###### Abstract  
With the massive access of distributed renewable energy sources, many uncertain renewable energy power components have been added to the low-voltage lines in substations in addition to the loads of definite classification. From the perspective of economy and cleanliness, it is necessary to quantitatively analyze the renewable energy share among them and improve the power quality level of users. For the power quality information at low-voltage feeders, this paper proposes a quantitative analysis algorithm based on improved wavelet energy entropy and LSTM neural network. The method is based on wavelet transform, based on sym8 wavelet basis function; it divides the long time sequence into equal-length small time sequences, calculates each feature component obtained from wavelet transform decomposition separately, then borrows the concept of information entropy to find its energy entropy. After obtaining the energy entropy sequence of each feature component, it then borrows the concept of kurtosis to weighted differentiation of each energy entropy sequence to highlight the feature information and finally, uses the LSTM neural network to classify the power quality information of different renewable energy sources to determine to which interval segment they belong. By building a simulation model to simulate the actual data in the field, the percentage of renewable energy can be quantitatively analyzed efficiently and accurately. renewable energy source; quantitative analysis; improved wavelet energy entropy; LSTM neural network; kurtosis +
Footnote †: journal: Medical  
of the signal at a certain time by finding the spectrum of the original signal [4]. However, it cannot reflect the local characteristics of the signal and is not suitable for analyzing the power quality problems with fast changes and short durations, such as transient pulses and transient oscillations due to renewable energy access. In the literature [5], the short-time Fourier transform is used to obtain multiple short-time signals by sliding and adding windows to the signals, after which Fourier transform is performed separately to obtain the time-frequency characteristics of the signals; this improves, to some extent, the drawback that the Fourier transform cannot reflect the local characteristics, but it is not adaptive because the length of the selected time-frequency window is fixed. The wavelet transform is a multi-scale time-frequency transform that can reflect both the full process and local information of the signal, so it is widely used to analyze abrupt signals and unsteady signals [6], but there are problems, such as the difficulty of selecting the basis function. The literature [7] uses Hilbert-Huang transform, combining two algorithms of empirical mode decomposition and Hilbert transform, which has better adaptivity, but has disadvantages, such as modal confusion [8] and endpoint effects. Wavelet energy entropy [9; 10] is an analysis algorithm that performs a secondary calculation of the feature vector, and the energy entropy of each component is obtained as a new feature vector with more obvious spectral characteristics by further analysis and calculation of the feature vectors obtained from the wavelet transform. At present, algorithms involving quantitative analysis are less studied, using mostly machine learning methods. The more-frequently applied methods based on feature selection are population-based genetic algorithms [11], particle swarm optimization [12] and ant colony optimization [13] algorithms, and other population intelligence algorithms, which are all effective for the input data drive algorithms of classifiers. The literature [14] based on improved GA to filter and combine wavelet extracted features and classify them by multiple models effectively improves the training speed and accuracy of the classifier, but the feature screening algorithm is not integrated with the subsequent classifier, and the GA tuning reference is more complicated, and the scene fit is lower.