Method for correcting super-short-term prediction of photovoltaic power of ARMA module in real time through light metering network

Abstract

The invention discloses a method for correcting super-short-term prediction of the photovoltaic power of an ARMA module in real time through a light metering network. The method includes the steps of inputting data to obtain auto-regression sliding average model parameters, inputting light resource monitoring system data and running monitoring system data, correcting the startup capacity in real time according to the running monitoring system data, setting up an auto-regression sliding average model so as to obtain a photovoltaic power super-short-term prediction result, and introducing real-time light metering station data so as to correct the photovoltaic power super-short-term prediction result in real time. By introducing the real-time light metering station data to correct the photovoltaic power super-short-term prediction result in real time, the defect that in the existing ARMA technology, photovoltaic generation power super-short-term prediction accuracy is low is overcome, and high-precision photovoltaic power generation super-short-term prediction is achieved.

Description

technical field [0001] The invention relates to the technical field of photovoltaic power prediction in the process of new energy power generation, in particular to an ARMA model ultra-short-term prediction method for photovoltaic power with real-time correction of a photometric network. Background technique [0002] After my country's photovoltaic power generation enters the stage of large-scale development, most of the large-scale new energy bases are located in the "three north regions" (Northwest, Northeast, and North China). Large-scale new energy bases are generally far away from the load center, and their power needs to be transmitted through long distances and high voltages. Go to the load center for consumption. Due to the intermittence, randomness and volatility of wind and light resources, the output of wind power and photovoltaic power generation in large-scale new energy bases will fluctuate in a large range, which will further lead to fluctuations in the chargin...

AI Technical Summary

Problems solved by technology

The ARMA method has many advantages, so it is widely used in the ultra-short-term prediction of photovoltaic power generation, but the biggest disadvantage of ARMA is the hysteresis of its prediction—that is, when the output of photovoltaic power generation changes, the change speed of the result predicted by ARMA is generally slower than the actual Variation speed of photovoltaic power generation output

Therefore, it seriously affects the prediction accuracy of ARMA

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