Online fault diagnosis method and device for intelligent situation awareness of booster station

Abstract

The present application discloses an online fault diagnosis method and device for intelligent situational awareness of a booster station, which relates to the field of fault diagnosis technology. The method includes: real-time collection of operating parameters of switchgear in the booster station, the operating parameters including electrical data, temperature data and gas chromatographic data, extracting electrical features in the electrical data based on Fourier transform, extracting temperature features in the temperature data based on time series analysis, extracting gas chromatographic features in the gas chromatographic data based on peak alignment algorithm, determining the performance score of the switchgear based on the electrical features, the temperature features and the gas chromatographic features, and generating fault information of the switchgear based on the electrical features, the temperature features and the gas chromatographic features when the performance score is less than a preset score. In the above manner, the operating status of the switchgear is detected from multiple dimensions, and the fault degree and fault type of the switchgear are determined from multiple angles.

Description

Technical Field

[0001] The present application relates to the technical field of fault diagnosis, and in particular to an online fault diagnosis method and device for intelligent situation awareness of a substation. Background Art

[0002] The operation and maintenance of booster stations mainly rely on a time-based preventive maintenance strategy. This strategy requires that equipment be inspected and maintained within a fixed time period, regardless of the actual operating status of the equipment. Although this method can prevent equipment failures to a certain extent, since the maintenance is based on time rather than the actual status of the equipment, it may lead to unnecessary power outages and maintenance when the equipment is still in good condition, affecting the continuity and reliability of power supply. In addition, preventive maintenance strategies often require regular replacement of parts, even if these parts have not yet reached the end of their service life, resulting in a waste of material and labor costs. Secondly, during the maintenance process, due to improper operation or inaccurate equipment status assessment, equipment accidents and personal safety risks may increase.

[0003] The above contents are only used to assist in understanding the technical solution of the present invention and do not constitute an admission that the above contents are prior art. Summary of the invention

[0004] The main purpose of the present application is to provide an online fault diagnosis method and device for intelligent situational awareness of a substation, aiming to solve the technical problem in the prior art that power outages are required for maintenance of switchgear in substations, which affects the normal operation of equipment.

[0005] To achieve the above objectives, the present application provides an online fault diagnosis method for intelligent situation awareness of a booster station, the method comprising:

[0006] Real-time collection of operating parameters of switchgear in the booster station, including electrical data, temperature data and gas chromatography data;

[0007] Extracting electrical features from the electrical data based on Fourier transform, extracting temperature features from the temperature data based on time series analysis, and extracting gas chromatographic features from the gas chromatographic data based on peak alignment algorithm;

[0008] determining a performance score of the switchgear based on the electrical characteristic, the temperature characteristic, and the gas chromatographic characteristic;

[0009] When the performance score is less than a preset score, fault information of the switch device is generated according to the electrical characteristics, the temperature characteristics and the gas chromatographic characteristics.

[0010] In one embodiment, the step of collecting the operating parameters of the switchgear in the booster station in real time, wherein the operating parameters include electrical data, temperature data and gas chromatography data, comprises:

[0011] Obtaining a device number of a switch device in the booster station, and determining a corresponding sensor group based on the device number;

[0012] Determine the sensor type of each sensor in the sensor group, and generate a time series data sequence of a corresponding type according to the sensor type;

[0013] Collecting the electrical real-time data, temperature real-time data and gas chromatography real-time data of the switch device in real time, and writing the electrical real-time data, the temperature real-time data and the gas chromatography real-time data into the time series data sequence of the corresponding type based on the time series, so as to obtain the electrical time series data sequence, the temperature time series data sequence and the chromatography time series data sequence;

[0014] Electrical data, temperature data and gas chromatography data are obtained according to the electrical time series data sequence, the temperature time series data sequence and the chromatography time series data sequence.

[0015] In one embodiment, the step of extracting electrical features from the electrical data based on Fourier transform includes:

[0016] Generate an electrical waveform diagram according to the electrical real-time data and timing information in the electrical data, and determine a time domain signal in the electrical waveform diagram;

[0017] Performing Fourier transform on the time domain signal to convert the time domain signal into a frequency domain signal to obtain harmonic components, amplitudes and phases at various frequencies;

[0018] determining the electrical harmonic signature based on the harmonic component, the amplitude, and the phase;

[0019] The electrical characteristics are obtained by superimposing a plurality of the electrical harmonic characteristics.

[0020] In one embodiment, the step of performing Fourier transform on the time domain signal to convert the time domain signal into a frequency domain signal to obtain harmonic components, amplitudes and phases at various frequencies comprises:

[0021] Separating the time domain signal to obtain a voltage time domain signal and a current time domain signal;

[0022] Performing Fourier transform on the voltage time domain signal and the current time domain signal respectively to obtain a voltage frequency domain signal and a current frequency domain signal;

[0023] Determine the voltage harmonic components, voltage amplitude and voltage phase corresponding to the voltage frequency domain signal at each frequency;

[0024] The current harmonic components, current amplitude and current phase corresponding to the current frequency domain signal at each frequency are determined.

[0025] In one embodiment, the step of superimposing a plurality of electrical harmonic features to obtain an electrical feature comprises:

[0026] Determining the phase difference of each of the electrical harmonics, and superimposing the electrical harmonics based on the phase difference to obtain a total electrical harmonic;

[0027] Determine the harmonic occupancy rate of the total electrical harmonics, and determine the root mean square value of the harmonic occupancy rate;

[0028] The harmonic distortion rate is obtained by taking the ratio of the root mean square value of the harmonic occupancy rate to the root mean square value of the fundamental wave;

[0029] An electrical characteristic is obtained according to the harmonic distortion rate.

[0030] In one embodiment, the step of extracting temperature features from the temperature data based on time series analysis includes:

[0031] Determine a temperature change curve in the temperature data based on the time series, and determine a change rate in the temperature change curve;

[0032] Determine a plurality of time intervals in the temperature change curve that are greater than a safety temperature threshold, and determine the temperature abnormality time according to the plurality of time intervals;

[0033] A temperature characteristic is obtained according to the change rate and the temperature abnormality time.

