OLTC fault vibration and current signal combined monitoring device and monitoring method thereof

Abstract

The invention discloses an on-load tap-changer (OLTC) fault vibration and current signal combined monitoring device, which comprises three vibration sensors, a current sensor, a signal conditioning module, a data acquisition unit and an upper computer, and is characterized in that the three vibration sensors are respectively arranged on the outer wall of a transformer provided with an OLTC; the current sensor is installed on a power line of the OLTC driving motor and used for detecting a current signal of the OLTC driving motor. The three vibration sensors and the current sensor are respectively connected with the input end of the signal conditioning module; the output end of the signal conditioning module, the data acquisition unit and the upper computer are connected in sequence; the OLTC operation condition is diagnosed and evaluated by detecting and analyzing the vibration acoustic signal and the driving motor current signal generated when the OLTC acts, early mechanical problems can be found in time, sudden accidents are avoided, and safe power supply is guaranteed. The invention further discloses an OLTC fault vibration and current signal combined monitoring method.

Description

Technical Field

[0001] The present invention relates to a combined monitoring device and method for OLTC fault vibration and current signals. By using sensor technology, data processing, and spectrum analysis, the device aims to more accurately diagnose the operating status of the OLTC by fusing multiple parameters such as discharge signals and vibration signals and verifying each other. Background Art

[0002] On-load tap-changing transformers (OLTCs) play a vital role in interconnecting power grids, regulating reactive power flows, and stabilizing voltage at load centers. They maintain high voltage levels, optimize reactive power, and thus reduce line losses and improve the economic efficiency of the grid, leading to their widespread use in power grids. On-load tap-changers (OLTCs), key components in the on-load voltage regulation of tap-changing transformers, not only reduce and prevent large voltage fluctuations but also enforce load flow distribution, optimize reactive and active power output, ensure safe and reliable operation of the power system, and increase grid dispatch flexibility. However, as the use of on-load tap-changing transformers in power grids increases, so too does the incidence of OLTC failures.

[0003] At present, there is basically no testing of OLTC in operation in China. The main method is to test the load switch by testing the DC resistance of the transformer and adjusting the OLTC gear, and to adopt the method of offline regular maintenance. Regular maintenance has the following disadvantages: (1) The workload of testing and inspection is large. During the test, not only is it required to lift out the core of the load switch, but also it is necessary to perform tedious processes such as oil drainage and oil filling, disassembly of the transmission mechanism and sealing cover, and at the same time consume a lot of manpower and material resources. (2) There are defects that are not easy to recover, especially some models of composite load switches, which have more "brush-type" contact structures in the transition resistance circuit. (3) The workload of status maintenance is large. The maintenance of the load switch is large (referring to the core lifting inspection), and in fact, the inspection and overhaul are often combined. (4) Faults that change the working sequence of the load switch cannot be detected.

[0004] An OLTC switching operation involves a series of events, such as spring loading, selector switch actuation, transfer switch switching, and contact collision. These events contain a wealth of mechanical vibration information, and a specific timing sequence must be met to ensure proper OLTC switching. Currently, researchers both domestically and internationally widely believe that using vibroacoustic principles to monitor OLTC operating status is a low-cost and effective diagnostic technique. Research has already been conducted on vibroacoustic monitoring of OLTCs, with considerable success.

[0005] The motor operating mechanism is the position control and transmission device for the OLTC switching operation. Mounted on the side of the transformer tank, it is connected to the tap changer via a horizontal drive shaft, a bevel gear box, and a vertical drive shaft. It serves as the power source for the OLTC's other mechanisms. During the OLTC switching process, changes in the performance of the energy storage spring or jamming of the mechanism inevitably cause changes in the motor's drive torque, which in turn causes changes in the drive motor current. Serious faults such as a temporary jam in the drive mechanism, increased friction due to oil shortage in connecting rods, or insufficient drive torque from a drive motor failure can all be reflected in changes in the drive motor current. Therefore, diagnosing drive mechanism faults based on current is an ideal method. However, most research focuses on analyzing or solving specific issues at a theoretical or technical level. To date, no research has demonstrated multi-dimensional monitoring of OLTC operating performance.

