CN117791876A - Substation equipment operation state monitoring method and abnormal control system - Google Patents
Substation equipment operation state monitoring method and abnormal control system Download PDFInfo
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Abstract
The invention relates to the technical field of operation and maintenance of power transformation equipment, in particular to a power transformation equipment operation state monitoring method and an abnormal control system, which improve the capability of accurately monitoring and evaluating the power transformation equipment operation state and are beneficial to improving the safety and reliability of a power system; the method comprises the following steps: and acquiring operation parameters of the power transformation equipment in real time, wherein the operation parameters of the power transformation equipment comprise temperature, vibration frequency, current, voltage and noise. Aiming at each operation parameter of the power transformation equipment, carrying out data extraction and imaging processing according to a preset time window to obtain fluctuation image data of each operation parameter of the power transformation equipment; calculating the difference between adjacent peaks and valleys according to the fluctuation image data of the operation parameters of each piece of power transformation equipment to generate an operation parameter fluctuation feature set; and traversing each difference value in the corresponding operation parameter fluctuation feature set by utilizing a preset operation parameter fluctuation threshold value.
Description
Technical Field
The invention relates to the technical field of operation and maintenance of power transformation equipment, in particular to a method for monitoring the operation state of the power transformation equipment and an abnormal control system.
Background
With the continuous improvement of the automation level of a power system and the development of a smart grid, the operation state monitoring and fault diagnosis technology of the power transformation equipment is increasingly paid attention to. In modern power systems, stable operation of key facilities such as transformer equipment is critical to ensure safe and efficient operation of the whole power system. However, these devices may suffer from performance degradation, early failure, etc. during long-term operation due to various internal or external factors, thereby affecting power quality and reliability. The existing transformer equipment operation state monitoring method is mainly used for directly monitoring key operation parameters such as temperature, current, voltage and the like, and judging whether the operation state is stable or not according to a comparison result of a monitoring result and a normal operation range.
However, existing monitoring methods have some limitations, particularly when equipment fails implicitly or when there is a subtle but critical dynamic change in operating parameters. Implicit faults are difficult to find by conventional threshold monitoring methods because their effects may be masked by normal operating parameter fluctuations. Even if the operating parameters of the device remain within normal ranges, if these parameters fluctuate too much over a short period of time, potential problems with the device may be indicated. In this case, the early warning of faults may be missed by a threshold comparison that depends only on the normal operating range.
Disclosure of Invention
In order to solve the technical problems, the invention provides the transformer equipment operation state monitoring method which improves the accurate monitoring and evaluation capability of the transformer equipment operation state and is beneficial to improving the safety and reliability of a power system.
In a first aspect, the present invention provides a method for monitoring an operation state of a power transformation device, the method comprising:
acquiring operation parameters of the power transformation equipment in real time, wherein the operation parameters of the power transformation equipment comprise temperature, vibration frequency, current, voltage and noise;
aiming at each operation parameter of the power transformation equipment, carrying out data extraction and imaging processing according to a preset time window to obtain fluctuation image data of each operation parameter of the power transformation equipment;
calculating the difference between adjacent peaks and valleys according to the fluctuation image data of the operation parameters of each piece of power transformation equipment to generate an operation parameter fluctuation feature set;
traversing each difference value in the corresponding operation parameter fluctuation feature set by utilizing a preset operation parameter fluctuation threshold value, counting the number of difference values exceeding the operation parameter fluctuation threshold value in the operation parameter fluctuation feature set, and taking the counted number of difference values as the destabilizing parameters of the operation parameter type of the corresponding power transformation equipment;
Summarizing instability parameters corresponding to operation parameters of the power transformation equipment in the same time window and different types to generate an operation stability evaluation vector of the power transformation equipment;
inputting the operation stability evaluation vector of the power transformation equipment into a pre-constructed operation state evaluation model of the power transformation equipment to generate an operation state evaluation index of the power transformation equipment;
comparing the operation state evaluation index of the power transformation equipment with a preset operation state evaluation threshold, and if the operation state evaluation index of the power transformation equipment exceeds the operation state evaluation threshold, generating equipment abnormality alarm information and displaying the equipment abnormality alarm information to operation and maintenance personnel; and if the operation state evaluation index of the power transformation equipment does not exceed the operation state evaluation threshold, no action is performed.
Further, the power transformation device operating parameters include temperature, vibration frequency, current, voltage, and noise.
Further, the method for obtaining the fluctuation image data of the operation parameters of each power transformation device comprises the following steps:
extracting operation data acquired by each data acquisition node from the original data, wherein the operation data comprises a temperature value, a vibration frequency value, a current value, a voltage value and a noise decibel value; each item of operation data is marked with an acquisition time stamp;
Setting a time window according to the type of the power transformation equipment and the monitoring requirement;
screening the operation data with the acquisition time stamp falling in the time window as the composition data of the fluctuation image data;
and converting the composition data into a waveform chart according to the time sequence of the acquisition time stamp, wherein the waveform chart is the fluctuation image data of the operation parameters of the corresponding power transformation equipment.
Further, a method for calculating the difference between adjacent peaks and valleys to generate an operating parameter fluctuation feature set includes:
for the fluctuating image data of each operation parameter, using a peak detection algorithm to find all peaks and troughs in the image;
according to the time sequence, calculating the difference between adjacent wave crests and wave troughs, wherein the difference can reflect the amplitude of each fluctuation of the operation parameters;
and (3) arranging and summarizing the calculated differences according to a time sequence to generate an operation parameter fluctuation feature set.
Further, the method for counting the number of differences exceeding the operation parameter fluctuation threshold in the operation parameter fluctuation feature set comprises the following steps:
setting an operation parameter fluctuation threshold according to historical data, industry standards and expert experience;
extracting the difference value quantity exceeding a preset operation parameter fluctuation threshold value from the operation parameter fluctuation feature set;
And taking the extracted difference value number as a destabilizing parameter corresponding to the operation parameter type of the power transformation equipment.