[0034] In one embodiment, the step of extracting gas chromatographic features from the gas chromatographic data based on a peak alignment algorithm comprises:

[0035] Extracting the gas chromatographic data, and performing component separation on the gas chromatographic data to determine the target gas type contained in the gas chromatographic data;

[0036] Determine a target gas peak height of the target gas in the gas chromatographic data, compare the target gas peak height with a standard gas peak height, and determine a target content of the target gas;

[0037] A gas chromatogram signature is generated based on the target gas type and the target content.

[0038] In one embodiment, the step of determining the performance score of the switch device according to the electrical characteristics, the temperature characteristics and the gas chromatographic characteristics comprises:

[0039] respectively determining initial performance score weights corresponding to the electrical characteristic, the temperature characteristic, and the gas chromatographic characteristic;

[0040] Respectively comparing the electrical characteristic, the temperature characteristic, and the gas chromatographic characteristic with corresponding preset standard characteristics, and when at least one of the electrical characteristic, the temperature characteristic, or the gas chromatographic characteristic is greater than the corresponding preset standard characteristic, setting the corresponding performance score weight as an abnormal characteristic weight;

[0041] Determining the initial performance score weight and / or the abnormal feature weight determines a performance score of the switchgear.

[0042] In one embodiment, when the performance score is less than a preset score, the step of generating fault information of the switch device according to the electrical characteristics, the temperature characteristics and the gas chromatographic characteristics comprises:

[0043] Determining a preset score for the switchgear to reach a maintenance state;

[0044] When the performance score is less than the preset score, determining an abnormal feature among the electrical feature, the temperature feature, and the gas chromatographic feature;

[0045] Fault information of the switch device is generated according to the abnormal characteristics.

[0046] In addition, to achieve the above purpose, the present application also proposes an online fault diagnosis device for intelligent situation awareness of a booster station, and the online fault diagnosis device for intelligent situation awareness of a booster station includes:

[0047] A data acquisition module, used to collect operating parameters of switchgear in the booster station in real time, the operating parameters including electrical data, temperature data and gas chromatography data;

[0048] A feature extraction module, used for extracting electrical features from the electrical data, extracting temperature features from the temperature data, and extracting gas chromatographic features from the gas chromatographic data;

[0049] a performance evaluation module, configured to determine a performance score of the switchgear according to the electrical characteristics, the temperature characteristics and the gas chromatographic characteristics;

[0050] A fault location module is used to generate fault information of the switch device according to the electrical characteristics, the temperature characteristics and the gas chromatographic characteristics when the performance score is less than a preset score.

[0051] In addition, to achieve the above-mentioned purpose, the present application also proposes an online fault diagnosis device for intelligent situational awareness of a substation, wherein the online fault diagnosis device for intelligent situational awareness of a substation comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the online fault diagnosis method for intelligent situational awareness of a substation as described above.

[0052] In addition, to achieve the above-mentioned purpose, the present invention also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the online fault diagnosis method for intelligent situation awareness of a substation are implemented as described above.

[0053] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the online fault diagnosis method for intelligent situation awareness of a substation as described above.

[0054] The present application provides an online fault diagnosis method for intelligent situational awareness of a booster station, which collects the operating parameters of the switchgear in the booster station in real time, the operating parameters include electrical data, temperature data and gas chromatographic data, extracts electrical features in the electrical data based on Fourier transform, extracts temperature features in the temperature data based on time series analysis, extracts gas chromatographic features in the gas chromatographic data based on a peak alignment algorithm, determines the performance score of the switchgear based on the electrical features, the temperature features and the gas chromatographic features, and generates fault information of the switchgear based on the electrical features, the temperature features and the gas chromatographic features when the performance score is less than a preset score. In the above manner, the operating status of the switchgear is detected from multiple dimensions, and the fault degree and fault type of the switchgear are determined from multiple angles. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0056] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0057] Figure 1 A flow chart of the first embodiment of the online fault diagnosis method for intelligent situation awareness of a booster station of the present application;

[0058] Figure 2 It is a temperature time series curve diagram of an embodiment of an online fault diagnosis method for intelligent situation awareness of a booster station of the present application;

[0059] Figure 3 A harmonic schematic diagram of an embodiment of an online fault diagnosis method for intelligent situation awareness of a booster station of the present application;

[0060] Figure 4 This is a schematic diagram of the module structure of an online fault diagnosis device for intelligent situation awareness of a booster station according to an embodiment of the present application;

[0061] Figure 5 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the online fault diagnosis method for intelligent situation awareness of a substation in an embodiment of the present application.

[0062] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0063] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0064] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0065] The main solution of the embodiment of the present application is: real-time collection of operating parameters of the switchgear in the booster station, the operating parameters including electrical data, temperature data and gas chromatography data;

[0066] Extracting electrical features from the electrical data based on Fourier transform, extracting temperature features from the temperature data based on time series analysis, and extracting gas chromatographic features from the gas chromatographic data based on peak alignment algorithm;

[0067] determining a performance score of the switchgear based on the electrical characteristic, the temperature characteristic, and the gas chromatographic characteristic;

[0068] When the performance score is less than a preset score, fault information of the switch device is generated according to the electrical characteristics, the temperature characteristics and the gas chromatographic characteristics.

[0069] At present, the operation and maintenance of booster stations mainly rely on a time-based preventive maintenance strategy. This strategy requires that equipment be inspected and maintained within a fixed time period, regardless of the actual operating status of the equipment. Although this method can prevent equipment failures to a certain extent, since the maintenance is based on time rather than the actual status of the equipment, it may lead to unnecessary power outages for maintenance when the equipment is still in good condition, affecting the continuity and reliability of power supply. In addition, preventive maintenance strategies often require regular replacement of parts, even if these parts have not yet reached the end of their service life, resulting in a waste of material and labor costs. Secondly, during the maintenance process, due to improper operation or inaccurate equipment status assessment, equipment accidents and personal safety risks may increase.

