Multi-dimensional power generation equipment state monitoring method and related system
By pre -processing and establishing an ARIMA model through the status parameters of power generation equipment, the multi -dimensional monitoring and prediction of the status of power generation equipment is realized, and the problems of lack of manual dependence and prediction capabilities in traditional monitoring methods have been solved. Accuracy.
Patent Information
- Application Number
- CN202411513346.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-28
- Publication Date
- 2025-01-28
AI Technical Summary
Traditional power generation equipment monitoring methods rely on manual inspection and empirical troubleshooting, making it difficult to achieve real-time and continuous equipment status monitoring, and lack in-depth analysis and forward-looking prediction of equipment operation data, resulting in insufficient prediction capabilities.
Multi -dimensional power generation equipment status monitoring methods include pre -processing of obtaining equipment status parameters, establishing an ARIMA model, making data estimates and prediction, and sending prediction results in time. Specific steps include the use of independent functions and partially related functions to establish the Arima model to estimate and predict the pre -processing data.
By eliminating noise and abnormal values, improve the effect of model prediction, can more accurately capture the inherent relationships of time sequence data, enhance the accuracy and reliability of prediction, realize real -time monitoring of power generation equipment, identify equipment failure in advance, reduce shutdown and maintenance Cost, improve operational efficiency.
Smart Images
Figure CN119377912A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of power generation equipment monitoring, and specifically relates to a multi-dimensional power generation equipment status monitoring method and a related system. Background Art
[0002] In the context of today's rapid industrial development, the application of automation and intelligent technologies is becoming more and more widespread. As a core component of the power system, the safety and reliability of the operating status of power generation equipment is directly related to the stability of the entire power supply network. Traditional power generation equipment maintenance strategies mainly rely on regular manual inspections and experience-based troubleshooting. This method has the following limitations when facing complex and changing operating environments and increasing performance requirements for equipment: Limitations of manual monitoring: Traditional manual monitoring is not only time-consuming and labor-intensive, but also limited by the experience and technical level of personnel, making it difficult to achieve continuous and real-time monitoring of equipment status. Insufficient predictive capabilities: Traditional methods lack in-depth analysis and forward-looking prediction of equipment operation data, making it difficult to provide effective early warning before a failure occurs. Summary of the invention
[0003] The purpose of the present invention is to overcome the above-mentioned shortcomings of low working efficiency and poor prediction ability, and to provide a multi-dimensional power generation equipment status monitoring method and related system.
[0004] In order to achieve the above object, the present invention adopts the following technical solution: In a first aspect, the present invention provides a multi-dimensional power generation equipment status monitoring method, comprising the following steps: Obtain the status parameters of the power generation equipment, and perform pre-processing on the status parameters for cleaning and standardization; The ARIMA model is established using the autocorrelation function and partial autocorrelation function; The ARIMA model is used to estimate the preprocessed data to obtain the estimation results; Predict the estimated results to obtain the predicted results; Send prediction results.
[0005] A further improvement of the present invention is that the specific method for preprocessing the state parameters for cleaning and standardization is as follows: The first-order difference formula is used to stabilize the state parameter process and remove outliers to obtain the time series; Perform several differences on the time series to complete the preprocessing.
[0006] A further improvement of the present invention is that the first-order difference formula is as follows:
[0007] in, is the change in time t, is the value of the time series at time t, is the value of the time series at the previous moment of time 𝑡.
[0008] A further improvement of the present invention is that when performing several differences on the time series, the following method is adopted:
[0009] in, is the difference operator, is the hysteresis coefficient, is the number of differences.
[0010] A further improvement of the present invention is that the autocorrelation function is:
[0011] in, The hysteresis coefficient is The autocorrelation function of The hysteresis coefficient is The autocovariance of is the covariance of the series; The partial autocorrelation function is:
[0012] in, The hysteresis coefficient is The partial autocorrelation function of The hysteresis coefficient is The partial autocorrelation function of .
