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Dual-stage Classification Framework for Detecting Rare or Unseen Patterns Based on Novelty Detection and Supervised Learning
(2024-01-01)
Rungtalay, Natthakritta
;
Kaitwanidvilai, Somyot
In this article, we propose a dual-stage classification framework designed for identifying rare or unseen patterns in the hard disk drive (HDD) industrial test process. The proposed framework integrates novelty detection and supervised learning methodologies to effectively address the challenges associated with imbalanced datasets and the detection of infrequent or unseen patterns within continuously changing environments. By employing novelty detection as the first-stage classifier followed by supervised learning as the second-stage classifier, the proposed method demonstrates an increased capacity to adapt to fluctuating environments, consequently enhancing the overall accuracy of process classification in practical manufacturing settings. To strengthen the robustness of novelty detection methods, an ensemble model technique is employed. Notably, the accuracy of the novelty detection methods in the first stage can be further enhanced with the incorporation of supervised learning techniques, particularly when a sufficiently large number of labeled samples are amassed. The proposed method consistently maintains accuracy, even in the face of changing environments, as it demonstrates the ability to adapt to data drift without necessitating the acquisition of new labeled data in the initial stage. This adaptability makes it particularly well suited for managing imbalanced datasets, rendering it highly practical for industrial applications. In a comprehensive case study conducted within the HDD industry, the framework exhibits immediate adaptability to rapidly changing environments while preserving high accuracy. This highlights the practical effectiveness of the proposed dual-stage classification framework in addressing the unique challenges posed by industrial scenarios.
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Comparative Fault Detection Between DWT and STFT in Overcurrent Relays
(2024-01-01)
Chiradeja, Pathomthat
;
Ananwattanaporn, Santipont
;
Lertwanitrot, Praikanok
;
Ngaopitakkul, Atthapol
;
Kunakorn, Anantawat
This study proposes a protection relay using a microcontroller to detect and classify faults in transmission lines based on the Wavelet transform. An experimental model was constructed from an actual 115 kV transmission system prototype. The current signal was observed based on the fault type, phase, and position. Clark's transform and the discrete Wavelet transform (DWT) were applied to transform signals for analysis. The positive and zero sequences obtained from Clark's transform were used for fault detection and fault classification, respectively. Moreover, the performances between the DWT and the short-time Fourier Transform (STFT) were compared in terms of accuracy and processing time. In addition, the double-detection technique was used to confirm the accuracy of fault detection. Results show that the proposed method is efficient for fault detection and classification. This finding allows the researcher to choose the appropriate analytical method. Moreover, it can also be used as the basis for overcurrent relay algorithm design in the effort to develop more advanced technologies.
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Fault Detection in Transmission Lines Using CNN
(2024-01-01)
Kanwal, Shazia
;
Jiriwibhakorn, Somchat
Transmission line faults pose a significant risk to power systems, potentially leading to widespread outages. Detecting these faults using advanced algorithms is crucial for preventing major disruptions in power supply. In this paper, we work on a fault detection technique for the IEEE 9-bus system based on deep learning. By training a Convolutional Neural Network (CNN) on features extracted from both normal and faulty conditions, we achieve an accuracy of 86%. This high accuracy underscores the potential of CNNs for real-world implementation in fault detection systems. The robustness of the CNN approach suggests its viability for deployment in complex, real-time systems, offering improved reliability and resilience against transmission line faults. Additionally, utilizing deep learning techniques opens avenues for further refinement and optimization of fault detection strategies in the power grid.
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Fault Detection and Predictive Maintenance Paradigm for Solar PV Systems
(2024-01-01)
Limpaporn, Poompat
;
Khomfoi, Surin
This research paper introduces the Box Plot model for the fault detection and degradation analysis in solar photovoltaic (PV) systems. The suggested method contains a mathematics equation and systematic flowchart algorithm to detect and categorize various fault types, such as direct current (DC) ground fault, DC overvoltage fault, and partial shading. The technique evaluates and monitors the performance and degradation of PV panel by processing comprehensive input data such as voltage, current, irradiance, temperature, power, solar PV systems related data and PV panel parameter data. The Box Plot model efficiently monitors fault analysis and degradation detection, adapting to various operational conditions with accuracy, resulting in a powerful tool for assessing the reliability and performance of solar PV systems. The Box Plot model is tested by using the three sets of the solar PV power plant collected data from the PV panel strings in the same array that nearby each other and verifying the Box Plot model effectiveness by comparing the model analysis result with the solar PV power plant operation and maintenance report. This methodology makes a major contribution to predictive maintenance operations and ensuring more reliability for the PV systems.

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