[0006] OLTCs are primarily maintained through regular preventive testing, periodic inspections, and troubleshooting. As power grids increasingly demand higher reliability from power equipment, the drawbacks of these traditional maintenance methods are becoming increasingly apparent. First, regular preventive testing and maintenance based on operating hours and the number of operations fail to account for the variability of individual tapchangers, making it difficult to promptly detect and monitor latent defects that can fluctuate significantly over a short period of time. Furthermore, they are unable to address OLTC failures that occur between inspections or between tests. Second, regular preventive testing and maintenance based on operating hours and the number of operations fail to account for the actual operating state of the OLTC. This leads to excessive testing, maintenance, and unnecessary power outages when abnormal OLTCs are rare. Furthermore, OLTC maintenance primarily relies on regular offline maintenance, rarely requiring standalone outages. Instead, OLTCs are often inspected along with transformers to minimize transformer outages. The inability to monitor the OLTC's status and provide early warnings during operation prevents early detection and proper maintenance arrangements, potentially exacerbating the situation and potentially expanding the scope of the fault. Existing mature technical approaches include:

[0007] (1) Dynamic impedance measurement method: During transformer OLTC overhaul or core inspection, the OLTC transition resistance is measured using the bridge method. The transition resistance measurement results should comply with the manufacturer's specifications and the deviation from the nameplate value should not exceed ±10%. The OLTC operation status is analyzed based on the comparison results. However, this method is relatively difficult to implement.

[0008] (2) Oil chromatography: This method uses the content of dissolved gas in the insulating oil in the OLTC tank to determine whether the running OLTC has potential faults such as overheating and discharge. However, this method is not very real-time.

[0009] (3) Thermal image analysis: Based on the principle of thermal radiation, the heat distribution of each OLTC component is detected. The obtained heat distribution image is compared with the normal distribution image to determine the OLTC operating status. However, the operating OLTC is isolated from the outside world by insulating oil and the outer shell. The measured heat distribution and temperature are significantly different from the actual ones, which may lead to missed judgments and misjudgments.

[0010] (4) Simply detecting and analyzing voiceprint signals. The establishment of a fault diagnosis model can realize the identification, classification and early warning of some faults, but the establishment and training of the model lack the support of a large amount of fault data.

[0011] However, the above methods can only identify a limited number of fault types, making it impossible to timely understand the true operating status of OLTCs and failing to meet the needs of condition-based maintenance. A single detection method faces challenges such as limited detection accuracy and fault diagnosis, and is unable to comprehensively, promptly, and effectively detect common OLTC fault hazards. Summary of the Invention

[0012] The present invention aims to overcome the shortcomings of the prior art by providing a device for monitoring OLTC fault vibration and current signals, focusing on the timing of various mechanical events occurring during OLTC switching operations, changes in the torque of the drive motor, and changes in its mechanical properties. By detecting and analyzing the vibration and acoustic signals generated during OLTC operation and the drive motor current signals, the device can diagnose and evaluate the OLTC operating status, enabling timely detection of early mechanical problems, preventing unexpected accidents, and ensuring safe power supply.

[0013] In a first aspect, the present invention provides an OLTC fault vibration and current signal joint monitoring device, comprising three vibration sensors, a current sensor, a signal conditioning module, a data acquisition unit, and a host computer, wherein:

[0014] The three vibration sensors are respectively installed on the outer wall of the transformer corresponding to the three phases of the OLTC, so as to simultaneously collect the vibration signals on each terminal when the three-phase OLTC is in operation;

[0015] The current sensor is installed on the power line of the OLTC drive motor and is used to detect the current signal of the OLTC drive motor;

[0016] The three vibration sensors and one current sensor are respectively connected to the input end of the signal conditioning module;

[0017] The output end of the signal conditioning module, the data acquisition unit and the host computer are connected in sequence.

[0018] In the above-mentioned OLTC fault vibration and current signal joint monitoring device, the vibration sensor adopts a piezoelectric acceleration sensor integrated with a charge amplifier to convert the vibration acceleration signal into a proportional voltage analog signal.

