Abstract: An observator is combined with fuzzy set theory and neural networks for monitoring transformer operation-state. It has overcome the limitations such as the dependency on experts for fuzzy rule and no self-adaptation for fuzzy set. The self-regulation of membership functions and the self-organization of fuzzy rule are realized by the self-learning of the neural networks. This approach is proven by experiments.

Key words: observator, transformer, fuzzy neural network (FNN), membership function

摘要： 把模糊集合理论与神经网络技术相结合设计模糊神经网络观测器，并对变压器运行状态进行动态监控。基于神经网络技术克服了模糊规则产生对专家的依赖性及模糊集的非自适应性的问题。隶属函数的自适应及模糊规则的自组织通过神经网络的自学习和竞争获得。该方法实现了变压器运行状态监控中模糊规则的自动确定和隶属函数的动态调节。通过实例分析验证了该方法的有效性和实用性。

关键词: 观测器, 变压器, 模糊神经网络, 隶属函数