[0070] The present application provides a solution, which collects the operating parameters of the switchgear in the booster station in real time, wherein the operating parameters include electrical data, temperature data and gas chromatographic data, extracts the electrical features in the electrical data based on Fourier transform, extracts the temperature features in the temperature data based on time series analysis, extracts the gas chromatographic features in the gas chromatographic data based on peak alignment algorithm, determines the performance score of the switchgear based on the electrical features, the temperature features and the gas chromatographic features, and generates the fault information of the switchgear based on the electrical features, the temperature features and the gas chromatographic features when the performance score is less than the preset score. In the above manner, the operating status of the switchgear is detected from multiple dimensions, and the fault degree and fault type of the switchgear are determined from multiple angles.

[0071] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, an online fault diagnosis device for intelligent situation awareness of a booster station, etc., and this embodiment does not specifically limit this. The following takes the online fault diagnosis device for intelligent situation awareness of a booster station as an example to illustrate this embodiment and the following embodiments.

[0072] The embodiment of the present application provides an online fault diagnosis method for intelligent situation awareness of a booster station, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the online fault diagnosis method for intelligent situation awareness of a booster station in the present application.

[0073] In this embodiment, the online fault diagnosis method of the booster station intelligent situation awareness includes steps S10 to S40:

[0074] Step S10, collecting operating parameters of the switchgear in the booster station in real time, the operating parameters including electrical data, temperature data and gas chromatography data;

[0075] It should be noted that the switchgear of the substation is an important component of the power system, ensuring the safe operation of the power system and being able to quickly cut off power supply when a fault occurs to prevent the accident from expanding or damaging other equipment.

[0076] It should be understood that electrical data refers to various measurements and parameters in the power system, which can reflect the current operating status of the power system. Temperature data refers to the equipment temperature of the switchgear when it is in working state. Temperature data is related to the heat generation. When the heat generation increases, the temperature of the switchgear will rise. Gas chromatography data refers to the chromatographic signal of the gas dissolved in the transformer insulating oil under the influence of temperature. It can be understood that the higher the temperature, the higher the solubility of the insulating oil.

[0077] In the specific implementation, due to the particularity of the booster station and the important role played by the switchgear in the booster station, when the switchgear is abnormal, it will cause a huge power supply loss. Therefore, it is extremely important to ensure that the operating state of the switchgear is in good working condition. The current traditional maintenance method is power-off maintenance. It is conceivable that when the power-off maintenance is carried out, it will cause a certain period of time to affect the power supply, and the scale of the impact is directly related to the scale of the maintenance. Furthermore, when the power-off maintenance is carried out, in order to reduce the impact of the power outage, the load of other working equipment will increase, which will further affect the life of the equipment with increased load. Therefore, this fault diagnosis method has obvious disadvantages.

[0078] Based on this, the present embodiment can add multiple types of sensors to the switchgear of the booster station, and can describe the performance of the switchgear from multiple dimensions. The present embodiment monitors the switchgear in three dimensions, namely, the electrical dimension, the temperature dimension, and the gas chromatography dimension. The specific dimensions are not limited in comparison in this embodiment, and the detection dimensions can be adaptively increased or decreased. When performing online intelligent situation detection on the switchgear in the booster station, the collected data of each sensor can be collected at the same time, and the data consistency in time can be guaranteed to obtain the operating parameters of the switchgear in the booster station. At this time, it is parallel processing, that is, the operating parameters of all switchgear in the area can be obtained at the same time, and the operating parameters include at least electrical data, temperature data, and gas chromatography data.

[0079] In a feasible implementation manner, the step of real-time acquisition of operating parameters of the switchgear in the booster station, wherein the operating parameters include electrical data, temperature data and gas chromatography data, comprises:

[0080] Obtaining a device number of a switch device in the booster station, and determining a corresponding sensor group based on the device number;

[0081] Determine the sensor type of each sensor in the sensor group, and generate a time series data sequence of a corresponding type according to the sensor type;

[0082] Collecting the electrical real-time data, temperature real-time data and gas chromatography real-time data of the switch device in real time, and writing the electrical real-time data, the temperature real-time data and the gas chromatography real-time data into the time series data sequence of the corresponding type based on the time series, so as to obtain the electrical time series data sequence, the temperature time series data sequence and the chromatography time series data sequence;

[0083] Electrical data, temperature data and gas chromatography data are obtained according to the electrical time series data sequence, the temperature time series data sequence and the chromatography time series data sequence.

[0084] It should be noted that each switch device in the booster station has a unique device number, and the specific location of the switch device can be determined based on the device number, and the operating parameters of the switch device can be uniformly managed based on the device number.

[0085] In the specific implementation, when performing online fault diagnosis of intelligent situational awareness, the device code of each switch device in the current booster station can be obtained, such as (SW001, SW002...). Each switch device is deployed with multiple sensors, and the device code and the sensor are configured to form a sensor group, which includes various types of sensors, which can be specifically expressed as:

[0086] SW001->[CT1,PT1,TempSensor1,GasAnalyzer1]

[0087] SW002->[CT2,PT2,TempSensor2,GasAnalyzer2]

[0088] Among them, CT stands for current transformer, PT stands for voltage sensor, TempSensor is temperature sensor, and GasAnalyzer is gas analyzer.

[0089] After determining the sensor type, a time series data sequence of the corresponding type can be generated, that is, the sensors collect data at the same time, which can ensure that the data are consistent in the collection time. The electrical real-time data, temperature real-time data and gas chromatography real-time data of the switch device are collected in real time, and the electrical real-time data, temperature real-time data and gas chromatography real-time data are written into the time series data sequence of the corresponding type based on the time series to obtain the electrical time series data sequence, temperature time series data sequence and chromatography time series data sequence, and then the electrical data, temperature data and gas chromatography data are obtained according to the electrical data sequence, temperature time series data sequence and chromatography time series data sequence.

