k-NN based fault detection and classification methods for power transmission systems

Majd, Aida Asadi; Samet, Haidar; Ghanbari, Teymoor

doi:10.1186/s41601-017-0063-z

Cited by 41 publications

(8 citation statements)

References 30 publications

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“…Furthermore, a similar study made it possible to detect and classify all the possible faults of a six-phase transmission system [100]. Two new KNN-based diagnostic methods for classifying eight types of faults of a power transmission system yielded a success rate of 98% of detected faults [101]. Furthermore, Madeti et al proposed the KNN model to detect and classify bypass diode, line-to-line and open-circuit faults in a PV system.…”

Section: K-nearest Neighbor Algorithm (Knn)mentioning

confidence: 99%

A Comprehensive Review of Supervised Learning Algorithms for the Diagnosis of Photovoltaic Systems, Proposing a New Approach Using an Ensemble Learning Algorithm

Toche Tchio,

Kenfack,

Kassegne

et al. 2024

Applied Sciences

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Photovoltaic systems are prone to breaking down due to harsh conditions. To improve the reliability of these systems, diagnostic methods using Machine Learning (ML) have been developed. However, many publications only focus on specific AI models without disclosing the type of learning used. In this article, we propose a supervised learning algorithm that can detect and classify PV system defects. We delve into the world of supervised learning-based machine learning and its application in detecting and classifying defects in photovoltaic (PV) systems. We explore the various types of faults that can occur in a PV system and provide a concise overview of the most commonly used machine learning and supervised learning techniques in diagnosing such systems. Additionally, we introduce a novel classifier known as Extra Trees or Extremely Randomized Trees as a speedy diagnostic approach for PV systems. Although this algorithm has not yet been explored in the realm of fault detection and classification for photovoltaic installations, it is highly recommended due to its remarkable precision, minimal variance, and efficient processing. The purpose of this article is to assist technicians, engineers, and researchers in identifying typical faults that are responsible for PV system failures, as well as creating effective control and supervision techniques that can minimize breakdowns and ensure the longevity of installed systems.

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Section: K-nearest Neighbor Algorithm (Knn)mentioning

confidence: 99%

A Comprehensive Review of Supervised Learning Algorithms for the Diagnosis of Photovoltaic Systems, Proposing a New Approach Using an Ensemble Learning Algorithm

Toche Tchio,

Kenfack,

Kassegne

et al. 2024

Applied Sciences

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“…Abed and AlRikabi [8] who presented a conference paper in 2021 focused on the detection of faults in underground cables as transmission lines, used IoT applications to monitor and detect underground cable faults. In the work done by Majd et al [16], the protection and control of power systems were investigated. In their work, a technique for the detection of transmission line faults was presented.…”

Section: A Literature Surveysmentioning

confidence: 99%

Detection of power transmission lines faults based on voltages and currents values using K-nearest neighbors

Abed

Al-Yasriy

et al. 2023

IJPEDS

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The critical factors to consider when implementing a maintenance plan for energy transmission lines are, accuracy, speed, and time, because of the increased global demand for electricity power caused by rapid development, and overuse of electric power transmission lines (both underground cables and overhead transmission lines), which in turn reduces the efficiency of the lines. Consequently, the efficiency of the lines may be reduced as a result of overuse or other activities like excavation that may have tampered with the cables. Thus, it becomes important to investigate the faults to which the lines are exposed. To this end, this article focuses on the detection of fault in transmission lines through the use of k-nearest neighbor algorithm. Using this algorithm, the characteristics were obtained (voltage, current), and these characteristics enable the identification of faults in the transmission lines, and in the specific location (the entire system, phase B, and phase A). The benefits that can be derived from the use of this algorithm include time, accuracy, speed, which are the requirements for the maintenance of transmission lines. Euclidean distance used in the application of the k-nearest neighbor technique for weights, and K = 3 for number of neighbors. The dataset was split into two parts, 70% training set and 30% testing set.

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“…This model uses a machine learning algorithm for classification using the Bayes theorem. This classifier assumes strong independence between the features [8] kNN KNN does not rely on any specific assumptions, earning its reputation as a non-parametric algorithm [9] J48 This algorithm results from improving and implementing the C4.5 algorithm [10] SVM SVM is another famous ML algorithm that forms a hyperplane to classify data [11] Random Forest…”

Section: Naive Bayesmentioning

confidence: 99%

Transfer Learning Based Fault Detection for Suspension System Using Vibrational Analysis and Radar Plots

et al. 2023

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The suspension system is of paramount importance in any automobile. Thanks to the suspension system, every journey benefits from pleasant rides, stable driving and precise handling. However, the suspension system is prone to faults that can significantly impact the driving quality of the vehicle. This makes it essential to find and diagnose any faults in the suspension system and rectify them immediately. Numerous techniques have been used to identify and diagnose suspension faults, each with drawbacks. This paper’s proposed suspension fault detection system aims to detect these faults using deep transfer learning techniques instead of the time-consuming and expensive conventional methods. This paper used pre-trained networks such as Alex Net, ResNet-50, Google Net and VGG16 to identify the faults using radar plots of the vibration signals generated by the suspension system in eight cases. The vibration data were acquired using an accelerometer and data acquisition system placed on a test rig for eight different test conditions (seven faulty, one good). The deep learning model with the highest accuracy in identifying and detecting faults among the four models was chosen and adopted to find defects. The results state that VGG16 produced the highest classification accuracy of 96.70%.

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k-NN based fault detection and classification methods for power transmission systems

Cited by 41 publications

References 30 publications

A Comprehensive Review of Supervised Learning Algorithms for the Diagnosis of Photovoltaic Systems, Proposing a New Approach Using an Ensemble Learning Algorithm

A Comprehensive Review of Supervised Learning Algorithms for the Diagnosis of Photovoltaic Systems, Proposing a New Approach Using an Ensemble Learning Algorithm

Detection of power transmission lines faults based on voltages and currents values using K-nearest neighbors

Transfer Learning Based Fault Detection for Suspension System Using Vibrational Analysis and Radar Plots

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