A method for monitoring faults in power equipment based on sound spectrum analysis

Abstract

The present invention belongs to the technical field of fault monitoring based on sound analysis, and specifically relates to a method for monitoring faults of electric power equipment based on sound spectrum analysis, the steps of which include: analyzing basic information of electric power equipment, comparing and obtaining sensor deployment schemes and fault assessment deviation values, analyzing basic characteristics of the sound spectrum of electric power equipment, judging whether the sound spectrum of electric power equipment is qualified, and judging whether there are faults in substations, transmission towers, and power transmission and transformation equipment. The present invention solves the problems of insufficient dynamic adaptability and lack of sound spectrum quality verification process in traditional electric power equipment fault monitoring methods. Combined with the fault assessment deviation values ​​of specific equipment, the accuracy of judging the type and degree of faults can be improved, and misjudgment and missed judgment can be reduced. Systematic sound spectrum monitoring and fault assessment can reduce the risk of unexpected downtime, improve the reliability and stability of the entire power system, and ensure the continuity and safety of power supply.

Description

Technical Field

[0001] The present invention belongs to the technical field of fault monitoring based on sound analysis, and in particular relates to a method for monitoring faults of electric power equipment based on sound spectrum analysis. Background Art

[0002] Power equipment, such as transformers, generators, and switchgear, are core components of power systems, and their failures can result in significant economic losses and service interruptions. Therefore, improving the reliability of these devices and reducing unexpected downtime have become important goals for the industry. Early detection of potential equipment failures can prevent the failure from expanding and enable preventive maintenance, thereby reducing unexpected downtime and maintenance costs. Sound spectrum analysis, as a non-invasive monitoring method, can be performed without interrupting equipment operation. By analyzing the spectrum of abnormal sounds generated during equipment operation, specific types of faults can be identified.

[0003] However, existing fault monitoring research based on sound spectrum has some shortcomings, such as insufficient dynamic adaptability and lack of sound spectrum quality verification process, which makes the monitoring system unable to adapt to environmental changes or changes in the working status of the equipment, and may lead to increased false alarm or missed alarm rates. When the operating conditions or environment of power equipment change, the lack of dynamic adaptability may lead to delayed fault detection and failure to respond to potential faults in a timely manner. Sound spectrum data that has not been strictly verified may lead to distrust of analysis results based on these data, affecting decision making. Sound spectra that lack quality verification may contain errors or noise, resulting in impaired accuracy of fault analysis and prediction. Summary of the invention

[0004] In view of the deficiencies of the prior art, the present invention provides a method for monitoring power equipment faults based on sound spectrum analysis, which realizes continuous monitoring of the power equipment status and early fault diagnosis, reduces the risk of unexpected shutdown, and improves the reliability and stability of the entire power system.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for monitoring power equipment faults based on sound spectrum analysis, comprising the following steps:

[0006] Analyze the basic information of power equipment, and compare to obtain the sensor deployment plan and fault assessment deviation value. The fault assessment deviation value specifically includes the substation fault assessment deviation value, the transmission tower fault assessment deviation value, and the transmission and transformation equipment fault assessment deviation value;

[0007] Based on the sensor deployment solution, the basic characteristics of the sound spectrum of the power equipment are analyzed to determine whether the sound spectrum of the power equipment is qualified:

[0008] If the sound spectrum of the electric equipment is unqualified, re-obtain the sound spectrum of the electric equipment;

[0009] If the sound spectrum of the power equipment is qualified:

[0010] Analyze the substation sound spectrum and combine it with the substation fault assessment deviation value to determine whether there is a fault in the substation;

[0011] Analyze the sound spectrum of the transmission tower and determine whether there is a fault in the transmission tower by combining the fault assessment deviation value of the transmission tower;

[0012] The sound spectrum of the power transmission and transformation equipment is analyzed, and combined with the fault assessment deviation value of the power transmission and transformation equipment, it is determined whether there is a fault in the power transmission and transformation equipment.

[0013] Preferably, the comparison obtains the sensor deployment scheme and the fault assessment deviation value, and the analysis process is:

[0014] Obtaining a data set of basic information of electric power equipment, wherein the data set of basic information of electric power equipment specifically includes an average service life of electric power equipment, a total number of electric power equipment, and an average number of times electric power equipment is repaired;

[0015] The average service life of power equipment, the total number of power equipment, and the average number of maintenance times of power equipment are stored as designated tags, and the designated tags are compared with the sensor deployment plans and fault assessment deviation values ​​corresponding to the designated tags stored in the database to obtain the sensor deployment plans and fault assessment deviation values ​​corresponding to the designated tags.

[0016] Preferably, the basic characteristics of the sound spectrum of the electric equipment are analyzed to determine whether the sound spectrum of the electric equipment is qualified, and the analysis process is:

[0017] Acquire a data set of basic characteristics of the sound spectrum, and obtain basic characteristic values ​​of the sound spectrum through comprehensive analysis based on the acquired data set of basic characteristics of the sound spectrum. The basic characteristic values ​​of the sound spectrum are used as an analysis basis for judging whether the sound spectrum of the electric equipment is qualified.

[0018] Compare the basic characteristic values ​​of the sound spectrum with the basic characteristic reference values ​​of the sound spectrum stored in the database;

[0019] If the basic characteristic value of the sound spectrum is not lower than the basic characteristic reference value of the sound spectrum, the sound spectrum of the electric equipment corresponding to the basic characteristic value of the sound spectrum is qualified;

[0020] If the basic characteristic value of the sound spectrum is lower than the basic characteristic reference value of the sound spectrum, the sound spectrum of the electric equipment corresponding to the basic characteristic value of the sound spectrum is unqualified.

[0021] Preferably, the sound spectrum basic feature data set specifically includes sound spectrum clarity, sound spectrum resolution, and sound spectrum completeness.

