Method and system for detecting abnormal vibration of power transformation equipment under environmental noise

Abstract

The invention discloses a method and a system for detecting abnormal vibration of power transformation equipment under environmental noise. The method comprises the following steps: acquiring a first target signal and a first noise signal; presetting a first suppression algorithm, and processing the first target signal and the first noise signal through the first suppression algorithm to obtain a first processing signal; and carrying out first judgment operation on the first processing signal, and carrying out abnormal vibration detection according to a judgment result. And the operation safety and reliability of the power transformation equipment can be effectively improved. The method can adapt to various complex environmental noise conditions, and ensures that abnormal vibration of the power transformation equipment can still be accurately detected under noise interference. Therefore, the detection accuracy is improved, and the maintenance cost caused by false alarm or missing alarm is greatly reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of abnormal vibration detection of substation equipment under environmental noise, and particularly to a method and system for detecting abnormal vibration of substation equipment under environmental noise. Background Art

[0002] Substation equipment is widely used in fields such as power station construction, nuclear power development, petrochemical development, and urban construction system improvement. From the simplest voltage transformation and distribution to complex power system control and protection, substation equipment plays an indispensable role. They are not only the core hubs for power transmission and distribution, but also directly related to the safe and stable operation of the power grid and energy utilization efficiency.

[0003] In petrochemical development, abnormal vibration of substation equipment can lead to a series of serious problems, directly threatening production safety, equipment life, and economic benefits. Abnormal vibration of substation equipment can cause problems such as accelerated equipment damage, decreased insulation performance, increased safety hazards, and reduced production efficiency. In severe cases, it may even trigger safety accidents, posing a serious threat to production operation and personnel safety. For example, abnormal vibration can increase the mechanical stress of substation equipment, causing the fasteners inside the equipment to loosen or break, which may in turn lead to faults such as poor electrical connection, short circuit, or open circuit; vibration may cause the insulation layer inside the equipment to rupture, triggering arc discharge or fire; or cause abnormal vibration of transformer oil, leading to environmental pollution and fire risks. Summary of the Invention

[0004] The purpose of this part is to outline some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this part, as well as in the abstract and title of the present application, to avoid obscuring the purpose of this part, the abstract, and the title. However, such simplifications or omissions shall not be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides a method and system for detecting abnormal vibration of substation equipment under environmental noise, which can solve the problems mentioned in the background art.

[0007] To solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, the present invention provides a method for detecting abnormal vibration of substation equipment under environmental noise, including:

[0009] Obtain a first target signal and a first noise signal;

[0010] Preset a first suppression algorithm, and process the first target signal and the first noise signal through the first suppression algorithm to obtain a first processed signal;

[0011] Perform a first judgment operation on the first processed signal, and perform abnormal vibration detection according to the judgment result.

[0012] As a preferred solution of the abnormal vibration detection method for substation equipment under environmental noise according to the present invention, wherein: the first suppression algorithm includes:

[0013] The first suppression algorithm is any algorithm for suppressing the first noise signal and filtering out the noise in the first target signal;

[0014] The result of suppressing the first noise signal is used to filter out the noise in the first target signal.

[0015] As a preferred solution of the abnormal vibration detection method for substation equipment under environmental noise according to the present invention, wherein: the first judgment operation includes:

[0016] Configure a first energy threshold;

[0017] Calculate the target energy of the first processed signal;

[0018] Compare and judge the target energy with the first energy threshold.

[0019] As a preferred solution of the abnormal vibration detection method for substation equipment under environmental noise according to the present invention, wherein: the calculation of the target energy of the first processed signal includes:

[0020] Perform a first decomposition on the first processed signal;

[0021] Calculate the energy proportion of each frequency band according to the result of the first decomposition;

[0022] Calculate the target energy of the first processed signal according to the energy proportion.

