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Optimal locations and capacities of multiple BESSs in a RES-integrated distribution network: a real-world case study
(2026-12-01)
Khunkitti, Sirote
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Wichitkrailat, Krit
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Siritaratiwat, Apirat
The global transition toward renewable energy sources (RESs) has introduced technical challenges in distribution networks, including voltage instability, increased power losses, and peak demand fluctuations. Battery Energy Storage Systems (BESSs) provide an effective solution through voltage regulation, loss minimization, and peak shaving. However, their effectiveness strongly depends on optimal location and capacity, and a single BESS may be insufficient for network with increasing RES penetration. This study proposes an optimization framework employing the crayfish optimization algorithm (COA) to determine the optimal locations and capacities of multiple BESSs within a distribution network integrated with RESs. The objective is to minimize the total system costs, including BESS investment and performance-related costs associated with voltage deviation, transmission loss, and peak power reductions. The proposed framework is applied to a real-world system, comprising 102 buses incorporating photovoltaic (PV) and biomass distributed generation. Three installation scenarios including one, two, and three BESS units are analyzed and compared against other optimization algorithms. The results confirm the optimal BESS locations and capacities found are technically feasible for real-world deployment. Moreover, COA consistently outperforms comparative methods, particularly in cost minimization and loss reduction. Notably, the two-BESS case yields the most balanced and cost-effective performance.
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Mining User Mobility Insights from Public Wi-Fi Data Using Association Rules in Urban Riverfront Areas
(2026-07-01)
Matarat, Korakot
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Surawanitkun, Chayada
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Wongsinlatam, Wullapa
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Remsungnen, Tawun
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Nokkaew, Manussawee
Understanding user mobility patterns is essential for effective urban planning and resource management. This study employs Association Rule Mining to analyze public Wi-Fi data collected from 11 access points along the Mekong River in Sri Chiang Mai, Thailand, from May 2022 to December 2023. By examining co-occurring movement patterns in over 73.7 million connection records, the research uncovers key insights into human behavior in the area. The findings highlight the area in front of the Fresh Market as a central destination, with confidence values exceeding 0.99 for related movement rules. The results reveal pronounced temporal variations in movement patterns, with transitions from commercial areas in the mornings to leisure spaces in the afternoons and evenings. Weekday patterns differ notably from weekend behaviors, reflecting how time influences urban space utilization. These insights provide urban planners and policymakers with data-driven evidence to optimize infrastructure development, enhance public spaces, and improve resource allocation. Although this study is limited to Wi-Fi data, it provides significant contributions to the development of smart cities that are more responsive and sustainable, paving the way for improved urban living experiences and more efficient resource management. Future work could integrate multiple data sources to enable more comprehensive mobility analysis and advance sustainable urban development goals.
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Aspect-Level Sentiment Analysis Using WangchanBERTa for Fine-Grained Service Insight Extraction in Hotel Reviews
(2026-06-01)
Suwan, Thanachok
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Nokkaew, Manussawee
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Surawanitkun, Chayada
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Sorn-In, Kanda
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Mueanrit, Nongram
Online booking site reviews substantially influence Thai SME hotel reputations and consumer decisions. Hotels should readily extract useful information from unstructured Thai-language ratings. WangchanBERTa, a Thai deep learning model, automates hotel sentiment analysis and strategic insight development in this study. System is two-stage. Phase 1 divides 10,040 Thai hotel reviews from Agoda, Booking.com, Traveloka, and Trip.com into good and negative attitudes and determines price, service quality, and cleanliness. Phase 2 extracts aspect-level information across 11 service characteristics to discover complex trends like consumers being satisfied with service but unhappy with cost. The sentiment categorization model performed well with 91.63% accuracy and 89.69% macro F1-score in experiments. The aspect-based sentiment analysis system achieved 91.63% accuracy, 91.55% macro precision, 91.63% recall, 91.52% F1-score, and real-world insight extraction. This methodology helps hoteliers listen to customers, integrate data into business ideas, and compete in Thailand’s tourism market.
