Generator transformer bank protection device performance detection system and method

Abstract

The invention provides a generator transformer bank protection device performance detection system and method, relates to the technical field of generator transformer bank protection, and solves the problem of single dimension of monitoring data in the traditional technology through multi-dimensional data acquisition and fusion. The method can reflect the operation state of equipment in all directions, extracts key features from signals through modular feature extraction and accurate recognition of electrical, acoustic, mechanical and thermal anomalies by the system, and generates a transformation anomaly index and a power generation anomaly index by establishing a mathematical analysis model. The comprehensive analysis capability can more accurately evaluate the overall health condition of the equipment, the performance detection module combines the transformation anomaly index and the power generation anomaly index to calculate the comprehensive anomaly index, and the system can timely identify the potential fault state of the equipment by comparing the comprehensive anomaly index with a preset threshold.

Description

Technical Field

[0001] The present invention relates to the technical field of generator transformer group protection, and in particular to a generator transformer group protection device performance detection system and method. Background Art

[0002] Generator transformers are the core equipment in the power system, which are mainly responsible for converting the electric energy generated by the generator into the voltage and current form suitable for transmission needs. The stability and reliability of their operation are of great significance to the safety of the entire power network. These devices usually operate under high load and complex environment, so real-time monitoring and preventive maintenance are the key to ensure the efficient operation of the power system.

[0003] Currently, the monitoring technology of generator transformer groups mainly relies on the monitoring of a single or a few parameters, such as temperature monitoring for detecting overheating, vibration monitoring for identifying mechanical failures, and voltage monitoring for judging electrical anomalies. These monitoring systems usually use a simple threshold-based judgment method to determine the status of the equipment. Once a parameter exceeds the set safety range, the system will issue an alarm. However, this method is difficult to capture the complex interactive relationship between multiple signals, so false alarms or missed alarms may occur in practical applications.

[0004] The main shortcomings of existing technologies are the singleness of their monitoring dimensions and the limitations of their analytical capabilities. Due to the inability to effectively integrate and analyze multiple operating data, potential early fault signals of equipment may be overlooked. In addition, relying on manual experience for fault judgment also increases the uncertainty of diagnosis. Therefore, improving the comprehensive analysis capabilities of fault diagnosis and monitoring systems, integrating multi-source data, and achieving a comprehensive assessment of equipment status are important directions for current technological development. This will help improve the accuracy and timeliness of fault warnings and ensure the stable operation of the power system.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The object of the present invention is to provide a generator transformer group protection device performance detection system and method to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A generator transformer group protection device performance detection system, comprising:

[0009] A data acquisition module, the data acquisition module is used to collect the power generation state data of the generator and the voltage transformation state data of the transformer in the past k time length, the power generation state data includes the vibration signal of the bearing housing of the generator and the temperature signal of the surface of the stator winding housing, and the voltage transformation state data includes the noise signal of the transformer operation and the voltage signal of the primary side;

[0010] A first processing module, the first processing module is used to extract features from the voltage signal on the primary side of the transformer, obtain the harmonic content and distortion rate in the voltage signal, and perform fast Fourier transform on the noise signal to obtain the peak frequency of the main frequency of the noise signal from the transformed frequency signal;

[0011] A second processing module, the second processing module is used to extract a vibration root mean square value and a peak factor of the vibration signal from the vibration signal, and to extract a maximum value, a minimum value and a temperature change rate from the temperature signal;

[0012] A data analysis module, which is used to analyze and process the harmonic content, distortion rate and peak frequency of the main noise frequency in the voltage signal, generate the transformer abnormality index, and establish a mathematical analysis model based on the vibration mean square score, peak factor and maximum and minimum values ​​of the temperature and the temperature change rate of the vibration signal to obtain the power generation abnormality index of the generator;

[0013] The performance detection module is used to combine the transformer abnormality index and the power generation abnormality index to calculate the comprehensive abnormality index, compare the comprehensive abnormality index with the abnormality threshold, and judge the operating status of the equipment.

[0014] Furthermore, the power generation status data and the transformer transformation status data are both continuous time series data, and the time interval of the collected power generation status data and transformation status data is [t0-k, t0], t0 represents the current moment value, and k represents the length of the time interval of the collected power generation status data and transformation status data as time series data.

[0015] Furthermore, the method for obtaining the harmonic content and distortion rate in the voltage signal is:

[0016] Perform Fourier transform on the voltage signal on the primary side of the transformer, convert the voltage signal from the time domain to the frequency, transform the voltage signal into the form of the sum of a series of complex numbers through Fourier transform, express the number of complex numbers as the number of frequency components of the internal components of the voltage signal, express the imaginary part of the frequency component of each complex number as the phase of the corresponding frequency component of the internal component of the voltage signal, express the real part of the frequency component of each complex number as the amplitude of the corresponding frequency component, and identify the frequency components of the fundamental wave and harmonics in the voltage signal;

[0017] The amplitude of each harmonic in the voltage signal is compared with the amplitude of the fundamental wave to generate the harmonic content and distortion rate, based on the formula:

[0018]

[0019] Where Hd represents the harmonic content, T hd Represents the distortion rate in the voltage signal, V i Represents the amplitude of the i-th harmonic in the voltage signal, V J It represents the amplitude of the fundamental wave in the voltage signal, v represents the number of harmonics in the voltage signal, and i represents the number of harmonics in the voltage signal.

[0020] Furthermore, the method for obtaining the peak frequency of the main frequency in the noise signal is:

[0021] The noise signal is sampled at a set sampling rate to obtain a discrete time signal of the noise signal, and a fast Fourier transform is applied to the sampled discrete signal to convert the discrete time signal of the noise signal into a frequency domain to obtain a spectrum of the noise signal and obtain a frequency resolution of the noise signal;

[0022] The amplitude of the frequency component of each noise signal is obtained according to the spectrum of the noise signal, the position of the maximum amplitude is identified, and the frequency position corresponding to the maximum amplitude is multiplied by the frequency resolution of the noise signal to obtain the peak frequency of the main frequency of the noise signal.

