JPH05209922A - Abnormality diagnosing method for transformation equipment - Google Patents

Abstract

PURPOSE:To provide an abnormality diagnosing method for transformation equipment which can make diagnosis with fewer detectors and high accuracy. CONSTITUTION:At transformation equipment 1, a detector for abnormality detection, for example, a partial discharge detector 2 is provided to obtain a detected value Cx, a detector for voltage detection 3 for detecting voltage charged on the transformation equipment 1, a detector for humidity detection 4 for detecting humidity in the ambient atmosphere which is the ambient environmental state, and a detection time detector are provided as detectors for state detection for detecting a quantity of state of the state of the transformation equipment 1 or the ambient environmental state. At a diagnosis part 8, an effective ratios Q (vx), Q (hx) and Q (tx) are obtained using membership functions Q (v), Q (h) and Q (t) in the fuzzy theory from the quantity of state of these detectors for state detection, the detected value Cx by the detector for abnormality detection 2 is corrected by these effective ratios so as to calculate a corrected value Cx', and this corrected value Cx' is compared with a predetermined set value for diagnosis.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method for diagnosing abnormalities in substation equipment and, more particularly, to a method for diagnosing abnormalities in substation equipment suitable for detecting anomalies in power distribution and insulation.

[0002]

2. Description of the Related Art As a conventional abnormality diagnosis method for substation equipment,
Various detectors are provided to monitor insulation abnormality, conduction abnormality, etc., and when a detection value of these detectors exceeds a certain set value, it is determined that an abnormality has occurred. However, in such an abnormality diagnosing method, for example, when detecting an internal partial discharge as an insulation abnormality of a substation device, particularly when it is raining, the partial discharge is detected outside the substation device, for example, an aerial partial discharge on an aerial overhead line. It may not be distinguished from the internal partial discharge that occurs inside the substation, resulting in a false diagnosis. On the other hand, in the abnormality diagnosis method described in JP-A-1-287475, in order to prevent erroneous diagnosis due to changes in the external environment, at the same time as the occurrence of abnormality, lightning, solar radiation, rain, wind, dust It is attempting to remove external noise by detecting the states such as the above, calculating the change in the state before and after the occurrence of the abnormality, and determining whether or not the amount of change is involved in the abnormality diagnosis.

[0003]

However, in the above-mentioned conventional abnormality diagnosis method, it is necessary to provide various detectors for detecting changes in the external environment that may cause malfunctions with respect to the internal abnormality diagnosis. The number of detectors increases, and the processing becomes complicated.

An object of the present invention is to provide a method for diagnosing abnormalities in substation equipment which enables accurate diagnosis with a small number of detectors.

[0005]

In order to achieve the above-mentioned object, the present invention provides an abnormality detection detector for detecting an internal abnormality of a substation equipment and a state quantity of the state of the substation equipment and the state of the surrounding environment. With at least one detector for detecting the state, to judge the effectiveness based on the membership function in the fuzzy theory from the state quantity, taking into account the above-mentioned effectiveness to the detection value by the anomaly detection detector. Characterized by abnormal diagnosis.

[0006]

As described above, the abnormality diagnosing method for the substation equipment according to the present invention uses the membership function in the fuzzy theory to correct the detected value by the abnormality detecting detector, thereby diagnosing the abnormality. By setting the membership function in advance in consideration of the condition of intrusion, even if external noise invades the anomaly detection detector, it can be removed by the correction value by the membership function. It is not necessary to capture individual information in order to remove the external noise itself from the detected value, and therefore the number of detectors can be reduced and accurate detection can be performed.

