Comparison of different classification algorithms for fault detection and fault isolation in complex systems

Due to the lack of sufficient results seen in literature, feature extraction and classification methods of hydraulic systems appears to be somewhat challenging. This paper compares the performance of three classifiers (namely linear support vector machine (SVM), distance-weighted k-nearest neighbor (WKNN), and decision tree (DT) using data from optimized and non-optimized sensor set solutions. The algorithms are trained with known data and then tested with unknown data for different scenarios characterizing faults with different degrees of severity. This investigation is based solely on a data-driven approach and relies on data sets that are taken from experiments on the fuel system. The system that is used throughout this study is a typical fuel delivery system consisting of standard components such as a filter, pump, valve, nozzle, pipes, and two tanks. Running representative tests on a fuel system are problematic because of the time, cost, and reproduction constraints involved in capturing any significant degradation. Simulating significant degradation requires running over a considerable period; this cannot be reproduced quickly and is costly

Measurements, Models, Systems and Design

531 s.

Modeling Under MATLAB by ANFIS of Three-Phase Tetrahedral Transformer Using in Microwave Generator for Three Magnetrons Per Phase

This work deals with the modeling of a new three-phase tetrahedral transformer of HV power supply, which feeds three magnetrons per phase. The design of this new power supply is composed of three single-phase with magnetic shunt transformers coupling in star; each one is size to feed voltage-doubling cells, thereby feeds a magnetron. In order to validate the functionality of this power supply, we simulate it under Matlab-Simulink environment. Thus, we modeled nonlinear inductance using a new approach of neuro-fuzzy (ANFIS); this method based on the interpolation of the curve B(H) of ferromagnetic material, the results obtained gives forms of both voltages and currents, which shows that they are in accordance with those of experimental tests, respecting the conditions recommended by the magnetron manufacture

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

Statistical and predictive modeling of automated meter reading system outages

An Automated Meter Reading (AMR) system is a metering technology that enables power utility companies to receive customers\u27 energy usage data centrally over a communication network. The installed automated meters also provide a daily log of outage events for each customer. A utility company can greatly benefit by using this information for outage management and to improve reliability. However, outage data is frequently corrupted and the outage flags registered by the customers\u27 meters do not necessarily reflect true outages. This thesis focuses on developing methods to analyze outage data and building a model to identify good data versus spurious indications. Outage data analysis is accomplished by comparison with known occurrences of outage events. A histogram analysis is performed to study the distribution of multiple outages. This thesis also introduces a fuzzy logic-based algorithm to analyze AMR meter outages and predict a degree of accuracy for each outage indication. A generalized model is developed to gather essential network information pertinent to outage indications. This information is combined with data from the outage analysis system and is used as an input to the fuzzy logic system that analyzes the information and provides a confidence index for the AMR outage flags --Abstract, page iii

Data Fusion and Machine Learning Integration for Transformer Loss of Life Estimation

Rapid growth of machine learning methodologies and their applications offer new opportunity for improved transformer asset management. Accordingly, power system operators are currently looking for data-driven methods to make better-informed decisions in terms of network management. In this paper, machine learning and data fusion techniques are integrated to estimate transformer loss of life. Using IEEE Std. C57.91-2011, a data synthesis process is proposed based on hourly transformer loading and ambient temperature values. This synthesized data is employed to estimate transformer loss of life by using Adaptive Network-Based Fuzzy Inference System (ANFIS) and Radial Basis Function (RBF) network, which are further fused together with the objective of improving the estimation accuracy. Among various data fusion techniques, Ordered Weighted Averaging (OWA) and sequential Kalman filter are selected to fuse the output results of the estimated ANFIS and RBF. Simulation results demonstrate the merit and the effectiveness of the proposed method

Small Local Dynamic Fuzzy Logical Models for Large-Scale Power Systems

In the power system stability problems the primary actors in the mathematical system model are the differential equations defining the dynamic state variables of generation and load. These differential equations are coupled together by load flow equations. Mathematically the load flow equations are nonlinear algebraic equations. These differential equations and nonlinear algebraic equations form the mathematical Differential Algebraic Equations (DAE) model for the power system. The fuzzy set theory is commonly used in analysis of dynamical nonlinear systems. In this paper, we build a set of local dynamical fuzzy logic models for the differential equations, thus transforming the differential equations into nonlinear algebraic equations, the DAE into nonlinear algebraic equations. We try to simulate the system by solving the nonlinear algebraic equations rather than by solving the DAE model. We also compare the application of two types of dynamical fuzzy models: the discrete-time model and discrete-event model in this approach. First we explain the approach by a small DAE example, and then we apply it to a 10-bus power system