[0023] Furthermore, the vibration root mean square value and peak factor of the vibration signal are extracted from the vibration signal as follows:

[0024] The continuous vibration signal is discretized into equal time intervals to obtain discrete samples of the vibration signal: x1, x2, ..., x j , …, x N ,x j represents the amplitude of the vibration signal at the jth sampling point in the discrete samples of the vibration signal, N represents the total number of discrete points in the discrete samples of the vibration signal, and j represents the number of the discrete point;

[0025] According to the amplitude of the total discrete points in the discrete samples of the vibration signal, the vibration root mean square number and peak factor of the vibration signal are calculated based on the formula:

[0026]

[0027] Among them, R z and Bl z They represent the vibration RMS value and peak factor of the vibration signal respectively, x(t) represents the vibration signal, and t is the time variable;

[0028] The specific methods for extracting the maximum value, minimum value and temperature change rate from the temperature signal are:

[0029] T max =max[Tw(t)]

[0030] T min =min[Tw(t)]

[0031]

[0032] in, and ΔTw represent the maximum value, minimum value and temperature change rate of the temperature signal respectively, Tw(t) represents the temperature signal, t max and t min Respectively represent the time values ​​corresponding to the maximum and minimum values ​​when the temperature reaches its maximum and minimum values.

[0033] Furthermore, the formula for generating the transformer abnormality index is as follows:

[0034]

[0035] Among them, By y Indicates the abnormal transformer index, Hd indicates the harmonic content, T hd It represents the distortion rate in the voltage signal, and Fp represents the peak frequency of the main frequency of the noise signal.

[0036] Furthermore, the power generation abnormality index of the generator is obtained as follows:

[0037]

[0038] Among them, Fd y Represents the power generation abnormality index, R z and Bl z Respectively represent the vibration root mean square value and peak factor of the vibration signal, and ΔTw represent the maximum value, minimum value and temperature change rate of the temperature signal respectively.

[0039] Furthermore, the specific logic for calculating the comprehensive anomaly index is:

[0040]

[0041] Where ZH represents the comprehensive abnormality index, α, β and γ represent the first weight coefficient, the nonlinear coefficient and the second weight coefficient respectively, and β>α>1>γ>0;

[0042] If ZH≤ZH y , then the generator transformer group is considered to be in normal operation. If ZH>ZH y , it is determined that the generator transformer group is abnormal and needs further inspection. y Indicates the anomaly threshold.

[0043] The present invention further provides a method for detecting the performance of a generator transformer group protection device. The detection method is obtained by executing the above-mentioned generator transformer group protection device performance detection system, and the specific steps include:

[0044] Step 1: Collect the power generation state data of the generator and the voltage transformation state data of the transformer in the past k time length, wherein the power generation state data includes the vibration signal of the bearing housing of the generator and the temperature signal of the surface of the stator winding housing, and the voltage transformation state data includes the noise signal of the transformer operation and the voltage signal of the primary side;

[0045] Step 2: Extract features from the voltage signal on the primary side of the transformer, obtain the harmonic content and distortion rate in the voltage signal, perform fast Fourier transform on the noise signal, and obtain the peak frequency of the main frequency of the noise signal from the transformed frequency signal;

[0046] Step 3: Extract the vibration root mean square value and peak factor of the vibration signal from the vibration signal, and extract the maximum value, minimum value and temperature change rate of the temperature from the temperature signal;

[0047] Step 4: Analyze and process the harmonic content, distortion rate and peak frequency of the main noise frequency in the voltage signal to generate the transformer abnormality index of the transformer, and establish a mathematical analysis model based on the vibration mean square score, peak factor and maximum, minimum and temperature change rate of the vibration signal to obtain the power generation abnormality index of the generator;

[0048] Step 5: Calculate the comprehensive abnormality index by combining the transformer abnormality index and the power generation abnormality index, compare the comprehensive abnormality index with the abnormality threshold, and determine the operating status of the equipment.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] The present invention solves the problem of single monitoring data dimension in traditional technologies through multi-dimensional data collection and fusion. The system collects multiple status parameters of generators and transformers, such as vibration, temperature, noise and voltage signals, and can fully reflect the operating status of the equipment. Through modular feature extraction, the system accurately identifies electrical, acoustic, mechanical and thermal anomalies, and uses fast Fourier transform and other technologies to extract key features from the signal, such as harmonic content, vibration root mean square value, temperature change rate, etc., thereby improving the accuracy of anomaly identification.

[0051] The present invention establishes a mathematical analysis model, integrates various types of characteristic information, and generates a transformer abnormality index and a power generation abnormality index. This comprehensive analysis capability can more accurately evaluate the overall health status of the equipment. The performance detection module combines the transformer abnormality index and the power generation abnormality index to calculate the comprehensive abnormality index. By comparing the comprehensive abnormality index with the preset threshold, the system can timely identify the potential fault state of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a schematic diagram of the overall system structure of the present invention;

[0053] Figure 2 It is a schematic diagram of the overall method flow of the present invention. DETAILED DESCRIPTION

[0054] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0055] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0056] Example:

[0057] See also Figure 1 , the present invention provides a technical solution:

[0058] A performance detection system for a generator transformer group protection device includes a data acquisition module, a first processing module, a second processing module, a data analysis module and a performance detection module, wherein:

[0059] The data acquisition module is used to collect the power generation status data of the generator and the voltage transformation status data of the transformer within the past k time lengths, the power generation status data includes the vibration signal of the bearing housing of the generator and the temperature signal of the surface of the stator winding housing, and the voltage transformation status data includes the noise signal of the transformer operation and the voltage signal of the primary side.

[0060] The generator bearing housing refers to the outside of the generator bearing. The function of the generator bearing housing is to support and maintain the rotation of the rotor. The vibration sensor installed on the bearing housing monitors the vibration of the bearing to determine the operating status and health status.

