During the operation of the novel power system with integrated energy as the main body

it is easy to be invaded by false data

and it is easy to be disturbed by data noise when identifying false data intrusion. In order to improve its power quality and operation stability

a false data intrusion detection method for power grid based on deep machine learning is proposed. The novel power grid data is preprocessed by de-noising

and the phasor measurement unit (PMU) is used to predict the real-time system state of comprehensive energy such as the novel power system. False data injection attacks (FDIAs) are obtained by continuously adding error measurement vectors in PMU to judge whether the power grid has been attacked by false information and predict the location value of possible attacks. Back propagation (BP) neural network based on wavelet de-noising is used to train the prediction results

the input layer

hidden layer and output layer are used to update the actual value in real time

and the deviation result is obtained by comparing with the threshold

which can detect the false data in power grid. Experimental results show that the proposed method can effectively remove noise in advance

improve the accuracy of false data intrusion detection in power grid

and the detection time is short.