The present application provides a detecting method of an operating state of a power supply and a detecting apparatus, where the power supply includes a primary circuit, a transformer and a secondary circuit. The secondary circuit includes a secondary current detecting unit, a temperature detecting unit and a secondary controlling unit. Firstly the secondary current detecting unit detects a current value of the secondary circuit, then the secondary controlling unit compares the current value of the secondary circuit with a preset current threshold. When the current value of the secondary circuit is less than or equal to the preset current threshold, the temperature detecting unit detects a temperature value of the power supply and the secondary controlling unit determines the operating state of the power supply according to the acquired temperature value of the power supply.

In at least some cases, an embodiment of an electric power industry structure monitor is arranged as a distribution transformer monitor. In at least some cases, an electric power industry structure monitor is arranged as a tilt sensor monitor. In at least some cases an electric power industry structure monitor is arranged as a high-voltage tower (e.g., power pole) monitor. In some cases, the electric power industry structure monitor includes a housing arranged for positioning on an electric power industry structure, and a sensor arranged in the housing. The sensor is positioned to generate digital data associated with at least one environmental condition that exists proximal to the electric power industry structure monitor. The monitor also includes a processing circuit arranged to determine from the generated digital data that the at least one environmental condition has crossed a threshold.

In at least some cases, an embodiment of an electric power industry structure monitor is arranged as a distribution transformer monitor. In at least some cases, an electric power industry structure monitor is arranged as a tilt sensor monitor. In at least some cases an electric power industry structure monitor is arranged as a high-voltage tower (e.g., power pole) monitor. In some cases, the electric power industry structure monitor includes a housing arranged for positioning on an electric power industry structure, and a sensor arranged in the housing. The sensor is positioned to generate digital data associated with at least one environmental condition that exists proximal to the electric power industry structure monitor. The monitor also includes a processing circuit arranged to determine from the generated digital data that the at least one environmental condition has crossed a threshold.

Transformer assembly including a first transformer stage having a plurality of first-stage transformer cells; and a second transformer stage. An input of the second transformer stage is connected to an output of the first transformer stage. A lightning impulse breakdown voltage of a transformer cell of the second stage is at least double of a lightning impulse breakdown voltage of transformer cells of the first stage.

Transformer assembly including a first transformer stage having a plurality of first-stage transformer cells; and a second transformer stage. An input of the second transformer stage is connected to an output of the first transformer stage. A lightning impulse breakdown voltage of a transformer cell of the second stage is at least double of a lightning impulse breakdown voltage of transformer cells of the first stage.

A current transformer includes a housing including generally cylindrical outer and inner walls defining an internal chamber, a front face enclosing one end of the internal chamber, a base, and a central opening defined by the inner wall. A generally toroidal current transformer core is disposed within the internal chamber. A secondary wiring is disposed about the transformer core and is configured to generate a current in response to magnetic flux in the transformer core. A pin housing is disposed on the front face of the housing adjacent the base. The pin housing has electrically conductive pins. A test wire passes through the central opening. The secondary wiring is electrically connected to a first pair of the pins and the test wire is electrically connected to a second pair of the pins.

A current transformer includes a housing including generally cylindrical outer and inner walls defining an internal chamber, a front face enclosing one end of the internal chamber, a base, and a central opening defined by the inner wall. A generally toroidal current transformer core is disposed within the internal chamber. A secondary wiring is disposed about the transformer core and is configured to generate a current in response to magnetic flux in the transformer core. A pin housing is disposed on the front face of the housing adjacent the base. The pin housing has electrically conductive pins. A test wire passes through the central opening. The secondary wiring is electrically connected to a first pair of the pins and the test wire is electrically connected to a second pair of the pins.

The disclosure discloses a power transformer winding fault positioning method based on deep convolutional neural network integrated with visual identification, including 1) a winding equivalent circuit is established, and a transfer function thereof is calculated; 2) a sine wave excitation source is set at one end of the power transformer winding to obtain the amplitude-frequency characteristic curve of each winding node; 3) circuits under various fault statuses are subjected to scanning frequency response analysis to extract amplitude-frequency characteristics; 4) a feature matrix is established based on the obtained amplitude-frequency characteristics; 5) scanning frequency response analysis is performed on the diagnosed power transformer to form a feature matrix; 6) the feature matrix is converted into an image, simulation and historical detection data are used as a training set, and a deep convolutional neural network is input for training; 7) diagnosed transformer is subjected to fault classification and positioning.

The disclosure discloses a power transformer winding fault positioning method based on deep convolutional neural network integrated with visual identification, including 1) a winding equivalent circuit is established, and a transfer function thereof is calculated; 2) a sine wave excitation source is set at one end of the power transformer winding to obtain the amplitude-frequency characteristic curve of each winding node; 3) circuits under various fault statuses are subjected to scanning frequency response analysis to extract amplitude-frequency characteristics; 4) a feature matrix is established based on the obtained amplitude-frequency characteristics; 5) scanning frequency response analysis is performed on the diagnosed power transformer to form a feature matrix; 6) the feature matrix is converted into an image, simulation and historical detection data are used as a training set, and a deep convolutional neural network is input for training; 7) diagnosed transformer is subjected to fault classification and positioning.

The invention relates to a method for personal protection during high-voltage testing on a test object, the method comprising the steps of outputting a high-voltage alternating current for the test object by means of a high-voltage generation device, which has a high-voltage transformer for generating the high-voltage alternating current. The method further has the steps of determining a curve over time of at least one electrical variable at the high-voltage transformer during the output of the high-voltage alternating current, and ending the output of the high-voltage alternating current on the basis of the curve over time of the at least one electrical variable.