Further, the construction method of the operation state evaluation model of the power transformation equipment comprises the following steps:
determining a model frame of an operation state evaluation model of the power transformation equipment according to the operation characteristics and monitoring requirements of the power transformation equipment;
extracting key characteristics from operation stability evaluation vectors of power transformation equipment, wherein the key characteristics comprise current instability parameters, voltage instability parameters, temperature instability parameters, noise instability parameters and vibration frequency instability parameters under different operation states;
training the power transformation equipment operation state evaluation model by utilizing the historical operation data and the corresponding operation states, and evaluating and optimizing the power transformation equipment operation state evaluation model;
the method comprises the steps of deploying a power transformation equipment operation state evaluation model into real-time monitoring, inputting a power transformation equipment operation stability evaluation vector obtained in real time into the model, and generating a power transformation equipment operation state evaluation index.
Further, the operation state evaluation model of the power transformation equipment adopts a multiple linear regression model, and the calculation formula of the operation state evaluation model of the power transformation equipment is as follows:
；
wherein,evaluation index for the operating state of the power transformation system, +. >Is a current instability parameter +.>Is a voltage instability parameter +.>Is a temperature instability parameter +.>Is a noise instability parameter +.>Is a vibration frequency instability parameter; />、/>、/>、/>Andrespectively representing the weight coefficients of the current instability parameter, the voltage instability parameter, the temperature instability parameter, the noise instability parameter and the vibration frequency instability parameter.
On the other hand, the application also provides a power transformation equipment operation state monitoring abnormality management and control system, which comprises:
the data acquisition module is used for acquiring the operation parameters of the power transformation equipment in real time, including temperature, vibration frequency, current, voltage and noise;
the data processing module is used for carrying out data extraction and imaging processing according to a preset time window aiming at each operation parameter of the power transformation equipment to obtain fluctuation image data of each operation parameter of the power transformation equipment;
the characteristic extraction module is used for calculating the difference value between adjacent peaks and valleys according to the fluctuation image data of the operation parameters of each piece of power transformation equipment to generate an operation parameter fluctuation characteristic set;
the destabilizing parameter calculation module is used for traversing each difference value in the corresponding operating parameter fluctuation feature set by utilizing a preset operating parameter fluctuation threshold value, counting the number of difference values exceeding the operating parameter fluctuation threshold value in the operating parameter fluctuation feature set, and taking the counted number of difference values as the destabilizing parameter of the operating parameter type of the corresponding power transformation equipment;
The evaluation vector generation module is used for summarizing instability parameters corresponding to the operation parameters of the power transformation equipment in the same time window and different types to generate an operation stability evaluation vector of the power transformation equipment;
the state evaluation module is used for inputting the operation stability evaluation vector of the power transformation equipment into a pre-constructed operation state evaluation model of the power transformation equipment to generate an operation state evaluation index of the power transformation equipment;
the alarm and display module is used for comparing the operation state evaluation index of the power transformation equipment with a preset operation state evaluation threshold, and generating equipment abnormality alarm information and displaying the equipment abnormality alarm information to operation and maintenance personnel if the operation state evaluation index of the power transformation equipment exceeds the operation state evaluation threshold; and if the operation state evaluation index of the power transformation equipment does not exceed the operation state evaluation threshold, no action is performed.
In a third aspect, the present application provides an electronic device comprising a bus, a transceiver, a memory, a processor and a computer program stored on the memory and executable on the processor, the transceiver, the memory and the processor being connected by the bus, the computer program implementing the steps of any of the methods described above when executed by the processor.
In a fourth aspect, the present application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of any of the methods described above.
Compared with the prior art, the invention has the beneficial effects that: the method acquires the operation parameters of a plurality of power transformation devices in real time, including a plurality of parameters such as temperature, vibration frequency, current, voltage and noise, and the operation state of the devices can be more comprehensively known by comprehensively monitoring the parameters; the fluctuation image data of the operation parameters of each transformer equipment are processed, the difference value between adjacent peaks and valleys is extracted as an operation parameter fluctuation feature set, and the dynamic feature extraction method can reflect the change of the operation state of the equipment more accurately, and is more sensitive to hidden faults or subtle but key dynamic changes;
the method based on statistics can better identify abnormal conditions of the running state of the equipment, including early-stage fault early-warning signals, by using a preset running parameter fluctuation threshold value and counting the number of difference values exceeding the threshold value in a running parameter fluctuation feature set as a destabilizing parameter; the destabilizing parameters of the operation parameters of all the power transformation equipment are assembled into the operation stability evaluation vector of the power transformation equipment, and then the operation stability evaluation vector is input into the operation state evaluation model of the power transformation equipment to generate a comprehensive operation state evaluation index, and the comprehensive evaluation can more intuitively reflect the overall operation state of the equipment and is beneficial to operation and maintenance personnel to quickly judge the health state of the equipment;
By comparing the device abnormal alarm information with a preset operation state evaluation threshold value, when the evaluation index exceeds the threshold value, generating device abnormal alarm information and displaying the device abnormal alarm information to operation and maintenance personnel, the automatic alarm mechanism can prompt the operation and maintenance personnel to pay attention to the abnormal condition of the device in time, and measures can be taken in time to maintain and repair the device, so that the reliability and stability of the device are improved;
in summary, the method can effectively solve the limitations of the existing monitoring method by comprehensively monitoring a plurality of operation parameters, extracting dynamic characteristics, identifying unstable parameters based on statistics, comprehensively evaluating indexes and other means, improves the accurate monitoring and evaluating capability of the operation state of the power transformation equipment, and is beneficial to improving the safety and reliability of a power system.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a flow chart of device operational state exception handling;
fig. 3 is a block diagram of a power transformation equipment operation state monitoring abnormality management and control system.
