Substations are subjected to multiple sound sources and complex environments and it is difficult to accurately extract the noises of high voltage reactors. In this study, we propose a noise extraction and evaluation method based on a multi-sound transmission array and deep learning for high voltage reactors. Firstly, the noise source was positioned on the basis of the reactor noise model by using the beamforming method, and the signal compensation was executed by combining the frequency domain-focused beamforming algorithm to guarantee high extraction accuracy of noise features. Then, echo was eliminated by using the improved local cepstrum, and the Deep Belief Network(DBN) evaluation model was improved by using genetic algorithms. Additionally, the processed sound pressure level signals were input into the improved DBN model to obtain the sound pressure level of the noises of high voltage reactors and achieve level evaluation. The proposed method was demonstrated experimentally by using the monitoring data of high voltage reactors in a 220 kV substation. The results showed that the proposed method can accurately locate the noise source, and the average percentage errors of the noise evaluation results in the daytime and at night were 2.7% and 3.5% respectively, both of which meet the requirements of practical application.