[0019] In the above-mentioned OLTC fault vibration and current signal joint monitoring device, the OLTC drive motor adopts a single-phase motor or a three-phase motor. When the OLTC drive motor is a three-phase motor, the current sensor is installed on any phase of the three-phase power line of the OLTC drive motor.

[0020] In the above-mentioned OLTC fault vibration and current signal joint monitoring device, the vibration sensor is connected to the outer wall of the transformer equipped with the OLTC by bolts.

[0021] In a second aspect, the present invention provides a method for jointly monitoring OLTC fault vibration and current signals, which uses the aforementioned OLTC fault vibration and current signal joint monitoring device to implement OLTC fault identification and diagnosis, including the following steps:

[0022] The three vibration sensors respectively collect vibration signals on each terminal when the three-phase OLTC is in operation. Each vibration signal is amplified and shaped by the signal conditioning module and then sent to the host computer through the data acquisition unit.

[0023] The current sensor detects the current signal of the OLTC drive motor, which is amplified and shaped by the signal conditioning module and then sent to the host computer through the data acquisition unit;

[0024] The host computer performs comprehensive OLTC fault diagnosis and trend prediction based on the three collected vibration signals and one current signal, a Gaussian Membership Function (GMF), and the introduction of weighted fuzzy comprehensive evaluation parameters in combination with a fuzzy rule base.

[0025] In the above-mentioned joint monitoring method for OLTC fault vibration and current signals, the change in the motor drive torque can be indirectly determined by monitoring the current signal of the OLTC drive motor. Since the sampled value of the drive current itself is constantly changing, it is difficult to detect slight changes in the motor drive torque caused by mechanical reasons by directly observing the instantaneous sampled value of the current. The absolute value sum method is used to determine the change in the motor drive torque, which is more effective. The principle formula is as follows:

[0026]

[0027] In formula (1), i(k) is the discrete sampling result of the current signal of the OLTC drive motor. During diagnosis, the envelope of the current signal is obtained and a threshold is set for monitoring. Once the current signal exceeds the limit, it indicates that the OLTC drive motor has a mechanical fault.

[0028] In the above-mentioned method for jointly monitoring OLTC fault vibration and current signals, in the fuzzy logic system, the membership function is used to describe the degree of attribution of the input data. The normal membership function expression is as follows:

[0029]

[0030] In formula (2), x is the characteristic value of the input signal, the collected vibration signal or current signal; c is the mean value of the characteristic under normal conditions; σ is the standard deviation of the characteristic, which determines the broadening degree of the curve; μ(x) ranges from [0, 1], indicating the degree to which the signal belongs to a healthy state; when x is close to c, the membership degree μ(x) is close to 1, indicating that the signal is normal; when x is far away from c, the membership degree decreases, indicating that the signal is abnormal.

[0031] In the above-mentioned method for jointly monitoring OLTC fault vibration and current signals, three vibration sensors respectively collect three vibration signals of the OLTC to monitor the mechanical operating status of different parts of the OLTC. The vibration features extracted from the vibration signals include:

[0032] Peak-to-peak PPV, which measures the maximum amplitude change and reflects the impact intensity of OLTC action;

[0033] Root mean square value RMS, which measures the vibration energy level;

[0034] Peak factor Pk, used to detect sudden shocks;

[0035] Frequency characteristics, the main frequency components extracted after FFT analysis;

[0036] According to the data under normal conditions, calculate the mean value c of each vibration characteristic v and standard deviation σ v , and establish the GMF model μ of vibration characteristics V (V i ):

[0037]

[0038] In formula (3), V1, V2, and V3 are the vibration characteristics of the three vibration signals; c v is the mean value of the vibration characteristics, σ v is the standard deviation of the vibration characteristics, when μ V (V i ) is low, indicating abnormal OLTC vibration and possible mechanical failure.