[0061] The stator winding shell surface refers to the outer surface of the stator winding. The stator is the fixed part of the generator, and the winding is its key component, which generates an electromagnetic field. The temperature sensor is installed on the surface of the stator winding shell to monitor temperature changes and prevent overheating from damaging the winding.

[0062] Noise signals are collected through acoustic sensors or microphones, which are installed on the transformer casing or near the core winding and other parts that are prone to noise, to capture and monitor noise signals. The data acquisition module obtains signals through sensors installed at key parts of the generator and transformer. The vibration sensor is installed on the generator bearing casing to detect mechanical vibration signals. The temperature sensor is located on the surface of the stator winding casing to monitor temperature changes. The noise signal of the transformer is captured by the acoustic sensor, and the voltage sensor records the voltage signal on the primary side.

[0063] The purpose of collecting this data is to monitor the operating status of the generator and transformer in real time. Vibration and temperature data can be used to determine the mechanical health of the generator and avoid potential failures. Noise and voltage signals are used to evaluate the electrical and mechanical performance of the transformer. For generators, the vibration signal of the bearing is the key to early identification of mechanical problems. Abnormal vibration may indicate bearing wear, imbalance or poor alignment. Timely monitoring can help prevent serious mechanical failures. In addition, the temperature signal of the stator winding is an important health indicator of the generator. Excessive temperature may cause insulation damage and electrical failures. Therefore, temperature monitoring can effectively avoid these risks.

[0064] For transformers, noise signals are an important method for detecting core and internal structural problems. The magnetostrictive effect of the core generates noise, and abnormal noise signals may indicate core failure or other mechanical problems. Therefore, noise monitoring helps to detect and correct potential structural failures at an early stage. In addition, the primary voltage signal directly affects the electrical performance of the transformer. Voltage fluctuations may indicate unstable load or electrical failure, affecting the quality of power supply and the safety of equipment.

[0065] In this embodiment, the power generation status data and the transformer transformation status data are both continuous time series data, and the time interval of the collected power generation status data and transformation status data is [t0-k, t0], t0 represents the current time value, and k represents the length of the time interval of the collected power generation status data and transformation status data as time series data.

[0066] In this embodiment, the power generation state data and transformer state data are collected and analyzed as continuous time series data. Time series data means that the data is arranged in chronological order, and each data point has a timestamp. By monitoring these data, trends and patterns that change over time can be observed, which is very important for predicting equipment status and detecting anomalies. Data collection covers an interval extending from the current moment t0 forward by k time units. This interval length k allows us to review and analyze data changes over a period of time, not just data at a single moment. This time series analysis helps identify long-term trends, cyclical changes, and short-term fluctuations, making the understanding of equipment status more comprehensive. Collecting and analyzing power generation state data and transformer state data as continuous time series data can more accurately predict equipment status changes and potential failures. Compared with data analysis at a single moment, time series data provides rich working information of generator transformer groups.

[0067] The first processing module is used to extract features from the voltage signal on the primary side of the transformer, obtain the harmonic content and distortion rate in the voltage signal, perform fast Fourier transform on the noise signal, and obtain the main frequency peak frequency of the noise signal from the transformed frequency signal.

[0068] By extracting features from the transformer primary voltage signal through the first processing module, including obtaining harmonic content and distortion rate, and performing fast Fourier transform on the noise signal, in-depth analysis of the signal can be achieved. Compared with the existing technology, this method can more accurately identify the harmonic characteristics and distortion degree in the voltage signal and quickly capture the main frequency peak of the noise signal. This process improves the resolution and accuracy of signal analysis, helps to more comprehensively evaluate the operating status of the equipment, and by accurately identifying and analyzing abnormal features in the signal, it provides reliable data support for subsequent fault identification and maintenance decisions, promotes the intelligence and efficiency of the overall solution, and ensures the stable operation of the system.

[0069] In this embodiment, the method for obtaining the harmonic content and distortion rate in the voltage signal is:

[0070] A Fourier transform is performed on the voltage signal on the primary side of the transformer to convert the voltage signal from the time domain to the frequency domain. The voltage signal is transformed into the form of the sum of a series of complex numbers through Fourier transform. The number of complex numbers is represented as the number of frequency components of the internal components of the voltage signal. The imaginary part of the frequency component of each complex number is represented as the phase of the corresponding frequency component of the internal component of the voltage signal. The real part of the frequency component of each complex number is represented as the amplitude of the corresponding frequency component, and the frequency components of the fundamental wave and harmonics in the voltage signal are identified.

[0071] By performing Fourier transform on the voltage signal on the primary side of the transformer, the time domain signal can be converted into a frequency domain signal. The Fourier transform decomposes the original voltage signal into a series of complex numbers, which represent different frequency components in the signal. The amplitude (real part) of each complex number represents the intensity of the frequency component, while the imaginary part represents the phase of the frequency component. This conversion allows us to identify the fundamental and harmonic frequency components in the voltage signal, and then analyze the harmonic content and distortion rate of the signal.

[0072] In specific operations, the output of Fourier transform can help us identify the main frequency components in the signal and their corresponding amplitudes, which is crucial for judging the quality of the voltage signal. By analyzing these frequency components, we can determine which components are fundamental waves and which are harmonics. The logic of this step is to deeply understand the internal composition of the voltage signal through the analysis of frequency and amplitude, thereby providing data support for subsequent signal quality assessment and anomaly detection.

[0073] The fundamental wave is the standard frequency of the generator transformer group. For example, in my country's AC power system, the fundamental wave is usually 50Hz, and the harmonics are integer multiples of the fundamental wave frequency. For example, if the fundamental wave is 50Hz, the harmonics may be 100Hz, 150Hz, etc. In the spectrum diagram, after finding the fundamental wave, identify the peaks of other frequencies. If these frequencies are integer multiples of the fundamental wave, they are harmonics.