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Fuzzy Analytical Hierarchy Process-Based Multi-Criteria Decision Framework for Risk-Informed Maintenance Prioritization of Distribution Transformers
(2026-01-01)
Rodkumnerd, Pannathon
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Pothinun, Thunpisit
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Phumpho, Suwilai
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Watson, Neville
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Siritaratiwat, Apirat
Effective asset management is crucial for improving the reliability, resilience, and cost efficiency of distribution networks throughout the asset life cycle. Distribution transformers are among the most critical components, as their failures can cause extensive service interruptions and substantial economic impacts. Therefore, robust and transparent maintenance prioritization strategies are essential, particularly for utilities managing several transformers. Traditional time-based maintenance, while simple to implement, often results in inefficient resource allocation. Condition-based maintenance provides a more effective alternative; however, its performance depends strongly on the reliability of indicator selection and weighting. This study proposes a systematic weighting framework for distribution transformer maintenance prioritization using a multi-criteria decision-making (MCDM) approach. Each transformer is evaluated across two dimensions, including health condition and operational impact, based on indicators identified from the literature and expert judgment. To address uncertainty and judgmental inconsistency, particularly when the consistency ratio (CR) exceeds the conventional threshold of 0.10, the Fuzzy Analytic Hierarchy Process (FAHP) is employed. Seven condition parameters characterize transformer health, while impact is quantified using five indicators reflecting failure consequences. The proposed framework offers a transparent, repeatable, and defensible decision-support tool, enabling utilities to prioritize maintenance actions, optimize resource allocation, and mitigate operational risks in distribution networks.
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Improving One-Day-Ahead Forecasting of Low-Latitude Amplitude Scintillation Using an Upsampling-Enhanced LSTM
(2026-01-01)
Muangkammuen, Patinya
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Suthisopapan, Puripong
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Tongkasem, Napat
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Supnithi, Pornchai
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Kruesubthaworn, Anan
The scintillation in radio wave propagation, particularly in regions near the magnetic equator, is found to be introduced by the ionospheric irregularities causing unsatisfactory performance in satellite-based applications. In order to mitigate this effect, we design a long short-term memory (LSTM) model to forecast amplitude scintillation at 1-min resolution. In addition, the upsampling-based feature preprocessing is introduced to improve forecasting performance, especially for short-term severe scintillation events. In terms of R$^{2}$, which is a popular forecast evaluation metric, our proposed model exhibits about 20% improvement over the same LSTM model without upsampling. Furthermore, although existing studies achieve good forecasting accuracy up to 4 h ahead, the proposed model sets a benchmark with one-day-ahead forecasting, but at the cost of longer training time due to upsampling.
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Label-Consistent Input Structuring Based on Second-Stage Sliding Window for PMSM Fault Diagnosis
(2026-01-01)
Nguyen, Thanh Son
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Khunkitti, Pirat
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Siritaratiwat, Apirat
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Seangwong, Pattasad
Accurate fault diagnosis of electrical machines is essential for operational reliability and safety. Handcrafted features remain attractive in practice because of their interpretability and computational efficiency. However, this traditional approach relies on individual feature vectors and is limited in preserving inherent temporal dynamics. To address this issue, a label-consistent input structuring based on the second-stage sliding window (SSSW) method is proposed. This approach retains the benefits of handcrafted features and arranges feature vectors into temporally coherent sequences while ensuring label consistency. A hyperparameter optimization scheme is incorporated with a long short-term memory classifier to reduce manual tuning. Performance evaluations demonstrate the robustness of the proposed SSSW method across diverse operating conditions and input signal configurations. These include single-phase currents, multiphase currents, vibration, and fused current-vibration signals. Notably, the proposed approach achieves classification accuracy of up to 100% and demonstrates stable learning behavior. Experimental verification on a laboratory-scale permanent magnet synchronous motor testbed further validates the proposed method under realistic measurement noise and interference conditions. Finally, combining handcrafted features with the proposed SSSW input structuring method provides a practical, scalable solution for reliable electrical machine fault diagnosis.
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A Self-Powered and Chemically Responsive Triboelectric Nanogenerator Based on Surface Protonation in SrO2Nanopowder/Graphene Oxide/epoxy Composite for pH Sensing
(2025-12-05)
Saengpoe, Prasert
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Supasai, Wisut
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Amorntep, Narong
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Nilnumpetch, Chatree
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Nokkaew, Manussawee
Practical implementation of triboelectric nanogenerators (TENGs) in autonomous systems is frequently impeded by their inadequate durability in chemically harsh environments. To address this limitation, we present a durable TENG utilizing a strontium dioxide nanopowders/graphene oxide/epoxy resin (SrO<inf>2</inf>NPOs/GO/ER) composite, positioning SrO<inf>2</inf>NPOs as an innovative, high-permittivity filler for triboelectric applications. By synergistically integrating the elevated dielectric constant of SrO<inf>2</inf>NPOs with the interfacial polarization of GO NPOs, our optimized composite achieves an outstanding output of approximately 136 V and 2.3 μA/cm2under a 100 N force, exceeding the performance of numerous advanced TENGs. Significantly, we convert a common degradation mechanism, i.e., surface protonation, into a functional sensing approach. The device leverages reversible protonation–deprotonation dynamics to convert environmental pH into distinct electrical signals, enabling self-powered, real-time pH sensing. The sensor exhibits excellent linearity (R2> 0.97) across three distinct operational regions (pH 1–12), demonstrating high sensitivity to acidity changes. The device has demonstrated remarkable durability, completing approximately 11,000 mechanical cycles. Also, the proposed device serves high chemical durability, maintaining stable performance (up to 6000 cycles) after 24 h immersion in neutral and alkaline solutions. Our work establishes a resilient, multifunctional platform that simultaneously harvests energy and senses its chemical surroundings by reframing protonation as a design principle. This breakthrough paves the way for next-generation TENGs for use in environmental monitoring, resilient IoT networks, and adaptive self-powered electronics that can function under conditions where the chemical environment changes.
