The invention discloses a space-time union data clustering analysis based abnormal status detection method for a power transmission and transformation device. The method comprises the following steps of adding time windows into time components of status data of the power transmission and transformation device to acquire time subsequences; obtaining space-time subsequences by combining a space component and the generated time subsequences; clustering the space-time subsequences by c mean value fuzzy clustering FCM according to different time windows to obtain block matrixes; describing a category existing in the time window corresponding to the block matrix by each block matrix; calculating an abnormal condition of a single space-time subsequence according to historical normal data of the status of the power transmission and transformation device, giving an abnormality value to the category of each time window by using the block matrix, and judging the abnormal condition of the device status according to the size of the abnormality value; and declaring that the abnormal condition exists in the device status if the abnormality value is high. The relation between categories of different time windows is built by using the block matrixes, and thus, the development condition of an abnormal type is visually observed.