[0013] A further improvement of the present invention is that the ARIMA model is used to estimate the preprocessed data, and the specific method for obtaining the estimation result is as follows: The formula for the ARIMA model is:
[0014] in, is the vector of the ARIMA model, is the preprocessed data, is the number of preprocessed data, is the error term, is the residual at time t.
[0015] A further improvement of the present invention is that the estimation result is predicted, and the specific method for obtaining the prediction result is as follows: The autoregressive model is used to predict the estimation results. The calculation method of the autoregressive model is as follows:
[0016] in, is the predicted value at time t, is a constant, is the autoregression coefficient, is the white noise error term.
[0017] In a second aspect, the present invention provides a multi-dimensional power generation equipment status monitoring system, comprising: A preprocessing module is used to obtain the state parameters of the power generation equipment and to perform cleaning and standardization preprocessing on the state parameters; Model building module, used to build ARIMA model using autocorrelation function and partial autocorrelation function; The estimation module is used to estimate the preprocessed data using the ARIMA model to obtain the estimation result; A prediction module is used to predict the estimation result and obtain the prediction result; The sending module is used to send the prediction results.
[0018] In a third aspect, the present invention provides an electronic device including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of a multi-dimensional power generation equipment status monitoring method when executing the computer program.
[0019] In a fourth aspect, the present invention provides a storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of a multi-dimensional power generation equipment status monitoring method.
[0020] Compared with the prior art, the present invention has the following beneficial effects: The present invention can eliminate noise and outliers by cleaning and standardizing state parameters, ensure the accuracy of input data, and thus improve the prediction effect of the model. The present invention uses autocorrelation function and partial autocorrelation function to establish ARIMA model, which can better capture the intrinsic relationship in time series data and improve the accuracy of prediction. ARIMA model is based on time series analysis and can effectively use historical data for modeling, making the prediction of future state more reliable. ARIMA model can dynamically adjust the prediction according to new data to adapt to the change of equipment state and enhance the flexibility of the model. The present invention can realize real-time monitoring of power generation equipment by sending prediction results in time, so as to facilitate timely discovery of potential problems and take measures to improve the operation efficiency of equipment. The present invention can accurately predict and identify equipment failures in advance, reduce unexpected downtime and maintenance costs, and improve overall operation efficiency. The present invention adopts automated monitoring and prediction processes to reduce manual intervention, improve work efficiency, and enable relevant personnel to focus on higher value tasks. In summary, the present invention can significantly improve the monitoring capability and operation efficiency of power generation equipment. BRIEF DESCRIPTION OF THE DRAWINGS
[0021] Figure 1 is a flow chart of the present invention; Figure 2 is a system diagram of the present invention; Figure 3 This is a system diagram of Example 5. DETAILED DESCRIPTION
[0022] In order to further understand the content of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the embodiments are only for explaining the present invention and are not intended to limit it.
[0023] Embodiment 1: See also Figure 1 , a multi-dimensional power generation equipment condition monitoring method, comprising the following steps: S1, obtaining the state parameters of the power generation equipment, and pre-processing the state parameters by cleaning and standardization.
[0024] S2, the ARIMA model is established using the autocorrelation function and partial autocorrelation function.
[0025] S3, using the ARIMA model to estimate the preprocessed data to obtain the estimated results.
[0026] S4, predicting the estimated results to obtain predicted results.
[0027] S5, sending the prediction result.
[0028] Embodiment 2: See also Figure 2 , multi-dimensional power generation equipment condition monitoring system, including: A preprocessing module is used to obtain the state parameters of the power generation equipment and to perform cleaning and standardization preprocessing on the state parameters; Model building module, used to build ARIMA model using autocorrelation function and partial autocorrelation function; The estimation module is used to estimate the preprocessed data using the ARIMA model to obtain the estimation result; A prediction module is used to predict the estimation result and obtain the prediction result; The sending module is used to send the prediction results.