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Automatic classification of spread‐F types in ionogram images using support vector machine and convolutional neural network
(2024-12-01)
Benchawattananon, Phongsachot
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Siritaratiwat, Apirat
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Supnithi, Pornchai
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Nishioka, Michi
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Perwitasari, Septi
An ionogram image serves as a valuable data for examining the ionospheric bottom side characteristics and variabilities. Spread-F is indicated or identified by plasma irregularity in the ionospheric region. Diffused echo in the ionogram images particularly pose challenges for efficient interpretation required in further applications. An automatic classification of spread-F is presented in this study. Ionogram images are automatically classified using preprocessing techniques to improve the classification performance. In this study, the classification is designed by two machine learning algorithms, including support vector machine (SVM) and convolutional neural network (CNN). The CNN model with preprocessing technique outperforms the SVM alternative based on 4,692 labelled ionogram images from the FMCW-type ionosonde at Chumphon station, Thailand. The model successfully classified clear, frequency spread-F (FSF), range spread-F (RSF), strong spread-F (SSF), and unidentified class with an accuracy of 98.0%, 85.1%, 90.7%, 66.7%, and 99.2%, respectively. The proposed automatic classification models achieved to classify classes of ionogram images. In addition, the image filtering and data preprocessing are useful with ionogram images for improving the model classification performance. Graphical Abstract: (Figure presented.)
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Enhancing Performance of Composite-Based Triboelectric Nanogenerators Through Laser Surface Patterning and Graphite Coating for Sustainable Energy Solutions
(2024-11-01)
Amorntep, Narong
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Siritaratiwat, Apirat
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Srichan, Chavis
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Sriphan, Saichon
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Wiangwiset, Thalerngsak
The performance of composite-based triboelectric nanogenerators (C–TENGs) was significantly enhanced through laser surface patterning and graphite coating. The laser etching process produced accurate and consistent patterns, increasing surface area and improving charge accumulation. SEM imagery confirmed the structural differences and enhanced surface properties of the laser-etched C–TENGs. Graphite fibers further augmented the contact surface area, enhancing charge accumulation and diffusion. Experimental results demonstrated that the optimized C–TENGs, especially those with line patterns and graphite coating, achieved a maximal 98.87 V open-circuit voltage (V<inf>OC</inf>) and a 0.10 µA/cm2 short-circuit current density (J<inf>SC</inf>) under a 20 N external force. Environmental tests revealed a slight decrease in performance with increased humidity, while long-term stability tests indicated consistent performance over three weeks. Practical application tests showed the potential of C–TENGs integrated into wearable devices, generating sufficient energy for low-power applications, thereby highlighting the promise of these devices for sustainable energy solutions.
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Simulation of magnetic footprints for heat assisted magnetic recording
(2017-05-01)
Pituso, Kotchakorn
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Khunkitti, Pirat
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Kruesubthaworn, Anan
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Chooruang, Komkrit
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Tongsomporn, Damrongsak
The heat assisted magnetic recording (HAMR) technology has been the promising candidate to overcome the thermal stability limitation at higher capacities of the hard disk drive. In this work, the characteristics of magnetic footprint of the medium written by HAMR were investigated through the micromagnetic simulations. The Voronoi granular media was firstly modeled, then the magnetic footprint technique was performed to observe the media behaviors at various linear densities. The results indicated that the pattern of magnetic footprint can be perverted at higher densities, which essentially causes a reduction of readback signal power. Also, the dependence of media grain size on the signal power shows that the larger grain size media can provide higher signal power at low linear density, while the smaller grain size media gives higher signal at high density. Thus, the magnetic footprint regarding the HAMR technology needs to be optimized to achieve the efficient recording system.

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