[0023] As a preferred solution of the abnormal vibration detection method for substation equipment under environmental noise according to the present invention, wherein: the performing a first decomposition on the first processed signal includes:

[0024] Decompose the first processed signal into several layers of wavelet packets;

[0025] Obtain the wavelet packet coefficients corresponding to the several layers of wavelet packets;

[0026] The wavelet packet coefficients are the result of the first decomposition.

[0027] As a preferred solution of the abnormal vibration detection method for substation equipment under environmental noise in the present invention, wherein: the comparison and determination of the target energy with the first energy threshold includes:

[0028] When the target energy is not greater than the first energy threshold, it is determined that the substation equipment has no abnormal vibration;

[0029] When the target energy is greater than the first energy threshold, it is determined that the substation equipment has abnormal vibration.

[0030] As a preferred solution of the abnormal vibration detection method for substation equipment under environmental noise in the present invention, wherein: the result after suppressing the first noise signal is used to filter the noise in the first target signal, including:

[0031] When the first noise signal is suppressed, the first noise signal becomes the first optimal estimation signal;

[0032] Filter out the part of the first optimal estimation signal in the first target signal to obtain the first processed signal.

[0033] In a second aspect, the present invention provides an abnormal vibration detection system for substation equipment under environmental noise, including:

[0034] A signal acquisition module, configured to acquire a first target signal and a first noise signal;

[0035] A signal processing module, configured to preset a first suppression algorithm and process the first target signal and the first noise signal through the first suppression algorithm to obtain a first processed signal;

[0036] A detection module, configured to perform a first judgment operation on the first processed signal and perform abnormal vibration detection according to the judgment result.

[0037] In a third aspect, the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the method described above are implemented.

[0038] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method described above are implemented.

[0039] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention proposes a method and system for detecting abnormal vibration of substation equipment under environmental noise, which acquires a first target signal and a first noise signal; presets a first suppression algorithm, and processes the first target signal and the first noise signal through the first suppression algorithm to obtain a first processed signal; performs a first judgment operation on the first processed signal, and performs abnormal vibration detection according to the judgment result. It can effectively improve the safety and reliability of the operation of substation equipment.

[0040] Specifically, the present invention obtains a first target signal and a first noise signal, and then applies a preset suppression algorithm to process these signals to obtain a first processed signal. Through the first judgment operation, it can accurately judge whether there is abnormal vibration in the substation equipment, and then take corresponding maintenance measures, avoiding safety accidents and economic losses caused by abnormal vibration of the equipment.

[0041] In addition, the detection method and system of the present invention have good adaptability and can adapt to various complex environmental noise conditions, ensuring that abnormal vibration of substation equipment can still be accurately detected under noise interference. This not only improves the detection accuracy, but also greatly reduces the maintenance cost caused by false alarms or missed alarms. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained according to these drawings. Among them:

[0043] Figure 1 is a flowchart of a method for detecting abnormal vibration of substation equipment under environmental noise provided by an embodiment of the present invention;

[0044] Figure 2 is a block diagram of a self-suppression algorithm for a method and system for detecting abnormal vibration of substation equipment under environmental noise provided by an embodiment of the present invention;

[0045] Figure 3 is a waveform schematic diagram of the original signal s(t) of a method and system for detecting abnormal vibration of substation equipment under environmental noise provided by an embodiment of the present invention;

[0046] Figure 4 is for a method and system for detecting abnormal vibration of substation equipment under environmental noise provided by an embodiment of the present invention 1 (t) waveform schematic diagram;

[0047] Figure 5Waveform diagram of e(t) for an abnormal vibration detection method and system of a substation equipment under environmental noise provided by an embodiment of the present invention;

[0048] Figure 6 Schematic diagram of the energy proportion of normal vibration of substation equipment for an abnormal vibration detection method and system of a substation equipment under environmental noise provided by an embodiment of the present invention;

[0049] Figure 7 Schematic diagram of the energy proportion of abnormal vibration of substation equipment for an abnormal vibration detection method and system of a substation equipment under environmental noise provided by an embodiment of the present invention;

[0050] Figure 8 Schematic diagram of the entire algorithm framework for an abnormal vibration detection method and system of a substation equipment under environmental noise provided by an embodiment of the present invention;