[0007]

Embodiments of the present invention will be described below with reference to the drawings. FIG. 1 is a block diagram showing an abnormality diagnosing device adopting an abnormality diagnosing method for substation equipment according to an embodiment of the present invention.
It is intended to detect electrical insulation abnormality from internal partial discharge as abnormality of substation equipment. A partial discharge detection detector 2 for detecting partial discharge is provided as an abnormality detection detector outside the closed container 1a of the substation device 1.
Further, as a state detection detector for detecting the state quantity of the substation device 1 or the state quantity of the surrounding environment state, a voltage detection detector 3 for detecting a voltage applied to the substation device 1 and a surrounding environment state A humidity detecting detector 4 for measuring the humidity in the atmosphere is provided. The signals from the detectors 2, 3 and 4 are input to the diagnosis unit 8 via the conversion units 5, 6 and 7 that convert them into electric signals. The diagnostic unit 8 is mainly composed of a computer, and processes the signals from the conversion units 6 and 7 based on a membership function in the fuzzy theory as will be described later in detail. A correction value obtained by correcting the detection value is derived, and a diagnosis is performed by comparing the correction value with a preset setting value. A man-machine device 9 is connected to the diagnosis section 8, and the man-machine device 9 can display a diagnosis result and input a set value.

FIG. 2 is a flow chart showing the processing operation of the above-mentioned diagnosis unit 8. In step S1, the detection value Cx by the partial discharge detection detector 2 is fetched, and at the same time, based on the detection value of the voltage detection detector 3 in step S2, the effective rate Q (vx) is calculated from the membership function Q (v) described later.
And in step S3, an effective rate Q (hx) is calculated from a membership function Q (h), which will be described later, based on the detected value of the humidity detecting detector 4, and in step S4, the effective rate Q (hx) The effective rate Q (tx) is calculated from the membership function Q (t) described later based on the detection time detector.
Then, in step S5, each of the above-mentioned effective rates Q (vx),
A correction value Cx 'is obtained from the product of Q (hz), Q (tx) and the detected value Cx, and this is compared with the preset value Co in step S6. If the correction value Cx' is larger, When an alarm or the like is issued and the correction value Cx 'is less than or equal to the set value Co, the above-described arithmetic processing is repeated to continuously monitor.

Next, the membership functions Q (v), Q (h) and Q (t) described above will be described with reference to FIG. FIG. 3A shows the membership function Q (v), where the horizontal axis represents the voltage v and the vertical axis represents the effective rate Q (vx). As can be seen from the figure, as the voltage v increases, the probability that internal partial discharge will occur increases in proportion thereto, so the effective rate Q (vx) is increased and then made constant. In other words, if an internal partial discharge is detected in a sound phase in which a one-line ground fault or the like has resulted in a high interphase voltage, the possibility that an internal partial discharge may actually occur is greater than in normal detection. Is also big. FIG. 3B shows the membership function Q (h), in which the horizontal axis indicates the humidity h and the vertical axis indicates the effective rate Q (hx). As can be seen from the figure, as the humidity increases, the probability of occurrence of partial discharge from the aerial overhead line or the like increases, especially during rainfall, and the possibility of detecting this with the partial discharge detection detector 2 increases. Therefore, the effective rate Q (hx) gradually decreases when the humidity exceeds a certain rate, and the effective rate Q (hx) decreases when the humidity is 100%.
x) is set to zero. FIG. 3C shows the membership function Q (t), in which the horizontal axis represents time t and the vertical axis represents the effective rate Q (ht). As can be seen from the figure, the effective rate during the daytime is shown. Q (ht) is the lowest and gradually increases before and after that. This is because the frequency band of the internal partial discharge to be detected influences a broadcast wave, a communication wave, or the like, and there is a high possibility that the partial discharge detection detector 2 will erroneously detect this, and this is corrected. The influence of these radio waves varies depending on the region, but changes with time, and increases during the daytime, for example, so the effective rate Q (h
t) is smaller than that at night.

As described above, the flow chart shown in FIG. 2 is based on at least one of these membership functions Q (v), Q (h) and Q (t), and each effective rate Q described above in step S5. (Vx), Q (hx), Q
Since the correction value Cx 'is obtained from the product of (tx) and the detected value Cx, when detecting the internal partial discharge, all elements that cause noise for the partial discharge detection detector 2 are assumed, and this is used as the other value. It is not necessary to detect with a detector and subtract from the detected value Cx, and highly accurate diagnosis can be performed with a small number of detectors.