[0039] In the above-mentioned method for jointly monitoring OLTC fault vibration and current signals, the current characteristics extracted from the current signal of the drive motor include:

[0040] Root mean square value RMS_I; reflects the overall change of current;

[0041] Total harmonic distortion (THD), used to detect nonlinear load problems;

[0042] Current transient change rate dI / dt, identifying abnormal current mutations;

[0043] According to the data under normal conditions, calculate the mean value c of each current characteristic I and standard deviation σ I , and establish the GMF model μ of current characteristics I (I):

[0044]

[0045] In formula (4): I is the current characteristic of the current signal of the driving motor, c I is the mean of the current characteristics, and the standard deviation σ I is the standard deviation of the current characteristics; if μ I (I) is lower than the set threshold, it indicates that the current is abnormal and an electrical fault may occur.

[0046] In the above-mentioned joint monitoring method of OLTC fault vibration and current signals, in order to comprehensively judge the health status of OLTC, a weighted fuzzy comprehensive evaluation parameter μ is introduced. 综合 , calculate the comprehensive health membership of multiple signals:

[0047] μ 综合 =ω1μ V (V1)+ω2μ V (V2)+ω3μ V (V3)+ω4μ I (I) (5)

[0048] In formula (5), ω1, ω2, ω3, and ω4 are weight coefficients, representing the contribution of each signal to fault detection. The weights are adjusted through experimental data analysis to optimize the diagnosis effect. When μ 综合 When the value drops below the set threshold, the OLTC is determined to be in an abnormal state, and the fault is further classified based on the fuzzy rule base. The fuzzy rule base includes the following fuzzy rules for fault classification and identification:

[0049] Abnormal vibration but normal current, μ V Low, μ I High, it is judged to be mechanical wear and poor contact;

[0050] Abnormal current but normal vibration, μ V High, μ I Low, it is determined that the drive motor is overloaded or electrically short-circuited;

[0051] Both vibration and current are abnormal, μ V Low, μ I Low, it is determined that the OLTC has a comprehensive fault, contact erosion and motor abnormality;

[0052] Both are normal, μ V High, μ I If high, the OLTC is judged to be in a healthy state.

[0053] The OLTC fault vibration and current signal combined monitoring device and monitoring method of the present invention detects vibration signals and current signals and jointly analyzes the status information of each action event generated by OLTC operation, effectively judging whether the contact opening and closing, switching is in place, and whether the operating mechanism is stuck or malfunctioning.

[0054] Compared with the existing technology, the present invention has the following advantages: according to the timing of various mechanical events occurring during OLTC switching operations, the changes in the torque of the drive motor, and the changes in its mechanical properties, a method is proposed to diagnose and evaluate the OLTC operating status by detecting and analyzing the vibration and acoustic signals and the drive motor current signals generated during OLTC operation. This can timely detect early mechanical problems, avoid sudden accidents, and ensure safe power supply. This method undoubtedly has important economic and social benefits and will have broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 Schematic diagram of the structure of an OLTC fault vibration and current signal joint monitoring device of the present invention;

[0056] Figure 2 Schematic diagram of the envelope curve of the vibration signal;

[0057] Figure 3 Schematic diagram of spectrum analysis of vibration signals. DETAILED DESCRIPTION

[0058] In order to enable those skilled in the art to better understand the technical solution of the present invention, the specific implementation methods thereof are described in detail below with reference to the accompanying drawings:

[0059] See also Figure 1 , an OLTC fault vibration and current signal joint monitoring device includes three vibration sensors 1, a current sensor 2, a signal conditioning module 3, a data acquisition unit 4 and a host computer 5.

[0060] Three vibration sensors 1 are bolted to the outer wall of a transformer 10 corresponding to the three phases of the OLTC, respectively, to simultaneously collect vibration signals from each terminal during operation of the three-phase OLTC. A current sensor 2 is installed on the power line of the OLTC drive motor to detect the current signal of the OLTC drive motor. The OLTC drive motor can be a single-phase motor or a three-phase motor. When the OLTC drive motor is a three-phase motor, the current sensor is installed on any phase of the three-phase power line of the OLTC drive motor.

[0061] The three vibration sensors 1 and the current sensor 2 are respectively connected to the input end of the signal conditioning module 3; the output end of the signal conditioning module 3, the data acquisition unit 4 and the host computer 5 are connected in sequence.