[0051] Figure 9 Internal structure diagram of a computer device for an abnormal vibration detection method and system of a substation equipment under environmental noise provided by an embodiment of the present invention. Detailed implementation manners

[0052] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will describe the detailed implementation manners of the present invention in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0053] Embodiment 1

[0054] Refer to Figures 1-9 , which is the first embodiment of the present invention. This embodiment provides an abnormal vibration detection method and system for substation equipment under environmental noise, including:

[0055] In the existing related technologies, there are some problems. For example, traditional vibration detection methods are easily interfered when the environmental noise is large, resulting in inaccurate detection results. In addition, these methods often require complex equipment and high costs, and are not easy to be widely applied in various environments.

[0056] This application provides a method that can effectively solve the above-mentioned problems. Next, multiple embodiments will be combined to elaborate in detail how to implement the abnormal vibration detection method for substation equipment under environmental noise;

[0057] Figure 1 Shows a method flow chart of an abnormal vibration detection method and system for substation equipment under environmental noise, including:

[0058] S101, obtain a first target signal and a first noise signal;

[0059] In an alternative embodiment, the first target signal and the first noise signal may be signals in different scenarios. For example, the first target signal may be the vibration signal of a power transformation device in a normal operating state, while the first noise signal may be the vibration signal caused by environmental noise in the same environment. By obtaining these two signals, specific algorithms can be used to process them to distinguish the abnormal vibration signal of the power transformation device.

[0060] In an alternative embodiment, different scenarios may include, but are not limited to, the operating states of a power transformation device under different load conditions and the operating states of a power transformation device under different weather conditions. For example, under high load conditions, the vibration mode of a power transformation device may be different from that under low load conditions, and adverse weather such as thunderstorms or strong winds may also affect the vibration characteristics of a power transformation device. By obtaining signals in these different scenarios, the accuracy and reliability of abnormal vibration detection can be further improved.

[0061] In an alternative embodiment, when actually using the acoustic emission method for abnormal vibration detection of a power transformation device, the noise of the cooling device and the noise caused by many uncontrollable environmental factors in the environment may also be transmitted to the acoustic emission sensor along with the useful signal. These noises, especially the noise of the cooling device, are difficult to remove.

[0062] It should be noted that the noise of the cooling device is manifested as transience and periodicity. Fourier transform filtering and wavelet rate wave are often used in engineering to filter signals. Whether it is Fourier transform filtering or wavelet filtering, the filtering process is completed in the frequency domain space. Assuming that the noise signal and the ideal signal occupy different frequency bands, the information in the ideal frequency band is retained, the signal amplitude in the noise signal frequency band is set to zero, or threshold processing is performed by selecting a threshold, and then signal reconstruction is performed to restore the time domain waveform.

[0063] In an alternative embodiment, since the useful signal of the abnormal vibration of a power transformation device and environmental noises such as the noise of the cooling device may often be distributed in the same frequency band, this results in that if Fourier transform filtering or wavelet filtering is used to filter the abnormal vibration signal of a power transformation device, the useful signal will also be filtered out.

[0064] In the embodiment of the present application, the first target signal is the actually collected vibration signal, and this signal contains the vibration information generated by the power transformation device during normal operation and abnormal vibration. In order to extract the characteristics of abnormal vibration from these signals;

[0065] In the embodiment of the present application, the first noise signal is a noise signal mainly containing the noise of the cooling device measured separately and is obtained by an independent sensor. This noise signal is used as a reference signal to be compared with the actually collected vibration signal in subsequent processing. Through this comparison, the characteristics of the abnormal vibration of the power transformation equipment can be more accurately identified, so that the abnormal state of the equipment can still be effectively detected under the influence of environmental noise.

