Internal and external faults in a power transformer are discriminated in this paper using an algorithm based on a combination of a discrete wavelet transform (DWT) and a probabilistic neural network (PNN). DWT decomposes high-frequency fault components using the maximum coefficients of a ¼ cycle DWT as input patterns for the training process in a decision algorithm. A division algorithm between a zero sequence of post-fault differential current waveforms and the differential current coefficient in the ¼ cycle DWT is used to detect the maximum ratio and faults. The simulation system uses various study cases based on Thailand’s electricity transmission and distribution systems. The simulation results demonstrated that the PNN and BPNN are effectively implemented and perform fault detection with satisfactory accuracy. However, the PNN method is most suitable for detecting internal and external faults, and the maximum coefficient algorithm is the most effective in detecting the fault. This study will be useful in differential protection for power transformers.

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 $dq$ 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.

To enhance efficient and sustainable energy usage in street lighting systems, a nano-grid infrastructure comprising an energy harvesting, storage, and management system is integrated. This paper investigated the feasibility in terms of energy production and economic evaluation of using various energy harvesting for photovoltaic, piezoelectric, and wind energy in a nano-grid street lighting system. The photovoltaic system was evaluated based on the factors of annual actual solar radiation, power losses, and system performance using the PVsyst software. The piezoelectric energy production was studied and designed. Optimal piezoelectric installation for maximum power generation was analyzed in terms of deformation and stress using ANSYS software. For wind power generation, the wind turbine characteristics, along with its location, were designed to optimize power output using computational fluid dynamic simulations in ANSYS software. After that, economic evaluation for the proposed energy harvesting systems for nano-grid street lighting system are analyzed and compared in terms of DPP, NPV, IRR, and LCOE. In addition, the optimization of using PV, a wind system, a hybrid PV—wind system for nano-grid street lighting systems was conducted using HOMER Pro software. The results indicated that generating power through PV, piezoelectric, and wind energy was feasible. However, economic evaluation unveiled the infeasibility of employing piezoelectric and wind energy systems due to their elevated investment costs relative to their power generation capabilities. The dynamics of power generation from PV and wind systems, along with street lighting consumption, significantly impacted the dimensions of energy harvesting and storage systems, as well as their economic feasibility. The hybrid PV-wind system exhibited strong economic feasibility.

Energy consumption in buildings has increased significantly as population and economic activities are concentrated in urban areas. Air conditioning accounts for a significant percentage of energy consumption in buildings, especially in tropical climates. The main area where heat can be transferred into the building is through glass windows. Thus, this study aims to evaluate feasibility in terms of overall thermal transfer value (OTTV), energy, and economics for retrofitting different glass materials in an office building in Thailand by using building energy code (BEC) software. The software uses Thailand’s building energy code as the standard to evaluate the energy performance of the case study building in comparison with different glass types used in retrofitted cases. From an economic perspective, the internal rate of return (IRR) and discounted payback periods (DPP) were used as determining indexes. The results demonstrated that retrofitted windows with the best energy-efficient glass might achieve energy performance, but installation cost can reduce economic feasibility, while the glass with the second lowest heat transfer coefficient can reduce the OTTV by 68.89% and building energy consumption by 16.87%. However, it can achieve the highest economic performance with 10.70% IRR and DPP at 11.83 years. Therefore, the balance between energy and economic factors must be considered to provide energy-efficient and investment-friendly glass materials for retrofit projects. In addition, the study focuses specifically on tropical climates. Thus, the finding may not be reflected similarly for buildings located in different regions.

In this study, an adaptive state-of-charge (SOC) boundary strategy (ASBS) is proposed that dynamically adjusts the admissible upper and lower SOC limits of second-life lithium-ion batteries in off-grid photovoltaic battery energy storage systems (PV-BESSs) based on real-time state of health (SOH) and temperature feedback. The strategy is formulated using a unified electrical–thermal–aging model with an online state estimator and ensures both electrical safety and power feasibility while remaining fully compatible with standard energy management functions. Two representative simulations—a single-day operating profile and a continuous thirty-day sequence—demonstrate the effectiveness of the ASBS. In the twenty-four-hour case, the duration spent in high state-of-charge conditions is reduced by approximately 0.30–0.50 h, the abrupt end-of-charging transition is eliminated, and the temperature rise is slightly moderated, all without any loss of energy supply. Over thirty days, the difference between the ASBS and a fixed state-of-charge window remains effectively zero for almost all hours, with only a brief midday deviation of −4 to −5 percentage points and no cumulative drift. Indicators of electrical and thermal stress improve substantially, including an approximate 70% reduction in the root mean square charging current. These results confirm that the ASBS provides a practical and non-intrusive means of mitigating stress on second-life lithium-ion batteries while preserving full energy autonomy in off-grid photovoltaic systems.

Energy consumption and environmental issues have become major drivers of increasing renewable energy penetration levels. The electricity generated from renewable energy sources is decentralized throughout distributed generation (DG), which is located at the distribution level. However, the presence of DG can change distribution system characteristics and affect protection systems. Thus, this study aims to investigate the impact of DG in term of its sizing and placement on distribution systems under both normal and fault conditions. In addition, the effects on voltage improvement under normal conditions and current under fault conditions are also considered. The case study system in this study was modelled after an actual section of a 22 kV distribution line from the Provincial Electricity Authority of Thailand using PSCAD software. For DG, wind turbine generation was selected as a renewable energy source. The simulation results demonstrated that the presence of DG has a significant impact on both voltage and current characteristics under both normal and fault conditions. These impacts on the distribution system caused by DG can affect the operation of conventional distribution systems, which require further analysis and preventive measures in order to ensure good system reliability.