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Analysis of Investment Feasibility for EV Charging Stations in Residential Buildings
(2025-09-01)
Chiradeja, Pathomthat
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Yoomak, Suntiti
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Sottiyaphai, Chayanut
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Ngaopitakkul, Atthapol
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Klomjit, Jittiphong
This 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.
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Development and Analysis of a Fast-Charge EV-Charging Station Model for Power Quality Assessment in Distribution Systems
(2025-09-01)
Chiradeja, Pathomthat
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Yoomak, Suntiti
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Srisuksai, Panu
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Klomjit, Jittiphong
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Ngaopitakkul, Atthapol
With the rapid rise in electric vehicle (EV) adoption, the deployment of EV charging infrastructure—particularly fast-charging stations—has expanded significantly to meet growing energy demands. While fast charging offers the advantage of reduced charging time and improved user convenience, it imposes considerable stress on existing power distribution systems due to its high power and current requirements. This study investigated the impact of EV fast charging on power quality within Thailand’s distribution network, emphasizing compliance with accepted standards such as IEEE Std 519-2014. We developed a control-oriented EV-charging station model in power systems computer-aided design and electromagnetic transients, including DC (PSCAD/EMTDC), which integrates grid-side vector control with DC fast-charging (CC/CV) behavior. Active/reactive power setpoints were mapped onto (Formula presented.) current references via Park’s transformation and regulated by proportional integral (PI) controllers with sinusoidal pulse-width modulation (SPWM) to command the voltage source converter (VSC) switches. The model enabled dynamic studies across battery state-of-charge and staggered charging schedules while monitoring voltage, current, and total harmonic distortion (THD) at both transformer sides, charger AC terminals, and DC adapters. Across all scenarios, the developed control achieved grid-current THDi of <5% and voltage THD of <1.5%, thereby meeting IEEE 519-2014 limits. These quantitative results show that the proposed, implementation-ready approach maintains acceptable power quality under diverse fast-charging patterns and provides actionable guidance for planning and scaling EV fast-charging infrastructure in Thailand’s urban networks.
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Comparison of artificial intelligence methods for fault classification of the 115-kv hybrid transmission system
(2020-06-01)
Klomjit, Jittiphong
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Ngaopitakkul, Atthapol
This research proposes a comparison study on different artificial intelligence (AI) methods for classifying faults in hybrid transmission line systems. The 115-kV hybrid transmission line in the Provincial Electricity Authority (PEA-Thailand) system, which is a single circuit single conductor transmission line, is studied. Fault signals in the transmission line were generated by the EMTP/ATPDraw software. Various factors such as fault location, type, and angle were considered. Then, fault signals were analyzed by coefficient details on the first scale of the discrete wavelet transform. Daubechies mother wavelet from MATLAB software was used to decompose the fault signal. The coefficient value of the mother wavelet behaved depending on the position, inception of fault angle, and fault type. AI methods including probabilistic neural networks (PNNs), back-propagation neural networks (BPNNs), and support vector machine (SVM) were used to identify faults. AI input used the maximum first peak coefficients of phase ABC and zero sequence. The results obtained from the study were found to be satisfactory with all AI methodologies having an average accuracy of more than 98% in the case study. However, the SVM technique can provide more accurate results than the PNN and BPNN techniques with less computation burden. Thus, it is suitable for being applied to actual protection systems.
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Comparison of various mother wavelets for fault classification in electrical systems
(2020-02-01)
Pothisarn, Chaichan
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Klomjit, Jittiphong
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Ngaopitakkul, Atthapol
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Jettanasen, Chaiyan
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Asfani, Dimas Anton
This paper presents a comparative study on mother wavelets using a fault type classification algorithm in a power system. The study aims to evaluate the performance of the protection algorithm by implementing different mother wavelets for signal analysis and determines a suitable mother wavelet for power system protection applications. The factors that influence the fault signal, such as the fault location, fault type, and inception angle, have been considered during testing. The algorithm operates by applying the discrete wavelet transform (DWT) to the three-phase current and zero-sequence signal obtained from the experimental setup. The DWT extracts high-frequency components from the signals during both the normal and fault states. The coefficients at scales 1-3 have been decomposed using different mother wavelets, such as Daubechies (db), symlets (sym), biorthogonal (bior), and Coiflets (coif). The results reveal different coefficient values for the different mother wavelets even though the behaviors are similar. The coefficient for any mother wavelet has the same behavior but does not have the same value. Therefore, this finding has shown that the mother wavelet has a significant impact on the accuracy of the fault classification algorithm.
