4results about How to "Maximize power generation efficiency" patented technology

This invention relates to the field of photovoltaic technology and discloses a method for optimizing the design of photovoltaic arrays under airspace constraints. The steps include: constructing a predicted elevation model for the target area, screening feasible point sets for photovoltaic placement, extracting boundary contours and drawing new boundaries that meet airspace requirements, calculating the initial layout based on the horizontal line method, optimizing the layout using a genetic algorithm, and automatically drawing photovoltaic modules. This invention establishes a generalized automatic photovoltaic layout model for airports, which can fully utilize land resources, maximize power generation efficiency, and achieve optimal design of photovoltaic arrays under airspace constraints.

The invention discloses a wind power plant real-time yaw cooperative control system and method fused with machine learning, and belongs to the technical field of wind power generation control. The invention aims to solve the problems of low precision, long calculation time and sparse samples of a wind power plant wake flow control model under complex terrains. According to the technical scheme, the method comprises the following steps: constructing a prior knowledge base by using OpenFOAM simulation and a virtual laser radar technology; establishing a hybrid deep neural network comprising a main prediction network and a residual error correction network, and realizing accurate flow field prediction of virtual and real data fusion; and based on a multi-agent reinforcement learning algorithm, a collaborative yaw strategy is generated by taking maximization of the whole-field generating capacity as a target. According to the invention, millisecond-level real-time response can be realized, the overall power generation benefit of the wind power plant under the complex terrain is effectively improved, and the equipment load is reduced.

The invention belongs to the technical field of reservoir water temperature prediction and regulation and control, and particularly discloses an upstream discharged water temperature prediction and flexible water retaining curtain wall regulation and control method and system. According to the method, a physical mechanism of an LSTM neuron structure is reconstructed, a water body stratification stability mechanism and a vertical thermal diffusion mechanism are deeply fused in a deep learning network, an improved LSTM model can well predict a water temperature stratification phenomenon of upstream incoming water, and high-precision prediction of vertical water temperature distribution of a reservoir under a complex meteorological condition is realized. A water temperature mixing model calculation formula is created for the first time, and the problem that the discharged water temperature requirement is difficult to calculate is solved. The vertical water temperature predicted by the improved LSTM is combined with the discharged water temperature inverted by the downstream ecological target, and the optimal water taking elevation is compared and determined, so that accurate lifting regulation and control of the flexible water retaining curtain wall are guided, the ecological water temperature requirement of a downstream river channel is strictly guaranteed, meanwhile, water resource waste is effectively avoided, and the power generation benefit and the flux utilization rate of a reservoir are maximized.

The invention relates to the technical field of meteorological information and photovoltaic trackers, and particularly discloses a photovoltaic tracker active protection system based on an AI meteorological large model. Comprising a meteorological data fusion module, an AI meteorological prediction module, a protection instruction generation module, an instruction issuing and execution module and a cloud operation and maintenance management platform used for managing the above modules which are connected in sequence, and the working process is as follows: fusing multi-source meteorological data and outputting station-level meteorological data; on the basis of the data, generating a disaster weather prediction result containing disaster types, time and intensity through a trained weather large model; dynamically generating a tracker protection instruction comprising an action type, a target angle and execution time according to the result; and finally, an instruction is issued to an edge side through the Internet of Things to control the photovoltaic tracker to complete active protection before a disaster occurs. According to the method, normal form transformation from lag response to precise pre-protection is realized, a seamless closed loop from prediction to control is constructed, and power generation benefits are optimized while equipment safety is guaranteed.