[0090] Step S20, extracting electrical features from the electrical data based on Fourier transform, extracting temperature features from the temperature data based on time series analysis, and extracting gas chromatographic features from the gas chromatographic data based on a peak alignment algorithm;

[0091] It should be noted that electrical characteristics, temperature characteristics and gas chromatographic characteristics are all characteristics that reflect the operating status of the switchgear. Electrical characteristics include current and voltage, temperature characteristics are the temperature distribution of the switchgear, and gas chromatographic characteristics are the gas component content in the area.

[0092] It can be understood that Fourier transform is a method to convert time domain signals into frequency domain representation, which can help us identify periodic and non-periodic components in electrical signals. EIts spectral features can be extracted by discrete Fourier transform (DFT) or fast Fourier transform (FFT). Assuming x[n] is the time series of electrical data, its discrete Fourier transform is defined as:

[0093]

[0094] where N is the number of data points, j is the imaginary unit, and X[k] is the complex value at frequency component k.

[0095] The electrical characteristics are then extracted and the amplitude |X[k]| and phase angle ∠X[k] of each frequency component are calculated.

[0096] Time series analysis is used to explore the change pattern of temperature data over time, and can be linearly fitted using the least squares method: T(t)=a+bt.

[0097] Where T(t) is the temperature at time t, and a and b are constants obtained by minimizing the sum of squared errors.

[0098] Gas chromatography data usually contains multiple peaks, which represent the concentrations of different gas components. Peak alignment algorithms are used to correct for peak position shifts caused by experimental conditions or instrument drift, so as to accurately compare the similarities between different samples. First, all significant peaks need to be found, and then the actual spectrum is adjusted to match the reference spectrum by comparing the position differences of the peaks between the reference spectrum and the actual measured spectrum. This is achieved by using dynamic time warping, and then the area and height of each peak are calculated as an indicator of the concentration of the gas component. The peak area can be calculated by numerical integration methods, while the peak height is the maximum value of the peak. The relative proportion of the peak area of ​​each component is calculated to reflect the composition of the gas mixture.

[0099] In a feasible implementation manner, the step of extracting electrical features from the electrical data based on Fourier transform includes:

[0100] Generate an electrical waveform diagram according to the electrical real-time data and timing information in the electrical data, and determine a time domain signal in the electrical waveform diagram;

[0101] Performing Fourier transform on the time domain signal to convert the time domain signal into a frequency domain signal to obtain harmonic components, amplitudes and phases at various frequencies;

[0102] determining the electrical harmonic signature based on the harmonic component, the amplitude, and the phase;

[0103] The electrical characteristics are obtained by superimposing a plurality of the electrical harmonic characteristics.

[0104] In a specific implementation, an electrical waveform diagram is generated based on the timing information in the electrical real-time data in the electrical data, and the time domain signal in the electrical waveform diagram can be determined. Then, the time domain signal is Fourier transformed to convert the time domain signal into a frequency domain signal to obtain the harmonic components, amplitudes, and phases at each frequency. When determining the harmonic components, the fundamental frequency and harmonics are usually considered, and the harmonics are integer multiples of the fundamental frequency. Harmonics can reflect the degree of distortion, which can be expressed as:

[0105]

[0106] Calculate the amplitude ratio of each harmonic relative to the fundamental wave to determine the contribution of different harmonic components:

[0107]

[0108] Finally, multiple electrical harmonic signatures are superimposed to form a comprehensive electrical signature.

[0109] In a feasible implementation manner, the step of performing Fourier transform on the time domain signal to convert the time domain signal into a frequency domain signal to obtain harmonic components, amplitudes and phases at various frequencies includes:

[0110] Separating the time domain signal to obtain a voltage time domain signal and a current time domain signal;

[0111] Performing Fourier transform on the voltage time domain signal and the current time domain signal respectively to obtain a voltage frequency domain signal and a current frequency domain signal;

[0112] Determine the voltage harmonic components, voltage amplitude and voltage phase corresponding to the voltage frequency domain signal at each frequency;

[0113] The current harmonic components, current amplitude and current phase corresponding to the current frequency domain signal at each frequency are determined.

[0114] In the specific implementation, refer to Figure 2 , Figure 2 is a harmonic diagram. The time domain signal is separated to obtain the voltage time domain signal V(t) and the current time domain signal I(t). Then the voltage time domain signal and the current time domain signal are Fourier transformed to obtain the voltage frequency domain signal V(k) and the current frequency domain signal I(k). Therefore, in the voltage frequency domain signal V(k), the voltage harmonic components, voltage amplitude and voltage phase at each frequency can be extracted. The determination method of the harmonic components is as described above. The voltage amplitude can calculate the amplitude |V[k]| of each frequency component, and the phase angle ∠V[k] can be determined by the real part and the imaginary part.

[0115]

[0116] Where Im(V[k]) is the imaginary part of V[k] and Re(V[k]) is the real part of V[k].

[0117] Similarly, when the current harmonic components, current amplitude |I[k]| and current phase ∠I[k] are determined, the processing method is similar to that of determining the voltage harmonic components, voltage amplitude and voltage phase, where the current phase can be expressed as:

[0118]

[0119] Where Im(I[k]) is the imaginary part of I[k], and Re(I[k]) is the real part of I[k].

[0120] In a feasible implementation manner, the step of superimposing a plurality of electrical harmonic features to obtain an electrical feature includes:

[0121] Determining the phase difference of each of the electrical harmonics, and superimposing the electrical harmonics based on the phase difference to obtain a total electrical harmonic;

[0122] Determine the harmonic occupancy rate of the total electrical harmonics, and determine the root mean square value of the harmonic occupancy rate;

[0123] The harmonic distortion rate is obtained by taking the ratio of the root mean square value of the harmonic occupancy rate to the root mean square value of the fundamental wave;

[0124] An electrical characteristic is obtained according to the harmonic distortion rate.