[0066] It should be noted that obtaining the first target signal and the first noise signal can lay a foundation for accurately separating the vibration signals generated by the power transformation equipment during normal operation and abnormal vibration. This is because the first noise signal provides a reference for environmental noise, so that when processing the first target signal, the influence of environmental noise can be more effectively filtered out, thereby extracting the characteristics related to the abnormal vibration of the power transformation equipment. In addition, by independently obtaining the first noise signal, it can be ensured that accurate abnormal vibration detection results can be obtained under different environmental noise conditions. This method of separating and extracting the characteristics of abnormal vibration not only improves the detection accuracy, but also reduces the risks of false alarms and missed alarms, thereby improving the safety and reliability of the operation of the power transformation equipment.

[0067] S102, preset a first suppression algorithm, and process the first target signal and the first noise signal through the first suppression algorithm to obtain a first processed signal;

[0068] In the embodiment of the present application, the first suppression algorithm includes:

[0069] The first suppression algorithm is any algorithm that suppresses the first noise signal and filters out the noise in the first target signal;

[0070] The result of suppressing the first noise signal is used to filter out the noise in the first target signal.

[0071] In an optional embodiment, the first suppression algorithm can use adaptive filtering technology, which can dynamically adjust the filtering parameters according to the change of environmental noise to achieve the best filtering effect. By real-time monitoring the characteristics of environmental noise and comparing it with the first noise signal, the adaptive filter can automatically adjust its internal algorithm, thereby effectively separating the signal components related to the abnormal vibration of the power transformation equipment from the first target signal. This method not only improves the detection sensitivity, but also can adapt to the detection requirements under different noise environments, ensuring the stability and reliability of the detection results.

[0072] In an alternative embodiment, the first suppression algorithm may also use a method based on wavelet transform. Wavelet transform is a mathematical tool that can provide both time and frequency information simultaneously, and is particularly suitable for processing non-stationary signals, such as the vibration signals of power transformation equipment. By selecting appropriate wavelet basis functions and decomposition levels, the signal can be decomposed into detail signals and approximation signals with different frequency components.

[0073] In another alternative embodiment, according to the characteristics of the signal, specific frequency components can be filtered to suppress noise and retain useful signals. This method is particularly effective when dealing with signals containing transient and periodic noise, as it can perform fine filtering operations on specific frequency ranges.

[0074] In an alternative embodiment, the first suppression algorithm may also use a method based on Fourier transform. Fourier transform is a mathematical tool that transforms a signal from the time domain to the frequency domain, and it can decompose a complex signal into a series of simple sine waves and cosine waves. By analyzing the frequency components of these waves, the noise and useful signals in the signal can be identified.

[0075] In another alternative embodiment, Fourier transform is used to analyze the spectral characteristics of the first target signal and the first noise signal, so as to achieve noise suppression and extraction of useful signals.

[0076] In an alternative embodiment, the first suppression algorithm may also adopt a method based on neural network. Neural network is a computational model that mimics the structure and function of human brain neurons. It can automatically extract features from signals by learning a large number of data samples, and perform classification or regression analysis on the signals. In this embodiment, the neural network can be trained to identify and suppress noise signals while retaining features related to abnormal vibrations of power transformation equipment.

[0077] In the embodiment of the present application, a self-suppression algorithm block diagram as shown in Figure 2 is designed, where d(t) is the desired signal, s(t) = d(t) + n 0 (t), where s(t) is the actually collected signal, and n 0 (t) is the noise signal mainly containing the noise of the cooling device, and n 1 (t) is the noise signal mainly containing the noise of the cooling device measured separately. The noise of n 1 (t) is output as When After iteration, when it is the best estimate of n 0 (t), n0(t) will be filtered out. The output of the self-suppression algorithm filtering is e(t).

[0078] In the embodiments of the present application, the result after suppressing the first noise signal is used to filter the noise in the first target signal, including:

[0079] After the first noise signal is suppressed, the first noise signal becomes the first optimal estimation signal;

[0080] Filter out the part of the first optimal estimation signal in the first target signal to obtain a first processed signal.

