Multi-energy disturbances might occur in the process of energy production

dispatch and use owing to the integration of electrical energy

natural gas

thermal energy

and so on. Traditional disturbance detection based on off-line data can hardly meet the real-time requirements of the energy internet. Data stream technology can be used to detect multi-energy disturbance signals on line. By proceeding from the interaction between energy and information

the energy internet multi-energy disturbance problem is analyzed

and mathematical models of different disturbance signals are developed. These disturbance signals are decomposed using wavelet transform and a signal data stream processing model of disturbance signals based on a sliding window with a synoptic data structure is built first. Secondly

for the purpose of rapid updating of the outline structure

the wavelet tree updating algorithm is improved. As a result the extraction of perturbation signal features is optimized and the decision tree algorithm is used to classify these signal features. Finally

the data stream processing model built in this article is applied to the identification of power quality disturbance and gas quality disturbance to verify the validity of the data stream model.