KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Search Results

Now showing 1 - 10 of 92

Some of the metrics are blocked by your
consent settings
Polarity-Dependent DC Dielectric Behavior of Virgin XLPO, XLPE, and PVC Cable Insulations
(2025-10-01)
Ruangwong, Khomsan
;
Pattanadech, Norasage
;
Pannil, Pittaya
Reliable DC cable insulation is crucial for photovoltaic (PV) systems and high-voltage DC (HVDC) networks. However, conventional materials such as cross-linked polyethylene (XLPE) and polyvinyl chloride (PVC) face challenges under prolonged DC stress—notably space charge buildup, dielectric losses, and thermal aging. Cross-linked polyolefin (XLPO) has emerged as a halogen-free, thermally stable alternative, but its comparative DC performance remains underreported. Methods: We evaluated the insulations of virgin XLPO, XLPE, and PVC PV cables under ±1 kV DC using time-domain indices (IR, DAR, PI, Loss Index), supported by MATLAB and FTIR. Multi-layer cable geometries were modeled in MATLAB to simulate radial electric field distribution, and Fourier-transform infrared (FTIR) spectroscopy was employed to reveal polymer chemistry and functional groups. Results: XLPO exhibited an IR on the order of 108–109 Ω, and XLPE (IR ~ 108 Ω) and PVC (IR ~ 107 Ω, LI ≥ 1) at 60 s, with favorable polarization indices under both polarities. Notably, they showed high insulation resistance and low-to-moderate loss indices (≈1.3–1.5) under both polarities, indicating controlled relaxation with limited conduction contribution. XLPE showed good initial insulation resistance but revealed polarity-dependent relaxation and higher loss (especially under positive bias) due to trap-forming cross-linking byproducts. PVC had the lowest resistance (GΩ-range) and near-unit DAR/PI, dominated by leakage conduction and dielectric losses. Simulations confirmed a uniform electric field in XLPO insulation with no polarity asymmetry, while FTIR spectra linked XLPO’s low polarity and PVC’s chlorine content to their electrical behavior. Conclusions: XLPO outperforms XLPE and PVC in resisting DC leakage, charge trapping, and thermal stress, underscoring its suitability for long-term PV and HVDC applications. This study provides a comprehensive structure–property understanding to guide the selection of advanced, polarity-resilient cable insulation materials.
Some of the metrics are blocked by your
consent settings
SOH Estimation Model Based on an Ensemble Hierarchical Extreme Learning Machine
(2025-05-01)
He, Yu
;
Pattanadech, Norasage
;
Sukemoke, Kasian
;
Chen, Lin
;
Li, Lulu
This paper addresses the challenges of accurately estimating the state of health (SOH) of retired batteries, where factors such as limited historical data, non-linear degradation, and unstable parameters complicate the process. We propose a novel SOH estimation model based on an Integrated Hierarchical Extreme Learning Machine (I-HELM). The model minimizes reliance on historical data and reduces computational complexity by introducing health indicators derived from constant charging time and charging current area. The hierarchical structure of the Extreme Learning Machine (HELM) effectively captures the non-linear relationship between health indicators and battery capacity, improving estimation accuracy and learning efficiency. Additionally, integrating multiple HELM models enhances the stability and robustness of the results, making the approach more reliable across varying operational conditions. The proposed model is validated on experimental datasets collected from two Samsung battery packs, four Samsung single cells, and two Panasonic retired batteries under both constant-current and dynamic conditions. Experimental results demonstrate the superior performance of the model: the maximum error for Samsung battery cells and packs does not exceed 2.2% and 2.6%, respectively, with root mean square errors (RMSEs) below 1%. For Panasonic retired batteries, the maximum error remains under 3%.
Some of the metrics are blocked by your
consent settings
The State of Health Estimation of Retired Lithium-Ion Batteries Using a Multi-Input Metabolic Gated Recurrent Unit
(2025-03-01)
He, Yu
;
Pattanadech, Norasage
;
Sukemoke, Kasiean
;
Pan, Minling
;
Chen, Lin
With the increasing adoption of lithium-ion batteries in energy storage systems, accurately monitoring the State of Health (SoH) of retired batteries has become a pivotal technology for ensuring their safe utilization and maximizing their economic value. In response to this need, this paper presents a highly efficient estimation model based on the multi-input metabolic gated recurrent unit (MM-GRU). The model leverages constant-current charging time, charging current area, and the 1800 s voltage drop as input features and dynamically updates these features through a metabolic mechanism. It requires only four cycles of historical data to reliably predict the SoH of subsequent cycles. Experimental validation conducted on retired Samsung and Panasonic battery cells and packs under constant-current and dynamic operating conditions demonstrates that the MM-GRU model effectively tracks SoH degradation trajectories, achieving a root mean square error of less than 1.2% and a mean absolute error of less than 1%. Compared to traditional machine learning algorithms such as SVM, BPNN, and GRU, the MM-GRU model delivers superior estimation accuracy and generalization performance. The findings suggest that the MM-GRU model not only significantly enhances the breadth and precision of SoH monitoring for retired batteries but also offers robust technical support for their safe deployment and asset optimization in energy storage systems.
