Improving Power Distribution Network Diagnosis and Classification through Fuzzy Intelligent Systems and PMUs
Distribution Network Disturbances with Phasor Measurement Unit
A phasor measurement unit (PMU) is a device that can measure the voltage and current phasors of a power system in real time. These measurements can be used to monitor the operating conditions and detect any disturbances in the power distribution network. However, the identification and classification of different types of disturbances is a challenging task that requires a high level of intelligence and accuracy. In this paper, we propose a fuzzy intelligent system that can diagnose and classify various disturbances in the power distribution network based on the PMU data. The proposed system consists of three stages: feature extraction, fuzzy inference, and decision making. In the feature extraction stage, we use wavelet transform and statistical methods to extract relevant features from the PMU data. In the fuzzy inference stage, we use fuzzy logic and fuzzy rules to infer the degree of membership of each feature to different disturbance categories. In the decision making stage, we use a fuzzy aggregation method to combine the membership degrees and determine the final classification of the disturbance. The proposed system is tested on a simulated power distribution network with different types of disturbances, such as voltage sag, voltage swell, harmonic distortion, and frequency deviation. The results show that the proposed system can achieve high accuracy and reliability in diagnosing and classifying the disturbances in the power distribution network by PMU.
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