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Overhead power transmission line optimized line selection method based on airborne laser radar data

The invention discloses an optimal route selection method for overhead transmission line route. Onboard lidar equipment is adopted to acquire laser point cloud data and aerial digital photo data of the transmission line route corridor range; the onboard lidar data is processed after the wave filtering and the sorting of the laser point cloud data, and the points of the ground surface are made into a digital elevation model with high precision; then data processing is carried out by utilizing the data of the digital elevation model with high precision and ortho-rectification is carried out to the aerial photo by utilizing the internal and external orientation elements of the aerial digital photo to generate digital orthophoto maps; through the overlying of the digital elevation model and the digital orthophoto maps, the tridimensional visualization of the transmission line route corridor can be realized to optimize the transmission line route selection; finally prearrangement of power pole and power pole arrangement are carried out according to the data of plane cross sections. The route selection platform of the invention has simple operation and lifelike tridimensional scene, thus being convenient for full roaming and multi-view observation and greatly improving the efficiency of the inner plane cross section survey operation. Compared with the optimal route selection technology based on the aerial photographing measuring method, the efficiency of the inner plane cross section survey operation can be improved by about 75 percent.
Owner:GUANGXI ELECTRIC POWER IND INVESTIGATION DESIGN & RES INST

Power consumption prediction method for system

ActiveCN105260803AImprove regularitySimple and regular power consumptionForecastingElectric power systemData acquisition
For accurately measuring the power consumption, the invention provides a power consumption prediction method for a system. The method comprises S1 data acquisition: acquiring the power utilization information of residents, industrial users and business users through an intelligent ammeter, acquiring the influence factor information through an external system, and generating a history table for the users at the same time; S2 analysis of power utilization rules for the users: performing fitting analysis of the acquired power utilization information of the users by means of combination with the history table, and obtaining the power utilization rules for the users through analysis; S3 analysis of influence factors: setting a load fluctuation range according to the user type, and performing analysis of influence factors for the time point surpassing the fluctuation range to obtain the influence value of each influence factor for the power consumption of the users; and S4 power consumption prediction for a system: predicting the future short-term power consumption for the system by means of combination of analysis of power utilization rules and analysis of influence factors. The prediction method shows a brand new development direction for short-term power consumption prediction in future, and can be widely applied to the prediction field with great significance.
Owner:STATE GRID CORP OF CHINA +1
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