Some of the metrics are blocked by your
consent settings
Experiment of Dielectric Frequency Response for Aged ZnO Surge Arrester
(2025-01-01)
Aiamrungruang, Phitthaya
;
Methavithit, Winai
;
Pannil, Pittaya
;
Chuayin, Chaitawat
;
Zinck, Matthieu
Condition-based monitoring of surge arresters has gained increasing attention in modern power system asset management, especially for medium-voltage equipment. This paper presents an investigation into the dielectric frequency response (DFR) analysis technique as a diagnostic method to evaluate aging and unhealthy conditions in 21-kV metal oxide surge arresters (MOSA). Four types of simulated defects, excessive number of lightning current stress, cracking, moisture ingress, and tracking, were introduced to represent common field failures. The DFR technique enables frequencydomain interpretation of dielectric properties, providing insight into insulation behavior over a wide frequency range. The test results show a significant variation in capacitance and dissipation factor corresponding to different defect types. Among these, moisture ingress exhibited the most severe deterioration. The findings suggest that DFR is a viable offline diagnostic method that can support preventive maintenance strategies for high-voltage surge arresters.
Some of the metrics are blocked by your
consent settings
Development of a Health Index for Transformer Condition Assessment Using an Online Dashboard
(2025-01-01)
Sripatawasumadee, Kornchet
;
Maneerat, Noppadol
;
Dechothamsathit, Vorawut
;
Kingkham, Sukanya
;
Jeenmuang, Siwakorn
Power transformers are critical components in electrical power systems in the transmission and distribution of electricity. Keeping them in good working condition is necessary to prevent power outages and ensure system stability. Traditional maintenance strategies, such as time-based maintenance, may not always be the best way to avoid problems or use resources efficiently. To improve this, the concept of a Health Index (HI) has been introduced as a systematic method for transformer condition assessment. By integrating various diagnostic data - such as load data, oil analysis, maintenance record, and on-load tap changer (OLTC) parts - into a unified HI score, asset managers can make informed decisions regarding maintenance prioritization and replacement planning. Furthermore, this paper proposes the development of an online dashboard system that facilitates the input, storage, and visualization of transformer health data. This user-friendly platform enables engineers to monitor condition trends in real time and supports strategic maintenance scheduling. The integration of Health Index evaluation with an accessible digital interface enhances the efficiency, transparency, and reliability of transformer asset management practices.
Some of the metrics are blocked by your
consent settings
The Experiment of Partial Discharge testing for Low Voltage Motor
(2025-01-01)
Sawangsri, Jaturaphat
;
Wiangtong, Theerayod
;
Pathanrat, Khomsan
;
Ruangwong, Khomsan
;
Jeenmuang, Siwakorn
Low-voltage (LV) motors are widely used and highly popular in industrial settings due to their critical role in production processes. Any malfunction or failure of an LV motor can lead to production downtime, resulting in reduced output and potential revenue loss. Therefore, it is essential to conduct diagnostic assessments that evaluate the reliability of the insulation system in LV motors. Partial Discharge (PD) testing is recognized as a highly effective and reliable method for assessing the condition of motor insulation. This paper presents PD testing on LV motors to investigate the behavior of partial discharges using multiple analysis techniques. Additionally, an acoustic camera is integrated into the testing process to accurately identify the discharge locations.
Some of the metrics are blocked by your
consent settings
Polarity-Dependent Dielectric Behavior of XLPO-Insulated PV Cables Under High-Voltage AC and DC Stress: Leakage Current and PRPD Analysis
(2025-01-01)
Ruangwong, Khomsan
;
Pattanadech, Norasage
;
Pannil, Pittaya
This study investigates the dielectric behavior of cross-linked halogen-free polyolefin (XLPO) insulation in H1Z2Z2-K photovoltaic cables under a direct current (DC) stress of ± 999.6 V. The results indicate significant polarity-dependent responses, with insulation resistance (IR) rising from 1.61 T Ω under negative DC to 2.30 T Ω under positive DC. Furthermore, the polarization index (PI) and dielectric absorption ratio (DAR) improve from 6.73 and 2.14 to 12.78 and 2.43, respectively. The Loss Index, obtained from polarization-depolarization current (PDC) analysis, increases from 0.935 to 0.991, suggesting enhanced dielectric stability with positive bias. Additionally, the partial discharge inception and extinction voltages (PDIV and PDEV) are recorded at 8.3 kV for DC and 1.93 kV for AC, exhibiting symmetrical behavior that indicates corona and surface discharge dominance and minimal space charge retention. These findings highlight the importance of considering space charge dynamics and polarity-aware diagnostics in evaluating non-certified XLPO-insulated cables in photovoltaic applications.