[0062] Vibration sensor 1 uses a piezoelectric accelerometer with an integrated charge amplifier to convert the vibration acceleration signal into a proportional voltage analog signal. This analog voltage signal is then converted by signal conditioning module 3 and data acquisition unit 4 and sent to host computer 5 for data processing. Current sensor 2 uses a current clamp that directly senses the current signal into the system. The obtained vibration signal and drive motor current signal undergo hardware data processing and are then stored in host computer 5, which is a portable computer.

[0063] Therefore, mechanical faults in the OLTC can be discovered by simultaneously detecting two signals: one is the vibration signal detected by the acceleration sensor when the OLTC is in operation, and the other is the current signal of the drive motor detected by the current clamp.

[0064] Vibration sensor 1 uses an acceleration sensor with a frequency range of 0.5 to 12 kHz to monitor OLTC and transformer vibrations, and a piezoelectric sensor with higher sensitivity at lower frequencies to monitor OLTC operation. During installation, first attach the base of vibration sensor 1 to the outer wall of the transformer, perpendicular to the OLTC main mechanism and as close as possible. Then, connect the vibration sensor 1 to its base and secure it to the transformer box.

[0065] Under normal operating conditions, the current of an OLTC drive motor is generally low, around 1A. However, when a fault such as a jam occurs, the current increases rapidly, typically reaching a maximum value of less than 10A. When selecting a current sensor, consider its operating temperature range, frequency response, and output range (i.e., transformation ratio). During installation, attach the current clamp to the OLTC drive motor's power cable (for single-phase motors) or any phase of the three-phase power cable (for three-phase motors).

[0066] Analysis of Mechanical Vibration Characteristics: Because mechanical vibration is susceptible to various random factors, it often exhibits certain chaotic characteristics. In practical engineering, many nonlinear system vibrations exist, which require the use of chaos theory for appropriate explanation. By observing the changes in the mechanical vibration modes of OLTCs from the perspective of chaotic dynamics and studying the characteristic parameters of chaotic dynamics that reflect the changes in different vibration modes during OLTC operation, this study provides a new scientific basis for online monitoring of OLTC mechanical conditions and fault diagnosis.

[0067] Envelope Curve Analysis: See Figure 2 Analytical software is used to generate envelope curves for the mechanical vibration signal spectrum, generating high-frequency and low-frequency envelope curves. To better analyze the spectrum, these envelope curves filter out irrelevant vibration data, retaining only the diagnostic-relevant portion of the original mechanical vibration data. The drive motor current envelope is the peak envelope of the original drive motor current signal (not the effective value). By superimposing high and low frequencies, calculating overlap, analyzing odd and even gear positions, and analyzing longitudinal trends on the envelope curves, faults such as OLTC switching asynchrony, brake failure, switch slippage, insufficient lubrication, or jamming can be effectively identified.

[0068] Energy Spectrum Analysis: See Figure 3 The occurrence and development of equipment failures often cause changes in the vibration signal frequency, primarily manifesting as the generation of new frequency components and an increase in the amplitude of existing frequencies. Therefore, energy spectrum analysis is a key tool in mechanical fault diagnosis. OLTC switching is a transient signal carrying a certain amount of energy. Determining the energy of this transient signal can provide insights into the vibration process.

[0069] Motor current analysis: By monitoring the OLTC drive motor's current signal, changes in the motor's drive torque can be indirectly determined. However, since the sampled drive current values ​​themselves are constantly changing, directly observing the instantaneous current sampled values ​​makes it difficult to detect small changes in the motor's drive torque caused by mechanical factors. Therefore, using the absolute value sum method to determine changes in the motor's drive torque is more effective. The principle formula is as follows:

[0070]

[0071] In formula (1), i(k) is the discrete sampling result of the current signal of the OLTC drive motor. During diagnosis, the envelope of the current signal is obtained and a threshold is set for monitoring. Once the current signal exceeds the limit, it indicates that the OLTC drive motor has a mechanical fault.