FIG. 4 is a flow chart showing a processing operation in the case of diagnosing an energization abnormality as an abnormality of the substation equipment in the diagnosis unit 8 described above. In this embodiment, a temperature detection detector is provided as a conduction abnormality detection detector in order to detect a conduction abnormality from a temperature rise due to an increase in contact resistance, and a membership function for correcting the detection value by this temperature detection detector. As the above, the state quantities of the energizing current, the temperature, the solar radiation and the detection time are used, and the detectors are respectively provided to obtain these state quantities. In step S7, a detection value Ax is taken in by a detector for detecting a conduction abnormality provided to detect an abnormality in conduction from a temperature rise due to an increase in contact resistance, and at the same time, based on the detection value of the detector for current detection in step S8. The effective rate Q (ix) is calculated from the membership function Q (i) described later, and the effective rate Q (θx) is calculated from the membership function Q (θ) described later based on the detection value of the temperature detecting detector in step S9. ) Is calculated, and the effective rate Q (sx) is calculated from the membership function Q (s) described later based on the detection value of the solar radiation amount detector in step S10, and further in step S11, the diagnosis unit 8 shown in FIG. The effective rate Q (tx) is calculated from the above-mentioned membership function Q (t) based on the detection time detector in. Then, in step S12, each effective rate Q (ix), Q (θx), Q
A correction value Ax ′ is obtained from the product of (sx) and Q (tx) and the detected value Ax, and this is compared with a preset value Ao in step S13. If the correction value Ax ′ is larger, an alarm is issued. Etc., while the correction value Ax ′ is the set value A
In the case of o or less, the above arithmetic processing is repeated to continuously monitor.

Next, the membership functions Q (i), Q (θ), Q (s) and Q (t) described above will be described with reference to FIG. The membership function Q is shown in FIG.
(I), the horizontal axis represents the conduction current i, and the vertical axis represents the effective rate Q (ix). As can be seen from the figure, when the energizing current i increases, the probability of occurrence of energization abnormality increases accordingly, so the effective rate Q (ix) is increased. The membership function Q (θ) is shown in FIG.
The temperature is shown on the horizontal axis and the effective rate Q (θx) is shown on the vertical axis.
Is taking. As can be seen from the figure, as the temperature rises, there is a greater possibility that this will be detected by the temperature detection detector serving as the current flow abnormality detection detector, so the effective rate Q (hx) will increase as the temperature rises. I am trying to decrease gradually. The figure (c) shows the membership function Q (s), where the horizontal axis shows the amount of solar radiation s and the vertical axis shows the effective rate Q (ht). As can be seen from FIG. The effective rate Q (ht) is also increased / decreased in proportion to. Also, FIG. 3D is the same as the membership function Q (t) described in FIG.

As described above, in the flow chart shown in FIG. 4, these membership functions Q are calculated in step S12.
Effective rates Q (ix), Q (θ) obtained based on at least one of (i), Q (θ), Q (s) and Q (t)
x), Q (sx) and Q (tx), and the correction value Ax 'is obtained from the product of the detected value Ax. Therefore, when detecting the energization abnormality, all elements that cause noise for the energization abnormality detection detector are detected. Assuming that this does not need to be detected by another detector and subtracted from the detection value Ax, a highly accurate diagnosis can be performed with a small number of detectors. In this way, the effective rate of the detection value Ax by the detector for detecting energization abnormality is highest at night when the energization current is large, the temperature is low, and there is no insolation. At this time, a highly sensitive and highly accurate diagnosis can be made. It will be possible.