[0081] In an alternative embodiment, after the first noise signal is suppressed, the first noise signal becomes the first optimal estimation signal, which can be obtained through a self-suppression algorithm. This algorithm utilizes the characteristics of the noise signal n 1 (t), and through iterative calculation, the output of n 1 (t) gradually approaches n 0 (t). During the iterative process, the algorithm continuously adjusts the parameters to minimize the estimation error until a predetermined convergence condition is reached. Once the output of n 1 (t) stabilizes and becomes the optimal estimation of n 0 (t), it can be considered that the noise signal n 0 (t) has been effectively filtered. At this time, subtracting the obtained estimation signal from the first target signal can obtain the first processed signal e(t) with the noise filtered out. Such processing not only improves the signal-to-noise ratio of the signal but also provides a clearer signal basis for subsequent abnormal vibration detection.

[0082] Exemplarily, Figure 3 is a waveform schematic diagram of the original signal s(t), Figure 4 is a waveform schematic diagram of n 1 (t), Figure 5 is a waveform schematic diagram of e(t).

[0083]

[0084] It should be noted that presetting the first suppression algorithm and processing the first target signal and the first noise signal through the first suppression algorithm to obtain the first processed signal can significantly reduce the interference of environmental noise on the detection of the vibration signal of the substation equipment. Through the processing of this algorithm, it can be ensured that the characteristics of the vibration signal are more prominent, thereby improving the accuracy and reliability of abnormal vibration detection. In addition, the introduction of this algorithm can also reduce the computational burden on subsequent signal processing steps because the signal e(t) with the noise filtered out is more concise and convenient for further analysis and processing.

[0085] S103, perform a first judgment operation on the first processed signal and perform abnormal vibration detection according to the judgment result.

[0086] In an alternative embodiment, the first determination operation can be performed according to a preset threshold. When the amplitude of the first processed signal exceeds this threshold, the system will determine that there is abnormal vibration. The setting of this threshold can be based on statistical analysis of historical data or determined according to the vibration characteristics during the normal operation of the power transformation equipment. In this way, abnormal vibration events can be effectively identified while avoiding misjudging normal vibration as abnormal.

[0087] In an alternative embodiment, the determination operation can also be combined with time series analysis to ensure that the abnormality of the vibration signal is not caused by transient and non-persistent interference. If the first processed signal exceeds the threshold in multiple consecutive sampling periods, the system will issue an alarm to prompt the operator to conduct further inspections and maintenance.

[0088] In an alternative embodiment, the first determination operation can also be performed according to a preset frequency range. The system will analyze the frequency components of the first processed signal. If an abnormal increase in the intensity of the vibration signal within a specific frequency range is detected, then the system will determine that there is abnormal vibration. This frequency-based determination method is particularly suitable for identifying situations where the vibration at specific frequencies may increase due to equipment aging or damage. In this way, the system can more accurately locate the source of the abnormal vibration, thereby providing more targeted maintenance suggestions for maintenance personnel.

[0089] In the embodiment of the present application, the first determination operation includes:

[0090] Configure a first energy threshold;

[0091] Calculate the target energy of the first processed signal;

[0092] Compare and determine the target energy with the first energy threshold.

[0093] In an alternative embodiment, the first energy threshold can be dynamically set by real-time monitoring of the vibration energy level of the power transformation equipment in the normal operation state and combining historical data. The system will adjust this threshold according to the type of equipment, working environment, and past maintenance records to ensure that it is neither too sensitive to cause frequent false alarms nor set too high to miss real abnormal vibration signals.

[0094] In an alternative embodiment, the setting of the first energy threshold can also consider the operation cycle of the equipment. For example, when the equipment starts and shuts down, due to large load changes, higher vibration energy may be generated. At this time, the system can appropriately increase the threshold to avoid misjudgment. Through this adaptive threshold setting method, the accuracy and reliability of the detection system can be effectively improved.