[0022] Preferably, the analysis process of determining whether there is a fault in the substation is as follows:

[0023] Analyze the substation sound spectrum to obtain the substation sound spectrum data set, which specifically includes the switch cabinet sound spectrum signal-to-noise ratio, the main transformer sound spectrum signal-to-noise ratio, and the GIS equipment sound spectrum harmonic distortion rate;

[0024] Based on the acquired substation sound spectrum data set, a comprehensive analysis is performed to obtain the substation sound spectrum characteristic value, which is used as the analysis basis for judging whether there is a fault in the substation;

[0025] Comparing the substation sound spectrum characteristic value with the substation sound spectrum characteristic reference value stored in the database;

[0026] If the substation sound spectrum characteristic value is not lower than the substation sound spectrum characteristic reference value, then the substation corresponding to the substation sound spectrum characteristic value does not have a fault;

[0027] If the substation sound spectrum characteristic value is lower than the substation sound spectrum characteristic reference value, then the substation corresponding to the substation sound spectrum characteristic value has a fault.

[0028] Preferably, the analysis process of the substation sound spectrum characteristic value is as follows:

[0029] ;

[0030] In the formula, α is the characteristic value of the substation sound spectrum, gxz is the signal-to-noise ratio of the switchgear sound spectrum, bxz is the signal-to-noise ratio of the main transformer sound spectrum, sxz is the harmonic distortion rate of the GIS equipment sound spectrum, ε1 is the compensation factor of the set gxz, ε2 is the compensation factor of the set bxz, ε3 is the compensation factor of the set sxz, and e is a natural constant.

[0031] Preferably, the analysis process of determining whether there is a fault in the transmission tower is as follows:

[0032] Analyze the sound spectrum of the transmission tower to obtain a data set of the sound spectrum of the transmission tower. The data set of the sound spectrum of the transmission tower specifically includes a signal-to-noise ratio of the sound spectrum of the transmission tower, a width of the sound spectrum of the transmission tower, and an absolute value of a difference between a fundamental frequency of the transmission tower and a reference fundamental frequency;

[0033] Based on the acquired transmission tower sound spectrum data set, a comprehensive analysis is performed to obtain the transmission tower sound spectrum characteristic values, which are used as the analysis basis for judging whether the transmission tower has a fault.

[0034] Compare the transmission tower sound spectrum characteristic value with the transmission tower sound spectrum characteristic reference value stored in the database;

[0035] If the transmission tower sound spectrum characteristic value is not lower than the transmission tower sound spectrum characteristic reference value, then the transmission tower corresponding to the transmission tower sound spectrum characteristic value does not have a fault;

[0036] If the transmission pole tower sound spectrum characteristic value is lower than the transmission pole tower sound spectrum characteristic reference value, then the transmission pole tower corresponding to the transmission pole tower sound spectrum characteristic value is faulty.

[0037] Preferably, the analysis process of the transmission tower sound spectrum characteristic value is as follows:

[0038] ;

[0039] Wherein, β is the characteristic value of the sound spectrum of the transmission tower, txz is the signal-to-noise ratio of the sound spectrum of the transmission tower, pk is the width of the sound spectrum of the transmission tower, jp is the absolute value of the difference between the fundamental frequency of the transmission tower and the reference fundamental frequency, σ1 is the compensation factor of the set txz, σ2 is the compensation factor of the set pk, and σ3 is the compensation factor of the set jp.

[0040] Preferably, the analysis process of determining whether there is a fault in the power transmission and transformation equipment is as follows:

[0041] Analyze the sound spectrum of the power transmission and transformation equipment to obtain the sound spectrum data set of the power transmission and transformation equipment. The sound spectrum data set of the power transmission and transformation equipment specifically includes the signal-to-noise ratio of the sound spectrum of the power transmission and transformation equipment, the absolute value of the difference between the fundamental frequency of the power transmission and transformation equipment and the reference fundamental frequency, and the absolute value of the difference between the amplitude of the sound spectrum of the power transmission and transformation equipment and the reference amplitude;

[0042] Based on the acquired sound spectrum data set of power transmission and transformation equipment, the sound spectrum characteristic values ​​of the power transmission and transformation equipment are obtained through comprehensive analysis. The sound spectrum characteristic values ​​of the power transmission and transformation equipment are used as the analysis basis for judging whether there is a fault in the power transmission and transformation equipment.

[0043] Compare the power transmission and transformation equipment sound spectrum characteristic value with the power transmission and transformation equipment sound spectrum characteristic reference value stored in the database;

[0044] If the characteristic value of the sound spectrum of the power transmission and transformation equipment is not lower than the reference value of the characteristic value of the sound spectrum of the power transmission and transformation equipment, then there is no fault in the power transmission and transformation equipment corresponding to the characteristic value of the sound spectrum of the power transmission and transformation equipment;

[0045] If the sound spectrum characteristic value of the power transmission and transformation equipment is lower than the sound spectrum characteristic reference value of the power transmission and transformation equipment, then the power transmission and transformation equipment corresponding to the sound spectrum characteristic value of the power transmission and transformation equipment has a fault.

[0046] Preferably, the analysis process of the sound spectrum characteristic value of the power transmission and transformation equipment is as follows:

[0047] ;

[0048] In the formula, γ is the characteristic value of the sound spectrum of the power transmission and transformation equipment, dxz is the signal-to-noise ratio of the sound spectrum of the power transmission and transformation equipment, sp is the absolute value of the difference between the fundamental frequency of the power transmission and transformation equipment and the reference fundamental frequency, zf is the absolute value of the difference between the amplitude of the sound spectrum of the power transmission and transformation equipment and the reference amplitude, τ1 is the compensation factor of the set dxz, τ2 is the compensation factor of the set sp, and τ3 is the compensation factor of the set zf.

[0049] The present invention has the following beneficial effects:

[0050] The present invention provides a power equipment fault monitoring method based on sound spectrum analysis, which continuously monitors the changes in the sound spectrum. The system can detect problems in time before the fault develops to a serious stage, thereby achieving early intervention. Combined with the fault assessment deviation value of the specific equipment, the judgment accuracy of the fault type and degree can be improved, and misjudgment and missed judgment can be reduced. For special analysis of different power equipment (such as substations, transmission towers, and power transmission and transformation equipment), the system can provide targeted maintenance suggestions and repair measures, thereby optimizing resource allocation and maintenance plans. Systematic sound spectrum monitoring and fault assessment can reduce the risk of unexpected downtime, improve the reliability and stability of the entire power system, and ensure the continuity and safety of power supply.

