Intelligent energy management system and method for power grid power equipment
An intelligent management system and power equipment technology, applied in neural learning methods, data processing applications, biological neural network models, etc., can solve problems such as reducing the safety and stability of power grids
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Embodiment 1
[0075] The main purpose of an intelligent energy management system for power grid power equipment of the present invention is to realize efficient and fast-response intelligent management of power equipment. Considering the transmission loss, the switch of the power equipment is managed as a whole.
[0076] Specifically, such as figure 1 The power grid power equipment energy intelligent management system shown, the system includes:
[0077] (1) The data acquisition module is used to acquire the basic information of the electric equipment, the basic information includes the aging degree of the equipment, the model of the equipment and the current running time of the equipment.
[0078] The above basic information of the electric equipment refers to the known basic attributes of the electric equipment, in which the rated power of the equipment corresponding to the equipment can be obtained according to the equipment model.
[0079](2) The electric energy output prediction modu...
Embodiment 2
[0123] This embodiment proposes a method for intelligent energy management of power grid power equipment, such as figure 2 shown, including the following steps:
[0124] S1, obtain the basic information of the electric equipment, the basic information includes the aging degree of the equipment, the equipment model and the running time of the equipment; also obtain the actual output power when the running time of the equipment is t;
[0125] S2. Among the basic information of all electric equipment, the basic information of a set number of electric equipment is arbitrarily selected in batches, and the basic information of each batch of electric equipment is used as a training sample, and each batch of training The samples are respectively used as input, and the actual output power of the equipment in each batch of training samples when the operating time is t is used as output, and the output power prediction network is trained separately, and the output power prediction netwo...
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