Abstract: The present invention discloses an adjustable-order fractional-order capacitor circuit and a control method therefor, including: using an improved fractional-order differential circuit; and using a junction field-effect transistor operating in a variable-resistance region to replace a fixed-value resistor in a common differential circuit, thereby achieving a fractional-order differential operation. In changing an order of the fractional-order circuit, an equivalent resistance of the junction field-effect transistor can be adaptively adjusted by the feedback tracking circuit to meet the order requirement. Without changing any element parameter in the differential circuit, an output signal of the differential circuit can lead an input signal in phase value by a value in an adjustable range, offering flexible adjustment.

Abstract: Disclosed in the present disclosure is a grid-forming microinverter and method based on bidirectional flyback converters, relating to the technical field of photovoltaic power generation. The method includes: calculating active power and reactive power through an output voltage and an output current of the grid-forming microinverter; generating a reference signal of a voltage loop by using a preset power reference value based on a power loop control principle; generating a reference signal of a peak current control loop according to an actual output voltage; and generating, according to primary-side current signals of the first bidirectional flyback converter and the second bidirectional flyback converter, a drive signal for each switch tube under regulation of the peak current control loop.

Abstract: A charging state analysis method of an electric vehicle based on electrical characteristic sequence analysis is provided. The method includes following steps: step S1, obtaining voltage sampling data and current sampling data of the electric vehicle during charging; step S2, setting a time interval, so as to divide the voltage sample data and the current sampling data obtained in step S1 into multiple data sets; step S3, calculating an electrical characteristic vector of each time interval; step S4. adding the calculation results of Step S3 to a temperature sensing value T, and generate an electrical characteristic sequence of whole charging cycle; step S5, inputting the electrical characteristic sequence of the electric vehicle into a trained TRNN in sequence to obtain corresponding results; if the result is 1, it is normal; if the result is 0, it is abnormal.

Abstract: The invention discloses a charging state analysis method of a electric vehicle based on electrical characteristic sequence analysis. The method includes following steps: step S1, obtaining voltage sampling data and current sampling data of the electric vehicle during charging; step S2, setting a time interval, so as to divide the voltage sample data and the current sampling data obtained in step S1 into multiple data sets; step S3, calculating an electrical characteristic vector of each time interval; step S4. adding the calculation results of Step S3 to a temperature sensing value T, and generate an electrical characteristic sequence of whole charging cycle; step S5, inputting the electrical characteristic sequence of the electric vehicle into a trained Time Recurrent Neural Network (TRNN) in sequence to obtain corresponding results; if the result is 1, it means normal; if the result is 0, it means abnormal.

Abstract: The invention discloses a non-invasive load decomposition method, which includes: step 1, obtaining the power fingerprint information of each load; step 2, clustering the operating state of loads through the clustering algorithm, calculate statistical values of each cluster, and encoding the operating state of electrical appliances; step 3, establishing a hidden Markov model with multiple-parameters and calculating the model parameters; step 4, performing state recognition based on Viterbi algorithm and obtaining predicted state sequence; step 5, according to the predicted state sequence and the statistical values of each cluster, decomposing the load power based on the maximum likelihood estimation principle; step 6, outputting the state sequence and power decomposition results. The invention solves the conventional load identification algorithm problems, such as complex model, insufficient use of electrical features and low accuracy of unknown information.