Detailed Description
In the description of the present application, those skilled in the art will appreciate that the present application may be embodied as methods, apparatuses, electronic devices, and computer-readable storage media. Accordingly, the present application may be embodied in the following forms: complete hardware, complete software (including firmware, resident software, micro-code, etc.), a combination of hardware and software. Furthermore, in some embodiments, the present application may also be embodied in the form of a computer program product in one or more computer-readable storage media, which contain computer program code.
Any combination of one or more computer-readable storage media may be employed by the computer-readable storage media described above. The computer-readable storage medium includes: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of the computer readable storage medium include the following: portable computer magnetic disks, hard disks, random access memories, read-only memories, erasable programmable read-only memories, flash memories, optical fibers, optical disk read-only memories, optical storage devices, magnetic storage devices, or any combination thereof. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, device.
The technical scheme of the application is that the acquisition, storage, use, processing and the like of the data meet the relevant regulations of national laws.
The present application describes methods, apparatus, and electronic devices provided by the flowchart and/or block diagram.
It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions. These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in a computer readable storage medium that can cause a computer or other programmable data processing apparatus to function in a particular manner. Thus, instructions stored in a computer-readable storage medium produce an instruction means which implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
The present application is described below with reference to the drawings in the present application.
Embodiment one: as shown in fig. 1 to 2, the method for monitoring the operation state of the power transformation device of the present invention specifically includes the following steps:
s1, acquiring operation parameters of power transformation equipment in real time, wherein the operation parameters of the power transformation equipment comprise temperature, vibration frequency, current, voltage and noise;
S1, a first step of a power transformation equipment operation state monitoring method, wherein operation parameters of the power transformation equipment are required to be obtained in real time; in the field of operation and maintenance of power transformation equipment, acquiring operation parameters in real time is a key basic step so as to ensure accurate monitoring and evaluation of equipment states; the method specifically comprises the following steps:
s11, selecting a sensor: the temperature sensor is used for measuring the temperature of key parts such as a transformer, an insulator and the like, and the sensors such as an infrared thermometer, a thermocouple, a thermistor and the like can be used for measuring the temperature under different working conditions; the vibration sensor is used for monitoring the vibration frequency of equipment, and can detect abnormal vibration of mechanical moving parts, such as windings or iron cores in the transformer; the current and voltage sensors are used for monitoring power grid parameters, so that normal operation of equipment is ensured, and the current transformer, the voltage transformer and other sensors can provide accurate electric parameter measurement; acoustic sensors are used to monitor noise levels around the device, the changes in which may reflect anomalies in the operating state of the device, especially when mechanical problems occur;
s12, real-time data acquisition: the operation parameters of the power transformation equipment can be acquired in real time through a sensor network; the SCADA system can integrate data of various sensors and provide real-time monitoring and remote control functions; connecting the sensors and devices to a monitoring system using a communication protocol (e.g., modbus, IEC 61850, etc.) to ensure real-time transmission of data;
S13, data processing and storage: processing the data acquired in real time by using a data processing algorithm, so as to ensure the accuracy and consistency of the data; storing the data acquired in real time in a reliable database system for subsequent data analysis and historical data query; secure transmission and storage of sensor data are ensured by adopting a secure protocol and an encryption technology; the redundancy design is adopted to improve the reliability of the system, and ensure that the system can still normally operate when a certain component or sensor fails.
In the step, through the deployment of various types of sensors, the multidimensional state of the power transformation equipment can be monitored in an omnibearing manner, and the comprehensive understanding of the health condition of the equipment is ensured, wherein the multidimensional state comprises a plurality of key parameters such as temperature, vibration, current, voltage, noise and the like; the real-time data acquisition can capture the tiny change in the running process of the equipment, and even very slight temperature rise, vibration abnormality, electric parameter fluctuation or noise increase and the like can be timely found, so that potential faults can be recognized in advance, and early warning and maintenance can be realized; the high-precision sensor technology such as a current transformer, a thermocouple and the like can provide an accurate data base, and is beneficial to accurately judging the running state and performance degradation condition of equipment; the SCADA system integrates a real-time data acquisition function, realizes remote monitoring and control, greatly improves operation and maintenance efficiency, reduces the workload of on-site inspection, and can respond to abnormal conditions at the first time; the redundancy design and the safety protocol are adopted, so that the reliability and the safety of the data transmission and storage process are ensured, the normal operation of the system can be ensured even if part of hardware fails, and meanwhile, the data leakage or the tampering is effectively prevented; the real-time data is not only used for current state evaluation, but also can be accumulated into a historical database, and provides powerful data support for subsequent equipment performance analysis, life prediction and optimization maintenance strategies.
S2, aiming at each operation parameter of the power transformation equipment, carrying out data extraction and imaging processing according to a preset time window to obtain fluctuation image data of each operation parameter of the power transformation equipment;
s2, playing a vital role in the operation state monitoring method of the power transformation equipment; the step mainly involves data extraction and imaging processing of substation equipment operation parameters acquired in real time, and aims to extract useful fluctuation information from the parameters; specifically, the step S2 includes the following:
s21, for each operation parameter of the power transformation equipment, such as temperature, vibration frequency, current, voltage, noise and the like, firstly, carrying out data extraction in the step S2; extracting operation data acquired by each data acquisition node from the original data, such as a temperature value, a vibration frequency value, a current value, a voltage value, a noise decibel value and the like; these critical operational data are critical to subsequent imaging processes and feature extraction;
s22, after data extraction, performing visual display on the extracted operation data by using a proper imaging processing technology, such as a waveform chart, a trend chart and the like in the step S2; this converts the raw data into a graphical form that is easy to understand and analyze; through imaging processing, the trend and mode of parameter fluctuation can be intuitively observed, and abnormal fluctuation and potential faults can be found;
S23, setting a proper time window when data extraction and imaging processing are performed; the size and the length of the time window are selected according to the type and the monitoring requirement of the power transformation equipment; smaller windows may provide finer wave information, while larger windows may provide more comprehensive trend analysis; the reasonable setting of the time window is important for accurately capturing the fluctuation characteristics of the running state of the equipment;
s24, extracting key features from the fluctuation image data of each operation parameter by using a specific algorithm or technology through data extraction and imaging processing, wherein the step S2 is further used for extracting key features from the fluctuation image data of each operation parameter; these characteristics may be amplitude, frequency, period, etc. of the fluctuations that reflect specific aspects of the operating state of the device; feature extraction is a key step because it can transform raw data into a compact, efficient feature set that facilitates subsequent state assessment and fault diagnosis.