FIG. 6 is a flowchart showing another processing operation of the diagnosis unit 8 shown in FIG. 1, that is, a control interval of detection by each detector. To do. As described above, the state quantities from various detectors are fetched due to the abnormality in the energization and are processed by the diagnosis unit 8 shown in FIG. 1. The processing interval is set to, for example, A in step S14 shown in FIG. There is. Furthermore,
This interval can be adjusted by checking whether or not the state quantity has changed in step S15. That is, when there is a change in the state quantity, this is detected in step S15, the detection interval is changed in step S16, and step S1 is changed.
At 7, the acquisition and diagnosis of the effective rate from each membership function shown in FIG. 6 is performed. This is because if the energizing current of the substation equipment is small, it is more effective to monitor it by making the detection interval coarse, even if the contact resistance increases a little. This is because it is possible to easily detect an increase in contact resistance and it is expected that the contact resistance will change from moment to moment, so it is more effective to closely monitor the detection intervals for monitoring.

7 and 8 show the detection timing and the detection interval correction characteristic associated with the change of the detection interval. As shown in FIG. 8, the energization current i on the horizontal axis is the set value i.
Detection intervals A, B, C and D are defined in relation to B, iC and iD. As shown in FIG. 7, when the energizing current i changes with respect to the normal detection interval A, a change command is issued so that the detection interval corresponds to it, and the detection timing becomes as shown in the figure. Therefore, effective diagnosis is made at the detection intervals according to the above-mentioned situations. It should be noted that such a change in the detection interval is not limited to the case of the abnormality detection of energization, but can be similarly applied to the case of the internal partial discharge detection.

[0016]

As described above, according to the present invention, since the correction value is added to the detection value of the abnormality detector using the membership function in the fuzzy theory, it is considered as external noise to remove external noise. It is not necessary to provide a detector for detecting all of the above, and accurate diagnosis can be performed with a small number of detectors.

[Brief description of drawings]

FIG. 1 is a block diagram of an abnormality diagnosing apparatus adopting an abnormality diagnosing method for substation equipment according to an embodiment of the present invention.

FIG. 2 is a flowchart showing one processing operation of the abnormality diagnosis device shown in FIG.

FIG. 3 is a characteristic diagram of each membership function shown in FIG.

4 is a flowchart showing another processing operation of the abnormality diagnosis device shown in FIG.

5 is a characteristic diagram of each membership function shown in FIG.

6 is a flowchart showing still another processing operation of the abnormality diagnosis device shown in FIG.

FIG. 7 is an explanatory diagram showing an example of detection timing by the method of FIG.

FIG. 8 is a characteristic diagram showing an example of detection intervals by the method of FIG.

[Explanation of symbols]

 1 Substation equipment 2 Partial discharge detector 3 Voltage detection detector 4 Humidity detection detector 5-7 Conversion unit 8 Diagnostic unit Q (v), Q (h), Q (t) Membership function Q (vx), Q (vx), Q (vx) Effective rate Cx Detection value Cx 'Correction value

Claims (4)

[Claims] 1. A method for diagnosing an abnormality in a substation device, comprising: providing at least one detector for abnormality detection in the substation device; and comparing the detected value by the detector for abnormality detection with a predetermined set value for diagnosis. Is provided and at least one detector for detecting the state quantity of the state of the surrounding environment is provided, and the effectiveness is judged from the state quantity based on the membership function in the fuzzy theory, and the above-mentioned effectiveness is added to the detected value. A method for diagnosing abnormalities in substation equipment, which is characterized by diagnosing abnormalities in consideration of the above. 2. The detector according to claim 1, wherein the abnormality detection detector is a partial discharge detector, and the state detection detector detects at least one of an applied voltage, humidity and detection time. A method for diagnosing abnormalities in substation equipment characterized by the above. 3. The detector according to claim 1, wherein the abnormality detection detector is a conduction abnormality detection detector, and the state detection detector is at least one of conduction current, temperature, insolation and detection time. A method for diagnosing abnormalities in substation equipment, which is characterized by detecting one of them. 4. The abnormality diagnosis method for substation equipment according to claim 1, wherein a detection interval by the abnormality detection detector is adjustable by at least one of the state quantities.