Abstract: An air conditioning control system including a motor and a controller is provided. The controller is configured to: filter a received signal using a filter function Q(Z), where the received signal is a difference between a feedback value of an electrical angular velocity of the motor and a given value of the electrical angular velocity of the motor; perform phase compensation on the filtered signal using a phase compensation function S(Z); determine a motor speed control signal, using a speed regulation function Gpi(Z), based on the phase-compensated signal; and discretize the motor speed control signal using a discrete transfer function Gp(Z), and control a speed of the motor based on the discretized motor speed control signal. Poles of a transfer function of the controller constructed based on the functions Q(Z), S(Z), Gpi(Z), and Gp(Z) are located within a unit circle.

Abstract: An electrolytic capacitor-less motor driving system includes a motor, a frequency converter and a controller. The controller is configured to: correct a coordinate transformation angle according to an electrical angular velocity of the motor, a bus voltage and a bus voltage reference value to obtain a corrected coordinate transformation angle, the coordinate transformation angle being an included angle between a current vector direction and a positive direction of an ? axis, and the corrected coordinate transformation angle is configured to cause an included angle between the current vector direction and a positive direction of a d axis to be zero; and determine the pulse width modulation signal according to a three-phase current output by the frequency converter, a d-axis reference current, a q-axis reference current and the corrected coordinate transformation angle, and input the pulse width modulation signal to the frequency converter.

Abstract: The present disclosure provides a magnetic pole position determining system, including a motor and a magnetic pole position determining apparatus. The motor includes a rotor. The apparatus is configured to: apply a high-frequency voltage to a direct axis of a two-phase rotating coordinate system of the motor at a first time point; determine a magnetic pole initial position of the rotor and a rotating speed of the rotor based on the high-frequency voltage; apply a zero voltage vector pulse to the motor at a second time point; determine a current vector position angle corresponding to the zero voltage vector pulse; and determine a magnetic pole position of the rotor based on the first time point, the second time point, the rotating speed of the rotor, the current vector position angle, a preset mapping table, and the magnetic pole initial position.

Abstract: A multi-split air-conditioning system, a fault positioning method, and a fault diagnosis model training method are provided. The multi-split air-conditioning system includes an outdoor unit, a plurality of indoor units, a plurality of electronic expansion valves, and a controller. The controller is configured to: obtain feature data of each of the plurality of indoor units in a case where an electronic expansion valve in the multi-split air-conditioning system is faulty, the feature data including a temperature difference between a temperature value of a liquid pipe correspondingly connected to the indoor unit and a temperature value of an air pipe correspondingly connected to the indoor unit; determine an abnormal indoor unit in the plurality of indoor units according to the feature data of each of the plurality of indoor units; and determine the electronic expansion valve corresponding to the abnormal indoor unit as a faulty electronic expansion valve.

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: An air conditioner system includes an air conditioner and a cloud platform. The air conditioner includes an indoor unit and an outdoor unit. The cloud platform is configured to: in a case where the air conditioner is in a heating mode, determine that the outdoor unit performs one of defrosting and ending defrosting; if determining that operating parameters of the outdoor unit satisfy a first preset condition, send a first sub-instruction to the outdoor unit, so that the outdoor unit starts defrosting according to the first sub-instruction; if determining that the operating parameters satisfy any one of a plurality of second preset conditions, send a second sub-instruction to the outdoor unit, so that the outdoor unit ends defrosting according to the second sub-instruction.

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: A non-intrusive load identification method based on the Power Fingerprint characteristics of the load is provided. The method includes: S1, collecting Power Fingerprint characteristic data of several loads of the same type; S2, after preprocessing Power Fingerprint characteristic data of load, establishing convolution neural network based on attention mechanism to learn load characteristics; S3, using sliding time window algorithm to realize load switching event detection, In order to extract the change of electrical data of user bus before and after the switching event, the non-intrusive load identification problem is converted into the single load identification problem; S4, the load identification is realized, and the extracted electrical information features of single load are identified using the trained model.