Transformer Winding Loose Fault Detection Method Based on Average Displacement Method

Abstract

The invention relates to a transformer winding looseness fault detection method based on an average displacement method, and belongs to the technical field of power transformer safety monitoring. The method comprises the steps that surface vibration data of a transformer with an unloosened winding are measured at preset time intervals within preset time range, the average displacement of the transformer is calculated, and a first average displacement sequence is obtained; surface vibration data of a transformer to be measured are obtained at the same time intervals within the same time range, the average displacement of the transformer is calculated, and a second average displacement sequence is obtained; difference values between the average displacements at the same time delay are calculated, the difference values are added, and evaluation factors for evaluating the winding looseness degree are obtained. The transformer winding looseness fault detection method is easy to implement, easy and convenient to operate and accurate in diagnosis, and the number of needed devices is small.

Description

technical field

[0001] The invention relates to a method for detecting loose faults of transformer windings, belonging to the technical field of power transformer safety monitoring. Background technique

[0002] Since the power transformer leaves the factory, its winding compression degree has gradually decreased with the transportation, installation, operation process and external short-circuit accidents, resulting in loose winding. When the winding is loose, its ability to withstand short-circuit will become worse, and the continuous short-circuit impact or lightning strike will further loosen or even deform the winding, further reducing the ability of the transformer to withstand short-circuit current impact. In addition, loose windings are also prone to vibration under the action of electromagnetic force, and mutual friction will damage the insulation. Over time, loose windings may eventually develop into fatal faults such as serious deformation of the winding or short c...

Examples

Embodiment 1

[0041] In this embodiment, a power transformer produced by Jiangsu Hongyuan Electric Co., Ltd. (the model of the transformer is S9-M-100 / 10) is set for loose winding faults, and fault detection is performed. The parameters of the transformer are shown in Table 1. Show.

[0042] Table 1

[0043] model

join group

voltage ratio

S9-M-100 / 10

Yyn0

10 / 0.4kV

High side I N

Low side I N

Short circuit impedance

5.77A

144.3A

3.98％

[0044] The short-circuit test method used in this embodiment is: short-circuit the three-phase windings on the low-voltage side of the experimental transformer, adjust the applied voltage through a voltage regulator on the high-voltage side, so that the short-circuit current on the low-voltage side is close to the rated current, that is, 140A, and simulate the high current of the transformer during rated operation. When the short-circuit current reaches the rated current, the vibration ...

Embodiment 2

[0062] This embodiment is basically the same as the first embodiment, the difference lies in the selection of j value.

[0063] What needs to be explained here is that the selection of the value of j in this embodiment is very important, and the value should be as large as possible in a meaningful situation, and when the calculation time delay is When the average displacement value between the values ​​is small, it is easy to cause large errors due to the small number of vibration data samples involved in the calculation; if it is too small, it cannot reflect the repeatability and regularity of the evaluation factor △s well. Therefore, usually the optimal value of j should be ,in is not greater than The largest integer of , for example, it is best when j is 3999 in this embodiment.

[0064] When j is 3999, according to past experience, the value of the warning threshold in this embodiment is 1.25×10 -3 j≈5, the fault threshold value is 3.01×10 -3 j≈12.

[0065] Adopt...

technical field

[0001] The invention relates to a method for detecting loose faults of transformer windings, belonging to the technical field of power transformer safety monitoring. Background technique

[0002] Since the power transformer leaves the factory, its winding compression degree has gradually decreased with the transportation, installation, operation process and external short-circuit accidents, resulting in loose winding. When the winding is loose, its ability to withstand short-circuit will become worse, and the continuous short-circuit impact or lightning strike will further loosen or even deform the winding, further reducing the ability of the transformer to withstand short-circuit current impact. In addition, loose windings are also prone to vibration under the action of electromagnetic force, and mutual friction will damage the insulation. Over time, loose windings may eventually develop into fatal faults such as serious deformation of the winding or short c...

Examples

Embodiment 1

[0041] In this embodiment, a power transformer produced by Jiangsu Hongyuan Electric Co., Ltd. (the model of the transformer is S9-M-100 / 10) is set for loose winding faults, and fault detection is performed. The parameters of the transformer are shown in Table 1. Show.

[0042] Table 1

[0043] model

join group

voltage ratio

S9-M-100 / 10

Yyn0

10 / 0.4kV

High side I N

Low side I N

Short circuit impedance

5.77A

144.3A

3.98％

[0044] The short-circuit test method used in this embodiment is: short-circuit the three-phase windings on the low-voltage side of the experimental transformer, adjust the applied voltage through a voltage regulator on the high-voltage side, so that the short-circuit current on the low-voltage side is close to the rated current, that is, 140A, and simulate the high current of the transformer during rated operation. When the short-circuit current reaches the rated current, the vibration ...

Embodiment 2

[0062] This embodiment is basically the same as the first embodiment, the difference lies in the selection of j value.

[0063] What needs to be explained here is that the selection of the value of j in this embodiment is very important, and the value should be as large as possible in a meaningful situation, and when the calculation time delay is When the average displacement value between the values ​​is small, it is easy to cause large errors due to the small number of vibration data samples involved in the calculation; if it is too small, it cannot reflect the repeatability and regularity of the evaluation factor △s well. Therefore, usually the optimal value of j should be ,in is not greater than The largest integer of , for example, it is best when j is 3999 in this embodiment.

[0064] When j is 3999, according to past experience, the value of the warning threshold in this embodiment is 1.25×10 -3 j≈5, the fault threshold value is 3.01×10 -3 j≈12.

[0065] Adopt...

Images