[0095] In an alternative embodiment, the first energy threshold can also be dynamically adjusted by real-time monitoring of the vibration energy of the power transformation equipment under different ambient noise levels and combining the operating status and historical data of the equipment. The system automatically adjusts the first energy threshold according to the intensity and type of the ambient noise and the operating status of the equipment to adapt to various complex detection environments. For example, in a high-noise environment, the system may lower the threshold to ensure that small abnormal vibrations of the equipment can be detected; while in a low-noise environment, the threshold can be appropriately increased to avoid false alarms caused by ambient noise. Through this intelligent adjustment mechanism, the detection system can more accurately identify the abnormal vibrations of the power transformation equipment, thereby improving the stability and safety of the entire power system.

[0096] In the embodiment of the present application, calculating the target energy of the first processed signal includes:

[0097] Performing a first decomposition on the first processed signal;

[0098] Calculating the energy proportion of each frequency band according to the result of the first decomposition;

[0099] Calculating the target energy of the first processed signal according to the energy proportion.

[0100] In an alternative embodiment, calculating the energy proportion of each frequency band can be achieved through Fourier transform. Fourier transform can decompose a signal into sine waves and cosine waves of different frequencies, thereby obtaining the energy distribution of each frequency band. By analyzing these energy distributions, it is possible to determine which frequency bands have abnormal energy proportions and then infer whether there are abnormal vibrations. This method is particularly effective for analyzing non-stationary signals because it can provide the frequency characteristics of the signal at different time points. After calculating the energy proportion of each frequency band, these proportions can be added up to obtain the total energy of the first processed signal. Then, this total energy is compared with a preset first energy threshold to determine whether there are abnormal vibrations. If the total energy of the first processed signal exceeds the first energy threshold, the system will determine that there are abnormal vibrations, thereby triggering corresponding alarms or maintenance operations. In this way, the system can effectively identify the abnormal vibrations of the power transformation equipment under complex ambient noise and ensure the stable operation of the power system.

[0101] In an alternative embodiment, calculating the energy proportion of each frequency band can also be accomplished through the Short-Time Fourier Transform (STFT). The Short-Time Fourier Transform divides the signal into shorter time intervals and performs Fourier Transform on each interval separately, thereby obtaining the frequency characteristics of the signal at different time points. This method is particularly suitable for processing signals that vary over time because it can capture the transient characteristics of the signal. In practical applications, selecting an appropriate time window length is crucial for accurately extracting signal features. If the window is too short, it may result in insufficient frequency resolution; while if the window is too long, it may not accurately reflect the transient changes of the signal. Therefore, through a carefully designed time window, combined with the Short-Time Fourier Transform, the vibration signals generated by power transformation equipment during operation can be analyzed more precisely, thereby improving the accuracy and reliability of anomaly detection.

[0102] In the embodiment of the present application, the first decomposition of the first processed signal includes:

[0103] Decomposing the first processed signal into several layers of wavelet packets;

[0104] Obtaining wavelet packet coefficients corresponding to the several layers of wavelet packets;

[0105] The wavelet packet coefficients are the first decomposition result.

[0106] In the embodiment of the present application, the comparison and determination of the target energy with the first energy threshold include:

[0107] If the target energy is not greater than the first energy threshold, it is determined that the power transformation equipment has no abnormal vibration;

[0108] If the target energy is greater than the first energy threshold, it is determined that the power transformation equipment has abnormal vibration.

[0109] In the embodiment of the present application, for the obtained signal, wavelet packet decomposition is performed on it, then the energy proportion of each frequency band is obtained, and the root mean square processing is performed on the energy proportion to obtain the parameter M.

[0110] In the embodiment of the present application, the wavelet basis function of db3 is used for wavelet packet decomposition. Taking the 3-layer wavelet packet decomposition as an example, after performing n-layer wavelet packet decomposition on e(t), wavelet packet coefficients of 8 frequency bands will be obtained: X 1 (N), X 2 (N), X 3 (N), …, X 1 (N) The energy is calculated using the following formula:

[0111]

[0112] Among them, X 1 (N)'s energy proportion calculation formula is as follows:

[0113]

[0114] Exemplarily, the following gives the wavelet packet energy proportion diagrams of the substation equipment with and without abnormal vibration, as Figure 6 , Figure 7 , first for the acquisition card with a sampling rate of 1M, after performing n-layer wavelet packet decomposition, the frequency range of each frequency band is H = 1000 / 2*2 n . That is, the frequency range of the first frequency band (the first column of the following bar chart) is 0 to H, and the frequency range of the second frequency band (the second column of the following bar chart) is H to 2H.