In the step, the operation parameters of the power transformation equipment are obtained in real time, data extraction and imaging processing are immediately carried out, and the step S2 can quickly respond to the fluctuation change of the equipment state and timely find out abnormal conditions; by data extraction and imaging processing, key fluctuation information can be accurately extracted from the original data, the influence of noise and other interference factors is reduced, and the accuracy and reliability of the data are improved; through imaging processing, the operation parameter fluctuation information is visually displayed, so that operation and maintenance personnel can intuitively observe the trend and mode of parameter fluctuation, and abnormal fluctuation and potential faults can be quickly found; the key features are extracted from the fluctuation image data of each operation parameter through a specific algorithm or technology, so that the original data can be converted into a simple and effective feature set, and an important basis is provided for subsequent state evaluation and fault diagnosis; allowing an appropriate time window to be set according to different equipment types and monitoring requirements so as to adapt to the operation characteristics and the monitoring requirements of different equipment; meanwhile, parameters of data extraction and imaging processing can be flexibly adjusted to optimize the processing effect.
S3, calculating the difference between adjacent peaks and valleys according to the fluctuation image data of the operation parameters of each piece of power transformation equipment, and generating an operation parameter fluctuation feature set;
in the step S3, the method for monitoring the running state of the power transformation equipment adopts a technical means based on fluctuation feature analysis so as to more finely identify potential fault signs; the step S3 mainly involves processing fluctuation image data of each substation equipment operation parameter, calculating a difference value between adjacent peaks and valleys, and generating an operation parameter fluctuation feature set, and specifically, the step S3 includes the following steps:
s31, in a given time window, for the fluctuation image data of each operation parameter, a signal processing method, such as a peak detection algorithm, can be used for finding peaks and valleys; the peak points and the valley points represent extreme values of the operation parameters and mark high and low points in the process of changing the parameters;
s32, once the wave crest and the wave trough are found, calculating the difference value between the wave crest and the wave trough; this difference reflects the degree of each fluctuation of the parameter, i.e. the amplitude of the variation of the parameter within each fluctuation; can be expressed as: difference = peak-trough value;
s33, classifying and summarizing the calculated difference values according to a certain rule to form an operation parameter fluctuation feature set; the fluctuation characteristic of the equipment operation parameters in a period of time can be comprehensively reflected by the operation parameter fluctuation characteristic set;
S34, selecting a representative difference characteristic for further processing according to actual requirements and monitoring experience, removing redundancy and noise, and optimizing a characteristic set;
s35, carrying out standardization processing on the selected difference value features, and converting features with different dimensions and orders into uniform dimensions so as to facilitate subsequent state evaluation and comparison analysis.
Through the processing of the step S3, a difference feature set reflecting the running state of the equipment can be extracted from the fluctuation image data of the running parameters of each piece of power transformation equipment; the feature sets provide important basis for subsequent state evaluation and fault diagnosis, and are helpful for finding potential equipment problems; therefore, the step S3 plays a key role in the operation state monitoring method of the power transformation equipment; it should be noted that the specific implementation of step S3 may vary depending on the type of device, monitoring requirements and data processing techniques; in practical application, links such as peak-valley value detection, difference value calculation, feature selection and optimization are required to be adjusted and optimized according to specific conditions so as to achieve the optimal monitoring effect; meanwhile, in order to improve the accuracy and reliability of the step S3, other technical means, such as artificial intelligence algorithm, pattern recognition, etc., may need to be combined for comprehensive analysis and processing.
S4, traversing each difference value in the corresponding operation parameter fluctuation feature set by utilizing a preset operation parameter fluctuation threshold value, counting the number of difference values exceeding the operation parameter fluctuation threshold value in the operation parameter fluctuation feature set, and taking the counted number of difference values as instability parameters of the operation parameter type of the corresponding power transformation equipment;
s4, playing an important role in the operation state monitoring method of the power transformation equipment, and mainly aiming at evaluating the operation state of the power transformation equipment by utilizing a preset operation parameter fluctuation threshold; specifically, the step S4 includes the following:
s41, firstly, setting an operation parameter fluctuation threshold; this threshold may be determined based on historical data, industry standards, or expert experience; the setting of the threshold is critical for the subsequent evaluation, since it determines the normal range of the operating state of the device;
s42, counting the number of differences exceeding a preset operation parameter fluctuation threshold in the operation parameter fluctuation feature set; the statistical result reflects the frequency or intensity of abnormal fluctuation of the operation parameters of the corresponding equipment in a specified time window; the method is used as a destabilizing parameter of the running parameter of the type, can reflect the stability condition of equipment in a certain moment window, and can capture subtle signs of fluctuation aggravation although the conventional threshold value is not exceeded compared with the traditional method of whether a single parameter value exceeds the limit;
S43, counting and analyzing the screened difference value exceeding the threshold value; specifically, the number of the differences is calculated and is used as a destabilizing parameter of the operation parameter type of the corresponding power transformation equipment; this destabilization parameter may reflect the degree of instability of the device over a certain parameter.
Through the step S4, the operation state of the power transformation equipment can be estimated by utilizing a preset operation parameter fluctuation threshold value; the method is helpful for timely finding out the abnormal condition of the equipment, and provides support for preventive maintenance and fault diagnosis; it should be noted that the setting of the threshold value needs to be adjusted according to the actual situation so as to adapt to the requirements of different equipment and different running conditions; meanwhile, in order to improve the accuracy and reliability of the evaluation, other technical means, such as artificial intelligence algorithm, pattern recognition, and the like, may need to be combined for comprehensive analysis and processing.