[0125] In the specific implementation, the harmonic components, amplitudes and phases of voltage and current at various frequencies are obtained through Fourier transform. For each harmonic component k, the phase difference between voltage and harmonics can be calculated:

[0126] Δφ k =∠V[k]-∠I[k]

[0127] Where ∠V[k] is the phase of the voltage frequency domain signal at the frequency component k, ∠I[k] is the phase of the current frequency domain signal at the frequency component k, and Δφ k is the phase difference at frequency component k.

[0128] In order to superimpose the various harmonic components into a total electrical harmonic, for each frequency component k, the harmonic component can be expressed as a complex number:

[0129] H k =|H[k]|e j∠H[k]

[0130] The total electrical harmonics can be obtained by complex summing all harmonic components:

[0131]

[0132] The harmonic occupancy rate refers to the ratio of the energy of each harmonic to the energy of the fundamental wave. For the kth harmonic, its harmonic occupancy rate can be defined as:

[0133]

[0134] Where H[k] is the amplitude of the kth harmonic, and H[1] is the amplitude of the fundamental wave.

[0135] And determine the RMS value of the harmonic occupancy, through the formula:

[0136]

[0137] At the same time, calculate the root mean square value of the fundamental wave, RMS base =|H[1]|, therefore, the harmonic distortion rate can be expressed as:

[0138]

[0139] Harmonic distortion (THD) is an important electrical characteristic that reflects the degree of nonlinear distortion in the electrical system. A high THD value may indicate a harmonic problem in the system, which may cause equipment overheating, reduced efficiency, poor power quality, and other problems. By monitoring THD, these problems can be discovered and resolved in a timely manner, thereby improving system reliability and performance.

[0140] In a feasible implementation manner, the step of extracting temperature features from the temperature data based on time series analysis includes:

[0141] Determine a temperature change curve in the temperature data based on the time series, and determine a change rate in the temperature change curve;

[0142] Determine a plurality of time intervals in the temperature change curve that are greater than a safety temperature threshold, and determine the temperature abnormality time according to the plurality of time intervals;

[0143] A temperature characteristic is obtained according to the change rate and the temperature abnormality time.

[0144] In the specific implementation, refer to Figure 3 , Figure 3 is the temperature time series curve. The temperature value of each time point in the temperature data is determined based on the time series information, and the temperature value of each time point is used to generate a temperature change curve, and the change rate of the temperature change curve is determined. Since the temperature rise is gradual, there will be no sudden changes like voltage or current. Therefore, the temperature change rate can be determined according to the temperature value of the preset time interval. Specifically, it can be expressed as for two adjacent time points t i and t i+1, the temperature change rate can be expressed as:

[0145]

[0146] At the same time, multiple time intervals in the temperature curve that are greater than the safety temperature threshold are determined, and the temperature abnormality time is determined based on the multiple time intervals. Therefore, the temperature characteristic can be determined by combining the temperature change rate and the temperature abnormality time.

[0147] In a feasible implementation manner, the step of extracting gas chromatographic features in the gas chromatographic data based on a peak alignment algorithm includes:

[0148] Extracting the gas chromatographic data, and performing component separation on the gas chromatographic data to determine the target gas type contained in the gas chromatographic data;

[0149] Determine a target gas peak height of the target gas in the gas chromatographic data, compare the target gas peak height with a standard gas peak height, and determine a target content of the target gas;

[0150] A gas chromatogram signature is generated based on the target gas type and the target content.

[0151] In specific implementation, gas chromatography is a technology for analyzing the components in a mixed gas. Through a gas chromatograph, different components in the mixed gas will be separated in the chromatographic column according to their physical and chemical properties, and different peaks will be generated in the detector. Each peak corresponds to a specific gas component. After the chromatographic data of the gas is extracted from the sensor, the gas chromatographic data can be separated into components to determine the target gas type contained in the gas chromatographic data. When separating the gas components, the position and height of each peak can be identified using a peak detection algorithm based on the chromatographic signal intensity C(t) at time t. Each peak represents a gas component. By comparing the chromatograms of known standard gases, the target gas type corresponding to each peak can be determined. Specifically, the target gas can be identified by comparing the actual measured retention time with the retention time of the standard gas. The residence time of each gas component in the chromatographic column is called the retention time t R Different gases have different retention times, so different gas components can be distinguished by retention time. In addition to retention time, the gas type can be further confirmed by comparing peak shapes (such as peak width, peak height, peak area, etc.). Once the peak position of the target gas is determined, the peak height of the target gas needs to be calculated. Peak height refers to the maximum signal intensity of the peak and is usually used for quantitative analysis. Let H target is the peak height of the target gas, i.e. the maximum signal intensity of the peak. The peak area A is determined by numerical integration target , which can be expressed as:

[0152]

[0153] Where t1 and t2 are the start and end time of the peak, respectively.

[0154] When the peak height or peak area is obtained, quantitative analysis can be performed to determine the specific content of the target gas C target , so it can be specifically expressed as:

[0155]

[0156] Finally, a gas chromatographic signature is generated based on the type and content of the target gas.

[0157] Step S30, determining a performance score of the switch device according to the electrical characteristics, the temperature characteristics and the gas chromatographic characteristics;

[0158] It should be noted that the performance score is used to measure the safety status of the switchgear. The higher the performance score, the more reliable the stability of the switchgear.

[0159] It is understandable that in order to accurately describe the actual quality of the switchgear and to avoid the limitations caused by a single data, this embodiment can measure the operating state of the switchgear by using data from multiple dimensions together, and there is a correlation between the parameters selected in this embodiment, which can more accurately and comprehensively describe the operating state of the switchgear. Therefore, by assigning a certain weight value to the data of each dimension, the performance score of the switchgear can be obtained through weighted calculation.

[0160] In a feasible implementation manner, the step of determining the performance score of the switch device according to the electrical characteristics, the temperature characteristics and the gas chromatographic characteristics comprises:

[0161] respectively determining initial performance score weights corresponding to the electrical characteristic, the temperature characteristic, and the gas chromatographic characteristic;

[0162] Respectively comparing the electrical characteristic, the temperature characteristic, and the gas chromatographic characteristic with corresponding preset standard characteristics, and when at least one of the electrical characteristic, the temperature characteristic, or the gas chromatographic characteristic is greater than the corresponding preset standard characteristic, setting the corresponding performance score weight as an abnormal characteristic weight;

[0163] Determining the initial performance score weight and / or the abnormal feature weight determines a performance score of the switchgear.