[0051] The present invention analyzes the basic information of the power equipment, compares the sensor deployment plan and the fault assessment deviation value, and analyzes the basic information of the power equipment to accurately determine which locations and environmental conditions are most suitable for deploying sensors, thereby optimizing the monitoring effect and equipment coverage. By comparing the fault assessment deviation values ​​obtained, a fault detection model can be customized for each device, thereby improving the accuracy of diagnosis, identifying and handling potential faults in a timely manner, and reducing the need for emergency repairs, thereby reducing maintenance costs and equipment downtime.

[0052] The present invention determines whether the sound spectrum of the power equipment is qualified by analyzing the basic characteristics of the sound spectrum of the power equipment. The problem can be discovered in time by analyzing the sound spectrum, which can significantly reduce the sudden failure rate of the equipment, thereby avoiding unexpected shutdown and production losses. The sound spectrum analysis provides quantitative data, which can be used to deeply analyze and understand the working status and performance trends of the equipment. Determining whether the sound spectrum of the power equipment is qualified helps to reduce misjudgments caused by unqualified sound spectrum. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0054] Figure 2 A flowchart of the steps for determining whether the sound spectrum of the electric equipment is qualified;

[0055] Figure 3It is an image of the basic characteristic values ​​of the sound spectrum changing with the completeness rate of the sound spectrum;

[0056] Figure 4 It is a scatter plot of the substation sound spectrum characteristic value as the GIS equipment sound spectrum harmonic distortion rate changes;

[0057] Figure 5 It is a three-dimensional graph showing the change of the substation sound spectrum characteristic value along with the GIS equipment sound spectrum harmonic distortion rate and the set GIS equipment sound spectrum harmonic distortion rate compensation factor;

[0058] Figure 6 It is an image of the characteristic value of the sound spectrum of the transmission tower changing with the absolute value of the difference between the fundamental frequency of the transmission tower and the reference fundamental frequency. DETAILED DESCRIPTION

[0059] The technical solutions in the embodiments of the present invention are clearly and completely described below in conjunction with the accompanying drawings.

[0060] like Figure 1 As shown, the present invention provides a method for monitoring power equipment faults based on sound spectrum analysis, including: analyzing basic information of the power equipment, and comparing to obtain a sensor deployment plan and a fault assessment deviation value, wherein the fault assessment deviation value specifically includes a substation fault assessment deviation value, a transmission tower fault assessment deviation value, and a transmission and transformation equipment fault assessment deviation value.

[0061] Specifically, the sensor deployment plan and the fault assessment deviation value are obtained by comparison, and the analysis process is as follows: obtaining a data set of basic information of power equipment, which specifically includes the average service life of power equipment, the total number of power equipment, and the average number of maintenance times of power equipment; storing the average service life of power equipment, the total number of power equipment, and the average number of maintenance times of power equipment as designated tags, and comparing the designated tags with the sensor deployment plans and fault assessment deviation values ​​corresponding to the designated tags stored in the database to obtain the sensor deployment plan and fault assessment deviation value corresponding to the designated tags.

[0062] By analyzing information such as the service life and maintenance times of power equipment, the probability of equipment failure can be predicted, and the sensor deployment plan can be optimized based on the equipment usage, thereby achieving more effective fault warning; the comparison results can help identify which sensor deployment plans perform better on specific types of equipment, thereby selecting the most appropriate monitoring plan for the equipment and improving monitoring efficiency and accuracy; by understanding the historical maintenance data of the equipment, the maintenance plan can be optimized, unnecessary maintenance can be reduced, and maintenance costs can be lowered. Combined with sensor data, more accurate condition monitoring and maintenance can be achieved.

[0063] Based on the sensor deployment plan, the basic characteristics of the sound spectrum of the power equipment are analyzed to determine whether the sound spectrum of the power equipment is qualified.

[0064] The analysis process is as follows: Figure 2 As shown, a data set of basic characteristic features of a sound spectrum is obtained, and based on the obtained data set of basic characteristic features of the sound spectrum, a comprehensive analysis is performed to obtain a basic characteristic value of the sound spectrum, and the basic characteristic value of the sound spectrum is used as an analysis basis for judging whether the sound spectrum of the electric equipment is qualified; the basic characteristic value of the sound spectrum is compared with a basic characteristic reference value of the sound spectrum stored in a database; if the basic characteristic value of the sound spectrum is not lower than the basic characteristic reference value of the sound spectrum, the sound spectrum of the electric equipment corresponding to the basic characteristic value of the sound spectrum is qualified; if the basic characteristic value of the sound spectrum is lower than the basic characteristic reference value of the sound spectrum, the sound spectrum of the electric equipment corresponding to the basic characteristic value of the sound spectrum is unqualified.

[0065] The above-mentioned sound spectrum analysis can provide precise data to help identify the problem in detail, rather than relying solely on the operator's subjective feelings or experience; maintenance work is only performed when necessary, avoiding the waste of resources and time due to excessive maintenance; timely fault diagnosis and processing can significantly reduce the situation of unexpected equipment downtime due to unqualified sound spectrum, ensuring production efficiency and continuity of power supply; systematic sound spectrum analysis provides strong data support to help management make data-based decisions.

[0066] The sound spectrum basic feature data set includes sound spectrum clarity, sound spectrum resolution, and sound spectrum completeness. Sound spectrum clarity refers to the degree of clarity of each frequency component in the sound spectrum, that is, the ratio of useful information to noise in the signal, which directly affects the understanding and analysis of the sound signal. Through a spectrum analyzer or audio analysis software, the collected audio signal can be analyzed in the frequency domain to evaluate the clarity of the audio spectrum.

