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

Vehicle level health assessment through integrated operational scalable prognostic reasoners

Today’s aircraft are very complex in design and need constant monitoring of the systems to establish the overall health status. Integrated Vehicle Health Management (IVHM) is a major component in a new future asset management paradigm where a conscious effort is made to shift asset maintenance from a scheduled based approach to a more proactive and predictive approach. Its goal is to maximize asset operational availability while minimising downtime and the logistics footprint through monitoring deterioration of component conditions. IVHM involves data processing which comprehensively consists of capturing data related to assets, monitoring parameters, assessing current or future health conditions through prognostics and diagnostics engine and providing recommended maintenance actions. The data driven prognostics methods usually use a large amount of data to learn the degradation pattern (nominal model) and predict the future health. Usually the data which is run-to-failure used are accelerated data produced in lab environments, which is hardly the case in real life. Therefore, the nominal model is far from the present condition of the vehicle, hence the predictions will not be very accurate. The prediction model will try to follow the nominal models which mean more errors in the prediction, this is a major drawback of the data driven techniques. This research primarily presents the two novel techniques of adaptive data driven prognostics to capture the vehicle operational scalability degradation. Secondary the degradation information has been used as a Health index and in the Vehicle Level Reasoning System (VLRS). Novel VLRS are also presented in this research study. The research described here proposes a condition adaptive prognostics reasoning along with VLRS

A Review of Classification Problems and Algorithms in Renewable Energy Applications

Classification problems and their corresponding solving approaches constitute one of the fields of machine learning. The application of classification schemes in Renewable Energy (RE) has gained significant attention in the last few years, contributing to the deployment, management and optimization of RE systems. The main objective of this paper is to review the most important classification algorithms applied to RE problems, including both classical and novel algorithms. The paper also provides a comprehensive literature review and discussion on different classification techniques in specific RE problems, including wind speed/power prediction, fault diagnosis in RE systems, power quality disturbance classification and other applications in alternative RE systems. In this way, the paper describes classification techniques and metrics applied to RE problems, thus being useful both for researchers dealing with this kind of problem and for practitioners of the field

Fault analysis using state-of-the-art classifiers

Fault Analysis is the detection and diagnosis of malfunction in machine operation or process control. Early fault analysis techniques were reserved for high critical plants such as nuclear or chemical industries where abnormal event prevention is given utmost importance. The techniques developed were a result of decades of technical research and models based on extensive characterization of equipment behavior. This requires in-depth knowledge of the system and expert analysis to apply these methods for the application at hand. Since machine learning algorithms depend on past process data for creating a system model, a generic autonomous diagnostic system can be developed which can be used for application in common industrial setups. In this thesis, we look into some of the techniques used for fault detection and diagnosis multi-class and one-class classifiers. First we study Feature Selection techniques and the classifier performance is analyzed against the number of selected features. The aim of feature selection is to reduce the impact of irrelevant variables and to reduce computation burden on the learning algorithm. We introduce the feature selection algorithms as a literature survey. Only few algorithms are implemented to obtain the results. Fault data from a Radio Frequency (RF) generator is used to perform fault detection and diagnosis. Comparison between continuous and discrete fault data is conducted for the Support Vector Machines (SVM) and Radial Basis Function Network (RBF) classifiers. In the second part we look into one-class classification techniques and their application to fault detection. One-class techniques were primarily developed to identify one class of objects from all other possible objects. Since all fault occurrences in a system cannot be simulated or recorded, one-class techniques help in identifying abnormal events. We introduce four one-class classifiers and analyze them using Receiver-Operating Characteristic (ROC) curve. We also develop a feature extraction method for the RF generator data which is used to obtain results for one-class classifiers and Radial Basis Function Network two class classification. To apply these techniques for real-time verification, the RIT Fault Prediction software is built. LabView environment is used to build a basic data management and fault detection using Radial Basis Function Network. This software is stand alone and acts as foundation for future implementations

One-Class Classification: Taxonomy of Study and Review of Techniques

One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection and concept learning. In this paper we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure