Identificación de fallas en sistemas eléctricos de potencia basado en el reconocimiento de patrones

En el presente artículo se realiza la identificación de fallas eléctricas en las líneas de transmisión de un Sistema de potencia mediante un algoritmo de Machine Learning, usando el algoritmo de los vecinos más cercanos que será implementado en Matlab y PSCAD. El machine learning es cada vez más aplicado a los sistemas eléctricos debido a que ayudan al estudio de señales eléctricas y desarrollo de diversas aplicaciones las cuales se van haciendo indispensables en la actualidad. De esta forma se busca que los sistemas de potencia con equipos de comunicación avanzados entreguen una mejor calidad de energía garantizando que en una desconexión por falla esta sea resuelta en el menor tiempo posible y el identificar el tipo de falla haga que se tomen los correctivos necesarios para mantener al sistema estable, el conocer qué tipo de falla es la que se produce no es solo importante a nivel de un sistema de alta potencia, también lo es a nivel industrial para siempre garantizar la calidad de la energía. Los patrones de falla son adquiridos desde un sistema inicial del cual se toman patrones de identificación de corriente para posteriormente realizar la simulación de casos de fallas en líneas de transmisión.This article identifies electrical failures in the transmission lines of a Power System using a Machine Learning algorithm, using the algorithm of the closest neighbors that will be implemented in Matlab and PSCAD. Machine learning is increasingly applied to electrical systems because they help the study of electrical signals and the development of various applications which are going to become indispensable today. In this way, the power systems with advanced communication equipment are sought to deliver a better quality of energy ensuring that in a disconnection due to failure this sea is resolved in the shortest possible time and the identifier the type of fault causes the necessary corrective measures to be taken to keeping the system stable, knowing what type of fault is the one that occurs is not only important at a high power system level, it is also an industrial level to always control the quality of energy. Failure patterns are acquired from an initial system from which current identification patterns are taken to subsequently simulate cases of transmission line failures

Fuzzy Inference System Approach for Locating Series, Shunt, and Simultaneous Series-Shunt Faults in Double Circuit Transmission Lines

Many schemes are reported for shunt fault location estimation, but fault location estimation of series or open conductor faults has not been dealt with so far. The existing numerical relays only detect the open conductor (series) fault and give the indication of the faulty phase(s), but they are unable to locate the series fault. The repair crew needs to patrol the complete line to find the location of series fault. In this paper fuzzy based fault detection/classification and location schemes in time domain are proposed for both series faults, shunt faults, and simultaneous series and shunt faults. The fault simulation studies and fault location algorithm have been developed using Matlab/Simulink. Synchronized phasors of voltage and current signals of both the ends of the line have been used as input to the proposed fuzzy based fault location scheme. Percentage of error in location of series fault is within 1% and shunt fault is 5% for all the tested fault cases. Validation of percentage of error in location estimation is done using Chi square test with both 1% and 5% level of significance

Fuzzy neural network pattern recognition algorithm for classification of the events in power system networks

This dissertation introduces advanced artificial intelligence based algorithm for detecting and classifying faults on the power system transmission line. The proposed algorithm is aimed at substituting classical relays susceptible to possible performance deterioration during variable power system operating and fault conditions. The new concept relies on a principle of pattern recognition and detects the existence of the fault, identifies fault type, and estimates the transmission line faulted section. The approach utilizes self-organized, Adaptive Resonance Theory (ART) neural network, combined with fuzzy decision rule for interpretation of neural network outputs. Neural network learns the mapping between inputs and desired outputs through processing a set of example cases. Training of the neural network is based on the combined use of unsupervised and supervised learning methods. During training, a set of input events is transformed into a set of prototypes of typical input events. During application, real events are classified based on the interpretation of their matching to the prototypes through fuzzy decision rule. This study introduces several enhancements to the original version of the ART algorithm: suitable preprocessing of neural network inputs, improvement in the concept of supervised learning, fuzzyfication of neural network outputs, and utilization of on-line learning. A selected model of an actual power network is used to simulate extensive sets of scenarios covering a variety of power system operating conditions as well as fault and disturbance events. Simulation results show improved recognition capabilities compared to a previous version of ART neural network algorithm, Multilayer Perceptron (MLP) neural network algorithm, and impedance based distance relay. Simulation results also show exceptional robustness of the novel ART algorithm for all operating conditions and events studied, as well as superior classification capabilities compared to the other solutions. Consequently, it is demonstrated that the proposed ART solution may be used for accurate, high-speed distinction among faulted and unfaulted events, and estimation of fault type and fault section

