An electrical system for locating fault in a power distribution network based on mixed recording waves, wherein the electrical system comprises devices for recording waveform at each monitoring point, wherein the electrical system is configured to: determine a common waveform section for each monitoring point based on a wide area time synchronization technique at points of time in a collection of waveform at each monitoring point; based on the common waveform section, sequentially piece together the waveform over the monitoring points as synchronized data; preprocess the synchronized data from each monitoring point into a high frequency component sequence and a low frequency component sequence; construct deep neural network model to include a multi-convolution layer, long and short term memory, LSTM, network unit groups of data block input; and further through machine training, obtain plurality of data block input deep neural network models; and finally choose an optimal model; thereafter, feed into this optimal model waveform data from the monitoring points, which includes the high frequency component sequence and the low frequency component sequence, to obtain relative positioning relationship between each monitoring point and a fault location; wherein the deep neural network model is a multi-component data block input deep neural network model that contains a high frequency and a low frequency data block input; and wherein the high frequency data block is input into a multi-layer network Module I, and the low frequency data block is input into a multi-layer network Module II; wherein the multi-layer network Module I includes multi-convolution layers and a full connection layer between convolutions layers; and wherein the multi-layer network Module II includes multi-convolution layers and a full connection layer between convolutions layers; wherein the multi-layer network Module I further includes a convolution block, and an average pooling layer; wherein the multi-layer network Module I output is superimposed with the output from the multi-layer network Module II based on weight factor, before being input into a shared Long and Short Term Memory (LSTM) cell, where a timing sequence is used to connect between the multiple LSTM cells through multiple computation channels. determine a common waveform section for each monitoring point based on a wide area time synchronization technique at points of time in a collection of waveform at each monitoring point; based on the common waveform section, sequentially piece together the waveform over the monitoring points as synchronized data; preprocess the synchronized data from each monitoring point into a high frequency component sequence and a low frequency component sequence; construct deep neural network model to include a multi-convolution layer, long and short term memory, LSTM, network unit groups of data block input; and further through machine training, obtain plurality of data block input deep neural network models; and finally choose an optimal model; thereafter, feed into this optimal model waveform data from the monitoring points, which includes the high frequency component sequence and the low frequency component sequence, to obtain relative positioning relationship between each monitoring point and a fault location; wherein the deep neural network model is a multi-component data block input deep neural network model that contains a high frequency and a low frequency data block input; and wherein the high frequency data block is input into a multi-layer network Module I, and the low frequency data block is input into a multi-layer network Module II; wherein the multi-layer network Module I includes multi-convolution layers and a full connection layer between convolutions layers; and wherein the multi-layer network Module II includes multi-convolution layers and a full connection layer between convolutions layers; wherein the multi-layer network Module I further includes a convolution block, and an average pooling layer; wherein the multi-layer network Module I output is superimposed with the output from the multi-layer network Module II based on weight factor, before being input into a shared Long and Short Term Memory (LSTM) cell, where a timing sequence is used to connect between the multiple LSTM cells through multiple computation channels.