[0029] Embodiment 3: This embodiment is a detailed description of a multi-dimensional power generation equipment status monitoring method, which specifically includes the following steps: Step 1: Collect the operating status data of the equipment at different time points, such as temperature, pressure, current, etc., and use the first-order difference formula to stabilize the status parameters:
[0030] in, is the change in time t, is the value of the time series at time t, is the value of the time series at the previous moment of time 𝑡.
[0031] Use difference operation to detrend the data and remove outliers to obtain the time series. Perform several differences on the time series and apply the difference operator:
[0032] in, is the difference operator, is the hysteresis coefficient, is the number of differences.
[0033] Step 2: Calculate the autocorrelation function and partial autocorrelation function:
[0034] in, The hysteresis coefficient is The autocorrelation function of The hysteresis coefficient is The autocovariance of is the covariance of the series; The partial autocorrelation function is:
[0035] in, The hysteresis coefficient is The partial autocorrelation function of The hysteresis coefficient is The partial autocorrelation function of .
[0036] Select appropriate ARIMA model parameters based on the calculation results.
[0037] Step 3: Use the ARIMA model to estimate the preprocessed data and calculate the estimated results using the following formula:
[0038] in, is the vector of the ARIMA model, is the preprocessed data, is the number of preprocessed data, is the error term, is the residual at time t.
[0039] Step 4: Predict the estimated results using the autoregressive model:
[0040] in, is the predicted value at time t, is a constant, is the autoregression coefficient, is the white noise error term.
[0041] Step five: Send the prediction results to relevant personnel or systems to facilitate real-time monitoring and decision-making.
[0042] This embodiment can effectively monitor the status of multi-dimensional power generation equipment, improve prediction accuracy and work efficiency, reduce failure risks, and optimize equipment maintenance strategies.
[0043] Embodiment 4: This embodiment performs status monitoring on a generator set, and the monitoring parameters include the temperature, vibration and current of the generator, including the following steps: Step 1: Obtain the status parameters of the power generation equipment: collect the temperature (unit: °C), vibration (unit: mm / s) and current (unit: A) of the generator every 5 minutes, including: Temperature: [70,72,71,73,75,76,74] Vibration: [0.5, 0.6, 0.55, 0.58, 0.57, 0.59, 0.6] Current: [150,152,151,153,155,154,156] Step 2: Use the first-order difference formula for stabilization: For the temperature series:
[0044] The result is: [2,−1,2,2,1,−2].
[0045] The outlier removal process is performed. The normal range of the preset temperature is [65,80], and the above data are all within the range. Since the data after the first-order difference is stable, it is directly processed to the next step.
[0046] Step 2: Calculate the autocorrelation function: is [1.0, 0.8, 0.5, 0.2], and we get: =1.0, =0.8, =0.5, =0.2 Establish an ARIMA(2,1,1) model: select p=2p and q=1 according to the value of the autocorrelation function.
[0047] Step 3: Use the ARIMA model for estimation and get the parameters by model fitting: θ=[0.5,0.3,0.2] Calculate the estimated results, assuming T=7, ;
[0048] Since the residual sum of squares is 5, then:
[0049] Step 4: Assume that the autoregressive coefficient is:
[0050] Predict the next moment :
[0051] is the white noise error term, with a value of 0.1.
[0052] Step 5: The prediction result is that the temperature of the generator at the next moment is expected to be 129.6°C, and it is evaluated whether it exceeds the normal range, triggering necessary warnings and sending the prediction results to relevant personnel or systems.
[0053] This embodiment provides a comprehensive data-driven solution that can effectively monitor the status of power generation equipment and make reliable predictions.
[0054] Embodiment 5: See also Figure 3 As shown, the present invention also provides an electronic device 100 for a multi-dimensional power generation equipment status monitoring method; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.
[0055] The memory 101 can be used to store the computer program 103, and the processor 102 implements the steps of the multi-dimensional power generation equipment state monitoring method described in Example 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data (such as audio data) created according to the use of the electronic device 100, etc. In addition, the memory 101 can include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices.
