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Different factors affecting the accuracy of fault location.
The fewer, yet more sensitively detected, fault features significantly improves the accuracy of fault diagnosis.
If the greater number of PMUs is installed in modeled system, the accuracy of fault area estimation will increase greatly.
However, the fixed basis functions which are not related to the given signal may lower the accuracy of fault detection.
However, due to the limitations of the fault detection and diagnostic technologies, there is a large variation in the accuracy of fault detection.
The application of artificial intelligence will improve the speed and accuracy of fault detection and analysis, which represents the future development of an intelligent diagnosis system.
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Thus, many works on transformer fault diagnosis have been proposed previously, which include artificial intelligence methods, to improve the accuracy of transformer fault diagnosis.
The presence of PMUs at all the buses provides several advantages and increases the accuracy of the fault analysis process.
Therefore, a feature selection process is necessary to reduce the computation complexity and improve the accuracy of the fault diagnosis.
Its success depends on the efficiency and accuracy of both fault simulators and the design models on which they operate.
The first 6 h of the time series of the water level was used to evaluate the accuracy of the fault models.
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