Abstract: Abstract: Residual current devices (RCDs), a type of protective equipment in low-voltage systems, are widely used to prevent and avoid leakage accident of power grid and protect the safety of life and property. At present, the operation of an RCD is based on sensing the root mean square value of residual current in an electrical circuit. The circuit will be interrupted on the action of a relay when the residual current exceeds a predetermined level (30 mA for human safety), known as the tripping current. Although such devices offer a large degree of protection, they are prone to nuisance tripping or maloperation in the actual operation of the grid due to the lack of the ability to diagnose electric shock type and identify touch current, which reduces the reliability and the rate of proper commissioning for RCDs. Thus, aiming at the problem that the measured electric shock signals are non-stationary and difficult to diagnose the type of electric shock, a new method of fault diagnosis of electric shock signal based on time-frequency singular spectrum of leakage current and fuzzy clustering is proposed. First of all, a simulation signal is used to compare and analyze the time-frequency analysis performance of short-time Fourier transformation (STFT), wigner-ville distribution (WVD) and smoothed pseudo Wigner-Ville distribution (SPWVD). The simulation results show that the STFT presents a lower time-frequency resolution because of the fixed window function, the WVD has serious crosstalk terms and it is difficult to determine the frequency components of the signal, and the SPWVD suppresses the crosstalk of WVD and reflects the distribution of signal frequency components with time through the smoothing of time-frequency window function. Therefore, SPWVD is chosen as the time-frequency analysis method in this paper. Then, numerous groups of total leakage current signals were measured using a recorder on the electric shock experiment platform of RCDs. We select a total of 0.04 s of data (one cycle before the electric shock and one cycle after the electric shock) as electric shock sample data. The SPWVD is used to analyze the total leakage current signal to obtain the time-frequency matrix, and the frequency band width of the main spectrum energy is 0-150 Hz, which can be divided into 13 sub-bands. The singular value decomposition (SVD) is adopted to decompose the time-frequency matrix formed by 13 sub-bands, and the singular values corresponding to the respective sub-frequency band are obtained as the feature vectors of the electric shock signal. And then the fuzzy C means (FCM) algorithm is applied to perform the clustering of extracted feature vectors to get the electric shock signal type. Finally, a total of 400 groups of animals and plants shock data are used as the research object. Among them, 140 groups of animal electric shock samples and 140 groups of plant electric shock samples are taken as known samples, and 60 groups of animal electric shock samples and 60 groups of plant electric shock samples are used as test samples. The experimental results show that there are 3 groups of samples in 120 groups of test samples which are wrongly identified and the recognition accuracy rate is 97.50%. Among them, the accuracy rate of plant electric shock test sample is 100%, and there are 3 samples in animal electric shock test samples, which are identified incorrectly and the recognition accuracy rate is 95.00%. The above results verify the correctness and validity of diagnosing the type of the electric shock fault signal by the extracted characteristic value of the total leakage current, which can lay a solid theoretical and technical foundation for developing new generations of adaptive residual current protection devices.