Research on Global Performance Evaluation Method for Metering Devices Based on Customer Load Characteristics

In the context of power systems, the daily load curve from substations serves as a tool for classifying and synthesizing load characteristics. The typical daily load curve data, after being acquired and processed by the SCADA system, forms the foundation for load classification studies. To tackle the issue of original curve pattern overlapping, a signal processing technique grounded in Variational Mode Decomposition has been introduced. Additionally, addressing the specific challenge of feature weighting in the Euclidean distance within the Fuzzy C-Means clustering algorithm, a feature-weighted fuzzy clustering approach was employed to develop a feature-weighted VMD-FCM clustering algorithm. Demonstrations reveal that the VMD algorithm can efficiently break down the inherent modes of signals, enhancing both the convergence rate and clustering precision of the FCM function; the introduction of a weight factor based on Euclidean distance boosts the practicality and accuracy of the FCM algorithm. The findings indicate that this enhanced method can effectively differentiate between various load categories, offering guidance for load forecasting, setting pricing criteria, and influencing marketing strategies.