Multiple Fault Diagnosis in Electrical Power Systems with Dynamic Load Changes Using Probabilistic Neural Networks
Diagnóstico de Fallas Múltiples en Sistemas Eléctricos de Potencia con Cambios de Carga Dinámicos Utilizando Redes Neuronales Probabilísticas
Juan Pablo Nieto González1, Luis Garza Castañón2 and Rubén Morales Menéndez3
1 I.T.E.S.M. Campus Saltillo, Departamento de Mecatrónica Saltillo, Coahuila, México juan.pablo.nieto@itesm.mx
2 I.T.E.S.M. Campus Monterrey, Departamento de Mecatrónica y Automatización, legarza@itesm.mx
]]> 3 I.T.E.S.M. Campus Monterrey, Centro de Automatización Industrial Monterrey, Nuevo León, México, rmm@itesm.mx
Article received on December 18, 2007
Accepted en February 27, 2009
Abstract
Power systems monitoring is particularly challenging due to the presence of dynamic load changes in normal operation mode of network nodes, as well as the presence of both continuous and discrete variables, noisy information and lack or excess of data. This paper proposes a fault diagnosis framework that is able to locate the set of nodes involved in multiple fault events. It detects the faulty nodes, the type of fault in those nodes and the time when it is present. The framework is composed of two phases: In the first phase a probabilistic neural network is trained with the eigenvalues of voltage data collected during normal operation, symmetrical and asymmetrical fault disturbances. The second phase is a sample magnitude comparison used to detect and locate the presence of a fault. A set of simulations are carried out over an electrical power system to show the performance of the proposed framework and a comparison is made against a diagnostic system based on probabilistic logic.
Keywords: Fault Diagnosis, Multiple Faults, Probabilistic Neural Networks, Correlation Matrix, Eigenvalues, Power System, Dynamic Load Changes.
Resumen
]]> El monitoreo de sistemas de potencia es particularmente retador debido a la presencia de cambios dinámicos de carga de los nodos de la red en modo de operación normal, así como la presencia de variables continuas y discretas, información con ruido y falta o exceso de datos. Este artículo propone un método de diagnóstico de fallas que es capaz de localizar el conjunto de nodos involucrado en eventos de fallas múltiples. El método detecta los nodos con falla, el tipo de falla y el tiempo en el cual está presente la falla. El método está compuesto de dos fases: En la primera fase una red neuronal probabilística es entrenada con los eigenvalores de los datos de voltaje obtenidos en operación normal así como con fallas simétricas y asimétricas. La segunda fase emplea una comparación entre las muestras para detectar y localizar la presencia de una falla. Se lleva a cabo un conjunto de simulaciones en un sistema eléctrico de potencia para mostrar el desempeño del método propuesto y se realiza una comparación contra un sistema de diagnóstico basado en lógica probabilística.Palabras clave: Diagnóstico de Fallas, Fallas Múltiples, Redes Neuronales Probabilísticas, Matriz de Correlación, Eigenvalores, Sistemas de Potencia, Cambios Dinámicos de Carga.
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