[0067] Sound spectrum resolution refers to the ability to distinguish two close frequency components in spectrum analysis. High resolution means that even two components with very close frequencies can be clearly distinguished. Sound spectrum resolution is mainly achieved by using a high-precision spectrum analyzer.

[0068] The sound spectrum completeness rate refers to the integrity of the sound spectrum data during the collection and analysis process, that is, whether all important frequency components are captured and reflected in the spectrum. The complete spectrum should contain all key frequency components from the lowest to the highest without omissions. Professional audio analysis instruments can be used to obtain complete sound spectrum information, including the intensity of each frequency point within the frequency range.

[0069] Clarity and resolution are usually complementary. High resolution can provide more detailed frequency component information, so that each frequency component can be clearly distinguished, thereby improving the clarity of the sound spectrum. High clarity of the sound spectrum usually means lower background noise, which also helps to better resolve and identify close frequency components, that is, improve resolution. Clarity provides information about the signal-to-noise ratio, which is the basis for evaluating the quality of spectral data. High clarity helps ensure that every part of the signal can be clearly captured and analyzed, which is a prerequisite for achieving completeness. The completeness of the sound spectrum emphasizes the presentation of all important components of the signal, while clarity ensures that these components are obvious enough and are not buried by noise. A sound spectrum with high resolution can finely depict frequency components and help capture the full range of signals from low frequency to high frequency, which directly affects the integrity of the spectrum. If the resolution is insufficient, some frequency components in the spectrum may be omitted or merged, thus affecting the completeness.

[0070] The analysis process of the basic characteristic values ​​of the above sound spectrum is as follows:

[0071] ;

[0072] Wherein, δ is the basic characteristic value of the sound spectrum, qx is the clarity of the sound spectrum, fb is the resolution of the sound spectrum, wz is the completeness of the sound spectrum, μ1 is the compensation factor of the set qx, μ2 is the compensation factor of the set fb, and μ3 is the compensation factor of the set wz.

[0073] In this embodiment, when calculating each formula, normalization processing is performed on the parameters involved as needed.

[0074] The clarity, resolution and completeness of the sound spectrum are important indicators for measuring the quality of audio signals. Through these characteristics, the degree of distortion and fidelity of the audio can be evaluated. By analyzing the spectrum characteristics, noise can be more effectively identified and corresponding noise reduction processing can be performed to improve the overall quality of the audio signal.

[0075] In this embodiment, each compensation factor is obtained from a database. For example, the set compensation factors of qx, fb, and wz are obtained from a database. A mapping set of historically measured sound spectrum clarity, sound spectrum resolution, sound spectrum completeness and compensation factors of qx, fb, and wz is established based on historical data to obtain the compensation factors of qx, fb, and wz corresponding to the current qx, fb, and wz. The same applies to the remaining compensation factors.

[0076] Figure 3It is an image of the basic eigenvalue of the sound spectrum changing with the sound spectrum completeness rate, where the x-axis represents the sound spectrum completeness rate and the y-axis represents the basic eigenvalue of the sound spectrum. It can help to intuitively understand how the sound spectrum completeness rate affects the basic eigenvalue of the sound spectrum. The larger the sound spectrum completeness rate, the larger the basic eigenvalue of the sound spectrum. As the sound spectrum completeness rate increases, the effect of the sound spectrum completeness rate on the basic eigenvalue of the sound spectrum remains unchanged. Set the sound spectrum clarity to 15, the sound spectrum resolution to 20, the compensation factor of the sound spectrum clarity to 0.2, the compensation factor of the sound spectrum resolution to 0.4, and the compensation factor of the sound spectrum completeness rate to 0.2. Only the size of the sound spectrum completeness rate is changed. The example values ​​of the sound spectrum completeness rate are as follows:

[0077] Table 1 Example values ​​of sound spectrum completeness rate in basic characteristic values ​​of sound spectrum

[0078]

[0079] In Table 1, for the sound spectrum completeness rate, 0.8 means 80%.

[0080] If the sound spectrum of the power equipment is unqualified, the sound spectrum of the power equipment is re-acquired; if the sound spectrum of the power equipment is qualified: analyze the sound spectrum of the substation, and combine it with the substation fault assessment deviation value to determine whether there is a fault in the substation; analyze the sound spectrum of the transmission tower, and combine it with the transmission tower fault assessment deviation value to determine whether there is a fault in the transmission tower; analyze the sound spectrum of the power transmission and transformation equipment, and combine it with the power transmission and transformation equipment fault assessment deviation value to determine whether there is a fault in the power transmission and transformation equipment.

[0081] Specifically, to determine whether there is a fault in the substation, the analysis process is as follows: analyzing the substation sound spectrum to obtain the substation sound spectrum data set, which specifically includes the switch cabinet sound spectrum signal-to-noise ratio, the main transformer sound spectrum signal-to-noise ratio, and the GIS equipment sound spectrum harmonic distortion rate;

[0082] The signal-to-noise ratio refers to the ratio of signal strength to noise strength, usually expressed in decibels (dB). In power equipment, a high signal-to-noise ratio means that the equipment is operating normally and the signal is clear, while a low signal-to-noise ratio may indicate that the equipment is faulty or abnormal. Based on a digital signal processor, it is used to process the captured audio signal and calculate the ratio of signal to noise to obtain the signal-to-noise ratio;

[0083] Harmonic distortion is an indicator for evaluating the degree of distortion of a sound waveform. It indicates the proportion of high-frequency harmonic components caused by nonlinear factors in the overall signal. It is usually expressed as a percentage. A higher harmonic distortion may indicate that the equipment is not operating properly or has a fault. A spectrum analyzer can be used to analyze the harmonic components in the signal, provide spectrum data, and help calculate the harmonic distortion. GIS equipment, or gas-insulated switchgear, is a type of switchgear used in power systems.