[0074] Through the above steps, the fundamental and harmonic components in the signal can be effectively distinguished. The amplitude of each harmonic in the voltage signal is compared with the amplitude of the fundamental wave to generate the harmonic content and distortion rate. The formula is:

[0075]

[0076]

[0077] Where Hd represents the harmonic content, T hd Represents the distortion rate in the voltage signal, V i Represents the amplitude of the i-th harmonic in the voltage signal, V J It represents the amplitude of the fundamental wave in the voltage signal, v represents the number of harmonics in the voltage signal, and i represents the number of harmonics in the voltage signal.

[0078] The harmonic content reflects the overall strength of the harmonic components in the voltage signal. Harmonics are integer multiples of the fundamental frequency and will affect the purity of the signal. When the harmonic content increases, it means that the harmonic components in the voltage signal increase, which means that the quality of the power signal decreases, because the ideal power signal should be a pure sine wave. Higher harmonic content will lead to increased heat loss in the generator transformer group, reduced equipment efficiency, and may cause equipment failure. When the harmonic content is generated, the independent variable is the amplitude of each harmonic. The increase in these values ​​will increase the harmonic content. Voltage with high harmonic content means that there may be problems with the generator transformer group, resulting in a decrease in power generation quality.

[0079] The distortion rate indicates the overall degree of distortion of the voltage signal by harmonics. It measures the degree to which the signal deviates from the ideal sine wave. An increase in the distortion rate indicates that the signal distortion has intensified. The deviation of the signal from the ideal state means that the power quality has deteriorated. A high distortion rate can cause the generator transformer unit to overheat, reduce efficiency, or even be damaged. An increase in the harmonic amplitude will increase the distortion rate, while an increase in the fundamental amplitude will reduce its value. A higher distortion rate means that the generator transformer unit may have defects, such as improper adjustment or load imbalance, which leads to a decrease in power generation quality.

[0080] When calculating the harmonic content, firstly, the amplitudes of all harmonics are accumulated to obtain a value representing the overall harmonic intensity. This part reflects the total amount of harmonics in the signal. In order to make the range of the result more stable, the formula uses a logarithmic transformation, which not only compresses the range of values, but also reduces the influence of extreme values, making the result smoother and easier to interpret. The logarithmic form provides a monotonically increasing relationship, which enables us to intuitively understand how the change in the total amount of harmonics affects the overall signal quality.

[0081] When calculating the distortion rate, first, each harmonic amplitude is squared and then summed. This step emphasizes the impact of large-amplitude harmonics on the result, thereby highlighting their contribution to signal distortion. This way of emphasizing the impact of large harmonics ensures that the role of large-amplitude harmonics is not ignored when evaluating distortion. Subsequently, the square root of the sum of squares is taken to be consistent with the original amplitude scale. Finally, by dividing the result by the fundamental amplitude, the formula standardizes the distortion rate to make it a relative value. This relative measurement method facilitates comparison between different signals and systems and clearly reflects the degree of impact of harmonics on the overall quality of the signal. This design allows us to better understand and control signal distortion when evaluating power quality.

[0082] Furthermore, the method for obtaining the peak frequency of the main frequency in the noise signal is:

[0083] The noise signal is sampled at a set sampling rate to obtain a discrete time signal of the noise signal, and a fast Fourier transform is applied to the sampled discrete signal to convert the discrete time signal of the noise signal into a frequency domain to obtain a spectrum of the noise signal and obtain a frequency resolution of the noise signal;

[0084] The amplitude of the frequency component of each noise signal is obtained according to the spectrum of the noise signal, the position of the maximum amplitude is identified, and the frequency position corresponding to the maximum amplitude is multiplied by the frequency resolution of the noise signal to obtain the peak frequency of the main frequency of the noise signal.

[0085] The sampling rate determines how many signal samples are collected per second. In order to accurately capture the frequency components in the signal, the sampling rate usually needs to be at least twice the highest frequency in the signal. Converting a continuous noise signal into a discrete time signal means dividing the signal into a series of equally spaced sampling points on the time axis.

[0086] The fast Fourier transform algorithm is used to convert discrete signals in the time domain into frequency domain signals. The fast Fourier transform is an efficient Fourier transform calculation method that can quickly calculate the spectrum of the signal. The spectrum is a signal representation in the frequency domain, where each point represents the amplitude of a specific frequency. The frequency resolution is determined by the sampling rate and the number of fast Fourier transform points, usually the sampling rate divided by the number of fast Fourier transform points. It represents the frequency interval between each point in the spectrum. Each frequency component in the spectrum is checked to obtain its corresponding amplitude. The amplitude reflects the intensity of the frequency component in the signal. The frequency component with the largest amplitude is found in the spectrum. The frequency corresponding to this position is the peak frequency of the main frequency of the signal, which is usually the most significant frequency component in the signal. The spectrum position corresponding to the maximum amplitude is multiplied by the frequency resolution to obtain the peak frequency of the main frequency of the signal. This step maps the position in the spectrum to the actual frequency value, and the result is the main frequency of the noise signal.

[0087] The peak frequency of the transformer noise signal can reflect the health status of the equipment. Abnormal frequency components may indicate internal faults, such as loose windings and core vibration. By monitoring the changing trend of the peak frequency, potential problems can be identified in advance, preventive maintenance can be performed, and greater losses can be avoided. The larger the peak frequency of the main frequency, the more high-frequency components are contained in the noise. High-frequency noise may come from the fast-vibrating parts of the transformer or electromagnetic interference. In some cases, high-frequency signals may indicate wear of mechanical parts or electrical failures.

[0088] During transformer operation, the extraction of the peak frequency of the main frequency in the noise signal is a key step. This frequency can reveal the operating status of the equipment and potential problems, such as loose windings or abnormal core vibration. Determining the peak frequency of the main frequency helps to detect and solve these problems in a timely manner, ensuring the normal operation of the equipment and extending its service life.

