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"fault location"
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Application of machine learning methods in fault detection and classification of power transmission lines: a survey
The rising development of power systems and smart grids calls for advanced fault diagnosis techniques to prevent undesired interruptions and expenses. One of the most important part of such systems is transmission lines. This paper presents a survey on recent machine learning-based techniques for fault detection, classification, and location estimation in transmission lines. In order to provide reliable and resilient electrical power energy, faster and more accurate fault identification tools are required. Costly consequences of probable faults motivate the need for immediate actions to detect them using intelligent methods, especially emerging machine learning approaches that are powerful in solving diagnosis problems. This paper presents a comprehensive review of various machine learning methodologies including naive Bayesian classifier, decision tree, random forest, k-nearest neighbor, and support vector machine as well as artificial neural networks such as feedforward neural network, convolutional neural network, and adaptive neuro-fuzzy inference system that have been used to detect, classify, and locate faults in transmission lines.
Journal Article
Fault Location for Distribution Smart Grids: Literature Overview, Challenges, Solutions, and Future Trends
Thanks to smart grids, more intelligent devices may now be integrated into the electric grid, which increases the robustness and resilience of the system. The integration of distributed energy resources is expected to require extensive use of communication systems as well as a variety of interconnected technologies for monitoring, protection, and control. The fault location and diagnosis are essential for the security and well-coordinated operation of these systems since there is also greater risk and different paths for a fault or contingency in the system. Considering smart distribution systems, microgrids, and smart automation substations, a full investigation of fault location in SGs over the distribution domain is still not enough, and this study proposes to analyze the fault location issues and common types of power failures in most of their physical components and communication infrastructure. In addition, we explore several fault location techniques in the smart grid’s distribution sector as well as fault location methods recommended to improve resilience, which will aid readers in choosing methods for their own research. Finally, conclusions are given after discussing the trends in fault location and detection techniques.
Journal Article
Recognizing and responding to normalization of deviance
\"Contains guidelines to assist facilities in recognizing and addressing the phenomenon of normalization of deviation--Provides techniques for addressing normalized deviations and techniques to eliminate waste in all manufacturing processes--Describes methods for identifying normalized deviation as well as where to find deviations--Includes techniques to reduce operational normalization of deviance and to reduce organizational normalization of deviance; Market description: Process safety professionals in all areas of manufacturing; Process safety consultants; Chemical engineering students; Certified safety professionals\"-- Provided by publisher.
BOOK
A Review of Fault Diagnosing Methods in Power Transmission Systems
Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field.
Journal Article
Photovoltaic Array Fault Diagnosis and Localization Method Based on Modulated Photocurrent and Machine Learning
Photovoltaic arrays are exposed to outdoor conditions year-round, leading to degradation, cracks, open circuits, and other faults. Hence, the establishment of an effective fault diagnosis system for photovoltaic arrays is of paramount importance. However, existing fault diagnosis methods often trade off between high accuracy and localization. To address this concern, this paper proposes a fault identification and localization approach for photovoltaic arrays based on modulated photocurrent and machine learning. By irradiating different frequency-modulated light, this method separates photocurrent and directly measures the photoelectric conversion efficiency of each panel, achieving both high accuracy and localization. Through machine learning classification algorithms, the current amplitude and frequency of each photovoltaic panel are identified to achieve fault identification and localization. Compared to other methods, the strengths of this method lie in its ability to achieve high-speed and high-accuracy fault identification and localization by measuring only the short-circuit current. Additionally, the equipment cost is low. The feasibility of the proposed method is demonstrated through practical experimentation. It is determined that when utilizing a neural network algorithm, the fault identification speed meets measurement requirements (5800 obs/s), and the fault diagnosis accuracy is optimal (97.8%).
Journal Article
SR-GNN Based Fault Classification and Location in Power Distribution Network
Accurately evaluating the fault type and location is important for ensuring the reliability of the power distribution network. A mushrooming number of distributed generations (DGs) connected to the distribution system brings challenges to traditional fault classification and location methods. Novel AI-based methods are mostly based on wide area measurement with the assistance of intelligent devices, whose economic cost is somewhat high. This paper develops a super-resolution (SR) and graph neural network (GNN) based method for fault classification and location in the power distribution network. It can accurately evaluate the fault type and location only by obtaining the measurements of some key buses in the distribution network, which reduces the construction cost of the distribution system. The IEEE 37 Bus system is used for testing the proposed method and verifying its effectiveness. In addition, further experiments show that the proposed method has a certain anti-noise capability and is robust to fault resistance change, distribution network reconfiguration, and distributed power access.
Journal Article
Large language models for prognostic analysis in mechanical fault diagnosis
With the in-depth development of industrial intelligence, as the core basic component of high-end equipment, the fault diagnosis and health management of rotating machinery has become a key link to ensure the reliability of complex systems. Although the intelligent diagnosis technology based on mechanical vibration signals has made remarkable progress, in complex mechanical systems, it is difficult to comprehensively cover the fault feature space using vibration signal data only.This paper proposes an intelligent diagnosis framework based on a large language model. By empowering the large language model through multimodal data feature fusion and constructing a ternary data system of “raw vibration signals - time-frequency spectrum features - fault knowledge text”, the framework realizes cross-modal joint representation of mechanical fault features and breaks through the bottlenecks of traditional methods, such as insufficient feature extraction capability under complex working conditions and limited cross-scenario generalization. The framework innovatively integrates the deep semantic understanding ability of pre-trained large language models with mechanical fault mechanisms. Through the method of plugging in principle knowledge bases, the model can not only output fault location results but also simultaneously generate interpretable reports including fault cause analysis and maintenance strategy suggestions.The model proposed in this paper has been strictly tested on bearing datasets. Experimental results demonstrate that the model exhibits excellent performance and adaptability in different industrial scenarios.
Journal Article
Research on Distribution Network Fault Location Based on Electric Field Coupling Voltage Sensing and Multi-Source Information Fusion
As the last link of power transmission, the safe operation of the distribution network directly affects the experience of power users, and short-time distribution network faults can cause huge economic losses. There are few fault recording devices in rural or suburban distribution networks, and it is difficult to upload information, which brings difficulties to accurate fault location. In order to improve the accuracy of fault location, this study proposes a fault location method for distribution networks based on electric field-coupled voltage sensing and multi-source information fusion. First, an optimized resource pool architecture is proposed, and a distribution network data fusion platform is established based on this architecture to effectively integrate voltage, current and other fault data. Second, in order to overcome the problem of expanding the fault location range that may be caused by the current-based matrix algorithm, this study proposes an improved directed graph-based matrix algorithm and combines it with the matrix algorithm of voltage quantities to form a joint location criterion, which improves the accuracy of fault location. Finally, for the single-ended ranging method, which is easily affected by the wave impedance discontinuity points in the system or the transition resistance in the line, this article introduces a fault ranging algorithm based on double-ended electrical quantities, which improves the accuracy and applicable range of fault ranging. Through simulation verification, we found that the matrix algorithm based on the electrical quantity can accurately locate the fault section in the case of a single fault with a single power supply. The proposed joint matrix algorithm can accurately locate the fault section in the case of a single fault with multiple power sources. The ranging algorithm based on double-ended electrical quantities has higher ranging accuracy in both interphase short circuits and grounded short circuits, and the ranging results are not affected by the fault type, fault location and transition resistance, which can effectively improve the efficiency and reliability of fault location.
Journal Article