[0084] Based on the acquired substation sound spectrum data set, a comprehensive analysis is performed to obtain the substation sound spectrum characteristic value, which is used as the analysis basis for judging whether there is a fault in the substation; the substation sound spectrum characteristic value is compared with the substation sound spectrum characteristic reference value stored in the database; if the substation sound spectrum characteristic value is not lower than the substation sound spectrum characteristic reference value, then the substation corresponding to the substation sound spectrum characteristic value does not have a fault; if the substation sound spectrum characteristic value is lower than the substation sound spectrum characteristic reference value, then the substation corresponding to the substation sound spectrum characteristic value has a fault.

[0085] The sound signals of the above-mentioned switchgear, main transformer and GIS equipment during operation are all derived from electrical and mechanical effects, such as switch operation, current flow and conversion, etc. If the equipment fails, it is usually accompanied by abnormal noise (for example, accompanied by abnormal vibration), which will affect the signal-to-noise ratio and cause the measured signal-to-noise ratio to decrease. A high signal-to-noise ratio indicates that the signal has high clarity relative to the background noise; when the equipment operates normally, the signal is clearer and the noise is less. By analyzing the signal-to-noise ratio of the sound spectrum of the switchgear and main transformer, we can warn of potential equipment failures, such as abnormal noise during switch operation, and the harmonic distortion rate of the GIS equipment can reflect the quality of the electrical insulation performance.

[0086] The above-mentioned real-time monitoring using sound spectrum characteristic values ​​can detect potential fault hazards early, thereby reducing the frequency of physical inspections of equipment and improving operation and maintenance efficiency. Through sound spectrum analysis, equipment problems can be accurately identified, unnecessary repairs and replacements can be avoided, and maintenance and downtime costs can be reduced. The collection and analysis of sound spectrum characteristic values ​​provides management with a data-driven decision-making basis, which can more accurately formulate repair and replacement plans.

[0087] Substation sound spectrum characteristic value, the analysis process is:

[0088] ;

[0089] In the formula, α is the characteristic value of the substation sound spectrum, gxz is the signal-to-noise ratio of the switchgear sound spectrum, bxz is the signal-to-noise ratio of the main transformer sound spectrum, sxz is the harmonic distortion rate of the GIS equipment sound spectrum, ε1 is the compensation factor of the set gxz, ε2 is the compensation factor of the set bxz, ε3 is the compensation factor of the set sxz, and e is a natural constant.

[0090] Figure 4 This is a scatter plot of the substation sound spectrum characteristic value as the GIS equipment sound spectrum harmonic distortion rate changes. Figure 5 It is a three-dimensional graph showing the change of the substation sound spectrum characteristic value along with the GIS equipment sound spectrum harmonic distortion rate and the set GIS equipment sound spectrum harmonic distortion rate compensation factor, such as Figure 4 and Figure 5 As shown in the figure, the greater the harmonic distortion rate of the sound spectrum of the GIS equipment, the smaller the characteristic value of the substation sound spectrum. As the harmonic distortion rate of the sound spectrum of the GIS equipment increases, the influence of the harmonic distortion rate of the sound spectrum of the GIS equipment on the characteristic value of the substation sound spectrum decreases.

[0091] By monitoring the sound spectrum characteristics of switch cabinets, main transformers and GIS equipment, abnormal noise or changes in spectrum characteristics can be identified, thereby discovering potential equipment failures at an early stage. Implementing a sound spectrum monitoring system can monitor the equipment in real time. Once abnormal sound frequency is detected, the system can immediately issue an alarm and take necessary emergency measures to ensure safe operation. The data obtained by analyzing the sound spectrum characteristics can provide data support for equipment maintenance and help formulate more scientific and reasonable maintenance strategies.

[0092] Specifically, to determine whether there is a fault in the transmission tower, the analysis process is as follows: analyzing the sound spectrum of the transmission tower to obtain a sound spectrum data set of the transmission tower, wherein the sound spectrum data set of the transmission tower specifically includes a signal-to-noise ratio of the sound spectrum of the transmission tower, a width of the sound spectrum of the transmission tower, and an absolute value of a difference between a fundamental frequency of the transmission tower and a reference fundamental frequency;

[0093] The signal-to-noise ratio refers to the ratio of signal strength to noise strength. In sound spectrum analysis, a high signal-to-noise ratio means that the effective signal (such as the sound generated by the operation of the equipment) is significantly stronger than the background noise (such as environmental noise). A high signal-to-noise ratio usually indicates that the measurement is more accurate and that fault signals can be more easily identified. An audio analyzer is used to capture sound signals and analyze frequency components, extract the required features from the audio signal, and calculate the signal-to-noise ratio.

[0094] Spectral width refers to the frequency range occupied by valid signals (such as fault signals) in the spectrum, usually expressed in Hertz (Hz). A wider spectrum width may indicate a complex signal with multiple frequency components, which may be related to equipment failure. Spectrum analyzers are used to analyze frequency components of equipment. They convert time domain signals into frequency domain signals through Fourier transform, thereby calculating the spectrum width.

[0095] Fundamental frequency refers to the most important frequency in the sound signal, which usually corresponds to the normal operating frequency of the equipment. When there is a difference between the actual measured fundamental frequency and the standard (reference) fundamental frequency, it may indicate that the operating status of the equipment has changed or there is a fault. The absolute value of the fundamental frequency difference is used to evaluate the severity of this deviation. The spectrum analyzer is used to measure and extract the fundamental frequency of the sound signal and compare the actual fundamental frequency with the reference fundamental frequency.

[0096] Based on the acquired transmission tower sound spectrum data set, a comprehensive analysis is performed to obtain the transmission tower sound spectrum characteristic value, which is used as an analysis basis for judging whether the transmission tower has a fault; the transmission tower sound spectrum characteristic value is compared with the transmission tower sound spectrum characteristic reference value stored in the database; if the transmission tower sound spectrum characteristic value is not lower than the transmission tower sound spectrum characteristic reference value, then the transmission tower corresponding to the transmission tower sound spectrum characteristic value does not have a fault; if the transmission tower sound spectrum characteristic value is lower than the transmission tower sound spectrum characteristic reference value, then the transmission tower corresponding to the transmission tower sound spectrum characteristic value has a fault. The transmission tower sound spectrum data set is acquired under different micro-meteorological conditions.