[0089] When the peak frequency value of the main frequency is large, it usually indicates that the signal contains more high-frequency components, which may reflect the rapid movement of mechanical parts or electromagnetic interference. Compared with traditional methods, this analysis technology provides more accurate diagnostic capabilities, making predictive maintenance more accurate and effectively reducing unplanned downtime and maintenance costs.

[0090] In this solution, obtaining and analyzing the peak frequency of the main frequency in the noise signal is the core component of the entire monitoring system. Through this step, the diagnostic accuracy of the system can be improved, providing an important reference for other monitoring data. This not only improves the reliability of transformer operation, but also enhances the effectiveness of the entire maintenance and management strategy.

[0091] The second processing module is used to extract the vibration root mean square value and peak factor of the vibration signal from the vibration signal, and to extract the maximum value, minimum value and temperature change rate of the temperature from the temperature signal.

[0092] In this embodiment, the vibration root mean square value and peak factor of the vibration signal are extracted from the vibration signal as follows:

[0093] The continuous vibration signal is discretized into equal time intervals to obtain discrete samples of the vibration signal: x1, x2, ..., x j , …, x N ,x j represents the amplitude of the vibration signal at the jth sampling point in the discrete samples of the vibration signal, N represents the total number of discrete points in the discrete samples of the vibration signal, and j represents the number of the discrete point;

[0094] According to the amplitude of the total discrete points in the discrete samples of the vibration signal, the vibration root mean square number and peak factor of the vibration signal are calculated based on the formula:

[0095]

[0096] Among them, R z and Bl z They represent the vibration root mean square value and peak factor of the vibration signal respectively, x(t) represents the vibration signal, and t is the time variable.

[0097] The vibration root mean square root reflects the energy level of the signal and is a comprehensive indicator of vibration intensity. The larger its value, the higher the overall intensity of the vibration. A high vibration root mean square root value may indicate abnormal vibration of the equipment and requires further inspection. The peak factor represents the ratio of the maximum amplitude in the signal to the vibration root mean square root. The larger its value, the more significant the peak in the signal. A large peak factor may indicate the presence of instantaneous impact or abnormal peak, indicating potential mechanical failure.

[0098] The RMS value of vibration is the square root of the mean of the sum of the squares of all vibration amplitudes. The size of each amplitude directly affects the RMS. The larger the amplitude, the larger the RMS value of vibration. The peak factor is determined by the maximum amplitude and the RMS value of vibration. The larger the maximum amplitude or the smaller the RMS value of vibration, the larger the peak factor.

[0099] The analysis of the mean square score and peak factor of the vibration signal provides an accurate assessment of vibration intensity and anomalies. Compared with existing technologies, this method can identify potential faults earlier, such as mechanical looseness or imbalance, and efficiently capture and quantify key features in vibration signals, making maintenance work more accurate and timely. By extracting key indicators from vibration signals, it can achieve continuous monitoring of equipment status. Compared with traditional methods, it can detect abnormal trends more quickly and improve the accuracy of fault diagnosis. In particular, it has the ability to identify transient shocks and abnormal peaks.

[0100] The specific methods for extracting the maximum value, minimum value and temperature change rate from the temperature signal are:

[0101] T max =max[tw(t)]

[0102] T min =min[Tw(t)]

[0103]

[0104] in, and ΔTw represent the maximum value, minimum value and temperature change rate of the temperature signal respectively, Tw(t) represents the temperature signal, t max and t min Respectively represent the time values ​​corresponding to the maximum and minimum values ​​when the temperature reaches its maximum and minimum values.

[0105] The maximum and minimum temperature values ​​reflect the operating status of the stator winding under extreme conditions. High temperatures may mean that the equipment is overloaded or the heat dissipation is insufficient, while low temperatures may be caused by environmental factors or the equipment is not running. The temperature change rate indicates the rate of temperature fluctuation in a short period of time. The greater the change rate, the more dramatic the temperature change, which may indicate an abnormality or potential failure of the equipment.

[0106] Extracting the maximum and minimum values ​​and the rate of change of temperature helps to effectively monitor the operating status of the generator. These indicators provide a scientific basis for predicting equipment failures, support preventive maintenance strategies, and reduce the possibility of unexpected downtime. In addition, by analyzing temperature signals, the stability and efficiency of equipment operation can be improved, the service life of equipment can be extended, and the reliability and safety of the power generation process can be ensured.

[0107] The data analysis module is used to analyze and process the harmonic content, distortion rate and peak frequency of the noise main frequency in the voltage signal, generate the transformer abnormality index of the transformer, and establish a mathematical analysis model based on the vibration mean square score, peak factor and maximum, minimum and temperature change rate of the vibration signal to obtain the power generation abnormality index of the generator.

[0108] In this embodiment, the formula for generating the transformer abnormality index is:

[0109]

[0110] Among them, By y Indicates the abnormal transformer index, Hd indicates the harmonic content, T hd It represents the distortion rate in the voltage signal, and Fp represents the peak frequency of the main frequency of the noise signal.

[0111] The transformer abnormality index combines the harmonic content, distortion rate and main frequency peak frequency of the noise signal in the voltage signal to reflect the overall status of the transformer voltage quality. The larger the index, the higher the degree of distortion of the voltage signal. The equipment may be subject to greater electrical stress and abnormal operating status. It helps to detect and diagnose transformer voltage problems and support decision-makers to take measures for maintenance and optimization to ensure the stability and safety of the power system. In the data processing on the transformer side, although the transformation has not been actually completed, the analysis of the harmonic content and distortion rate of the voltage signal is to evaluate the performance of the transformer under load changes or power system abnormalities in advance.

