基于数学形态学去噪的光伏发电限电异常数据识别算法

An Abnormal Data Recognition Algorithm Based on Mathematical Morphology Denoising Theory for PV Power Generation

光伏发电领域特有的限电异常数据,由于其来源于不确定的、突发的强制弃风弃光操作,完全无规律可循,使得依赖数据分布假设或经验模型的传统异常数据识别算法无法对其进行有效识别。为提高光伏限电异常数据的识别率,提出一种基于数学形态学去噪的限电异常数据识别算法。该算法将限电异常数据作为原始数据的噪声信号,对原始数据本身的分布特性没有任何要求,只需将原始数据转换为二值图像,通过膨胀腐蚀等数学形态学去噪的基本运算即可对限电异常数据进行自适应识别。通过实际采集数据进行仿真,结果表明,与传统异常数据识别算法相比,该算法可显著提高限电异常数据的识别率,从而验证了其在限电异常数据识别领域的适用性。

The curtailment data in PV power generation is a special type of abnormal data. Traditional abnormal data recognition algorithms rely on the data distribution hypothesis or empirical model and cannot work well for recognizing this special type of abnormal data. Aiming to address with this problem, an abnormal data recognition algorithm based on the mathematical morphology denoising theory was proposed in this paper. The proposed abnormal data recognition algorithm took the curtailment data as the noise signal of the original data, so it did not have any requirements on the distribution characteristics of the original data. It only needed to transform the original data into a binary image, and then adaptively identify the curtailment data through the mathematical morphology denoising operations such as dilation and erosion.The simulation results show that compared with the traditional abnormal data recognition algorithms, the proposed algorithm has significantly improved the recognition rate of the curtailment data, which verifies the applicability of the proposed algorithm in the field of the curtailment data recognition.