電力人工智能指標算法模型多場景魯棒性評價方法

摘要：為解決傳統的模型魯棒性評價方法存在描述相符性較低， 難以獲得精準的場景匹配數據的不足， 提出了一種新的電力人工智能指標算法模型多場景魯棒性評價方法。針對多場景數據進行提取， 設置局部空間的多場景數據擾動范圍區間， 控制空間范圍的區間移動距離， 在區間范圍內預測樣本點的數據獲取結果。輸入算法模型的基礎特征參數， 在輸入參數維度提高的同時選擇多場景數據獲取距離范圍數值， 根據選取的數值進行初始數據評估操作。針對不確定的控制目標的特點進行數據基礎分析， 確保系統處于穩定狀態中， 并保持系統的動態特征， 有效分析不同的系統參數之間的差異， 構建偏差值范圍， 判斷算法模型的多場景特點， 實現數據評價。實驗結果表明， 該電力人工智能指標算法模型多場景魯棒性評價方法能很好地變換采樣點坐標， 確保多場景采樣點數據圖像具備不變性， 從而克服場景數據旋轉敏感問題， 提高響應速度。與傳統評價方法相比， 筆者提出的評價方法在干擾魯棒性和仿射形變魯棒性等方面具有較強的優勢。

關鍵詞：電力人工； 人工智能； 指標算法； 模型多場景； 魯棒性評價； 評價方法

中圖分類號： TP39 文獻標志碼： A

Multi－Scenario Robustness Evaluation Method of Power Artificial Intelligence Index Algorithm Model

HUANG Yun1，2， DONG Tianyu1

（1. Anhui Jiyuan Inspection and Testing Technology Company Limited， Evaluation Experiment Center， State Grid Xintong Industry Group， Hefei 230031， China; 2. School of Computer Science and Information， Hefei University of Technology， Hefei 230031， China）

Abstract：To address the shortcomings of traditional model robustness evaluation methods， such as low description consistency and difficulty in obtaining accurate scene matching data， a new power artificial intelligence index algorithm model of multi scenario robustness evaluation method is proposed. The multi scene data is extracted， the disturbance range interval of multi scene data in local space is set， the interval movement distance of spatial range is controlled， and the data acquisition results of sample points within the interval range are predicted. The basic feature parameters of the algorithm model are input， the multiple scene data is selected to obtain distance range values while increasing the input parameter dimension， and the initial data evaluation operations are performed based on the selected values. Based on the characteristics of uncertain control objectives， conduct data foundation analysis to ensure that the system is in a stable state and maintains its dynamic characteristics. Effectively analyze the differences between different system parameters， construct a range of deviation values， judge the multi scenario characteristics of the algorithm model， and achieve data evaluation. The experimental results show that the multi scenario robustness evaluation method of the electric power artificial intelligence index algorithm model can effectively transform the coordinates of sampling points， ensure the invariance of multi scenario sampling point data images， overcome the problem of scene data rotation sensitivity， and improve response speed. Compared with traditional evaluation methods， the proposed evaluation method has strong advantages in interference robustness and affine deformation robustness.

Key words：electric power labor; artificial intelligence; index algorithm; model multi－scenario; robustness evaluation; evaluation method

0 引 言

隨著人工智能技術的飛速發展， 電力人工智能指標算法模型被廣泛應用于不同的電力系統運作場景中， 有效評價模型多場景魯棒性特征對算法模型的發展起著較為關鍵的作用?！?br>