[0112] Harmonic content and distortion rate are important components of voltage signal quality. Higher harmonic content may cause equipment overheating and damage, while higher distortion rate indicates that the voltage waveform deviates from normal and increases electrical equipment losses. Fp represents the most significant frequency component in the signal. When Fp increases, it means that there are strong specific frequency components in the signal, which may be related to some abnormal conditions. Therefore, the larger the Fp, the higher the possible abnormality, so By y The larger the value, the greater the influence of the three factors on the transformer abnormality index. The increase of harmonic content and distortion rate will increase the transformer abnormality index, indicating that the quality of voltage signal is deteriorating and the transformer is working abnormally. Therefore, the formula reflects the direct influence relationship between the independent variable and the dependent variable, which helps to identify and deal with electrical abnormalities.

[0113] The increase in the peak frequency of the main frequency indicates the presence of high-frequency noise in the transformer, which may mean partial discharge or other abnormal phenomena. These high-frequency components may cause aging or damage of the insulation material. The increase in high-frequency components may reflect wear or vibration problems of the mechanical parts, which will affect the normal operation of the transformer. High-frequency harmonics may come from the instability of the external power grid or the nonlinear characteristics of the load, which will cause additional losses and temperature rise of the transformer. Therefore, when the peak frequency of the main frequency of the noise signal increases, the transformer needs to be carefully inspected to prevent potential failures.

[0114] It is a Euclidean norm form, which represents the comprehensive size of harmonic content, distortion rate and peak frequency of the main frequency of the noise signal. By taking the square root of the sum of squares, the overall impact of these three factors on voltage quality can be determined. The sum of squares ensures that the impact of each factor is fully considered and will not be offset by signs. Reflects the relative relationship between harmonic content and distortion rate. This item emphasizes the proportional relationship between the two, thereby providing more information about the direction of voltage distortion. The inverse tangent function can be used to convert the ratio into an angle, helping to identify the main source of distortion.

[0115] Furthermore, the power generation abnormality index of the generator is obtained as follows:

[0116]

[0117] Among them, Fd y Represents the power generation abnormality index, R z and Bl z Respectively represent the vibration root mean square value and peak factor of the vibration signal, and ΔTw represent the maximum value, minimum value and temperature change rate of the temperature signal respectively.

[0118] The power generation abnormality index combines the vibration signal (root mean square value and peak factor) and temperature signal (maximum value, minimum value and temperature change rate) of the generator. The larger the power generation abnormality index, the greater the vibration and temperature fluctuations of the generator, which may mean abnormal equipment operation or increased risk of failure. It can help monitor and diagnose the operating status of the generator, support timely maintenance and optimization, and ensure the safety and efficiency of the generator.

[0119] The vibration root mean square value and peak factor reflect the vibration condition of the generator. The vibration root mean square value represents the vibration intensity, and the peak factor reflects the sharpness of the vibration. The temperature index reflects the thermal state of the generator. The extreme temperature and change rate are important factors in the thermal performance of the equipment. Abnormal changes in vibration and temperature will increase the power generation abnormality index, indicating possible operating problems of the equipment. The impact of vibration intensity and sharpness on the power generation abnormality index is positively correlated, which means that an increase in vibration will directly lead to an increase in the index, and the greater the temperature change, the greater the index.

[0120] The vibration signal is characterized by the vibration root mean square value and peak factor. The logarithmic transformation is used to reduce the values ​​of different magnitudes to a range that is easier to compare. The logarithmic transformation can effectively smooth the changes in values, reduce the impact of extreme values ​​on the overall calculation, make the anomaly index more stable, and avoid a single feature from excessively dominating the anomaly index, ensuring that the impact of each vibration feature is reasonably considered.

[0121] The square root of the sum of squares in the formula uses the mathematical form of Euclidean distance to comprehensively measure the vibration characteristics, taking the vibration intensity and sharpness into consideration as a whole. In this way, the formula can fully reflect the multi-faceted characteristics of the vibration signal in the calculation, ensuring that the anomaly index can accurately represent the overall abnormal condition of the vibration, thereby providing a warning of potential problems with the generator.

[0122] The temperature signal is described by the maximum value, minimum value and temperature change rate. The inverse tangent transformation in the formula converts the relationship between the temperature fluctuation amplitude and the change rate into an angle expression. This design emphasizes the trend and amplitude of temperature change and reflects the possibility of temperature anomaly through angle. The inverse tangent function not only provides a means to compare different temperature characteristics, but also maintains the stability of exponential calculation under extreme conditions. Combining these characteristics, the formula effectively identifies and quantifies the abnormal state of the generator and supports reliable operation and maintenance decisions.

[0123] The performance detection module is used to combine the transformer abnormality index and the power generation abnormality index to calculate the comprehensive abnormality index, compare the comprehensive abnormality index with the abnormality threshold, and determine the operating status of the equipment.

[0124] The specific logic for calculating the comprehensive anomaly index is:

[0125]

[0126] Wherein, ZH represents the comprehensive abnormality index, α, β and γ represent the first weight coefficient, the nonlinear coefficient and the second weight coefficient respectively, and β>α>1>γ>0.

[0127] The comprehensive abnormality index represents the degree of abnormality of the overall operating status of the generator transformer group. By combining the transformer abnormality index and the power generation abnormality index, a comprehensive indicator is provided to measure the health status of the equipment. The larger the comprehensive abnormality index value, the more the overall operating status of the generator transformer group deviates from the normal range, and there may be potential faults or abnormalities.

[0128] The transformer abnormality index and power generation abnormality index reflect the overall abnormal state of the generator transformer group respectively. Taking these two factors into consideration can more comprehensively evaluate the overall health of the equipment. The two are combined in the form of square sum to express their joint impact on equipment abnormality. The weight coefficient and nonlinear coefficient adjust their contribution to ensure the stability and accuracy of the calculation. As the transformer abnormality index and power generation abnormality index increase, the comprehensive abnormality index will also increase.