[0072] This invention provides a method for jointly monitoring OLTC fault vibration and current signals. This method utilizes the aforementioned combined monitoring device for OLTC fault vibration and current signals to identify and diagnose OLTC faults. A joint detection method based on the Gaussian Membership Function (GMF) is proposed, utilizing three vibration signals and one motor current signal for fault diagnosis and trend prediction. This method incorporates fuzzy logic theory to perform signal health assessments, enhancing the intelligent level of OLTC fault detection. The method includes the following steps:

[0073] The three vibration sensors 1 respectively collect vibration signals from each terminal when the three-phase OLTC is in operation. Each vibration signal is amplified and shaped by the signal conditioning module 3 and then sent to the host computer 5 through the data acquisition unit 4.

[0074] The current sensor 2 detects the current signal of the OLTC drive motor. The current signal is amplified and shaped by the signal conditioning module 3 and then sent to the host computer 5 through the data acquisition unit 4.

[0075] The host computer 5 performs comprehensive OLTC fault diagnosis and trend prediction based on the three vibration signals and one current signal collected, normal membership function, weighted fuzzy comprehensive evaluation parameters, and fuzzy rule base.

[0076] In the fuzzy logic system, the membership function is used to describe the degree of belonging of the input data. The normal membership function expression is as follows:

[0077]

[0078] In formula (2), x is the characteristic value of the input signal, the collected vibration signal or current signal; c is the mean value of the characteristic under normal conditions; σ is the standard deviation of the characteristic, which determines the broadening degree of the curve; μ(x) ranges from [0, 1], indicating the degree to which the signal belongs to a healthy state; when x is close to c, the membership degree μ(x) is close to 1, indicating that the signal is normal; when x is far away from c, the membership degree decreases, indicating that the signal is abnormal.

[0079] Three vibration sensors collect three vibration signals (V1, V2, and V3) from the OLTC to monitor the mechanical operating status of different parts of the OLTC. The vibration characteristics extracted from the vibration signals include:

[0080] Peak-to-peak PPV, which measures the maximum amplitude change and reflects the impact intensity of OLTC action;

[0081] Root mean square value RMS, which measures the vibration energy level;

[0082] Peak factor Pk, used to detect sudden shocks;

[0083] Frequency characteristics, the main frequency components extracted after FFT analysis;

[0084] According to the data under normal conditions, calculate the mean value c of each vibration characteristic v and standard deviation σ v , and establish the GMF model μ of vibration characteristics V (V i ):

[0085]

[0086] In formula (3), V1, V2, and V3 are the vibration characteristics of the three vibration signals; c v is the mean value of the vibration characteristics, σ v is the standard deviation of the vibration characteristics, when μ V (V i ) is low, indicating abnormal OLTC vibration and possible mechanical failure.

[0087] In the above-mentioned method for jointly monitoring OLTC fault vibration and current signals, the current characteristics extracted from the current signal of the drive motor include:

[0088] Root mean square value RMS_I; reflects the overall change of current;

[0089] Total harmonic distortion (THD), used to detect nonlinear load problems;

[0090] Current transient change rate dI / dt, identifying abnormal current mutations;

[0091] According to the data under normal conditions, calculate the mean value c of each current characteristic I and standard deviation σ I , and establish the GMF model μ of current characteristics I (I):

[0092]

[0093] In formula (4): I is the current characteristic of the current signal of the driving motor, c I is the mean value of the current characteristic, and the standard deviation σ I is the standard deviation of the current characteristics; if μ I (I) is lower than the set threshold, it indicates that the current is abnormal and an electrical fault may occur.