S5, summarizing instability parameters corresponding to the operation parameters of the power transformation equipment with the same window and different types, and generating an operation stability evaluation vector of the power transformation equipment;
s5, playing a key role in the monitoring method of the running state of the power transformation equipment, and mainly aiming at summarizing instability parameters corresponding to different types of running parameters of the power transformation equipment in the same time window to generate a running stability evaluation vector of the power transformation equipment; the evaluation vector can comprehensively reflect the running stability of the equipment in a period of time; specifically, the step S5 includes the following:
In the step S5, firstly, the instability parameters corresponding to the operation parameters of different types of power transformation equipment in the same time window are summarized; the instability parameters are obtained through calculation and statistics in the step S4 and represent the fluctuation condition of each operation parameter; the summarized instability parameters can further generate operation stability evaluation vectors of the power transformation equipment; the evaluation vector is a multi-dimensional data structure and comprises the instability degree of each operation parameter; by integrating the degrees of instability of different parameters into one vector, the overall operational stability of the device can be more comprehensively evaluated;
the operation stability evaluation vector of the power transformation equipment not only comprises the instability degree of each parameter, but also comprises the interrelationship among different parameters; the comprehensive evaluation mode is helpful for finding potential failure modes or abnormal trends, and provides more accurate equipment state judgment; through visual display of the operation stability evaluation vector of the power transformation equipment, operation and maintenance personnel can more intuitively know the operation state of the equipment; visualization means may be graphs, curves, thermodynamic diagrams, etc., to help quickly identify abnormal areas and potential problems; the generated operation stability evaluation vector of the power transformation equipment can be used as input data and input into a pre-constructed operation state evaluation model of the power transformation equipment; the model can further analyze and process the evaluation vector to generate a more accurate equipment running state evaluation index.
S5, the instability degrees of different parameters can be integrated into an evaluation vector, so that comprehensive data support is provided for subsequent state evaluation and fault diagnosis; the comprehensive evaluation mode is beneficial to finding potential problems and improving the accuracy and timeliness of equipment fault early warning; it should be noted that the specific implementation of step S5 may vary from device to device and from monitoring requirement to monitoring requirement; in practical application, links such as data summarization, evaluation vector generation and the like are required to be adjusted and optimized according to specific conditions so as to achieve the optimal monitoring effect; meanwhile, in order to improve the accuracy and reliability of the evaluation, other technical means, such as artificial intelligence algorithm, pattern recognition, and the like, may need to be combined for comprehensive analysis and processing.
S6, inputting the operation stability evaluation vector of the power transformation equipment into a pre-constructed power transformation equipment operation state evaluation model to generate a power transformation equipment operation state evaluation index;
in the step S6, a power transformation equipment operation state evaluation model is pre-constructed and is used for receiving a power transformation equipment operation stability evaluation vector and generating a power transformation equipment operation state evaluation index; the construction of the model needs to comprehensively consider the operation characteristics and the monitoring requirements of the power transformation equipment, and combines actual operation data and experience to train and optimize; specifically, the construction of the power transformation equipment operation state evaluation model in step S6 includes the following steps:
S61, selecting a proper model type according to the operation characteristics and monitoring requirements of the power transformation equipment; common model types include regression models, classification models, cluster models, and the like; the models can be classified, regressed or clustered according to the characteristics and parameters of the input vectors, so that more state information and characteristics are extracted;
s62, before a model is built, key features are required to be selected and extracted from the operation stability evaluation vector of the power transformation equipment; these features should be able to reflect the operating state and stability of the device, such as current destabilization parameters, voltage destabilization parameters, temperature destabilization parameters, noise destabilization parameters, and vibration frequency destabilization parameters, among others; feature selection and extraction are key steps of model construction, and accuracy and reliability of the model are directly affected;
s63, training the model by using historical operation data and known operation state labels; through training, the model can learn the characteristics and modes of the normal running state and the manifestation and difference of the abnormal state; this step typically requires a large amount of data and appropriate algorithms to optimize and adjust;
s64, after model training is completed, the model needs to be evaluated and optimized; the aim of the evaluation is to check the accuracy and reliability of the model, and calculate indexes such as the accuracy, recall rate, F1 value and the like of the model by comparing the predicted running state and the actual running state of the model; according to the evaluation result, the model can be further optimized and adjusted, and the performance of the model is improved;
S65, after model training and evaluation are completed, the model can be deployed into a real-time monitoring system; generating an operation state evaluation index of the power transformation equipment by inputting the operation stability evaluation vector of the power transformation equipment into the model; the index can reflect the running state and stability of the equipment in real time, and provides important basis for subsequent fault diagnosis and operation and maintenance decision.
The power transformation equipment operation state evaluation model adopts a multiple linear regression model, and the calculation formula of the power transformation equipment operation state evaluation model is as follows:
；
wherein,evaluation index for the operating state of the power transformation system, +.>Is a current instability parameter +.>Is a voltage instability parameter +.>Is a temperature instability parameter +.>Is a noise instability parameter +.>Is a vibration frequency instability parameter; />、/>、/>、/>Andrespectively representing the weight coefficients of the current instability parameter, the voltage instability parameter, the temperature instability parameter, the noise instability parameter and the vibration frequency instability parameter.