[0129] This part directly affects the base value of the comprehensive anomaly index and reflects the cumulative effect of the sum of squares. y -Fd y | Partially reflects the absolute value of the difference between the two. Although γ is small, it adjusts the balance of different types of abnormal impacts to ensure that the equipment can be accurately evaluated under various abnormal conditions. It puts transformer abnormalities and power generation abnormalities in the same framework and balances their impacts through square averaging. Square processing can amplify larger abnormal values ​​and increase sensitivity to extreme abnormalities. Exponent β adjusts the degree of nonlinearity to ensure that the calculation results are closer to the actual abnormal conditions.

[0130] When By y -Fd y When the two are similar, this contribution is small, emphasizing the consistency of the anomaly; when there is a large difference between the two, this item increases, and the difference between the two provides a measure of the inconsistency between the two anomaly indices. When there is a significant difference between the two, this part is given additional weight, indicating that extra attention should be paid to anomalies in a certain aspect, thereby enhancing the comprehensiveness of the comprehensive evaluation. The formula can not only accurately reflect the degree of abnormality of the current equipment, but also provide more detailed analysis and early warning according to the type of anomaly, balancing the sensitivity to anomalies and the stability of calculations, and avoiding the anomaly index being too biased towards a certain type of anomaly.

[0131] If ZH≤ZH y , then the generator transformer group is considered to be in normal operation. If ZH>ZH y , it is determined that the generator transformer group is abnormal and needs further inspection. yIndicates the abnormal threshold. The abnormal threshold is a pre-set standard value used to distinguish between normal and abnormal states of the equipment. It is usually determined based on historical data, equipment characteristics and experience values. When the comprehensive abnormal index is less than or equal to the threshold, it indicates that the operating state of the equipment is within the normal range. When the comprehensive abnormal index exceeds the threshold, it indicates that there may be abnormalities in the operation of the equipment, which requires further inspection and verification. By reasonably setting the threshold, false alarms and unnecessary maintenance operations can be reduced, abnormalities can be identified and handled early, and the overall reliability and safety of the equipment can be improved.

[0132] This embodiment solves the problem of single-dimensional monitoring data in traditional technologies through multi-dimensional data collection and fusion. The system collects multiple status parameters of generators and transformers, such as vibration, temperature, noise and voltage signals, and can fully reflect the operating status of the equipment. Through modular feature extraction, the system accurately identifies electrical, acoustic, mechanical and thermal anomalies, and uses technologies such as fast Fourier transform to extract key features from signals, such as harmonic content, vibration root mean square value, temperature change rate, etc., thereby improving the accuracy of anomaly identification.

[0133] This embodiment establishes a mathematical analysis model, integrates various types of feature information, and generates a transformer abnormality index and a power generation abnormality index. This comprehensive analysis capability can more accurately evaluate the overall health status of the equipment. The performance detection module combines the transformer abnormality index and the power generation abnormality index to calculate the comprehensive abnormality index. By comparing the comprehensive abnormality index with the preset threshold, the system can promptly identify the potential fault status of the equipment.

[0134] See also Figure 2 The present invention also provides a method for detecting the performance of a generator transformer group protection device. The detection method is obtained by executing the above-mentioned generator transformer group protection device performance detection system, and the specific steps include:

[0135] Step 1: Collect the power generation state data of the generator and the voltage transformation state data of the transformer in the past k time length, wherein the power generation state data includes the vibration signal of the bearing housing of the generator and the temperature signal of the surface of the stator winding housing, and the voltage transformation state data includes the noise signal of the transformer operation and the voltage signal of the primary side;

[0136] Step 2: Extract features from the voltage signal on the primary side of the transformer, obtain the harmonic content and distortion rate in the voltage signal, perform fast Fourier transform on the noise signal, and obtain the peak frequency of the main frequency of the noise signal from the transformed frequency signal;

[0137] Step 3: Extract the vibration root mean square value and peak factor of the vibration signal from the vibration signal, and extract the maximum value, minimum value and temperature change rate of the temperature from the temperature signal;

[0138] Step 4: Analyze and process the harmonic content, distortion rate and peak frequency of the main noise frequency in the voltage signal to generate the transformer abnormality index of the transformer, and establish a mathematical analysis model based on the vibration mean square score, peak factor and maximum, minimum and temperature change rate of the vibration signal to obtain the power generation abnormality index of the generator;

[0139] Step 5: Calculate the comprehensive abnormality index by combining the transformer abnormality index and the power generation abnormality index, and compare the comprehensive abnormality index with the abnormality threshold to determine the operating status of the equipment.

[0140] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0141] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0142] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0143] The above description is only a specific implementation manner 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.

Claims

1. A generator transformer group protection device performance detection system, characterized in that: include: A data acquisition module, the data acquisition module is used to collect the power generation state data of the generator and the voltage transformation state data of the transformer in the past k time length, the power generation state data includes the vibration signal of the bearing housing of the generator and the temperature signal of the surface of the stator winding housing, and the voltage transformation state data includes the noise signal of the transformer operation and the voltage signal of the primary side; A first processing module, the first processing module is used to extract features from the voltage signal on the primary side of the transformer, obtain the harmonic content and distortion rate in the voltage signal, and perform fast Fourier transform on the noise signal to obtain the peak frequency of the main frequency of the noise signal from the transformed frequency signal; A second processing module, the second processing module is used to extract a vibration root mean square value and a peak factor of the vibration signal from the vibration signal, and to extract a maximum value, a minimum value and a temperature change rate from the temperature signal; A data analysis module, which is used to analyze and process the harmonic content, distortion rate and peak frequency of the main noise frequency in the voltage signal, generate the transformer abnormality index, and establish a mathematical analysis model based on the vibration mean square score, peak factor and maximum and minimum values ​​of the temperature and the temperature change rate of the vibration signal to obtain the power generation abnormality index of the generator; The performance detection module is used to combine the transformer abnormality index and the power generation abnormality index to calculate the comprehensive abnormality index, compare the comprehensive abnormality index with the abnormality threshold, and judge the operating status of the equipment.