[0094] In the above-mentioned joint monitoring method of OLTC fault vibration and current signals, in order to comprehensively judge the health status of OLTC, a weighted fuzzy comprehensive evaluation parameter μ is introduced. 综合 , calculate the comprehensive health membership of multiple signals:

[0095] μ 综合 =ω1μ V (V1)+ω2μ V (V2)+ω3μ V (V3)+ω4μ I (I) (5)

[0096] In formula (5), ω1, ω2, ω3, and ω4 are weight coefficients, representing the contribution of each signal to fault detection. The weights are adjusted through experimental data analysis to optimize the diagnosis effect. When μ 综合 When the value drops below the set threshold, the OLTC is determined to be in an abnormal state, and the fault is further classified based on the fuzzy rule base. The fuzzy rule base includes the following fuzzy rules for fault classification and identification:

[0097] Abnormal vibration but normal current, μ V Low, μ I High, it is judged to be mechanical wear and poor contact;

[0098] Abnormal current but normal vibration, μ V High, μ I Low, it is determined that the drive motor is overloaded or electrically short-circuited;

[0099] Both vibration and current are abnormal, μ V Low, μ I Low, it is determined that the OLTC has a comprehensive fault, contact erosion and motor abnormality;

[0100] Both are normal, μ V High, μ I If high, the OLTC is judged to be in a healthy state.

[0101] Based on these rules, a GMF-based expert system can be constructed to achieve intelligent fault identification of OLTC.

[0102] In summary, the present invention's combined monitoring device and method for OLTC fault vibration and current signals addresses the timing of various mechanical events occurring during OLTC switching operations, changes in the drive motor's torque, and changes in its mechanical properties. By detecting and analyzing the vibration and acoustic signals and drive motor current signals generated during OLTC operation, the device and method diagnoses and evaluates OLTC operating conditions. This enables timely detection of early mechanical problems, avoids unexpected accidents, and ensures safe power supply. This device undoubtedly offers significant economic and social benefits and has broad application prospects.

[0103] Those skilled in the art should recognize that the above embodiments are merely intended to illustrate the present invention and are not intended to limit the present invention. As long as they are within the spirit of the present invention, any changes or modifications to the above embodiments will fall within the scope of the claims of the present invention.

Claims

1. An OLTC fault vibration and current signal joint monitoring device, characterized in that: It includes three vibration sensors, a current sensor, a signal conditioning module, a data acquisition unit and a host computer, including: The three vibration sensors are respectively installed on the outer wall of the transformer corresponding to the three phases of the OLTC, so as to simultaneously collect the vibration signals on each terminal when the three-phase OLTC is in operation; The current sensor is installed on the power line of the OLTC drive motor and is used to detect the current signal of the OLTC drive motor; The three vibration sensors and one current sensor are respectively connected to the input end of the signal conditioning module; The output end of the signal conditioning module, the data acquisition unit and the host computer are connected in sequence.

2. The OLTC fault vibration and current signal joint monitoring device according to claim 1, characterized in that: The vibration sensor adopts a piezoelectric acceleration sensor integrated with a charge amplifier to convert the vibration acceleration signal into a proportional voltage analog signal.

3. The OLTC fault vibration and current signal joint monitoring device according to claim 1, characterized in that: The OLTC drive motor is a single-phase motor or a three-phase motor. When the OLTC drive motor is a three-phase motor, the current sensor is installed on any one phase of the three-phase power line of the OLTC drive motor.

4. The OLTC fault vibration and current signal joint monitoring device according to claim 1, characterized in that: The vibration sensor is connected to the outer wall of the transformer equipped with the OLTC by means of bolts.

5. A method for jointly monitoring OLTC fault vibration and current signals, characterized in that: The OLTC fault vibration and current signal combined monitoring device according to claim 1 is used to implement OLTC fault identification and diagnosis, comprising the following steps: The three vibration sensors respectively collect vibration signals on each terminal when the three-phase OLTC is in operation. Each vibration signal is amplified and shaped by the signal conditioning module and then sent to the host computer through the data acquisition unit. The current sensor detects the current signal of the OLTC drive motor, which is amplified and shaped by the signal conditioning module and then sent to the host computer through the data acquisition unit; The host computer performs OLTC fault comprehensive diagnosis and trend prediction based on the three collected vibration signals and one current signal, normal membership function, weighted fuzzy comprehensive evaluation parameters, and fuzzy rule base.

