A non-intrusive, in-situ power method for measuring the loss associated with magnetic components (for example, an inductor) of a power converter is provided. The method involves first capturing a first set of voltage and current waveforms from the power converter. An additional capacitor is then connected to the power converter and a second set of voltage and current waveforms are captured. Based on the first set of waveforms and the second set of waveforms, a timing skew between the current and voltage waveforms captured from the power converter may be determined. This timing skew may then be used to determine the loss of the inductor. The loss may be used to design an optimized power converter.

The invention relates to a method for detecting a hardware configuration of an on-board device in an aircraft, capable of receiving as input a setpoint current (Ic), and of producing as output a response current (I), the method comprising the following steps: a) Send, to the input of the device, a setpoint current (Ic) at a given time (t.sub.0); b) Measure one or more values of the response current (I) at the output of the device in a measurement-time interval defined between two instants (t.sub.1 and t.sub.2) after the initial instant (t.sub.0); c) Infer the hardware configuration of the device, doing so from one or more values of the response current (I) measured.

Arrangement of interleaved secondary windings for power transformers that reduces the thickness of secondary windings and allows to build transformers with an odd number of layers.

A method for identifying asymmetrical vibrations during the operation of an electric device which is connected to a high-voltage grid: Operational noise of the electric device is detected using acoustic sensors which output measured values. The measured values and/or values derived therefrom are transmitted to a communication unit via a close-range communication connection and then transmitted to a data processing cloud via a far-range communication connection. The measured values and/or the values derived from the measured values are separated into frequency components by the data processing cloud using a Fourier transformation, thereby obtaining a frequency spectrum. Odd and even frequency components of the frequency spectrum are ascertained on the basis of a base frequency of the high-voltage supply grid and put into a ratio R relative to one another. The presence of asymmetrical vibrations is indicated if the ratio R exceeds a specified threshold.

A method of detecting a rotor bar fault of an electrical machine, said method comprising, acquiring a set of online signals from the electrical machine over a period, said set of online signals comprising at least one vibration signal, and a summed signal of magnetic flux signals, each magnetic flux signal obtained from a respective flux sensor positioned on an external surface of the electrical machine; extracting an online anomaly indicator value from the set of online signals; comparing the online anomaly indicator value with a baseline anomaly indicator value of a corresponding loading condition; and determining presence of the broken or cracked rotor when the online anomaly indicator value deviates from the baseline anomaly indicator value by a threshold.

A multilayer board includes a first insulator layer including a first coil pattern thereon, a second insulator layer including a second coil pattern thereon, a third insulator layer including a third coil pattern thereon, a first terminal on the first insulator layer and connected to one end of the first coil pattern, a first floating pattern on the first insulator layer and not connected to the first coil pattern, and a second terminal on the third insulator layer and connected to one end of the third coil pattern. The first, second, and third insulator layers are sequentially laminated. The first, second, and third coil patterns are respectively electrically connected in sequence. The first floating pattern overlaps the second coil pattern when viewed from a laminating direction.

The invention relates to a high-voltage transformer which is configured as a toroidal transformer. The high-voltage transformer has a magnetizable core (310) and a high-voltage winding (330) and a low-voltage winding (320) around the magnetizable core (310). The high-voltage winding (330) is embodied at least partially as a pilgrim step winding.

Disclosed is an intelligent on-line diagnosis method for winding deformation of power transformer. When a transformer is subjected to short-circuit shock or transportation collision, transformer windings may undergo local twisting, swelling or the like under the action of an electric power or mechanical force, which is called winding deformation and will cause a huge hidden danger to the safe operation of the power network. Commonly used diagnosis methods for winding deformation are all off-line diagnosis methods, which have the disadvantages that transformers need to be shut down and highly skilled operators are required. The present invention provide an intelligent on-line diagnosis method for winding deformation on the basis of combination of information entropy and support vector machine. By carrying out feature extraction of current and voltage signals based on permutation entropy and wavelet entropy, integrating the variation of the monitoring indicators of the power transformers in complexity, time-frequency domain and the like and automatically learning the diagnostic logic from fault features through the machine learning algorithm, intelligent diagnosis of winding deformation is realized, thereby reducing labor costs and improving diagnosis efficiency.

Disclosed is an intelligent on-line diagnosis method for winding deformation of power transformer. When a transformer is subjected to short-circuit shock or transportation collision, transformer windings may undergo local twisting, swelling or the like under the action of an electric power or mechanical force, which is called winding deformation and will cause a huge hidden danger to the safe operation of the power network. Commonly used diagnosis methods for winding deformation are all off-line diagnosis methods, which have the disadvantages that transformers need to be shut down and highly skilled operators are required. The present invention provide an intelligent on-line diagnosis method for winding deformation on the basis of combination of information entropy and support vector machine. By carrying out feature extraction of current and voltage signals based on permutation entropy and wavelet entropy, integrating the variation of the monitoring indicators of the power transformers in complexity, time-frequency domain and the like and automatically learning the diagnostic logic from fault features through the machine learning algorithm, intelligent diagnosis of winding deformation is realized, thereby reducing labor costs and improving diagnosis efficiency.

A solenoid valve diagnostic apparatus includes a plurality of solenoid valves that open or close an entrance/exit opening of a fuel tank, a current sensor that measures an operating current of the solenoid valves, and a controller that diagnoses whether the solenoid valves fail, based on the operating current measured by the current sensor.