BP neural network-based micro-energy device energy identification method

Abstract

The invention provides a BP neural network-based micro-energy device energy identification method, which comprises the following steps: S1, collecting a dynamic voltage of a micro-energy device in anopen-circuit state to obtain an original voltage signal, and performing adaptive threshold wavelet transform processing on the original voltage signal to remove noise; S2, extracting an R wave peak value of the denoised voltage signal so as to obtain model input data; S3, establishing a BP neural network model, inputting data to train the model, and stopping training when a training error is smaller than a preset value to obtain a qualified BP neural network model; and S4, identifying a to-be-identified voltage signal by using the BP neural network model obtained in the step S3. According to the invention, accurate and rapid energy identification and classification can be carried out, and the classification result is reliable. The method is high in anti-interference capability. A pluralityof characteristic quantities with relatively high influence proportions in energy signal comparison of the micro-energy device are selected.

Description

technical field [0001] The invention relates to the field of smart micro-energy systems, in particular to an energy identification method for micro-energy devices based on a BP neural network. Background technique [0002] At present, smart micro-energy systems featuring self-sensing, self-awakening, self-learning, and self-adaptation are gradually beginning to replace traditional energy management systems. Among them, accurate identification of input energy is the key to smart micro-energy systems. [0003] Since the voltage signal of the micro-energy device is interfered by external factors such as the material itself and the process technology, there may be a variety of noises with different strengths or frequencies in the voltage signal. It is one of the urgent problems to be solved in device energy identification. [0004] At present, the research directions of machine learning mainly include research on decision trees, random forests, artificial neural networks, and ...

AI Technical Summary

Problems solved by technology

Taking the smart micro-energy system as an example, if a common machine learning algorithm such as the naive Bayesian classifier algorithm is used to classify the output electrical forms of various micro-energy devices, the algorithm will only classify the current training set samples (that is, each The dynamic voltage when the micro energy device is open circuit) is used to classify and extract features, but the feature extraction effect for other data samples is poor

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