[0115] In the embodiment of the present application, after obtaining the energy proportion of each frequency band, calculate its root mean square. The formula is as follows:

[0116]

[0117] When M is less than the initially set threshold value (L), it means that the substation equipment has no abnormal vibration. When M is greater than the initially set threshold value (L), it means that the substation equipment has abnormal vibration. For the overall algorithm framework, as Figure 8 .

[0118] In summary, the present invention proposes a method for detecting abnormal vibration of substation equipment under environmental noise, which obtains a first target signal and a first noise signal; preset a first suppression algorithm, and processes the first target signal and the first noise signal through the first suppression algorithm to obtain a first processed signal; perform a first judgment operation on the first processed signal, and perform abnormal vibration detection according to the judgment result. It can effectively improve the safety and reliability of the operation of substation equipment. Specifically, the present invention obtains a first target signal and a first noise signal, and then applies a preset suppression algorithm to process these signals to obtain a first processed signal. Through the first judgment operation, it can accurately judge whether there is abnormal vibration in the substation equipment, and then take corresponding maintenance measures to avoid safety accidents and economic losses caused by abnormal vibration of the equipment. In addition, the detection method and system of the present invention have good adaptability, can adapt to various complex environmental noise conditions, and ensure that the abnormal vibration of the substation equipment can still be accurately detected under noise interference. This not only improves the detection accuracy, but also greatly reduces the maintenance cost caused by false alarms or missed alarms.

[0119] Embodiment 2

[0120] In a preferred embodiment, an acoustic emission sensor with a resonant frequency of 30 kHz is used to detect the substation equipment.

[0121] (1) Select on-site substation equipment for measurement. First, clean a certain amount of dust from the substation equipment, and then apply a coupling agent on the pre-prepared acoustic emission sensor. Use a sheet-like object to evenly apply the coupling agent on the acoustic emission sensor, and then attach the sensor to the substation equipment. When attaching the acoustic emission sensor to the substation equipment, be sure to evacuate the air between the sensor and the substation equipment, which is beneficial for better measurement and data collection. Another sensor is placed at a location in the field where there is no substation equipment to measure and collect data on environmental noise such as cooling devices.

[0122] (2) Connect the two-way acoustic emission sensors to the front-end amplifier, adjust the amplification factor of the preamplifier to 40 dB, and then connect the output of the preamplifier to a 16-bit data acquisition card. The data of the data acquisition card is wirelessly transmitted to a computer for data processing.

[0123] (3) For the two-way data transmitted to the computer, one is the abnormal vibration signal data of the substation equipment, and the other is the environmental noise signal data such as the cooling device. Transmit the data of the two signals to the self-suppression algorithm to obtain a signal with environmental noise filtered out. Then transmit this signal to the wavelet packet energy ratio root mean square algorithm. The algorithm determines whether the detected substation equipment has abnormal vibration by combining the wavelet packet energy ratio and the root mean square.

[0124] (4) When it is necessary to simultaneously detect the abnormal vibration conditions of multiple substation equipment, only need to place more sensors on the substation equipment to be measured, and transmit the data to the self-suppression algorithm and the wavelet packet energy ratio root mean square algorithm already written in the computer at the same time, then the abnormal vibration conditions of the measured substation equipment can be obtained simultaneously.

[0125] Embodiment 3

[0126] This embodiment also provides a detection system for abnormal vibration of substation equipment under environmental noise, including:

[0127] A signal acquisition module for acquiring a first target signal and a first noise signal;

[0128] A signal processing module for presetting a first suppression algorithm and processing the first target signal and the first noise signal through the first suppression algorithm to obtain a first processed signal;

[0129] A detection module for performing a first judgment operation on the first processed signal and performing abnormal vibration detection according to the judgment result.