6. The method for jointly monitoring OLTC fault vibration and current signals according to claim 5, wherein: By monitoring the current signal of the OLTC drive motor, the change of the motor drive torque can be indirectly judged. Since the sampling value of the drive current itself is constantly changing, it is difficult to detect the slight change of the motor drive torque caused by mechanical reasons by directly observing the instantaneous sampling value of the current. The use of the absolute value sum method to judge the change of the motor drive torque is more effective. The principle formula is as follows: In formula (1), i(k) is the discrete sampling result of the current signal of the OLTC drive motor. During diagnosis, the envelope of the current signal is obtained and a threshold is set for monitoring. Once the current signal exceeds the limit, it indicates that the OLTC drive motor has a mechanical fault.

7. The method for jointly monitoring OLTC fault vibration and current signals according to claim 5, wherein: In the fuzzy logic system, the membership function is used to describe the degree of belonging of the input data. The normal membership function expression is as follows: In formula (2), x is the characteristic value of the input signal, i.e., the collected vibration signal or current signal; c is the mean value of the characteristic under normal conditions; σ is the standard deviation of the characteristic, which determines the broadening degree of the curve; μ(x) ranges from [0, 1], indicating the degree to which the signal belongs to a healthy state; when x is close to c, the membership μ(x) is close to 1, indicating that the signal is normal; when x is far away from c, the membership decreases, indicating that the signal is abnormal.

8. The method for jointly monitoring OLTC fault vibration and current signals according to claim 7, wherein: Three vibration sensors collect three vibration signals from the OLTC to monitor the mechanical operating status of different parts of the OLTC. The vibration characteristics extracted from the vibration signals include: Peak-to-peak PPV, which measures the maximum amplitude change and reflects the impact intensity of OLTC action; Root mean square value RMS, which measures the vibration energy level; Peak factor Pk, used to detect sudden shocks; Frequency characteristics, the main frequency components extracted after FFT analysis; According to the data under normal conditions, calculate the mean value c of each vibration characteristic v and standard deviation σ v , and establish the GMF model μ of vibration characteristics V (V i ): In formula (3), V1, V2, and V3 are the vibration characteristics of the three vibration signals; c v is the mean value of the vibration characteristics, σ v is the standard deviation of the vibration characteristics, when μ V (V i ) is low, indicating abnormal OLTC vibration and possible mechanical failure.

9. The method for jointly monitoring OLTC fault vibration and current signals according to claim 7, wherein: The current characteristics extracted from the current signal of the driving motor include: Root mean square value RMS_I; reflects the overall change of current; Total harmonic distortion (THD), used to detect nonlinear load problems; Current transient change rate dI / dt, identifying abnormal current mutations; According to the data under normal conditions, calculate the mean value c of each current characteristic I and standard deviation σ I , and establish the GMF model μ of current characteristics I (I): In formula (4): I is the current characteristic of the current signal of the driving motor, c I is the mean value of the current characteristic, and the standard deviation σ I is the standard deviation of the current characteristics; if μ I (I) is lower than the set threshold, it indicates that the current is abnormal and an electrical fault may occur.

10. The method for jointly monitoring OLTC fault vibration and current signals according to claim 7, wherein: In order to comprehensively judge the health status of OLTC, the weighted fuzzy comprehensive evaluation parameter μ is introduced. 综合 , calculate the comprehensive health membership of multiple signals: m 综合 =ω1μ V (V1)+ω2μ V (V2)+ω3m V (V3)+ω4μ I (I) (5) In formula (5), ω1, ω2, ω3, and ω4 are weight coefficients, representing the contribution of each signal to fault detection. The weights are adjusted through experimental data analysis to optimize the diagnosis effect. When μ 综合 When the value drops below the set threshold, the OLTC is determined to be in an abnormal state, and the fault is further classified based on the fuzzy rule base. The fuzzy rule base includes the following fuzzy rules for fault classification and identification: Abnormal vibration but normal current, μ V Low, μ I High, it is judged to be mechanical wear and poor contact; Abnormal current but normal vibration, μ V High, μ I Low, it is determined that the drive motor is overloaded or electrically short-circuited; Both vibration and current are abnormal, μ V Low, μ I Low, it is determined that the OLTC has a comprehensive fault, contact erosion and motor abnormality; Both are normal, μ V High, μ I If high, the OLTC is judged to be in a healthy state.

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