[0164] It should be noted that the abnormal feature weight refers to a special weight based on when the feature is in an abnormal state. The abnormal feature weight in this embodiment is 0. This embodiment does not limit this and can be reasonably set according to specific circumstances.

[0165] In the specific implementation, first, an initial performance score weight is assigned to each feature. These weights can be determined based on expert experience, historical data analysis, or machine learning models. The weight values ​​of electrical features, temperature features, and gas chromatographic features are w1, w2, and w3, respectively. And they can be initially assigned values ​​to be 0.4, 0.3, and 0.3, respectively. At the same time, standard features are set for each feature, and the obtained electrical features, temperature features, and gas chromatographic features can be compared with the corresponding preset standard features. When it is determined that the electrical features, temperature features, and gas chromatographic features have abnormal features, the feature values ​​corresponding to the abnormal features are reset to the abnormal feature weights, and then the electrical features, temperature features, and gas chromatographic features are calculated with the corresponding reset weights to obtain a performance score.

[0166] Step S40: When the performance score is less than a preset score, generating fault information of the switch device according to the electrical characteristics, the temperature characteristics and the gas chromatographic characteristics.

[0167] In the specific implementation, it is first necessary to determine the preset score for the switchgear to reach the maintenance state, that is, when the performance score is less than the preset score, it indicates that the current switchgear needs to be repaired or replaced. When the performance score is less than the preset score, the abnormal features in the electrical features, the temperature features, and the gas chromatographic features can be determined, and whether it is an electrical abnormality, a temperature abnormality, or a gas chromatographic abnormality can be directly determined based on the abnormal features, and fault information is generated accordingly, and then the fault information and the device number of the current switchgear are packaged and output to inform the switchgear that needs to be repaired or replaced.

[0168] This embodiment provides an online fault diagnosis method for intelligent situational awareness of a booster station, which collects the operating parameters of the switchgear in the booster station in real time, the operating parameters including electrical data, temperature data and gas chromatographic data, extracts electrical features in the electrical data based on Fourier transform, extracts temperature features in the temperature data based on time series analysis, extracts gas chromatographic features in the gas chromatographic data based on a peak alignment algorithm, determines the performance score of the switchgear based on the electrical features, the temperature features and the gas chromatographic features, and generates fault information of the switchgear based on the electrical features, the temperature features and the gas chromatographic features when the performance score is less than a preset score. In the above manner, the operating status of the switchgear is detected from multiple dimensions, and the fault degree and fault type of the switchgear are determined from multiple angles.

[0169] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the online fault diagnosis method of intelligent situational awareness of the substation in the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0170] This application also provides an online fault diagnosis device for intelligent situation awareness of a booster station, please refer to Figure 4 The online fault diagnosis device of the booster station intelligent situation awareness includes:

[0171] The data acquisition module 10 is used to collect the operating parameters of the switchgear in the booster station in real time, and the operating parameters include electrical data, temperature data and gas chromatography data;

[0172] A feature extraction module 20, used for extracting electrical features from the electrical data, extracting temperature features from the temperature data, and extracting gas chromatographic features from the gas chromatographic data;

[0173] a performance evaluation module 30, for determining a performance score of the switchgear according to the electrical characteristics, the temperature characteristics and the gas chromatographic characteristics;

[0174] The fault location module 40 is used to generate fault information of the switch device according to the electrical characteristics, the temperature characteristics and the gas chromatographic characteristics when the performance score is less than a preset score.

[0175] In a feasible embodiment, the data acquisition module 10 is also used to obtain the device number of the switch device in the booster station, determine the corresponding sensor group based on the device number; determine the sensor type of each sensor in the sensor group, and generate a corresponding type of timing data sequence according to the sensor type; collect the electrical real-time data, temperature real-time data and gas chromatography real-time data of the switch device in real time, write the electrical real-time data, the temperature real-time data and the gas chromatography real-time data into the timing data sequence of the corresponding type based on the timing, and obtain the electrical timing data sequence, the temperature timing data sequence and the chromatography timing data sequence; obtain the electrical data, temperature data and gas chromatography data according to the electrical timing data sequence, the temperature timing data sequence and the chromatography timing data sequence.

[0176] In a feasible implementation manner, the feature extraction module 20 is also used to generate an electrical waveform diagram based on the electrical real-time data and timing information in the electrical data, and determine the time domain signal in the electrical waveform diagram; perform Fourier transform on the time domain signal, convert the time domain signal into a frequency domain signal, and obtain the harmonic components, amplitudes and phases at each frequency; determine the electrical harmonic characteristics based on the harmonic components, the amplitudes and the phases; and superimpose multiple electrical harmonic characteristics to obtain electrical characteristics.

[0177] In a feasible implementation manner, the feature extraction module 20 is also used to separate the time domain signal to obtain a voltage time domain signal and a current time domain signal; perform Fourier transform on the voltage time domain signal and the current time domain signal respectively to obtain a voltage frequency domain signal and a current frequency domain signal; determine the corresponding voltage harmonic components, voltage amplitude and voltage phase of the voltage frequency domain signal at each frequency; determine the corresponding current harmonic components, current amplitude and current phase of the current frequency domain signal at each frequency.

[0178] In a feasible implementation manner, the feature extraction module 20 is also used to determine the phase difference of each of the electrical harmonics, superimpose the electrical harmonics based on the phase difference to obtain total electrical harmonics; determine the harmonic occupancy of the total electrical harmonics, and determine the root mean square value of the harmonic occupancy; obtain the harmonic distortion rate by the ratio of the root mean square value of the harmonic occupancy to the root mean square value of the fundamental wave; and obtain electrical characteristics based on the harmonic distortion rate.