In the step, the stable evaluation vector is formed by integrating the operation parameters of the multi-dimensional power transformation equipment and is input into the model, so that the comprehensive and comprehensive evaluation of the operation state of the equipment can be realized, and the method is not limited to single parameter threshold judgment; the historical operation data and the known state label training model are utilized, so that the device has self-learning and self-adapting capabilities, complex modes and abnormal behaviors in the operation of the device can be captured, and the accuracy and predictability of fault detection are improved; the key characteristics are selected and extracted according to the characteristics and monitoring requirements of the power transformation equipment, redundant information is removed, and the core indexes truly reflecting the health conditions of the equipment are focused, so that the effectiveness and the efficiency of the model are improved; through the evaluation and optimization process after model training, the reliability of the performance of the model in practical application is ensured, the normal state and the abnormal state can be effectively distinguished, and the false alarm rate are reduced; the trained model is deployed into a real-time monitoring system, so that an operation state evaluation index can be quickly generated according to data acquired in real time, the real-time monitoring and early warning functions are realized, timely preventive measures are facilitated for operation and maintenance personnel, and safe and stable operation of the power system is ensured; based on the running state evaluation index provided by the model, scientific basis can be provided for operation and maintenance decision, and the power-assisted intelligent power grid realizes more refined and intelligent equipment management and maintenance.
S7, comparing the operation state evaluation index of the power transformation equipment with a preset operation state evaluation threshold, and if the operation state evaluation index of the power transformation equipment exceeds the operation state evaluation threshold, generating equipment abnormality alarm information and displaying the equipment abnormality alarm information to operation and maintenance personnel; if the operation state evaluation index of the power transformation equipment does not exceed the operation state evaluation threshold, no action is performed;
in the S7 step, comparing the operation state evaluation index of the power transformation equipment with a preset operation state evaluation threshold value, so as to determine whether to generate equipment abnormality alarm information and display the equipment abnormality alarm information to operation and maintenance personnel; the step S7 plays a crucial role in the field of operation and maintenance of the power transformation equipment, because the step S7 is directly related to fault detection of the equipment and stability guarantee of a system; specifically, the step S7 includes the following:
s71, setting an operation state evaluation threshold value: an operation state evaluation threshold value needs to be set in advance; the threshold value is a reference value and is used for judging whether the operation state of the power transformation equipment is normal or not; the determination of the threshold value usually needs to combine various information such as historical data, equipment specifications, manufacturer suggestions and the like so as to ensure that the threshold value can accurately reflect the normal operation range of the equipment;
S72, calculating an operation state evaluation index of the power transformation equipment: s6, generating a power transformation equipment operation stability evaluation vector which reflects the instability condition of each operation parameter in a certain time window; by inputting the vector into a pre-constructed power transformation equipment operation state evaluation model, a comprehensive operation state evaluation index can be obtained; this index reflects the current operating state of the device, including the impact of various operating parameters;
s73, comparing the running state evaluation index with a threshold value: comparing the calculated running state evaluation index with a preset running state evaluation threshold; the aim of the comparison is to judge whether the current running state of the equipment is normal or not; the comparison mode can be simple size comparison or more complex statistical method, and a proper comparison mode is selected according to specific conditions;
s74, generating equipment abnormality alarm information: if the running state evaluation index exceeds a set threshold value, indicating that the running state of the equipment is abnormal; in this case, the system should generate device abnormality alert information; the information can comprise the identification of the equipment, specific parameters of the abnormality, the degree of the abnormality and the like, so that the operation and maintenance personnel can quickly know the severity and specific position of the problem;
S75, showing to operation and maintenance personnel: the generated abnormal alarm information needs to be transmitted to relevant operation and maintenance personnel; this can be achieved in various ways, e.g. sending mail, short messages, app push, etc.; it is important to ensure timeliness and accuracy of the information so that the operation and maintenance personnel can quickly take necessary measures to cope with the equipment abnormality.
In the step, the running state evaluation index is continuously calculated and compared, so that the real-time monitoring of the health state of the equipment can be realized, potential faults and abnormal changes can be found in time, and accidents can be effectively prevented; compared with the traditional monitoring method which only depends on a single parameter threshold, the running state evaluation model adopted in the step can comprehensively consider a plurality of running parameter fluctuation characteristics, and the capability of complex and hidden fault recognition is improved; by setting a reasonable operation state evaluation threshold and generating an accurate operation state evaluation index by combining the operation stability evaluation vector of the power transformation equipment, the system can more accurately judge whether the equipment is in a normal state, and the possibility of false alarm or missing alarm is reduced; when the equipment is abnormal, the system automatically generates alarm information containing detailed information, so that operation and maintenance personnel can quickly know the specific condition of the fault, quickly locate the source of the problem, and greatly improve the operation and maintenance response speed and the working efficiency; the monitoring mode based on the running state evaluation index facilitates the transition from passive maintenance to active maintenance, is beneficial to prolonging the service life of equipment, reducing the maintenance cost and is beneficial to guaranteeing the safety and stability of the whole power system; the abnormal alarm information can be sent to operation and maintenance personnel through various communication means, so that timeliness and coverage range of information transmission are ensured, and powerful support is provided for realizing efficient operation and maintenance management.
Embodiment two: as shown in fig. 3, the abnormal monitoring and controlling system for the operation state of the power transformation equipment specifically comprises the following modules;
the data acquisition module is used for acquiring the operation parameters of the power transformation equipment in real time, including temperature, vibration frequency, current, voltage and noise;
the data processing module is used for carrying out data extraction and imaging processing according to a preset time window aiming at each operation parameter of the power transformation equipment to obtain fluctuation image data of each operation parameter of the power transformation equipment;
the characteristic extraction module is used for calculating the difference value between adjacent peaks and valleys according to the fluctuation image data of the operation parameters of each piece of power transformation equipment to generate an operation parameter fluctuation characteristic set;
the destabilizing parameter calculation module is used for traversing each difference value in the corresponding operating parameter fluctuation feature set by utilizing a preset operating parameter fluctuation threshold value, counting the number of difference values exceeding the operating parameter fluctuation threshold value in the operating parameter fluctuation feature set, and taking the counted number of difference values as the destabilizing parameter of the operating parameter type of the corresponding power transformation equipment;
the evaluation vector generation module is used for summarizing instability parameters corresponding to the operation parameters of the power transformation equipment in the same time window and different types to generate an operation stability evaluation vector of the power transformation equipment;
The state evaluation module is used for inputting the operation stability evaluation vector of the power transformation equipment into a pre-constructed operation state evaluation model of the power transformation equipment to generate an operation state evaluation index of the power transformation equipment;
the alarm and display module is used for comparing the operation state evaluation index of the power transformation equipment with a preset operation state evaluation threshold, and generating equipment abnormality alarm information and displaying the equipment abnormality alarm information to operation and maintenance personnel if the operation state evaluation index of the power transformation equipment exceeds the operation state evaluation threshold; and if the operation state evaluation index of the power transformation equipment does not exceed the operation state evaluation threshold, no action is performed.
