Modeling and simulation of substation fault diagnosis based on Petri nets
In order to improve the efficiency of the substation fault diagnosis with multiple protections, the proposed method describes protection grades by varied weight of the transition vector in the Petri net models. According to relays and circuit breakers, special markings are produced in the diagnosis results that enhance the identifying capability of error information. The process of fault diagnosis is accomplished by algebra matrix calculation. A simulation on actual substation fault diagnosis process indicates that the improved method has fast calculation speed and high veracity
Power systems fault diagnosis using Petri nets
A new method of detecting fault locations in power networks is proposed. The diagnostic process uses Petri net models. This can reduce processing time and increase accuracy when compared with the traditional pattern recognition and hypotheses testing methods. The method is more able than previous methods to adapt to changing network configurations. The methodology was tested using the IEEE 118-bus standard test system. Fast and accurate results were obtained. The method is very suitable for online applications.
Neural network applications in power systems
The first part of the presentation is a brief survey of the neural network models and their use in power systems, based on an analysis of more than 200 publications. The second part discusses the applications in static security assessment, with supervised and unsupervised neural network models. An example of static security assessment with the Kohonen classifier is developed: the Kohonen network is trained with vectors representing simulated states of the power system, with or without contingencies. The Kohonen map is used to assess unknown power system states in real time. The two approaches, supervised and unsupervised, are compared and finally, the perspective of industrial implementation is evaluated