[0179] In a feasible implementation, the feature extraction module 20 is also used to determine the temperature change curve in the temperature data based on time series, and determine the change rate in the temperature change curve; determine multiple time intervals in the temperature change curve that are greater than the safety temperature threshold, and determine the temperature abnormality time based on the multiple time intervals; obtain temperature characteristics based on the change rate and the temperature abnormality time.

[0180] In a feasible implementation, the feature extraction module 20 is also used to extract the gas chromatography data, and perform component separation on the gas chromatography data to determine the target gas type contained in the gas chromatography data; determine the target gas peak height of the target gas in the gas chromatography data, compare the target gas peak height with the standard gas peak height, and determine the target content of the target gas; generate gas chromatography features according to the target gas type and the target content.

[0181] In a feasible implementation manner, the performance evaluation module 30 is further used to respectively determine the initial performance score weights corresponding to the electrical characteristics, the temperature characteristics and the gas chromatographic characteristics; respectively compare the electrical characteristics, the temperature characteristics and the gas chromatographic characteristics with the corresponding preset standard characteristics, and when at least one of the electrical characteristics, the temperature characteristics or the gas chromatographic characteristics is greater than the corresponding preset standard characteristics, set the corresponding performance score weight to the abnormal characteristic weight; determine the initial performance score weight and / or the abnormal characteristic weight to determine the performance score of the switching device.

[0182] In a feasible implementation manner, the fault location module 40 is further used to determine a preset score for the switch device to reach a maintenance state; when the performance score is less than the preset score, determine abnormal features among the electrical features, the temperature features, and the gas chromatographic features; and generate fault information of the switch device based on the abnormal features.

[0183] The online fault diagnosis device for intelligent situational awareness of the substation provided by the present application adopts the online fault diagnosis method for intelligent situational awareness of the substation in the above-mentioned embodiment, which can solve the technical problem that the power outage for maintenance of the switch equipment of the substation is required, which will affect the normal operation of the equipment. Compared with the prior art, the beneficial effects of the online fault diagnosis device for intelligent situational awareness of the substation provided by the present application are the same as the beneficial effects of the online fault diagnosis method for intelligent situational awareness of the substation provided by the above-mentioned embodiment, and the other technical features of the online fault diagnosis device for intelligent situational awareness of the substation are the same as the features disclosed in the above-mentioned embodiment method, which will not be repeated here.

[0184] The present application provides an online fault diagnosis device for intelligent situational awareness of a substation, and the online fault diagnosis device for intelligent situational awareness of a substation includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the online fault diagnosis method for intelligent situational awareness of a substation in the above-mentioned embodiment one.

[0185] Reference below Figure 5 , which shows a schematic diagram of the structure of an online fault diagnosis device for intelligent situational awareness of a booster station suitable for implementing the embodiment of the present application. The online fault diagnosis device for intelligent situational awareness of a booster station in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The online fault diagnosis device for intelligent situation awareness of the substation shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0186] like Figure 5As shown, the online fault diagnosis device of the intelligent situational awareness of the booster station may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM: Read Only Memory) 1002 or the program loaded from the storage device 1003 to the random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the online fault diagnosis device of the intelligent situational awareness of the booster station are also stored. The processing device 1001, ROM1002 and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 can allow the online fault diagnosis device of the intelligent situational awareness of the booster station to communicate wirelessly or wired with other devices to exchange data. Although the figure shows the online fault diagnosis device of the intelligent situational awareness of the booster station with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have alternatively.

[0187] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0188] The online fault diagnosis device for intelligent situational awareness of booster stations provided by the present application adopts the online fault diagnosis method for intelligent situational awareness of booster stations in the above-mentioned embodiment, which can solve the technical problems of online fault diagnosis for intelligent situational awareness of booster stations. Compared with the prior art, the beneficial effects of the online fault diagnosis device for intelligent situational awareness of booster stations provided by the present application are the same as the beneficial effects of the online fault diagnosis method for intelligent situational awareness of booster stations provided by the above-mentioned embodiment, and the other technical features of the online fault diagnosis device for intelligent situational awareness of booster stations are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0189] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0190] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0191] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, the computer-readable program instructions being used to execute the online fault diagnosis method for intelligent situational awareness of a substation in the above-mentioned embodiment.

[0192] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0193] The above-mentioned computer-readable storage medium may be included in the online fault diagnosis device for intelligent situation awareness of the booster station; or it may exist independently without being assembled into the online fault diagnosis device for intelligent situation awareness of the booster station.

[0194] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the online fault diagnosis device of the intelligent situation awareness of the booster station, the online fault diagnosis device of the intelligent situation awareness of the booster station: collects the operating parameters of the switch equipment in the booster station in real time, and the operating parameters include electrical data, temperature data and gas chromatography data; extracts electrical features in the electrical data based on Fourier transform, extracts temperature features in the temperature data based on time series analysis, and extracts gas chromatographic features in the gas chromatographic data based on peak alignment algorithm; determines the performance score of the switch equipment according to the electrical features, the temperature features and the gas chromatographic features; when the performance score is less than the preset score, generates fault information of the switch equipment according to the electrical features, the temperature features and the gas chromatographic features.

[0195] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0196] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0197] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0198] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned online fault diagnosis method for intelligent situational awareness of booster stations, and can solve the technical problems of online fault diagnosis of intelligent situational awareness of booster stations. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the online fault diagnosis method for intelligent situational awareness of booster stations provided by the above-mentioned embodiments, and will not be repeated here.

[0199] The present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the online fault diagnosis method for intelligent situation awareness of a substation as described above.

[0200] The computer program product provided by the present application can solve the technical problem of online fault diagnosis of intelligent situational awareness of booster stations. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the online fault diagnosis method of intelligent situational awareness of booster stations provided by the above embodiments, which will not be repeated here.