[0130] The above-mentioned each unit module can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above each module.

[0131] This embodiment also provides a computer device, which may be a terminal, and its internal structure diagram may be as shown in Figure 9 . The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a method for detecting abnormal vibration of a power transformation device under environmental noise. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or may also be a button, a trackball, or a touchpad provided on the outer shell of the computer device, or may also be an external keyboard, a touchpad, or a mouse, etc.

[0132] This embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0133] Obtain a first target signal and a first noise signal;

[0134] Preset a first suppression algorithm, and process the first target signal and the first noise signal through the first suppression algorithm to obtain a first processed signal;

[0135] Perform a first judgment operation on the first processed signal, and perform abnormal vibration detection according to the judgment result.

[0136] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

[0137] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The solutions in the embodiments of the present application can be implemented in various computer languages. For example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript can be used.

[0138] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or combinations of blocks.

[0139] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or combinations of blocks.

[0140] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or combinations of blocks.

[0141] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they know the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications that fall within the scope of the present application.

[0142] Obviously, those skilled in the art can make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalent technologies, this application is also intended to include these modifications and variations.

Claims

1. A method for detecting abnormal vibration of substation equipment under environmental noise, characterized in that: include: Acquire a first target signal and a first noise signal; Preset a first suppression algorithm, and process the first target signal and the first noise signal by the first suppression algorithm to obtain a first processed signal; A first judgment operation is performed on the first processed signal, and abnormal vibration detection is performed according to the judgment result.

2. The method for detecting abnormal vibration of substation equipment under environmental noise as claimed in claim 1, characterized in that: The first suppression algorithm comprises: The first suppression algorithm is any algorithm for suppressing the first noise signal and filtering out noise in the first target signal; The result of suppressing the first noise signal is used to filter out the noise in the first target signal.

3. The method for detecting abnormal vibration of substation equipment under environmental noise as claimed in claim 2, characterized in that: The first determination operation includes: configuring a first energy threshold; calculating a target energy of the first processed signal; The target energy is compared with the first energy threshold.

4. The method for detecting abnormal vibration of substation equipment under environmental noise as claimed in claim 3, characterized in that: The calculating the target energy of the first processed signal comprises: performing a first decomposition on the first processed signal; Calculate the energy proportion of each frequency band according to the result of the first decomposition; The target energy of the first processed signal is calculated according to the energy proportion.

5. The method for detecting abnormal vibration of substation equipment under environmental noise as claimed in claim 4, characterized in that: The first decomposing the first processed signal comprises: Decomposing the first processed signal into a plurality of layers of wavelet packets; Obtaining wavelet packet coefficients corresponding to the wavelet packets of the plurality of layers; The wavelet packet coefficients are the first decomposition results.

6. The method for detecting abnormal vibration of substation equipment under environmental noise as claimed in claim 5, characterized in that: The comparing and judging the target energy with the first energy threshold comprises: If the target energy is not greater than the first energy threshold, it is determined that the substation equipment has no abnormal vibration; If the target energy is greater than the first energy threshold, it is determined that the substation equipment exhibits abnormal vibration.

7. The method for detecting abnormal vibration of substation equipment under environmental noise as claimed in claim 6, characterized in that: The result of suppressing the first noise signal is used to filter out the noise in the first target signal, including: After the first noise signal is suppressed, the first noise signal becomes a first optimal estimation signal; A first best estimation signal portion in the first target signal is filtered out to obtain a first processed signal.

8. A system for detecting abnormal vibration of substation equipment under environmental noise, characterized in that: include: A signal acquisition module, used to acquire a first target signal and a first noise signal; a signal processing module, configured to preset a first suppression algorithm, and process the first target signal and the first noise signal by using the first suppression algorithm to obtain a first processed signal; The detection module is used to perform a first judgment operation on the first processed signal and perform abnormal vibration detection according to the judgment result.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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