Some of the metrics are blocked by your
consent settings
Overload Duration Analysis of Mineral Oil and Natural Ester in Retrofilled Distribution Transformers
(2025-01-01)
Pongpitak, Siriwut
;
Pannil, Pittaya
;
Pattanadech, Norasage
Natural ester fluids such as FR3 provide higher fire safety, better biodegradability, and extended insulation life compared with mineral oil. Retrofilling existing transformers with FR3 offers a practical means to enhance safety and environmental performance without redesigning the unit. This study evaluates the thermal performance and overload endurance capability of a 160 kVA distribution transformer before and after retrofilling from mineral oil to FR3. Results show that steady-state temperature rises increased modestly after retrofilling (+1.3-4 K for top oil and windings) but remained within IEC limits. Under 1.5 p.u. normal cyclic loading, the mineral-oil unit reached its 120 °C hot-spot limit after 5 hours and 20 minutes, whereas the FR3 unit stabilized near 126 °C and did not reach its 140 °C limit within 10 hours. Under 1.8 p.u. long-time emergency loading, FR3 extended the time-to-limit to 5 hours and 20 minutes, compared with 4 hours and 30 minutes for mineral oil (18.5% increase). Overall, FR3 trades slightly higher temperature rises for a wider operational safety envelope and longer overload endurance, supporting safer peak-shaving and contingency operations in retrofilled assets. These findings provide actionable insights for utilities considering FR3 retrofilling as a cost-effective transformer upgrade strategy.
Some of the metrics are blocked by your
consent settings
Dielectric Frequency Response Analysis of 230 kV Service Aged OIP and RIP Bushings: Case Studies
(2025-01-01)
Pattanadech, Norasage
;
Jeenmuang, Siwakorn
;
Ostria, Elmer
;
Suksagoolpanya, Suthat
Transformer bushings are critical components that enable current to pass through the transformer enclosure while providing insulation between the high-voltage conductor and the grounded housing. A failure in the bushing could potentially result in transformer failure. This paper presents case studies on the evaluation of 230 kV Oil-Impregnated Paper (OIP) and 230 kV Resin-Impregnated Paper (RIP) bushings using Dielectric Frequency Response (DFR) analysis. The bushings investigated in this study were service-aged units removed from the field. For the 230 kV OIP bushing, the paper insulation was peeled off starting from the outermost layer. This allowed for DFR testing on individual insulation sections. The results indicated moisture absorption and provided information on the moisture distribution within the paper insulation. Furthermore, a 230 kV RIP bushing suspected of having poor contact between the outermost foil layer and the test tap connection was also investigated. The DFR results from this case revealed the observable impact of such connection issues on the DFR test. In summary, DFR analysis has proved that it is a very useful technique for testing and diagnosing various common bushing degradation mechanisms that can lead to severe failure of the bushings and the transformers.
Some of the metrics are blocked by your
consent settings
Power transformer fault warning combining support vector machine and improved grey wolf optimization algorithm
(2025-01-01)
Zhao, Shuzong
;
Pattanadech, Norasage
To optimize the parameter setting of the support vector machine and improve the classification performance and computational efficiency of power transformer fault diagnosis, this study proposes an improved grey wolf optimization algorithm. By optimizing the global search and local optimization capabilities of the grey wolf algorithm and combining them with stacked denoising autoencoders, a new power transformer fault warning model is constructed. Firstly, the grey wolf optimization algorithm is optimized through four strategies: elite reverse learning, nonlinear control parameters, Lévy flight, and particle swarm optimization, which improve its global search and local optimization capabilities. Secondly, the stacked denoising autoencoder is utilized to extract high-level features of fault data, and the improved GWO algorithm and SVM are combined to complete fault classification. The results indicated that the proposed diagnostic model achieved a diagnostic accuracy of 0.979, a recall rate of 0.986, and an F1 value of 0.983 in benchmark performance testing. In practical applications, the average fault diagnosis accuracy of this model could reach up to 99.21%, and the average diagnosis time was only 0.08 s. The developed power transformer fault warning model can provide an efficient and reliable technical solution for fault diagnosis in the power system.

KMITL

Browse

Filters

Settings

Sort By

Results per page

Search Results