7results about How to "Maximize power generation efficiency" patented technology

The invention relates to the technical field of power equipment operation and maintenance, and provides a photovoltaic power station subarray box transformer stable no-load switching method, which comprises the following steps of: monitoring operation data of a photovoltaic power station subarray box in real time, constructing a switching risk prediction model by utilizing a machine learning algorithm, predicting a switching risk, automatically matching an execution command, and after switching execution, carrying out automatic switching on the subarray box transformer substation. Feedback data are collected, stability detection is conducted, and if the stability does not meet the standard, an optimization algorithm is adopted to adjust the command till the stability requirement is met. The stable no-load switching operation and maintenance efficiency of the photovoltaic power station subarray box transformer substation is improved, the stable no-load switching risk of the photovoltaic power station subarray box transformer substation is reduced, the system stability of the photovoltaic power station is improved, refined operation and maintenance of the photovoltaic power station are achieved, and the overall performance and economic benefits are improved.

The invention discloses a breeze power generation system and method and related assemblies thereof, and relates to the field of breeze power generation, and the breeze power generation method comprises the following steps: step 1, deploying and initializing all parts, and completing the installation of an air duct, an air guide piece, a Venturi tube and a spiral blade; 2, wind direction optimization treatment is conducted through a wind guide piece, natural breeze is introduced into the wind cylinder to be converted into vertically-downward airflow, the airflow interacts with spiral blades in the wind cylinder and then is converted into mechanical energy, and residual breeze enters the Venturi tube and then interacts with spiral blades in the Venturi tube and then is converted into mechanical energy; through comprehensive analysis of historical data and real-time data, a power generation strategy is automatically adjusted in combination with an intelligent algorithm, potential trend changes and abnormal conditions are rapidly identified, then an operation strategy is responded and adjusted in time, breeze energy can be more effectively captured and converted through design optimization of airflow guiding and conversion, the power generation capacity is maximized, and the power generation efficiency is improved. And challenges of different meteorological conditions can be handled.

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.

The present invention relates to the technical field of electric energy balance strategies, and discloses an intelligent decision-making method and system based on artificial intelligence, wherein an intelligent decision-making method based on artificial intelligence comprises the following steps: collecting historical hydrological and meteorological data, historical electric energy demand data, historical hydropower station operation data, historical transmission line monitoring data and historical meteorological department forecast data of all hydropower stations in a hydropower station group for preprocessing; triggering an early warning if the average rainfall in the basin where the hydropower group is located in the next day is greater than a set rainfall threshold; encoding and combining the collected data of the basin into a first sequence of data and graph structure data; inputting the first sequence of data into a first neural network, and outputting a selected action; decoding and selecting a corresponding electric energy balance strategy according to the selected action; the present invention can issue an early warning and provide an electric energy balance strategy when extreme weather occurs.