[0097] The above-mentioned higher signal-to-noise ratio usually indicates that the effective component of the signal is significantly higher than the background noise. In this case, the frequency components of the signal can be more clearly distinguished during spectrum analysis, so the measurement of the spectrum width is more accurate; if the spectrum width is wide, it may indicate that the signal contains multiple frequency components. This may cause the energy of the signal to disperse, thereby affecting the signal-to-noise ratio. When the spectrum width increases, if the nature of the signal does not change significantly, the signal-to-noise ratio may decrease; if the difference between the fundamental frequency and the reference fundamental frequency is small, it means that the device is normal and the signal quality is high. Usually in this case, the signal-to-noise ratio will also be relatively high, reflecting that the signal strength is good and the background noise is not strong; if the absolute value of the difference is large, it may indicate that there is an abnormality in the device. At this time, the signal-to-noise ratio may decrease. The change of the fundamental frequency can affect the spectrum characteristics. If the device performance is normal, the difference between the fundamental frequency and the reference fundamental frequency is small, and the spectrum width will generally remain in a normal range. If the fundamental frequency deviates greatly, it may indicate that the device has a fault, causing the signal spectrum to become more complex, and the spectrum width may increase at this time.

[0098] The above-mentioned continuous monitoring of the sound spectrum can achieve early identification of potential faults in transmission towers, and necessary maintenance measures can be taken before the fault expands on a large scale, thereby reducing the accident rate; sound spectrum analysis is a non-invasive detection method that can be performed without interfering with the normal operation of the transmission tower, which means that maintenance and detection can be carried out under continuous power supply, thereby not affecting the power supply; through the comprehensive analysis of multiple sound spectrum characteristics (such as signal-to-noise ratio, spectrum width and fundamental frequency difference), the health status of the transmission tower can be more comprehensively evaluated, while the analysis of a single characteristic may not be sufficient to accurately reflect the actual operating status of the equipment; by establishing a transmission tower sound spectrum feature data set and comparing it with historical data, decisions can be made in a data-driven manner, which can provide operation and maintenance personnel with clear fault location and maintenance suggestions, and improve the accuracy and scientificity of decision-making.

[0099] The characteristic value of the sound spectrum of the transmission tower, the analysis process is:

[0100] ;

[0101] Wherein, β is the characteristic value of the sound spectrum of the transmission tower, txz is the signal-to-noise ratio of the sound spectrum of the transmission tower, pk is the width of the sound spectrum of the transmission tower, jp is the absolute value of the difference between the fundamental frequency of the transmission tower and the reference fundamental frequency, σ1 is the compensation factor of the set txz, σ2 is the compensation factor of the set pk, and σ3 is the compensation factor of the set jp.

[0102] Figure 6 It is an image of the characteristic value of the sound spectrum of the transmission tower as the absolute value of the difference between the fundamental frequency of the transmission tower and the reference fundamental frequency changes, such as Figure 6 As shown, the greater the absolute value of the difference between the fundamental frequency of the transmission tower and the reference fundamental frequency, the smaller the characteristic value of the sound spectrum of the transmission tower. As the absolute value of the difference between the fundamental frequency of the transmission tower and the reference fundamental frequency increases, the influence of the absolute value of the difference between the fundamental frequency of the transmission tower and the reference fundamental frequency on the characteristic value of the sound spectrum of the transmission tower decreases.

[0103] Monitoring the sound spectrum characteristics can detect abnormal behavior of transmission towers, issue alarms in time, facilitate rapid response, and reduce accident risks. Monitoring and analyzing sound characteristics can reduce sudden downtime caused by faults, thereby improving overall operating efficiency. Analyzing the signal-to-noise ratio, spectrum width, and the difference between the fundamental frequency and the reference fundamental frequency of the sound spectrum of transmission towers can effectively improve the operating safety, stability, and maintenance efficiency of the equipment, and provide strong support for ensuring the reliability of power transmission.

[0104] Specifically, it is determined whether there is a fault in the power transmission and transformation equipment. The analysis process is as follows: analyzing the sound spectrum of the power transmission and transformation equipment to obtain a sound spectrum data set of the power transmission and transformation equipment. The sound spectrum data set of the power transmission and transformation equipment specifically includes a signal-to-noise ratio of the sound spectrum of the power transmission and transformation equipment, an absolute value of a difference between a fundamental frequency of the power transmission and transformation equipment and a reference fundamental frequency, and an absolute value of a difference between an amplitude of the sound spectrum of the power transmission and transformation equipment and a reference amplitude;

[0105] A higher signal-to-noise ratio means better signal quality and less noise impact on the signal. In power transmission and transformation equipment, the signal is usually the normal operating sound of the equipment, and the noise is environmental noise or other irrelevant sounds. Professional sound analyzers, digital oscilloscopes or audio recording equipment (such as high-sensitivity microphones combined with audio analysis software) can be used to measure and calculate the strength of the signal and noise.

[0106] Fundamental frequency refers to the frequency component with the largest energy in the sound spectrum. The fundamental frequency of power transmission and transformation equipment should be within a specific standard range. If there is a difference between the fundamental frequency of the current equipment and the reference fundamental frequency, and the absolute value is taken, it can be used to evaluate the operating status of the equipment. Significant changes in the fundamental frequency may indicate an abnormality of the equipment. The fundamental frequency is extracted from the equipment sound recording through a spectrum analyzer, digital signal processor (DSP) or audio analysis software;

[0107] Amplitude is the strength of a sound signal, usually expressed in decibels (dB). The amplitude of an audio signal affects the loudness of the sound perceived by the human ear. Comparing the actual amplitude measured by the device with the reference amplitude and taking the absolute value of the difference can be used to detect whether the device has abnormal vibration or noise. Use a sound analyzer, sound level meter, or audio analysis software to obtain the sound intensity data generated by power transmission and transformation equipment during operation;

