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Low-quality power grid image data can greatly affect the effect of deep learning in the power industry. Therefore, adopting accurate image quality assessment techniques is essential for screening high-quality power grid images. Although current blind image quality assessment (BIQA) methods have made some progress, they usually use only one type of feature and ignore other factors that affect the quality of images, such as noise and brightness, which are highly relevant to low-quality power grid images with noise, underexposure, and overexposure. Therefore, we propose a multi-dimension distortion feature network (MDFN) based on CNN and Transformer, which considers high-frequency (edges and details) and low-frequency (semantic and structural) features of images, along with noise and brightness features, to achieve more accurate quality assessment. Specifically, the network employs a dual-branch feature extractor, where the CNN branch captures local distortion features and the Transformer branch integrates both local and global features. We argue that separating low-frequency and high-frequency components enables richer distortion features. Thus, we propose a frequency selection module (FSM) which extracts high-frequency and low-frequency features and updates these features to achieve global spatial information fusion. Additionally, previous methods only use the CLS token for predicting the quality score of the image. Considering the issues of severe noise and exposure in power grid images, we design an effective way to extract noise and brightness features and combine them with the CLS token for the prediction. The results of the experiments indicate that our method surpasses existing approaches across three public datasets and a power grid image dataset, which shows the superiority of our proposed method.