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چکیده
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Abstract: Due to the importance of continued energy suppling to subscribers, especially industrial subscribers, who are severely affected by any disruption in network parameters and network outages, we decided to use new technology and software to reduce network outages and even reducing the shutdown time. In this study, equipment and relays are used to diagnosis and clear faults in transmission networks based on a novel Hopfield neural network. Then, the 230Kv power transmission network of Ilam province and its 10 substations (63 kV) and several power plants located in the province are modeled using DIgSILENT Power Factory software. In this regard, first, the load distribution in normal network and the values of network parameters are recorded, then the values of network parameters are recorded in faulty network. These values are feed to a novel fuzzy Hopfield neural network as training data, the results are compared with initial values and it is observed that fuzzy Hopfield neural network can quickly and accurately locate network faults.
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