Détection, classification et localisation des défauts dans les lignes de transmission par les réseaux de neurones artificiels

La croissance rapide des systèmes d’énergie électrique observée au cours des dernières décennies a entraîné une forte augmentation du nombre de lignes de transmission et de distribution en service dans le monde. En outre, la commercialisation et la déréglementation introduites partout dans le monde imposent des exigences de plus en plus restrictives pour assurer une alimentation électrique continue et de bonne qualité, sans augmentation significative du coût de l’énergie fournie. Les défauts électriques sont l’un des facteurs les plus importants qui entravent la fourniture continue d’électricité et de courant. La détection des défauts sur les lignes de transmission constitue une partie majeure et importante de la surveillance et contrôle des systèmes électriques, l’intégration d’un système de protection intelligent va permettre de détecter rapidement voire prévoir l’occurrence d’un défaut, par conséquent éviter les dommages catastrophiques aux biens matériels et humains. Ce projet analyse l’utilisation des réseaux de neurones pour la détection, classification et localisation des défauts dans les lignes de transport de l’énergie électrique pour soutenir une nouvelle génération de système de relais de protection à grande vitesse et avec précision. Les défauts entraînent des temps d’arrêt du système, des dommages aux équipements et présentent un risque élevé pour l’intégrité du réseau électrique, et affectent son opérabilité et sa fiabilité. Le réseau de neurones de type feedforward sera utilisé ainsi qu’un algorithme de rétropropagation (backpropagation) pour chacune des trois phases pour indiquer l’absence ou la présence du défaut, le classifier en fonction de ses caractéristiques transitoires et pointer son emplacement sur une ligne de transmission

Artificial Neural Networks for loudspeaker modelling and fault detection

This thesis is the result of a collaborative project between Cardiff University and Harman/Becker Automotive Systems. It investigates the application of Artificial Neural Networks to loudspeaker fault detection and modelling of the loudspeaker transfer function. The aim was to utilise the ability of artificial neural networks to model high order nonlinear systems to generate a model of the loudspeaker transfer function which could be used in a linearisation scheme to reduce distortion in loudspeaker output. This thesis investigates a practical approach to transfer function modelling through the use of musical excitation signals. This would allow data to be collected during normal operation of the loudspeaker and, as the transfer function changes over time due to time dependent nonlinearities, would facilitate regular updating of the neural network model to incorporate these nonlinearities. It was determined that although very accurate models could be produced over long training periods, a significant compromise in ANN training set size and number of training epochs were required to reduce the ANN training duration to the required time period, which ultimately resulted in a decline in performance. The aim in the case of fault detection was to improve on current end of production line testing for loudspeaker distortion. Neural networks were trained with harmonic distortion data in order to emulate the end of line test result. Excellent classification accuracy was achieved when neural network classification results were compared to the end of line test results. An investigation was also conducted to determine if neural networks could be trained to recognise specific loudspeaker faults. In a development of the end of line test, a system of neural networks were trained to produce an output vector that described which of five frequency regions the loudspeaker distortion levels were above the limits, thus giving an indication of the possible fault.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Artificial Neural Networks for loudspeaker modelling and fault detection

This thesis is the result of a collaborative project between Cardiff University and Harman/Becker Automotive Systems. It investigates the application of Artificial Neural Networks to loudspeaker fault detection and modelling of the loudspeaker transfer function. The aim was to utilise the ability of artificial neural networks to model high order nonlinear systems to generate a model of the loudspeaker transfer function which could be used in a linearisation scheme to reduce distortion in loudspeaker output. This thesis investigates a practical approach to transfer function modelling through the use of musical excitation signals. This would allow data to be collected during normal operation of the loudspeaker and, as the transfer function changes over time due to time dependent nonlinearities, would facilitate regular updating of the neural network model to incorporate these nonlinearities. It was determined that although very accurate models could be produced over long training periods, a significant compromise in ANN training set size and number of training epochs were required to reduce the ANN training duration to the required time period, which ultimately resulted in a decline in performance. The aim in the case of fault detection was to improve on current end of production line testing for loudspeaker distortion. Neural networks were trained with harmonic distortion data in order to emulate the end of line test result. Excellent classification accuracy was achieved when neural network classification results were compared to the end of line test results. An investigation was also conducted to determine if neural networks could be trained to recognise specific loudspeaker faults. In a development of the end of line test, a system of neural networks were trained to produce an output vector that described which of five frequency regions the loudspeaker distortion levels were above the limits, thus giving an indication of the possible fault.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Quadri-dimensional approach for data analytics in mobile networks

