# Adaptive Leakage Protection for Low-Voltage Distribution Systems Based on SSA-BP Neural Network
## 1 Introduction  
Most of the low-voltage power grids in China are directly grounded at neutral points, and the consequences of leakage accidents are thus very serious [1; 2]. Leakage protection is crucial to preventing the leakage fires and personal electric shock casualties in low-voltage distribution networks [3; 4; 5]. According to IEC 60947-2 (circuit breaker incorporating residual current protection) and IEC 60755 (general requirements for residual current operated protective devices), residual current devices (RCDs) should react to leakage currents from 30 mA to tens of amperes [6; 7]. There is a certain amount of normal leakage current (residual current) that exists when the low-voltage distribution line is operating regularly [8]. In order to ensure the leakage protector operates normally, the current action setting of the leakage protector should be selected as low as possible under the premise of avoiding the normal leakage current of the line [9; 10]. In practice, the normal leakage currents of low-voltage distribution lines have a large numerical fluctuation affected by the external environment, power grid structure, and load size, and has obvious regional, climatic, and seasonal characteristics. For example, in the southern region of China, there is often a problem that the normal leakage current of the line increases during the rainy season, resulting in the failure of the leakage protection to be put into operation [11; 12]. Therefore, ensuring the practical operational proportion of the leakage protector without reducing its sensitivity has become an urgent problem to be solved. In recent years, in order to improve the reliability of leakage protection, a variety of residual leakage protection technologies have been developed by researchers [13; 14; 15]. A method in [8] takes the leakage current variation in addition to the instantaneous root-mean-square (rms) value of the leakage current as the indicator of leakage fault. Althoughit solves the problem of a protection dead zone to a certain extent, the application effect is less than perfect because there are still protection misoperation and rejection phenomena. Ref. [16] proposed a method for detecting and identifying the earth leakage current among a group of electrical appliances using frequency domain analysis. Although it somewhat increases the device's action reliability, the issue of circuit tripping and power failures still remains unsolved. The enterprise standard of the China Southern Power Grid puts forward the regulations and references for the use of different currents for leakage protection in rainy and non-rainy seasons. The standard considers the influence of humidity on leakage current, but not the temperature, load current, and line aging. In addition, with the development of modern signal processing techniques, some related techniques, such as variable mode decomposition (VMD), big data analysis, neural network algorithms, and machine learning approaches [17; 18; 19; 20; 21], have been applied to the detection of residual current. In [17], an adaptive residual current detection method based on VMD and a dynamic fuzzy neural network (DFNN) was proposed. A residual current warning technique based on big data analysis was proposed in [18]. The combination of wavelet transform (WT) and backpropagation neural network (BPNN) was proposed to preprocess the signal with multiscale wavelets, and then to use the processed signal as a sample for detection and analysis by BPNN, in [19]. The WT must predetermine the fundamental wave, decomposition layer, threshold, and threshold function, which reduces the algorithm's adaptability [20]. In [21], a residual current detection method was proposed, based on machine learning approaches. Although these methods have improved the residual current detection accuracy to a certain extent, they cannot predict normal leakage current and cannot set the protection action threshold in advance, which means some application limits.