[0108] Based on the acquired sound spectrum data set of power transmission and transformation equipment, a comprehensive analysis is performed to obtain the sound spectrum characteristic values ​​of the power transmission and transformation equipment, and the sound spectrum characteristic values ​​of the power transmission and transformation equipment are used as the analysis basis for judging whether the power transmission and transformation equipment has a fault; the sound spectrum characteristic values ​​of the power transmission and transformation equipment are compared with the sound spectrum characteristic reference values ​​of the power transmission and transformation equipment stored in the database; if the sound spectrum characteristic values ​​of the power transmission and transformation equipment are not lower than the sound spectrum characteristic reference values ​​of the power transmission and transformation equipment, then the power transmission and transformation equipment corresponding to the sound spectrum characteristic values ​​of the power transmission and transformation equipment does not have a fault; if the sound spectrum characteristic values ​​of the power transmission and transformation equipment are lower than the sound spectrum characteristic reference values ​​of the power transmission and transformation equipment, then the power transmission and transformation equipment corresponding to the sound spectrum characteristic values ​​of the power transmission and transformation equipment has a fault.

[0109] When the equipment is operating normally, the base frequency is close to the reference base frequency, and the amplitude change is within an acceptable range. The signal-to-noise ratio is usually high, and the noise interference to the signal is small. Once the equipment fails, the base frequency may shift, the amplitude may increase or decrease significantly, and the signal-to-noise ratio may decrease due to the enhancement of the noise component. These changes can be used as fault warning signals. The signal-to-noise ratio affects the quality of the signal, making the measurement results of the base frequency and amplitude more reliable. The change of the base frequency can reflect the health status of the equipment, and the change of the amplitude indicates the strength of the equipment operation and the possible fault type. The comprehensive analysis of these three can more effectively monitor the operation status of the power transmission and transformation equipment.

[0110] The sound spectrum of the above-mentioned monitoring and analysis equipment can detect anomalies at the early stage of a fault, help operation and maintenance personnel take measures early, reduce the impact of equipment failure, and reduce equipment downtime. Timely identification of faults can improve the operating safety of power transmission and transformation equipment, prevent power system failures caused by equipment failures, and improve the stability of the entire power grid. By continuously accumulating sound spectrum data and fault characteristic information, a richer data set can be obtained, providing a basis for subsequent analysis and accumulating experience and lessons.

[0111] The characteristic value of the sound spectrum of power transmission and transformation equipment, the analysis process is:

[0112] ;

[0113] In the formula, γ is the characteristic value of the sound spectrum of the power transmission and transformation equipment, dxz is the signal-to-noise ratio of the sound spectrum of the power transmission and transformation equipment, sp is the absolute value of the difference between the fundamental frequency of the power transmission and transformation equipment and the reference fundamental frequency, zf is the absolute value of the difference between the amplitude of the sound spectrum of the power transmission and transformation equipment and the reference amplitude, τ1 is the compensation factor of the set dxz, τ2 is the compensation factor of the set sp, and τ3 is the compensation factor of the set zf.

[0114] Signal-to-noise ratio monitoring can help identify abnormal sounds of power equipment and detect potential faults in a timely manner. A high signal-to-noise ratio usually means a clear signal, while a low signal-to-noise ratio may indicate equipment failure or damage. Monitoring the difference between fundamental frequency and amplitude can detect changes in the operating status of the equipment and identify abnormal conditions of the equipment early, which helps avoid accidents. By regularly recording and analyzing the characteristic values ​​of the sound spectrum, the health status of the equipment can be evaluated, whether the equipment is operating normally, and appropriate repair or maintenance measures can be taken in a timely manner.

Claims

1. A method for monitoring faults of electric power equipment based on sound spectrum analysis, characterized in that: The following steps are involved: Analyze the basic information of power equipment and compare it to get the sensor deployment plan; Based on the sensor deployment solution, the basic characteristics of the sound spectrum of the power equipment are analyzed to determine whether the sound spectrum of the power equipment is qualified: If the sound spectrum of the electric equipment is unqualified, re-obtain the sound spectrum of the electric equipment; If the sound spectrum of the power equipment is qualified: Analyze the sound spectrum of the substation to determine whether there is a fault in the substation; The analysis process of judging whether there is a fault in the substation is as follows: Analyze the substation sound spectrum to obtain the substation sound spectrum data set, which specifically includes the switch cabinet sound spectrum signal-to-noise ratio, the main transformer sound spectrum signal-to-noise ratio, and the GIS equipment sound spectrum harmonic distortion rate; Based on the acquired substation sound spectrum data set, a comprehensive analysis is performed to obtain the substation sound spectrum characteristic value, which is used as the analysis basis for judging whether there is a fault in the substation; Comparing the substation sound spectrum characteristic value with the substation sound spectrum characteristic reference value stored in the database; If the substation sound spectrum characteristic value is not lower than the substation sound spectrum characteristic reference value, then the substation corresponding to the substation sound spectrum characteristic value does not have a fault; If the substation sound spectrum characteristic value is lower than the substation sound spectrum characteristic reference value, then the substation corresponding to the substation sound spectrum characteristic value has a fault; The analysis process of the substation sound spectrum characteristic value is as follows: In the formula, α is the characteristic value of the substation sound spectrum, gxz is the signal-to-noise ratio of the switch cabinet sound spectrum, bxz is the signal-to-noise ratio of the main transformer sound spectrum, sxz is the harmonic distortion rate of the GIS equipment sound spectrum, ε1 is the compensation factor of the set gxz, ε2 is the compensation factor of the set bxz, ε3 is the compensation factor of the set sxz, and e is a natural constant; Analyze the sound spectrum of the transmission tower to determine whether there is a fault in the transmission tower; Analyze the sound spectrum of the power transmission and transformation equipment to determine whether there is any fault in the power transmission and transformation equipment.