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Fault classification on the hybrid transmission line system between overhead line and underground cable
(2017-08-30)
Klomjit, Jittiphong
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Ngaopitakkul, Atthapol
This paper illustrates fault classification on hybrid transmission line. Current signals were analyzed by coefficients of discrete wavelet transform (DWT). Daubechies4 (db4) is employed as mother wavelet to decompose high frequency components from fault signals. In this paper, ATP/EMTP is used to simulate fault signal from current signals. Hybrid system between overhead line and underground cable of 115 kV from Provincial Electricity Authority (PEA-Thailand) system in case single circuit single conductor with overhead and underground was used as simulation case study. Various factors such as location of fault, fault type and fault angle have been taken into consideration. DWT is then applied on phase current and zero sequence signals using MATLAB software in order to obtain coefficient in scale 1 for further analysis. This value is mainly used to design algorithm for fault classification. Result obtained from the study is satisfactory.
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Comparison of mother wavelet for classification fault on hybrid transmission line systems
(2017-07-01)
Klomjit, Jittiphong
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Ngaopitakkul, Atthapol
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Sreewirote, Bancha
This 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.
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Selection of proper input pattern in fuzzy logic algorithm for classifying the fault type in underground distribution system
(2017-02-08)
Klomjit, Jittiphong
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Ngaopitakkul, Atthapol
This paper present the proper input pattern of fuzzy logic algorithm for fault type classification in underground cable. The proposed algorithm using combination of discrete wavelet transform (DWT) and fuzzy logic. The DWT is applied to extract high frequency component from fault current waveform using mother wavelet daubechies4 (db4). The maximum coefficients detail of DWT and maximum ratio of DWT, obtained from phase A, B, C and zero sequence of fault current waveforms have been used as an input variables for decision algorithm. The obtained results in term of average accuracy have shown that the maximum ratio of DWT can achieved satisfactory accuracy in fault type classification.
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Discriminating between external short circuit and internal winding fault in power transformer using rbf neural networks
(2013-07-12)
Klomjit, Jittiphong
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Ngaopitakkul, Atthapol
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Jettanasen, Chaiyan
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Pothisarn, Chaichan
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Thongsuk, Surakit
In the literature for fault detection, several decision algorithms have been developed to be employed in the protective relay. In previous research works, the behaviour analysis of signals is performed using DWT. The results obtained from the analysis will be useful in the development of a detected fault scheme for power transformer in this paper. This paper proposes an algorithm based on a combination of discrete wavelet transform (DWT) and radial basis function neural network (RBFNN) for discriminating between external fault and internal winding fault of three-phase two-winding transformer. The DWT is employed for extracting the high frequency component contained in the post-fault differential current waveforms, and the coefficients of the first scale from the DWT that can detect fault are investigated as an input for the training pattern. Various cases studies based on Thailand electricity transmission and distribution systems have been investigated so that the algorithm can be implemented. Results show that the proposed technique is highly satisfactory.
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Behaviour of interturn fault in power transformer winding using high frequency components of discrete wavelet transform
(2012-12-01)
Klomjit, Jittiphong
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Ngaopitakkul, Atthapol
Behaviour of interturn winding fault signals in a three-phase two-winding transformer with delta connected primary and wye connected secondary, using high frequency components of DWT is proposed in this paper. The mother wavelet daubechies4 (db4) is employed to decompose high frequency components from signals. Various case studies have been done including the variation of fault inception angles, fault types, and fault locations. The result will be useful in the development of a fault detecting scheme for power transformer in the future. © 2012 IEEJ Industry Appl Soc.
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Discriminating among inrush current, external fault and internal fault in power transformer using low frequency components comparison of DWT
(2012-12-01)
Jettanasen, Chaiyan
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Pothisarn, Chaichan
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Klomjit, Jittiphong
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Ngaopitakkul, Atthapol
A technique using discrete wavelet transform (DWT) in order to discriminate among inrush current, internal fault, and external fault has been proposed. Daubechies4 (db4) is employed as mother wavelet in order to decompose low frequency components from fault signals. A ratio between per unit differential current and per unit time is calculated and performed as comparison indicator. The results obtained from the proposed technique have good accuracy to discriminating fault in the considered system. © 2012 IEEJ Industry Appl Soc.

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