Ngaopitakkul, Atthapol
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Preferred name
Ngaopitakkul, Atthapol
Alternative Name
Ngaopitakkul, A.
Main Affiliation
Email
atthapol.ng@kmitl.ac.th
14 results
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Item type:Publication, Comparison of artificial intelligence methods for fault classification of the 115-kv hybrid transmission system(2020-06-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Development and Analysis of a Fast-Charge EV-Charging Station Model for Power Quality Assessment in Distribution Systems(2025-09-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Analysis of Investment Feasibility for EV Charging Stations in Residential Buildings(2025-09-01); ; Klomjit, JittiphongThis study investigates the financial and operational feasibility of deploying electric vehicle (EV) charging infrastructure within high-density residential buildings, utilizing empirical operational data combined with comprehensive financial modeling. A 14-day monitoring period conducted at a residential complex comprising 958 units revealed distinct charging behaviors, with demand peaking during weekday evenings between 19:00 and 22:00 and displaying more dispersed yet lower overall utilization during weekends. Energy efficiency emerged as a significant operational constraint, as standby power consumption contributed substantially to total energy losses. Specifically, while total energy consumption reached 248.342 kW, only 138.24 kW were directly delivered to users, underscoring the necessity for energy-efficient hardware and intelligent load management systems to minimize idle consumption. The financial analysis identified pricing as the most critical determinant of project viability. Under current cost structures, financial break-even was attainable only at a profit margin of 0.2286 USD (8 THB) per kWh, while lower margins resulted in persistent financial deficits. Sensitivity analysis further demonstrated the considerable vulnerability of the project’s financial performance to small fluctuations in profit share and utilization rate. A 10% reduction in either parameter entirely eliminated the project’s ability to reach payback, while variations in energy costs, capital expenditures (CAPEX), and operational expenditures (OPEX) exerted comparatively limited influence. These findings emphasize the importance of precise demand forecasting, adaptive pricing strategies, and proactive government intervention to mitigate financial risks associated with residential EV charging deployment. Policy measures such as capital subsidies, technical regulations, and transparent pricing frameworks are essential to incentivize private sector investment and support sustainable expansion of EV infrastructure in residential sectors. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Discriminating among inrush current, external short circuit and internal winding fault in power transformer using coefficient of DWT(2012-01-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Fault classification on the hybrid transmission line system between overhead line and underground cable(2017-08-30) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Comparison of mother wavelet for classification fault on hybrid transmission line systems(2017-07-01); Sreewirote, BanchaThis paper proposes comparison mother wavelets for fault classification on hybrid transmission line systems. Hybrid system consists of overhead line and underground cable of 115 kV. ATP/EMTP software has been used for generating fault signals. Then it varies location of fault, fault type and angle. Current signals and zero sequence are analyzed by Discrete Wavelet Transform (DWT) in MATLAB software. DWT decomposes high frequency components from fault signals. Coefficient in scale 1 has been decomposed from Mother Wavelets such as Daubechies (db), Symlets (sym), Biorthogonal (bior) and Coiflets (coif). The coefficient for any mother wavelet has same behavior but different value. Design algorithm for fault classification and compare the result. Therefore, comparison of mother wavelet for fault classification is important to provide the high accuracy. Daubechies (db) can give accuracy more than any mother wavelet. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Behaviour of interturn fault in power transformer winding using high frequency components of discrete wavelet transform(2012-12-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Discriminating among inrush current, external fault and internalwinding fault using coefficient of DWT(2012-06-12); ; This paper proposes a technique for discriminating among inrush current, external fault and internal winding fault of three-phase two-winding transformer which variations of coefficients of high frequency component obtained from DWT of differential current are analyzed. The maximum coefficient details of DWT are performed as comparison indicator. Various cases based on Thailand electricity transmission and distribution systems are studied to verify the validity of the proposed algorithm. Results show that the proposed technique has good accuracy in the considered system. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Application of back-propagation neural network for transformer differential protection schemes part 2 identification the phase with fault appearance in power transformer(2012-12-01); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Discriminating among inrush current, external fault and internal fault in power transformer using low frequency components comparison of DWT(2012-12-01); ;