2. The method for monitoring power equipment faults based on sound spectrum analysis according to claim 1, characterized in that: The sensor deployment scheme is obtained by comparison, and the analysis process is as follows: Obtaining a data set of basic information of electric power equipment, wherein the data set of basic information of electric power equipment specifically includes an average service life of electric power equipment, a total number of electric power equipment, and an average number of times electric power equipment is repaired; The average service life of power equipment, the total number of power equipment, and the average number of maintenance times of power equipment are stored as designated tags, and the designated tags are compared with the sensor deployment plans corresponding to the designated tags stored in the database to obtain the sensor deployment plan corresponding to the designated tags.

3. The method for monitoring power equipment faults based on sound spectrum analysis according to claim 1, characterized in that: The basic characteristics of the sound spectrum of the electric equipment are analyzed to determine whether the sound spectrum of the electric equipment is qualified. The analysis process is as follows: Acquire a data set of basic characteristics of the sound spectrum, and obtain basic characteristic values ​​of the sound spectrum through comprehensive analysis based on the acquired data set of basic characteristics of the sound spectrum. The basic characteristic values ​​of the sound spectrum are used as an analysis basis for judging whether the sound spectrum of the electric equipment is qualified. Compare the basic characteristic values ​​of the sound spectrum with the basic characteristic reference values ​​of the sound spectrum stored in the database; If the basic characteristic value of the sound spectrum is not lower than the basic characteristic reference value of the sound spectrum, the sound spectrum of the power equipment corresponding to the basic characteristic value of the sound spectrum is qualified; If the basic characteristic value of the sound spectrum is lower than the basic characteristic reference value of the sound spectrum, the sound spectrum of the electric equipment corresponding to the basic characteristic value of the sound spectrum is unqualified.

4. The method for monitoring faults of electric power equipment based on sound spectrum analysis according to claim 3 is characterized in that: The sound spectrum basic feature data set specifically includes sound spectrum clarity, sound spectrum resolution, and sound spectrum completeness.

5. The method for monitoring power equipment faults based on sound spectrum analysis according to claim 1, characterized in that: The analysis process of judging whether there is a fault in the transmission tower is as follows: Analyze the sound spectrum of the transmission tower to obtain a data set of the sound spectrum of the transmission tower. The data set of the sound spectrum of the transmission tower specifically includes a signal-to-noise ratio of the sound spectrum of the transmission tower, a width of the sound spectrum of the transmission tower, and an absolute value of a difference between a fundamental frequency of the transmission tower and a reference fundamental frequency; Based on the acquired transmission tower sound spectrum data set, a comprehensive analysis is performed to obtain the transmission tower sound spectrum characteristic values, which are used as the analysis basis for judging whether the transmission tower has a fault. Compare the transmission tower sound spectrum characteristic value with the transmission tower sound spectrum characteristic reference value stored in the database; If the transmission tower sound spectrum characteristic value is not lower than the transmission tower sound spectrum characteristic reference value, then the transmission tower corresponding to the transmission tower sound spectrum characteristic value does not have a fault; If the transmission pole tower sound spectrum characteristic value is lower than the transmission pole tower sound spectrum characteristic reference value, then the transmission pole tower corresponding to the transmission pole tower sound spectrum characteristic value is faulty.

6. The method for monitoring power equipment faults based on sound spectrum analysis according to claim 5, characterized in that: The analysis process of the transmission tower sound spectrum characteristic value is as follows: Wherein, β is the characteristic value of the sound spectrum of the transmission tower, txz is the signal-to-noise ratio of the sound spectrum of the transmission tower, pk is the width of the sound spectrum of the transmission tower, jp is the absolute value of the difference between the fundamental frequency of the transmission tower and the reference fundamental frequency, σ1 is the compensation factor of the set txz, σ2 is the compensation factor of the set pk, and σ3 is the compensation factor of the set jp.

7. The method for monitoring power equipment faults based on sound spectrum analysis according to claim 1, characterized in that: The analysis process of judging whether there is a fault in the power transmission and transformation equipment is as follows: Analyze the sound spectrum of the power transmission and transformation equipment to obtain the sound spectrum data set of the power transmission and transformation equipment. The sound spectrum data set of the power transmission and transformation equipment specifically includes the signal-to-noise ratio of the sound spectrum of the power transmission and transformation equipment, the absolute value of the difference between the fundamental frequency of the power transmission and transformation equipment and the reference fundamental frequency, and the absolute value of the difference between the amplitude of the sound spectrum of the power transmission and transformation equipment and the reference amplitude; Based on the acquired sound spectrum data set of power transmission and transformation equipment, the sound spectrum characteristic values ​​of the power transmission and transformation equipment are obtained through comprehensive analysis. The sound spectrum characteristic values ​​of the power transmission and transformation equipment are used as the analysis basis for judging whether there is a fault in the power transmission and transformation equipment. Compare the power transmission and transformation equipment sound spectrum characteristic value with the power transmission and transformation equipment sound spectrum characteristic reference value stored in the database; If the characteristic value of the sound spectrum of the power transmission and transformation equipment is not lower than the reference value of the characteristic value of the sound spectrum of the power transmission and transformation equipment, then there is no fault in the power transmission and transformation equipment corresponding to the characteristic value of the sound spectrum of the power transmission and transformation equipment; If the sound spectrum characteristic value of the power transmission and transformation equipment is lower than the sound spectrum characteristic reference value of the power transmission and transformation equipment, then the power transmission and transformation equipment corresponding to the sound spectrum characteristic value of the power transmission and transformation equipment has a fault.

8. The method for monitoring electric power equipment faults based on sound spectrum analysis according to claim 7, characterized in that: The analysis process of the sound spectrum characteristic value of the power transmission and transformation equipment is as follows: In the formula, γ is the characteristic value of the sound spectrum of the power transmission and transformation equipment, dxz is the signal-to-noise ratio of the sound spectrum of the power transmission and transformation equipment, sp is the absolute value of the difference between the fundamental frequency of the power transmission and transformation equipment and the reference fundamental frequency, zf is the absolute value of the difference between the amplitude of the sound spectrum of the power transmission and transformation equipment and the reference amplitude, τ1 is the compensation factor of the set dxz, τ2 is the compensation factor of the set sp, and τ3 is the compensation factor of the set zf.

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