2. A generator transformer group protection device performance detection system according to claim 1, characterized in that: The power generation status data and transformer transformation status data are both continuous time series data. The time interval of the collected power generation status data and transformation status data is [t0-k, t0], t0 represents the current time value, and k represents the length of the time interval of the collected power generation status data and transformation status data as time series data.

3. A generator transformer group protection device performance detection system according to claim 2, characterized in that: The method for obtaining the harmonic content and distortion rate in the voltage signal is: Perform Fourier transform on the voltage signal on the primary side of the transformer, convert the voltage signal from the time domain to the frequency, transform the voltage signal into the form of the sum of a series of complex numbers through Fourier transform, express the number of complex numbers as the number of frequency components of the internal components of the voltage signal, express the imaginary part of the frequency component of each complex number as the phase of the corresponding frequency component of the internal component of the voltage signal, express the real part of the frequency component of each complex number as the amplitude of the corresponding frequency component, and identify the frequency components of the fundamental wave and harmonics in the voltage signal; The amplitude of each harmonic in the voltage signal is compared with the amplitude of the fundamental wave to generate the harmonic content and distortion rate, based on the formula: Where Hd represents the harmonic content, T hd Represents the distortion rate in the voltage signal, V i Represents the amplitude of the i-th harmonic in the voltage signal, V J It represents the amplitude of the fundamental wave in the voltage signal, v represents the number of harmonics in the voltage signal, and i represents the number of harmonics in the voltage signal.

4. A generator transformer group protection device performance detection system according to claim 2, characterized in that: The method to obtain the peak frequency of the main frequency in the noise signal is: The noise signal is sampled at a set sampling rate to obtain a discrete time signal of the noise signal, and a fast Fourier transform is applied to the sampled discrete signal to convert the discrete time signal of the noise signal into a frequency domain to obtain a spectrum of the noise signal and obtain a frequency resolution of the noise signal; The amplitude of the frequency component of each noise signal is obtained according to the spectrum of the noise signal, the position of the maximum amplitude is identified, and the frequency position corresponding to the maximum amplitude is multiplied by the frequency resolution of the noise signal to obtain the peak frequency of the main frequency of the noise signal.

5. A generator transformer group protection device performance detection system according to claim 2, characterized in that: The vibration root mean square value and peak factor of the vibration signal are extracted from the vibration signal as follows: The continuous vibration signal is discretized into equal time intervals to obtain discrete samples of the vibration signal: x1, x2, ..., x j , …, x N ,x j represents the amplitude of the vibration signal at the jth sampling point in the discrete samples of the vibration signal, N represents the total number of discrete points in the discrete samples of the vibration signal, and j represents the number of the discrete point; According to the amplitude of the total discrete points in the discrete samples of the vibration signal, the vibration root mean square number and peak factor of the vibration signal are calculated based on the formula: Among them, R z and Bl z They represent the vibration RMS value and peak factor of the vibration signal respectively, x(t) represents the vibration signal, and t is the time variable; The specific methods for extracting the maximum value, minimum value and temperature change rate from the temperature signal are: in, and ΔTw represent the maximum value, minimum value and temperature change rate of the temperature signal respectively, Tw(t) represents the temperature signal, t max and t min Respectively represent the time values ​​corresponding to the maximum and minimum values ​​when the temperature reaches its maximum and minimum values.

6. A generator transformer group protection device performance detection system according to claim 1, characterized in that: The formula for generating the transformer abnormality index is: Among them, By y Indicates the abnormal transformer index, Hd indicates the harmonic content, T hd It represents the distortion rate in the voltage signal, and Fp represents the peak frequency of the main frequency of the noise signal.

7. A generator transformer group protection device performance detection system according to claim 6, characterized in that: The specific power generation abnormality index of the generator is obtained as follows: Among them, Fd y Represents the power generation abnormality index, R z and Bl z Respectively represent the vibration root mean square value and peak factor of the vibration signal, and ΔTw represent the maximum value, minimum value and temperature change rate of the temperature signal respectively.

8. A generator transformer group protection device performance detection system according to claim 7, characterized in that: The specific logic for calculating the comprehensive anomaly index is: Where ZH represents the comprehensive abnormality index, α, β and γ represent the first weight coefficient, the nonlinear coefficient and the second weight coefficient respectively, and β>α>1>γ>0; If ZH≤ZH y , then the generator transformer group is considered to be in normal operation. If ZH>ZH y , it is determined that the generator transformer group is abnormal and needs further inspection. y Indicates the anomaly threshold.

9. A method for detecting performance of a generator transformer protection device, characterized in that: The detection method is obtained by executing a generator transformer group protection device performance detection system according to any one of claims 1 to 8, and the specific steps include: Step 1: Collect the power generation state data of the generator and the voltage transformation state data of the transformer in the past k time length, wherein the power generation state data includes the vibration signal of the bearing housing of the generator and the temperature signal of the surface of the stator winding housing, and the voltage transformation state data includes the noise signal of the transformer operation and the voltage signal of the primary side; Step 2: Extract features from the voltage signal on the primary side of the transformer, obtain the harmonic content and distortion rate in the voltage signal, perform fast Fourier transform on the noise signal, and obtain the peak frequency of the main frequency of the noise signal from the transformed frequency signal; Step 3: Extract the vibration root mean square value and peak factor of the vibration signal from the vibration signal, and extract the maximum value, minimum value and temperature change rate of the temperature from the temperature signal; Step 4: Analyze and process the harmonic content, distortion rate and peak frequency of the main noise frequency in the voltage signal to generate the transformer abnormality index of the transformer, and establish a mathematical analysis model based on the vibration mean square score, peak factor and maximum, minimum and temperature change rate of the vibration signal to obtain the power generation abnormality index of the generator; Step 5: Calculate the comprehensive abnormality index by combining the transformer abnormality index and the power generation abnormality index, compare the comprehensive abnormality index with the abnormality threshold, and determine the operating status of the equipment.

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