The telecommunication market is growing at a very fast pace with the evolution of new technologies to support high speed throughput and the availability of a wide range of services and applications in the mobile networks. This has led to a need for communication service providers (CSPs) to shift their focus from network elements monitoring towards services monitoring and subscribers’ satisfaction by introducing the service quality management (SQM) and the customer experience management (CEM) that require fast responses to reduce the time to find and solve network problems, to ensure efficiency and proactive maintenance, to improve the quality of service (QoS) and the quality of experience (QoE) of the subscribers. While both the SQM and the CEM demand multiple information from different interfaces, managing multiple data sources adds an extra layer of complexity with the collection of data. While several studies and researches have been conducted for data analytics in mobile networks, most of them did not consider analytics based on the four dimensions involved in the mobile networks environment which are the subscriber, the handset, the service and the network element with multiple interface correlation. The main objective of this research was to develop mobile network analytics models applied to the 3G packet-switched domain by analysing data from the radio network with the Iub interface and the core network with the Gn interface to provide a fast root cause analysis (RCA) approach considering the four dimensions involved in the mobile networks. This was achieved by using the latest computer engineering advancements which are Big Data platforms and data mining techniques through machine learning algorithms.Electrical and Mining EngineeringM. Tech. (Electrical Engineering

Artificial Neural Networks for loudspeaker modelling and fault detection

This thesis is the result of a collaborative project between Cardiff University and Harman/Becker Automotive Systems. It investigates the application of Artificial Neural Networks to loudspeaker fault detection and modelling of the loudspeaker transfer function. The aim was to utilise the ability of artificial neural networks to model high order nonlinear systems to generate a model of the loudspeaker transfer function which could be used in a linearisation scheme to reduce distortion in loudspeaker output. This thesis investigates a practical approach to transfer function modelling through the use of musical excitation signals. This would allow data to be collected during normal operation of the loudspeaker and, as the transfer function changes over time due to time dependent nonlinearities, would facilitate regular updating of the neural network model to incorporate these nonlinearities. It was determined that although very accurate models could be produced over long training periods, a significant compromise in ANN training set size and number of training epochs were required to reduce the ANN training duration to the required time period, which ultimately resulted in a decline in performance. The aim in the case of fault detection was to improve on current end of production line testing for loudspeaker distortion. Neural networks were trained with harmonic distortion data in order to emulate the end of line test result. Excellent classification accuracy was achieved when neural network classification results were compared to the end of line test results. An investigation was also conducted to determine if neural networks could be trained to recognise specific loudspeaker faults. In a development of the end of line test, a system of neural networks were trained to produce an output vector that described which of five frequency regions the loudspeaker distortion levels were above the limits, thus giving an indication of the possible fault

Dynamic Analysis and Fault Detection of Multi Cracked Structure Under Moving Mass Using Intelligent Methods

The present thesis explores an inclusive research in the era of moving load dynamic problems. The responses of vibrating structures due to the moving object and different methodologies for damaged identification process have been investigated in this analogy. The theoretical-numerical solutions of the multi-cracked structure with different end conditions subjected to transit mass have been formulated. The Runge-Kutta fourth order integration approach has been applied to determine the response of the structures numerically. The effects of parameters like mass and speed of the traversing object, crack locations, and depth on the response of the structures are investigated. The proposed numerical method has been verified using FEA and experimental investigations. The novel damage prediction processes are developed on the knowledge-based concepts of recurrent neural networks (RNNs) and statistical process control (SPC) methods as inverse approaches. The Jordan’s recurrent neural networks (JRNNs), Elman’s recurrent neural network (ERNNs), the integrated approach of the JRNNs, and ERNNs, the autoregressive (AR) process in the domain of SPC and the combined hybrid neuro-autoregressive process have been developed to identify and quantify the faults in the structure. The accuracy and exactness of each approach has been verified with experiments and FEA. The proposed methods can be useful for the online condition monitoring of faulty cracks in structures