[0056] The at least one processor 102 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor, etc. The processor 102 is the control center of the electronic device 100, and uses various interfaces and lines to connect various parts of the entire electronic device 100.
[0057] The memory 101 in the electronic device 100 stores a plurality of instructions to implement a multi-dimensional power generation equipment status monitoring method, and the processor 102 can execute the plurality of instructions to implement: Obtain the status parameters of the power generation equipment, and perform pre-processing on the status parameters for cleaning and standardization; The ARIMA model is established using the autocorrelation function and partial autocorrelation function; The ARIMA model is used to estimate the preprocessed data to obtain the estimation results; Predict the estimated results to obtain the predicted results; Send prediction results.
[0058] Embodiment 6: If the module / unit integrated in the electronic device 100 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory and read-only memory (ROM, Read-Only Memory).
[0059] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0060] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0061] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.
[0062] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.
[0063] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.
Claims
1. A multi-dimensional power generation equipment status monitoring method, characterized in that: The following steps are involved: Obtain the status parameters of the power generation equipment, and perform pre-processing on the status parameters for cleaning and standardization; The ARIMA model is established using the autocorrelation function and partial autocorrelation function; The ARIMA model is used to estimate the preprocessed data to obtain the estimation results; Predict the estimated results to obtain the predicted results; Send prediction results.
2. The multi-dimensional power generation equipment status monitoring method according to claim 1, characterized in that: The specific method for preprocessing the state parameters for cleaning and standardization is as follows: The first-order difference formula is used to stabilize the state parameter process and remove outliers to obtain the time series; Perform several differences on the time series to complete the preprocessing.
3. The multi-dimensional power generation equipment status monitoring method according to claim 2, characterized in that: The first-order difference formula is as follows: in, is the change in time t, is the value of the time series at time t, is the value of the time series at the previous moment of time 𝑡.
4. The multi-dimensional power generation equipment status monitoring method according to claim 2, characterized in that: When performing several differences on a time series, the following method is used: in, is the difference operator, is the hysteresis coefficient, is the number of differences.
5. The multi-dimensional power generation equipment status monitoring method according to claim 1, characterized in that: The autocorrelation function is: in, The hysteresis coefficient is The autocorrelation function of The hysteresis coefficient is The autocovariance of is the covariance of the series; The partial autocorrelation function is: in, The hysteresis coefficient is The partial autocorrelation function of The hysteresis coefficient is The partial autocorrelation function of .
6. The multi-dimensional power generation equipment status monitoring method according to claim 1, characterized in that: The ARIMA model is used to estimate the preprocessed data. The specific method to obtain the estimation result is as follows: The formula for the ARIMA model is: in, is the vector of the ARIMA model, is the preprocessed data, is the number of preprocessed data, is the error term, is the residual at time t.
7. The multi-dimensional power generation equipment status monitoring method according to claim 1, characterized in that: The specific method of predicting the estimated results and obtaining the predicted results is as follows: The autoregressive model is used to predict the estimation results. The calculation method of the autoregressive model is as follows: in, is the predicted value at time t, is a constant, is the autoregression coefficient, is the white noise error term.
8. Multi-dimensional power generation equipment status monitoring system, characterized in that: include: A preprocessing module is used to obtain the state parameters of the power generation equipment and to perform cleaning and standardization preprocessing on the state parameters; Model building module, used to build ARIMA model using autocorrelation function and partial autocorrelation function; The estimation module is used to estimate the preprocessed data using the ARIMA model to obtain the estimation result; A prediction module is used to predict the estimation result and obtain the prediction result; The sending module is used to send the prediction results.
9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the multi-dimensional power generation equipment status monitoring method described in any one of claims 1 to 7 are implemented.
10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the multi-dimensional power generation equipment status monitoring method described in any one of claims 1 to 7 are implemented.