Novel power system load curve clustering method

Abstract

The invention relates to a novel power system load curve clustering method. The novel power system load curve clustering method comprises the following steps: step S1, obtaining daily load curve active power data in five or more days of loads or transformers to be classified from a monitoring, controlling and data-collecting system; step S2, taking a mean value of all workdays at every moment of each load or transformer as a user typical load curve according to the daily load curve active power data obtained in the step S1; step S3, carrying out standardization treatment on the user typical load curve obtained in the step S2; step S4, culturing the user typical load curve by using an affinity propagation algorithm to obtain preliminary classification results; step S5, calculating a user load form characteristic index, and carrying out min-max standardization treatment to obtain a standardized load form characteristic index; and step S6, carrying out secondary classification on the preliminary classification results in the step S4 based on the standardized load form characteristic index obtained in the step S5.

Description

technical field [0001] The invention relates to the field of power systems, in particular to a novel power system load curve clustering method. Background technique [0002] With the development of smart grid technology, various advanced measurement devices have been widely used in power systems. The power generation, transmission, distribution, and power consumption of the power system generate massive amounts of data. How to extract valuable information from massive power data is a current research hotspot in power systems. In the electricity market, electricity sellers need to implement differentiated marketing based on the characteristics of users' time-sharing electricity consumption behaviors to reflect the differences in electricity costs in different periods of time on a daily time scale. However, the number of power users is huge, and it is difficult to formulate marketing rules for each user. It needs to be classified according to the characteristics of users' el...

AI Technical Summary

Problems solved by technology

However, the above method also has shortcomings. The essence of using Euclidean distance is the similarity of the geometric mean distance, which cannot fully guarantee the similarity of the shape or outline of the time series.

And the above method is susceptible to extreme values ​​and noise

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