Power energy managing system and method

Abstract

The invention relates to a power energy managing system and method. The power energy managing system comprises a data measuring device and a local computing device; the local computing device is used for estimating parameters to be computed on the basis of a Kalman filtering algorithm of an RC model, so that estimation on the state of a power energy device is achieved. The power energy managing method comprises the step that state posteriori estimation and state error covariance matrix posteriori estimation of the power energy device are obtained on the basis of the Kalman filtering algorithm of the RC model. According to the power energy managing system and method, computing loss can be effectively reduced, the computing speed can be increased, smooth local computing can be promoted, and then the bandwidth load and the computing load of the power energy managing system are reduced.

Description

【Technical field】 [0001] The invention relates to the field of electric energy management, in particular to an electric energy management system and a method thereof. 【Background technique】 [0002] In the existing electric energy management system, it is often necessary to estimate various parameters of the system to monitor the operating status and life of the system in real time. But so far, the parameter estimation of the electric energy management system needs more calculation content, and the calculation loss is relatively large. In addition, since the host computer in the network is generally used as the computing unit, the transmission of relevant parameters and estimation results may be affected by factors such as data bandwidth, network delay, data peak period, and line failure, resulting in high computing load. 【Content of invention】 [0003] In order to overcome the deficiencies in the prior art, the present invention provides an electric energy management sys...

AI Technical Summary

Problems solved by technology

But so far, the parameter estimation of the electric energy management system needs more calculation content, and the calculation loss is relatively large.

In addition, because the host computer in the network is generally used as the computing unit, the transmission of relevant parameters and estimation results may be affected by factors such as data bandwidth, network delay, data peak period, and line failure, resulting in high computing load.

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