[0201] The above are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. An online fault diagnosis method for intelligent situation awareness of a booster station, characterized in that: The online fault diagnosis method for intelligent situation awareness of the booster station includes: Real-time collection of operating parameters of switchgear in the booster station, including electrical data, temperature data and gas chromatography data; Extracting electrical features from the electrical data based on Fourier transform, extracting temperature features from the temperature data based on time series analysis, and extracting gas chromatographic features from the gas chromatographic data based on a peak alignment algorithm; determining a performance score of the switchgear based on the electrical characteristic, the temperature characteristic, and the gas chromatographic characteristic; When the performance score is less than a preset score, fault information of the switch device is generated according to the electrical characteristics, the temperature characteristics and the gas chromatographic characteristics.

2. The method according to claim 1, characterized in that The step of collecting the operating parameters of the switchgear in the booster station in real time, wherein the operating parameters include electrical data, temperature data and gas chromatographic data, comprises: Obtaining a device number of a switch device in the booster station, and determining a corresponding sensor group based on the device number; Determine the sensor type of each sensor in the sensor group, and generate a time series data sequence of a corresponding type according to the sensor type; Collecting the electrical real-time data, temperature real-time data and gas chromatography real-time data of the switch device in real time, and writing the electrical real-time data, the temperature real-time data and the gas chromatography real-time data into the time series data sequence of the corresponding type based on the time series, so as to obtain the electrical time series data sequence, the temperature time series data sequence and the chromatography time series data sequence; Electrical data, temperature data and gas chromatography data are obtained according to the electrical time series data sequence, the temperature time series data sequence and the chromatography time series data sequence.

3. The method according to claim 1, characterized in that The step of extracting electrical features from the electrical data based on Fourier transform comprises: Generate an electrical waveform diagram according to the electrical real-time data and timing information in the electrical data, and determine a time domain signal in the electrical waveform diagram; Performing Fourier transform on the time domain signal to convert the time domain signal into a frequency domain signal to obtain harmonic components, amplitudes and phases at various frequencies; determining an electrical harmonic signature based on the harmonic component, the amplitude, and the phase; The electrical characteristics are obtained by superimposing a plurality of the electrical harmonic characteristics.

4. The method according to claim 3, characterized in that The step of performing Fourier transform on the time domain signal to convert the time domain signal into a frequency domain signal to obtain harmonic components, amplitudes and phases at various frequencies comprises: Separating the time domain signal to obtain a voltage time domain signal and a current time domain signal; Performing Fourier transform on the voltage time domain signal and the current time domain signal respectively to obtain a voltage frequency domain signal and a current frequency domain signal; Determine the voltage harmonic components, voltage amplitude and voltage phase corresponding to the voltage frequency domain signal at each frequency; The current harmonic components, current amplitude and current phase corresponding to the current frequency domain signal at each frequency are determined.

5. The method according to claim 3, characterized in that The step of superimposing a plurality of electrical harmonic features to obtain an electrical feature comprises: Determining the phase difference of each of the electrical harmonics, and superimposing the electrical harmonics based on the phase difference to obtain a total electrical harmonic; Determine the harmonic occupancy rate of the total electrical harmonics, and determine the root mean square value of the harmonic occupancy rate; The harmonic distortion rate is obtained by taking the ratio of the root mean square value of the harmonic occupancy rate to the root mean square value of the fundamental wave; An electrical characteristic is obtained according to the harmonic distortion rate.

6. The method according to claim 1, characterized in that The step of extracting temperature features from the temperature data based on time series analysis includes: Determine a temperature change curve in the temperature data based on the time series, and determine a change rate in the temperature change curve; Determine a plurality of time intervals in the temperature change curve that are greater than a safety temperature threshold, and determine the temperature abnormality time according to the plurality of time intervals; A temperature characteristic is obtained according to the change rate and the temperature abnormality time.

7. The method according to claim 1, characterized in that The step of extracting gas chromatographic features in the gas chromatographic data based on a peak alignment algorithm comprises: Extracting the gas chromatographic data, and performing component separation on the gas chromatographic data to determine the target gas type contained in the gas chromatographic data; Determine a target gas peak height of the target gas in the gas chromatographic data, compare the target gas peak height with a standard gas peak height, and determine a target content of the target gas; A gas chromatogram signature is generated based on the target gas type and the target content.

8. The method according to claim 1, characterized in that The step of determining the performance score of the switchgear according to the electrical characteristic, the temperature characteristic and the gas chromatographic characteristic comprises: respectively determining initial performance score weights corresponding to the electrical characteristic, the temperature characteristic, and the gas chromatographic characteristic; Respectively comparing the electrical characteristic, the temperature characteristic, and the gas chromatographic characteristic with corresponding preset standard characteristics, and when at least one of the electrical characteristic, the temperature characteristic, or the gas chromatographic characteristic is greater than the corresponding preset standard characteristic, setting the corresponding performance score weight as an abnormal characteristic weight; Determining the initial performance score weight and / or the abnormal feature weight determines a performance score of the switchgear.

9. The method according to claim 1, characterized in that When the performance score is less than a preset score, the step of generating fault information of the switch device according to the electrical characteristics, the temperature characteristics and the gas chromatographic characteristics comprises: Determining a preset score for the switchgear to reach a maintenance state; When the performance score is less than the preset score, determining an abnormal feature among the electrical feature, the temperature feature, and the gas chromatographic feature; Fault information of the switch device is generated according to the abnormal characteristics.

10. An online fault diagnosis device for intelligent situation awareness of a booster station, characterized in that: The online fault diagnosis device for intelligent situation awareness of the booster station includes: A data acquisition module, used to collect operating parameters of switchgear in the booster station in real time, the operating parameters including electrical data, temperature data and gas chromatography data; A feature extraction module, used for extracting electrical features from the electrical data, extracting temperature features from the temperature data, and extracting gas chromatographic features from the gas chromatographic data; a performance evaluation module, configured to determine a performance score of the switchgear according to the electrical characteristics, the temperature characteristics and the gas chromatographic characteristics; A fault location module is used to generate fault information of the switch device according to the electrical characteristics, the temperature characteristics and the gas chromatographic characteristics when the performance score is less than a preset score.

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