In the embodiment, the system monitors the traditional key operation parameters (such as temperature, current and voltage) in real time, and also brings in non-traditional but same important indexes such as vibration frequency and noise, so that the overall evaluation capability of the overall health condition of the equipment is improved; by setting a time window and extracting data to form fluctuation image data, the subtle change trend of the operation parameters of the equipment along with time can be captured, and the potential dynamic performance degradation condition can be found;
the feature extraction module adopts an adjacent peak Gu Chazhi calculation method, so that the identification capability of the parameter fluctuation details is enhanced, and tiny anomalies or hidden faults which are not easy to detect by conventional threshold detection can be revealed; the destabilization parameter calculation module counts the number of difference values exceeding the threshold value based on a preset fluctuation threshold value, and the quantification mode avoids false alarm and missing alarm possibly caused by simple threshold value comparison, and improves the accuracy of fault diagnosis;
The system integrates the unstable parameters of different types of operation parameters into a stable evaluation vector, and generates an evaluation index by utilizing an advanced operation state evaluation model, so that the process reflects the comprehensive evaluation capability of a complex system, and the problem that a single parameter cannot comprehensively reflect the state of equipment is effectively solved; the alarm and display module can timely send out an equipment abnormality alarm according to the comparison result of the operation state evaluation index of the power transformation equipment and a preset threshold value, so that operation and maintenance personnel can take preventive maintenance measures before a fault occurs, the fault risk is reduced, and the stable operation of the power system is ensured;
in summary, the abnormal monitoring and controlling system for the running state of the power transformation equipment can comprehensively and accurately evaluate the running state of the power transformation equipment through the cooperative work of the functional modules such as real-time monitoring, data processing, feature extraction, destabilization parameter calculation, evaluation vector generation, state evaluation, alarm and display, and the like, discover potential faults and abnormal conditions in time, and provide early warning information for operation and maintenance personnel; the method is beneficial to improving the stability and reliability of the power system, reducing the failure rate and maintenance cost of equipment and improving the operation efficiency of the whole power system.
In addition, the application further provides an electronic device, which comprises a bus, a transceiver, a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the transceiver, the memory and the processor are respectively connected through the bus, and when the computer program is executed by the processor, the processes of the method embodiment for controlling output data are realized, and the same technical effects can be achieved, so that repetition is avoided and redundant description is omitted.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that it will be apparent to those skilled in the art that modifications and variations can be made without departing from the technical principles of the present invention, and these modifications and variations should also be regarded as the scope of the invention.
Claims (10)
1. A method for monitoring the operation state of a power transformation device, the method comprising:
acquiring operation parameters of power transformation equipment in real time;
aiming at each operation parameter of the power transformation equipment, carrying out data extraction and imaging processing according to a preset time window to obtain fluctuation image data of each operation parameter of the power transformation equipment;
calculating the difference between adjacent peaks and valleys according to the fluctuation image data of the operation parameters of each piece of power transformation equipment to generate an operation parameter fluctuation feature set;
Traversing each difference value in the corresponding operation parameter fluctuation feature set by utilizing a preset operation parameter fluctuation threshold value, counting the number of difference values exceeding the operation parameter fluctuation threshold value in the operation parameter fluctuation feature set, and taking the counted number of difference values as the destabilizing parameters of the operation parameter type of the corresponding power transformation equipment;
summarizing instability parameters corresponding to operation parameters of the power transformation equipment in the same time window and different types to generate an operation stability evaluation vector of the power transformation equipment;
inputting the operation stability evaluation vector of the power transformation equipment into a pre-constructed operation state evaluation model of the power transformation equipment to generate an operation state evaluation index of the power transformation equipment;
comparing the operation state evaluation index of the power transformation equipment with a preset operation state evaluation threshold, and if the operation state evaluation index of the power transformation equipment exceeds the operation state evaluation threshold, generating equipment abnormality alarm information and displaying the equipment abnormality alarm information to operation and maintenance personnel; and if the operation state evaluation index of the power transformation equipment does not exceed the operation state evaluation threshold, no action is performed.
2. The power transformation device operational state monitoring method of claim 1, wherein the power transformation device operational parameters comprise temperature, vibration frequency, current, voltage, and noise.
3. The power transformation device operation state monitoring method according to claim 2, wherein the method of obtaining the fluctuation image data of each power transformation device operation parameter comprises:
extracting operation data acquired by each data acquisition node from the original data, wherein the operation data comprises a temperature value, a vibration frequency value, a current value, a voltage value and a noise decibel value; each item of operation data is marked with an acquisition time stamp;
setting a time window according to the type of the power transformation equipment and the monitoring requirement;
screening the operation data with the acquisition time stamp falling in the time window as the composition data of the fluctuation image data;
and converting the composition data into a waveform chart according to the time sequence of the acquisition time stamp, wherein the waveform chart is the fluctuation image data of the operation parameters of the corresponding power transformation equipment.
