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Analysis of voltage drop using transformer tap changer and placement of capacitor bank with genetic algorithm
(2025-12-01)
Siregar, Yulianta
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Saragi, Agus Kivander
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Ngamroo, Issarachai
The demand for electrical energy is increasing due to high economic growth and population. The impact is that electrical energy operates excessively to meet the required demand. Unbalanced loads, higher power losses on the line, and voltage drops that are higher than allowed are just a few of the issues that may result from this. Adding tap changers and capacitor banks is one method of improving the voltage profile and power losses. To conduct this study, tap changers and capacitor banks were added to the IEEE 33 bus network system. The value, capacity, and location of the tap changers and capacitor banks in the system were ascertained using the genetic algorithm (GA) approach. According to the simulation results, the voltage profile, which initially had 21 buses outside the IEEE standard limits, may be ideal by installing two tap changers and two capacitor banks. Additionally, reactive power losses decreased from 41.8 kVar to 93.3 kVar, and active power losses decreased from 202.7 kW to 130.7 kW, a decrease of 72 kW.
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Artificial intelligence optimization for producing high quality foam-mat dried tomato powder and its application in nutritional soup
(2024-12-01)
Thuy, Nguyen Minh
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Giau, Tran Ngoc
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Hao, Hong Van
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Minh, Vo Quang
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Tai, Ngo Van
The present work aims to investigate the effect of foam-mat drying on drying rate and lycopene content of tomato powder using a three-level Box-Behnken experimental design of Response Surface Methodology (RSM). Three process parameters included egg albumin (EA) ranging from 3 to 9 % as a foaming agent, carboxymethyl cellulose (CMC) from 0.2 to 0.6 % as a foam stabilizer and drying temperatures (60, 70, and 80<sup>o</sup>C). The responses measured drying rate (DR) and lycopene content, which are the indication of drying process and product quality. Optimization of drying process using RSM and artificial neural network coupled genetic algorithm (ANN-GA) models has been also investigated. Foam mat dried tomato powder produced under optimal conditions was then used to prepare a nutritious soup powder with 4 designed recipes with other nutritious ingredients. The results showed that the ANN-GA model (network structure of 3-10-2) could predict and optimize better than the RSM model. The optimal conditions for foam-mat drying process were EA of 6.67 %, CMC of 0.381 %, and drying temperature of 70.6<sup>o</sup>C. These gave the DR and lycopene to be 3.004 g water/g dry matter/min and 392.8 μg/g, respectively. Validation optimal condition was performed and showed that the experimental values obtained were greatly close to the predicted values. From the 4 designed soup formulas, the macronutrient content in formula F2 met the range for AMDR with the percentage of calories from protein, lipid, and carbohydrate being 21.28 %, 20.28 %, and 58.44 %, respectively. It was proven that ANN-GA is a more reliable and robust predictive modelling tool for foam-mat tomato powder production optimization compared to RSM model. Also, the promising application of tomato powder in nutritious soup production also was shown in this study, which could further research in larger scale.
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Optimization of the Solid-State Copper Brazing Condition Using Desirability Function and Genetic Algorithm
(2024-11-01)
Jattakul, Prajak
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Mhoraksa, Thiti
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Kanlayasiri, Kannachai
This research studies on optimization of solid-state copper brazing condition and comparatively investigates the ability of the desirability function and genetic algorithm (GA) optimization schemes with regard to the optimal brazing condition that yields the maximum brazed-joint tensile shear force, where the brazing parameters included the brazing temperature, holding time and loading pressure. To that end, a second-order mathematical model was first derived based on the Box–Behnken experimental design and the maximum-response desirability function. The findings suggested that the tensile shear force of the brazed joints was significantly influenced by all three brazing parameters. The optimal brazing condition was at 620 °C brazing temperature, 30-min holding time and 12.173 kPa loading pressure. The desirability function- and GA-predicted optimal brazing conditions were effectively identical, thus confirming the comparable power of both optimization schemes. Further experiments were conducted to validate the optimization outcomes, whereby the confirmation tests were carried out under the optimal brazing condition. The results suggest that both optimization schemes are viable for solid-state copper brazing, with the GA demonstrating a slightly higher prediction accuracy.
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BP Neural Intelligent Residential Demand Volume Data Collection System Based on Improved Genetic Algorithm
(2024-01-01)
Zhang, Xiaoxing
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Jumsai na Ayudhya, Thirayu
There are problems with the quality and consistency of traditional residential demand volume data, leading to data inaccuracies and biases, affecting system analysis and decision-making results. In order to solve these problems, this article proposes a BP neural data acquisition system based on an improved genetic algorithm. This study adopts the method of experimental testing, after determining the structure of the network, including the number of nodes in the input layer, hidden layer, and output layer, uses an improved genetic algorithm to initialize and optimize the weights and biases of the network. Through experimental verification, the accuracy range of data collected based on this method is 89–96%. The BP neural intelligent residential demand volume data collection system based on the improved genetic algorithm designed in this study shows high prediction accuracy and efficiency. Compared with traditional methods, this system can better capture the complex relationships between input features and optimize network parameters through an improved genetic algorithm, improving the performance and convergence speed of the model.
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Optimum Operation and Cost Scenarios of a Hybrid Wind/PV/Battery in a Radial Network using Genetic Algorithm and Particle Swarm Optimization
(2022-01-01)
Abdelwareth, Mohamed Els S.
