绝缘子运行状态的评估关乎到输电工程的安全运行。紫外成像技术提供了一种绝缘子评估的量化手段, 为此, 提出了一种基于改进自适应神经模糊推理系统(adaptive neuro-fuzzy inference system, ANFIS)的绝缘子紫外光斑评估方法。首先, 搭建了绝缘子污秽放电测试平台, 开展了不同测试距离和增益下的绝缘子放电强度研究。其次, 将增益以及紫外光斑面积作为训练数据, 建立了基于贝叶斯推理的 ANFIS模型。最后, 进行了现场验证测试。结果表明, 该方法具有良好的预测精度和测试效率, 适用于绝缘子紫外成像量化评估, 为绝缘子运行状态的评估提供了技术支撑。

Evaluation of insulator operation is important for safely operating transmission lines. Ultraviolet (UV) imaging is a quantitative method for evaluating insulators. Therefore, a UV spot area assessment method based on an improved ANFIS for insulators was proposed. First, an insulator pollution discharge test platform was built to study the discharge intensity of the insulators at different test distances and gains. Second, an ANFIS(adaptive neuro-fuzzy inference system) model based on Bayesian inference is established using the gain and ultraviolet spot area as training data. Finally, a field verification test was conducted. The results show that this method has good prediction accuracy and test efficiency and is suitable for the quantitative evaluation of ultraviolet imaging for insulators, which provides technical support for the evaluation of the operating state of insulators.