Jettanasen, Chaiyan
Loading...
Preferred name
Jettanasen, Chaiyan
Alternative Name
Jettanasen, C.
Main Affiliation
Email
chaiyan.je@kmitl.ac.th
94 results
Now showing 1 - 10 of 94
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, An Evaluation Study on Electric Appliance Characteristics and Load Patterns in Residential Buildings(2026-01-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Performance and electromagnetic interference mitigation of DC-DC converter connected to photovoltaic panel(2019-11-01); Renewable energy has been rapidly increased in terms of installed capacity. However, the electronic components inside such a system can cause a serious threat such as Electromagnetic Interference (EMI) to nearby sensitive devices/equipment. This paper aims to study on performance of DC-DC boost converter connected to photovoltaic panel and how to reduce EMI emissions issued in the system. A passive EMI low-pass filtering is one of various mitigation techniques. The designed converter connected to photovoltaic panel and EMI filter are tested in both simulation software and field testing by using EN55022 standard as a reference. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Transient Analysis to Distinguish Mechanically Switched Capacitors Using Discrete Wavelet Transform and Artificial Intelligence(2026-01-01); ; Capacitor banks are widely used in modern power systems for reactive power compensation and voltage regulation. However, switching operations of mechanically switched capacitors (MSCs) can generate transient phenomena, such as inrush currents, which may resemble fault currents and lead to misoperation of protection systems. Therefore, accurate detection and classification of transient events are essential for reliable system operation. This study proposes a hybrid approach for transient signal analysis and classification by integrating the discrete wavelet transform (DWT) with artificial intelligence (AI) techniques, including probabilistic neural networks and fuzzy inference systems (FIS). The DWT performs time–frequency analysis to extract multi-scale wavelet features from three-phase current signals. The proposed method enables both discrimination between inrush and fault currents and multi-class classification of transient events among six capacitor switching conditions, namely base case, pre-insertion resistor, pre-insertion inductor, current limiting reactor, 6% reactor, and synchronous closing. The methodology is validated using PSCAD/EMTDC simulations under isolated and back-to-back capacitor switching scenarios. The results demonstrate that the proposed DWT–AI approach achieves high classification accuracy exceeding 95%, outperforming conventional methods based on DWT alone and DWT combined with FIS. Furthermore, the proposed method improves protection system performance by reducing false tripping caused by transient inrush currents, while maintaining reliable fault detection capability. The findings confirm that integrating time–frequency signal processing with AI-based classification provides an effective and practical solution for transient event discrimination in MSC capacitor bank systems. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Discrete wavelet transform and back-propagation neural networks algorithm for fault classification in underground cable(2011-07-26); ; ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The conducted emission attenuation of micro-inverters for nanogrid systems(2020-01-01); Road lighting systems require a significant amount of electric energy. To compensate for the utilized energy, the concept of a nanogrid road lighting system is presented. A solar panel is installed on the top of a lighting pole to generate electric power. In this research, a photovoltaic simulator (PV simulator), which is used to simulate solar behavior such as current, voltage, and power based on temperature and solar irradiance levels, is employed to replace a solar panel. In the nanogrid system, grid-connected and stand-alone micro-inverters are employed to convert the electric power. The inverters comprise switching devices that can generate electromagnetic interference (EMI) when operating, which is harmful to the grid system and the electrical equipment. In general, EMI has been studied and reduced in electrical appliances, which only receive electric power. However, for the nanogrid system, which supplies electricity to the grid system, there is less study on the EMI topic because the usage is still not widespread. In the future, the nanogrid system will be widely used delivering high power directly into the electrical grid system. Therefore, the study and attenuation of EMI in the nanogrid system are very promising. Conducted emission (CE) is one form of EMI that flows through a cable connecting several appliances in the frequency range of 150 kHz to 30 MHz. CE of grid-connected and stand-alone micro-inverters have high levels in the low-frequency range between 150 kHz-5 MHz and then decreases steadily. CE attenuation is important for this inverter in a solar power system. This research studies the effect of CE mitigation on the nanogrid system. The result is compared with the Comite International Special des Perturbations Radio (CISPR) 14-1 standard. Finally, the passive EMI filter can reduce CE and meets the CISPR 14-1 standard. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Design of a Personal Mobility Device for Elderly Users(2021-01-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Identification of fault location for simultaneous fault in distribution system using discrete wavelet transform(2011-04-01); ; ; Apisit, C.Currently, the most effective technique for identifying fault location, based on a travelling wave, has been proposed in several research papers. However, the effects of simultaneous faults have been neglected. In order to overcome this problem, a new algorithm will be developed in order to predict fault location precisely. This paper presents a technique to detect fault locations, during simultaneous fault, in an underground distribution system using discrete wavelet transform (DWT). The DWT is used to detect the high frequency components. The time that the fault signal uses to reach the ends of the distribution line is considered, then, applied so that the distance of fault can be calculated. The result is found that the proposed algorithm gives satisfactory both in case of single fault and simultaneous fault. ICIC International © 2011. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A novel probabilistic neural network-based algorithm for classifying internal fault in transformer windings(2013-01-01); The major function of protective devices in a power system is to detect the occurrence of faults and to isolate the faulty sections from the rest of the system. Much progress has been made in the development algorithms for detecting faults in power transformers, which depend on transients-based techniques. This paper presents an algorithm based on a combination of discrete wavelet transforms and probabilistic neural networks (PNNs) for classifying internal faults in a two-winding three-phase transformer. Fault conditions of the transformer are simulated using alternative transients program/electromagnetic transients program (ATP/EMTP) in order to obtain current signals. The mother wavelet Daubechies4 is employed to decompose the high-frequency components from these signals. All three phases of the differential current signals are used in the fault detection decision algorithm. The variations of first-scale high-frequency component that detects fault are used as an input for the training pattern. The training process for the neural network and fault diagnosis decision is implemented using toolboxes on MATLAB/Simulink. Various cases and fault types based on the Thailand electricity transmission and distribution systems are studied to verify the validity of the algorithm. Backpropagation neural network is also compared with the PNN in this paper. It is found that the proposed method gives satisfactory accuracy with less training time, and will be particularly useful in the development of a modern differential relay for a transformer protection scheme. © 2013 Institute of Electrical Engineers of Japan. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An Approach for Voltage Drop Improvement in Distribution Line Using High-Voltage Capacitor Bank(2025-01-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Selection of proper activation functions in back-propagation neural networks algorithm for identifying the phase with fault appearance in transformer windings(2012-06-01); This paper presents an algorithm based on a combination of Discrete Wavelet Transforms and back-propagation neural networks for identifying the types of fault including the phase with fault appearance of a two-winding three-phase power transformer. Fault conditions of the transformer are simulated using ATP/EMTP in order to obtain current signals. The training process for the neural network and fault diagnosis decision are implemented using toolboxes on MATLAB. Various cases and fault types based on Thailand electricity transmission and distribution systems are studied to verify the validity of the algorithm. Various activation functions in each hidden layer and the output layer are compared in order to select the best activation function for identifying the types of internal fault of the transformer winding. It is found that average accuracy obtained from hyperbolic tangent-hyperbolic tangent-linear activation function gives satisfactory accuracy, and will be particularly useful in the development of a modern differential relay. © 2012 ICIC International.