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Riawan, Dedet Candra
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Chompoo-Inwai, Chow
Hybrid generation systems took the attention of many researchers searching for the best energy source that can be optimum, reliable, and scalable instead of dependency on the traditional fossil fuels sources; researchers are developing Artificial Intelligence (AI) algorithms to optimize those systems. This paper will study the optimum generating power from a Wind turbine, PV, and Battery linked to the radial network in Tomia Island, considering the optimum cost using Particle Swarm Optimization (PSO) and Genetic Algorithm (GA).
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AI-Enhanced Predictive Maintenance in Manufacturing Processes
(2022-01-01)
Netisopakul, Ponrudee
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Phumee, Nawarat
This research aims to apply artificial intelligence technology to a manufacturing industry, specifically, to forecast temperature and insulation values of motors from the CNC machine. Dataset from motor sensors are collected and forecasting models are trained using four deep learning models, namely, multilayer perceptron (MLP), long-short term memory (LSTM), LSTM autoencoder, and bidirectional LSTM (Bi-LSTM). Models are evaluated by measuring the deviation of forecasting values from the real values. Two measures, root mean square error (RMSE) and mean absolute error (MAE), are used to assess model's performance. Experiments are conducted and found that the Bi-LSTM yielded the lowest RMSE and MAE numbers, hence, the best model to be selected. Further development has been implemented by integrating Bi-LSTM and genetic algorithm (GA) in order to optimize the model performance. Instead of searching the huge hyperparameter space of the neural network, the integrate GA-LSTM model using RMSE as a fitness function to reduce the search space and obtain the optimal or near optimal hyperparameters. The empirically best model is found which yields a lower RMSE value of 0.041 comparing to 0.18 when not optimized.
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Construction of punctured polar codes based on genetic algorithm
(2021-05-19)
Mueadkhunthod, Krittiyaporn
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Min Myint, Lin Min
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Supnithi, Pornchai
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Phakphisut, Watid
In this work, we propose a new construction of punctured polar codes, which provides a good error-correcting performance at arbitrary code lengths and code rates. We exploit the density evolution to evaluate the error probability of punctured polar codes. Then, the puncturing pattern and frozen bit positions are selected by genetic algorithm to construct a punctured polar code with low error probability. The results show that the proposed technique provides superior block error rate performance than the quasi-uniform puncturing technique at a high code rate. At a low code rate, the proposed technique can show the better performance than the shortening technique.
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Implementation and Optimization of Two Degree of Freedom H∞ Loop Shaping Control for Average Current Mode Control Buck Converter using Genetic Algorithm
(2021-01-01)
Kaitwanidvilai, Somyot
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Phurahong, Nuttapon
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Suwan-Ngam, Warachart
In this paper, we focus on the implementation and optimization of two degree of freedom (2DOF) H∞ loop shaping control for the DC-DC buck converter. The output voltage is controlled using the current control mode called the average current mode control (ACMC). The technique using the fixed structure robust controller technique, as well as genetic algorithm (GA), is applied, resulting in the reduction of the controller order and optimal parameter for the robust proportional integral (PI) controller. In this paper, the performance of the proposed controller is compared with those using the conventional 2DOF H∞ loop shaping controller and other techniques. According to both simulation and experimental results, the robust controller designed by the proposed technique is simple, low order, and practical, yet still retains both performance and robustness.
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A Genetic Algorithm Approach for Intermodal Cooperation with High-Speed Rail: The Case of Thai Transportation System
(2020-12-01)
Boongasame, Laor
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Temdee, Punnarumol
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Kumnungkit, Kanchana
The Thai government has a plan to start the first operation of the Thai High-Speed Rail (THSR) in 2021. However, ensuring the profit of THSR while limiting the project impacts on existing transport options is challenging. In this study, an approach for identifying the optimal travel frequency for impacted transportation services after the THSR operation is implemented. The genetic algorithm (GA) is introduced with specific value functions of various transport options, including rail, bus, and van, to reschedule each travelling option under the intermodal cooperation model. The constraint of GA is that the profit of the individual transport option in the next generation will have to be higher than the total profit of the previous generation. From the case study between Nakhon Ratchasima and Bangkok, the simulation results show that THSR and other transport options have overall gain higher profits after the start of THSR operation. Regarding social welfare theory, the simulation results show that the profit of each transport option is proven to be stable concerning travel schedule frequency after implementation of the THSR system.
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Effective simulation approach for lightning impulse voltage tests of reactor and transformer windings
(2020-10-16)
Tuethong, Piyapon
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Yutthagowith, Peerawut
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Kunakorn, Anantawat
In this paper, an effective simulation method for lightning impulse voltage tests of reactor and transformer windings is presented. The method is started from the determination of the realized equivalent circuit of the considered winding in the wide frequency range from 10 Hz to 10 MHz. From the determined equivalent circuit and with the use of the circuit simulator, the circuit parameters in the impulse generator circuit are adjusted to obtain the waveform parameters according to the standard requirement. The realized equivalent circuits of windings for impulse voltage tests have been identified. The identification approach starts from equivalent circuit determination based on a vector fitting algorithm. However, the vector fitting algorithm with the equivalent circuit extraction is not guaranteed to obtain the realized equivalent circuit. From the equivalent circuit, it is possible that there are some negative parameters of resistance, inductance, and capacitance. Using such circuit parameters from the vector fitting approach as the beginning circuit parameters, a genetic algorithm is employed for searching equivalent circuit parameters with the constraints of positive values. The realized equivalent circuits of the windings can be determined. The validity of the combined algorithm is confirmed by comparison of the simulated results by the determined circuit model and the experimental results, and good agreement is observed. The proposed approach is very useful in lightning impulse tests on the reactor and transformer windings.

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