency domain of known harmonic voltages are investigated. The methodology of the proposed GA applied to parameter estimation of the unsymmetrical two-phase induction motor is fully given. Carrier-based unbalanced Space Vector Pulse With Modulation (SVPWM) is employed and implemented on a low-cost microcontroller. In order to prove the validity of the model with the parameters obtained by GA, performance comparison with the experiment and the model with the parameters obtained by conventional test in laboratory has been made. The results of simulation with the proposed parameters and experiment are in good agreement.
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Selecting an access point for indoor localization system using frequency analysis
(2019-07-01)
Suwannawach, Piyapan
;
Chivapreecha, Sorawat
Even though Indoor localization system has been continuously developed, there have also been some amounts of errors in the positioning due to the variance of the signal. This paper proposes a method to help selecting an access point to reduce the variance of received signal strength indicator (RSSI) using frequency analysis and applied genetic algorithm to search the optimal weights for weighted distant fingerprint algorithm (WDF). Experiments were conducted in the indoor environment using android mobile received signal strength from the access point and the proposed algorithm compared with K-Nearest Neighbor (KNN) algorithm and conventional weighted distant fingerprint (WDF) algorithm. Results have demonstrated the proposed algorithm that can improve an increased accuracy up to 89.75% for more accurate identification.
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Selection of a minimal number of significant porcine snps by an information gain and genetic algorithm hybrid model
(2019-01-01)
Rathasamuth, Wanthanee
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Pasupa, Kitsuchart
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Tongsima, Sissades
A panel of a large number of common Single Nucleotide Polymorphisms (SNPs) distributed across an entire porcine genome has been widely used to represent genetic variability of pigs. With the advent of SNP-array technology, a genome-wide genetic profile of a specimen can be easily observed. Among the large number of such variations, there exists a much smaller subset of the SNP panel that could equally be used to correctly identify the corresponding breed. This work presents a SNP selection heuristic that can still be used effectively in the breed classification. The features were selected by combining a filter method and a wrapper method-information gain method and genetic algorithm-plus a feature frequency selection step, while classification used a support vector machine. We were able to reduce the number of significant SNPs to 0.86 % of the total number of SNPs in a swine dataset with 94.80 % classification accuracy.
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Grounding system design of substation using matlab program
(2017-10-19)
Kapijan, Prateep
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Potivejkul, Siriwat
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Yutthagowith, Peerawut
Substation grounding system of Provincial Electricity Authority (PEA) are normally designed using CDEGS software. However, the copyright and limited user licenses affect the practical use. Therefore, using an alternative software should be more beneficial and cost-effective. MATLAB, an engineering software, has been utilized in this study to design the substation grounding system of non-uniform soil. The formulas used in this program are cited from previous literatures and followed the IEEE80-2000. The parameters, including grid resistance (Rg), grid potential rise (GPR), mesh voltage (Em), and step voltage (Es), are in accordance with the results from CDEGS software.
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Auto-Tuning of parameters in hybrid sampling method for class imbalance problem
(2017-02-21)
Sanguanmak, Yotsathon
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Hanskunatai, Anantaporn
The class imbalance is a major problem in machine learning. This problem affects the performance of a model prediction. The DBSM algorithm, a hybrid-sampling technique, was developed to deal with the class imbalance for two-class classification problem. Although the DBSM algorithm is the effective solution, there are too many parameters for tuning in the algorithm. Thus, this paper proposes an automatic parameter tuning for the DBSM algorithm by using a genetic algorithm (GA), called GADBSM. The experimental results of GADBSM are compared with the DBSM algorithm. The results show that the GADBSM can enhance the classification performance of the DBSM algorithm. Moreover, the GADBSM provides the best in F-measure and AUC in all datasets.
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Robust DC motor system and speed control using genetic algorithms with two degrees of freedom and h infinity control
(2017-01-01)
Chitsanga, N.
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Kaitwanidvilai, S.
This paper presents a new design controller called "Robust and fixed-structure 2DOF control" for a DC motor and speed control system. The controller is designed using the concept of 2DOF control with robust loop shaping. The specification of time domain includes in the controller design by using the reference model. Moreover, the structures of both pre-filter and feedback controllers are fixed as a simple structure to make the system robust and still useful in practical works. Instead of solving mathematically complicated equations, Genetic Algorithm is proposed to solve the process of the design control problem. The proposed control is implemented in a speed control of DC Motor. As seen in the results, the proposed system performs better performance, compared to the 1DOF Fixed Structure control and Robust Loop Shaping Control. When the system payloads are changed from 0 kg to 16 kg, the step response from the conventional 1 DOF controller is oscillated obviously at 16 kg., while the proposed controller performs the smooth and stable response.
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A combined GA and IPM approach for unit commitment problem
(2015-01-30)
Nakawiro, Worawat
Unit commitment involves determination of a large number of variables (both discrete and continuous). The well-known curse of dimensionality is very pronounced when dealing with large-scale power systems. This paper presents an approach combined between genetic algorithm (GA) and interior point method (IPM). GA is used to determine the on/off status of generators (discrete variables) whereby IPM finds the optimal power dispatch of the committed generators. Simulations were carried out for the case with 10, 30, 60 and 100 units. The results show that the proposed method helps improve quality of the final result and also reduce computing time.
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A modified particle swarm optimization with mutation and reposition
(2014-12-01)
Ratanavilisagul, Chiabwoot
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Kruatrachue, Boontee
The common problems of particle swarm optimization (PSO) are those of trapping in local optimum and premature convergence. This research paper aims to develop a solution to both problems by introducing mutation around particles and employing the reposition technique. The concurrent use of the introduced mutation and reposition has proved to solve both problems and enhanced the PSO performance; and thus is employed in this research. The proposed technique is termed MRPSO. MRPSO is tested on sixteen benchmark functions and the multidimensional knapsack problems (MKP). MRPSO yields the more satisfactory search results than the genetic algorithm (GA) and PSOs for the benchmark functions and the MKPs.
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Predictive voltage control for a distribution network with renewable energy sources
(2014-10-15)
Nakawiro, Worawat
This paper presents a predictive voltage control strategy for power distribution systems with renewable energy sources. A mixed-integer nonlinear programming problem was formulated and solved by genetic algorithm (GA). The on-load tap changer transformer and reactive power set point of wind and solar farms are determined. The day-ahead control horizon is considered and optimization is carried out at every hour. The wind and solar power are predicted by artificial neural network. The proposed methodology is implemented on a test distribution network to verify its effectiveness. It is demonstrated that the proposed method is capable of maintaining the system voltage close to the nominal as compared to the case of fixed control set-points.
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2DOF H infinity Control for DC Motor using Genetic Algorithms
(2014-01-01)
Chitsanga, Natchanon
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Kaitwanidvilai, Somyot
This paper presents a new method of 2DOF H infinity Control for DC Motor. The proposed technique applies the Genetic Algorithms to achieve the specified structure robust control design. The robustness in terms of robust 2DOF control is achieved by the proposed design and the control results are compared with the conventional 2DOF H infinity control. As results indicated, the proposed method is simpler and the order of the proposed controller is lower than that of the conventional robust control technique. The comparison is done by performing the simulation using the transfer function of the real motor system.

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