4. A method of monitoring the operational state of a power transformation device as defined in claim 3, wherein the method of calculating the difference between adjacent peaks and valleys to generate a set of operational parameter fluctuation features comprises:
for the fluctuating image data of each operation parameter, using a peak detection algorithm to find all peaks and troughs in the image;
according to the time sequence, calculating the difference between adjacent wave crests and wave troughs, wherein the difference can reflect the amplitude of each fluctuation of the operation parameters;
And (3) arranging and summarizing the calculated differences according to a time sequence to generate an operation parameter fluctuation feature set.
5. The method for monitoring the operation state of power transformation equipment according to claim 4, wherein the method for counting the number of differences exceeding the operation parameter fluctuation threshold in the operation parameter fluctuation feature set comprises the following steps:
setting an operation parameter fluctuation threshold according to historical data, industry standards and expert experience;
extracting the difference value quantity exceeding a preset operation parameter fluctuation threshold value from the operation parameter fluctuation feature set;
and taking the extracted difference value number as a destabilizing parameter corresponding to the operation parameter type of the power transformation equipment.
6. The power transformation equipment operation state monitoring method according to claim 1, wherein the power transformation equipment operation state evaluation model construction method comprises the following steps:
determining a model frame of an operation state evaluation model of the power transformation equipment according to the operation characteristics and monitoring requirements of the power transformation equipment;
extracting key characteristics from operation stability evaluation vectors of power transformation equipment, wherein the key characteristics comprise current instability parameters, voltage instability parameters, temperature instability parameters, noise instability parameters and vibration frequency instability parameters under different operation states;
Training the power transformation equipment operation state evaluation model by utilizing the historical operation data and the corresponding operation states, and evaluating and optimizing the power transformation equipment operation state evaluation model;
the method comprises the steps of deploying a power transformation equipment operation state evaluation model into real-time monitoring, inputting a power transformation equipment operation stability evaluation vector obtained in real time into the model, and generating a power transformation equipment operation state evaluation index.
7. The power transformation equipment operation state monitoring method according to claim 6, wherein the power transformation equipment operation state evaluation model adopts a multiple linear regression model, and a calculation formula of the power transformation equipment operation state evaluation model is as follows:
；
wherein,evaluation index for the operating state of the power transformation system, +.>Is a current instability parameter +.>Is a voltage instability parameter +.>Is a temperature instability parameter +.>Is a noise instability parameter +.>Is a vibration frequency instability parameter; />、/>、/>、/>And->Respectively representing the weight coefficients of the current instability parameter, the voltage instability parameter, the temperature instability parameter, the noise instability parameter and the vibration frequency instability parameter.
8. A power transformation equipment operating condition monitoring anomaly management and control system, the system comprising:
the data acquisition module is used for acquiring the operation parameters of the power transformation equipment in real time, including temperature, vibration frequency, current, voltage and noise;
The data processing module is used for carrying out data extraction and imaging processing according to a preset time window aiming at each operation parameter of the power transformation equipment to obtain fluctuation image data of each operation parameter of the power transformation equipment;
the characteristic extraction module is used for calculating the difference value between adjacent peaks and valleys according to the fluctuation image data of the operation parameters of each piece of power transformation equipment to generate an operation parameter fluctuation characteristic set;
the destabilizing parameter calculation module is used for traversing each difference value in the corresponding operating parameter fluctuation feature set by utilizing a preset operating parameter fluctuation threshold value, counting the number of difference values exceeding the operating parameter fluctuation threshold value in the operating parameter fluctuation feature set, and taking the counted number of difference values as the destabilizing parameter of the operating parameter type of the corresponding power transformation equipment;
the evaluation vector generation module is used for summarizing instability parameters corresponding to the operation parameters of the power transformation equipment in the same time window and different types to generate an operation stability evaluation vector of the power transformation equipment;
the state evaluation module is used for inputting the operation stability evaluation vector of the power transformation equipment into a pre-constructed operation state evaluation model of the power transformation equipment to generate an operation state evaluation index of the power transformation equipment;
The alarm and display module is used for comparing the operation state evaluation index of the power transformation equipment with a preset operation state evaluation threshold, and generating equipment abnormality alarm information and displaying the equipment abnormality alarm information to operation and maintenance personnel if the operation state evaluation index of the power transformation equipment exceeds the operation state evaluation threshold; and if the operation state evaluation index of the power transformation equipment does not exceed the operation state evaluation threshold, no action is performed.
9. A substation equipment operation state monitoring electronic device comprising a bus, a transceiver, a memory, a processor and a computer program stored on the memory and operable on the processor, the transceiver, the memory and the processor being connected by the bus, characterized in that the computer program when executed by the processor realizes the steps in the method according to any of claims 1-7.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method according to any of claims 1-7.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118312908A (en) * | 2024-06-06 | 2024-07-09 | 国网湖北省电力有限公司信息通信公司 | Power data processing system based on cloud computing |
| CN118425839A (en) * | 2024-04-29 | 2024-08-02 | 国网宁夏电力有限公司电力科学研究院 | Transformer core and clamp state monitoring method, medium and system |
| CN118568647A (en) * | 2024-08-01 | 2024-08-30 | 佛山市星际云数字科技有限公司 | Industrial equipment fault intelligent detection method and system based on digital twin |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118425839A (en) * | 2024-04-29 | 2024-08-02 | 国网宁夏电力有限公司电力科学研究院 | Transformer core and clamp state monitoring method, medium and system |
| CN118425839B (en) * | 2024-04-29 | 2024-10-18 | 国网宁夏电力有限公司电力科学研究院 | Transformer core and clamp state monitoring method, medium and system |
| CN118312908A (en) * | 2024-06-06 | 2024-07-09 | 国网湖北省电力有限公司信息通信公司 | Power data processing system based on cloud computing |
| CN118312908B (en) * | 2024-06-06 | 2024-09-03 | 国网湖北省电力有限公司信息通信公司 | Power data processing system based on cloud computing |
| CN118568647A (en) * | 2024-08-01 | 2024-08-30 | 佛山市星际云数字科技有限公司 | Industrial equipment fault intelligent detection method and system based on digital twin |
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