CN117591994A - Power equipment state data prediction method, intelligent platform, equipment and medium - Google Patents
Power equipment state data prediction method, intelligent platform, equipment and medium Download PDFInfo
- Publication number
- CN117591994A CN117591994A CN202311553100.2A CN202311553100A CN117591994A CN 117591994 A CN117591994 A CN 117591994A CN 202311553100 A CN202311553100 A CN 202311553100A CN 117591994 A CN117591994 A CN 117591994A
- Authority
- CN
- China
- Prior art keywords
- data
- state
- power equipment
- equipment
- state data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 59
- 238000001914 filtration Methods 0.000 claims abstract description 26
- 230000007774 longterm Effects 0.000 claims abstract description 17
- 238000007418 data mining Methods 0.000 claims abstract description 14
- 230000002159 abnormal effect Effects 0.000 claims abstract description 10
- 238000013135 deep learning Methods 0.000 claims abstract description 9
- 238000012423 maintenance Methods 0.000 claims abstract description 8
- 238000007619 statistical method Methods 0.000 claims abstract description 7
- 230000004927 fusion Effects 0.000 claims abstract description 6
- 238000007781 pre-processing Methods 0.000 claims abstract description 4
- 230000015654 memory Effects 0.000 claims description 29
- 230000003044 adaptive effect Effects 0.000 claims description 13
- 238000013500 data storage Methods 0.000 claims description 12
- 238000004590 computer program Methods 0.000 claims description 8
- 238000012800 visualization Methods 0.000 claims description 7
- 230000006872 improvement Effects 0.000 claims description 5
- 238000005070 sampling Methods 0.000 claims description 4
- 230000008901 benefit Effects 0.000 abstract description 6
- 238000005516 engineering process Methods 0.000 description 15
- 230000008859 change Effects 0.000 description 13
- 238000007405 data analysis Methods 0.000 description 10
- 238000012545 processing Methods 0.000 description 8
- 238000001514 detection method Methods 0.000 description 7
- 238000011161 development Methods 0.000 description 6
- 238000004458 analytical method Methods 0.000 description 5
- 238000004891 communication Methods 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 238000010276 construction Methods 0.000 description 2
- 238000003745 diagnosis Methods 0.000 description 2
- 230000005684 electric field Effects 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 238000007689 inspection Methods 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 230000004913 activation Effects 0.000 description 1
- 150000001875 compounds Chemical class 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 238000007500 overflow downdraw method Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00001—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/10—Pre-processing; Data cleansing
- G06F18/15—Statistical pre-processing, e.g. techniques for normalisation or restoring missing data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
- G06F18/2131—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on a transform domain processing, e.g. wavelet transform
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/25—Fusion techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/26—Discovering frequent patterns
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/27—Regression, e.g. linear or logistic regression
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
- G06N3/0442—Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00032—Systems characterised by the controlled or operated power network elements or equipment, the power network elements or equipment not otherwise provided for
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/048—Activation functions
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Computation (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Power Engineering (AREA)
- Business, Economics & Management (AREA)
- Health & Medical Sciences (AREA)
- Economics (AREA)
- General Health & Medical Sciences (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Biomedical Technology (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Probability & Statistics with Applications (AREA)
- Human Computer Interaction (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to a power equipment state data prediction method, an intelligent platform, equipment and a medium, wherein the method comprises the following steps: s1, acquiring power equipment state data detected by on-site operation and maintenance, and filtering and preprocessing invalid abnormal data by adopting a self-adaptive wavelet filtering algorithm; s2, analyzing small-scale state data in the power equipment by using a statistical analysis data mining method to obtain a short-term trend of the state of the power equipment; analyzing the large-scale state data in the power equipment by using a deep learning data mining method to obtain the long-term trend of the state of the power equipment; and S3, respectively carrying out weight distribution on the short-term trend and the long-term trend of the power equipment state, and carrying out fusion prediction. Compared with the prior art, the method has the advantage of high prediction accuracy.
Description
Technical Field
The invention relates to the field of big data processing, in particular to a power equipment state data prediction method, an intelligent platform, equipment and a medium.
Background
Because the state change and sudden faults of the power equipment occur under the action of a high-voltage electric field, heat, mechanical force, operation working conditions, meteorological environment and other factors, the latent faults generated by the equipment in operation are difficult to discover timely and accurately, and different characteristic parameter values and the change trend thereof must be comprehensively analyzed at multiple angles to improve the accuracy of equipment state evaluation, diagnosis and prediction.
The state change and fault evolution rules of the power equipment are contained in various state information such as electrified detection, on-line monitoring, inspection tests, operation conditions, environmental climate, power grid operation and the like, the equipment detection means are continuously enriched along with the construction and continuous development of the intelligent power grid, the data quantity generated by the power grid operation and the equipment detection is exponentially increased, and the difficulty of power state data prediction is increased. And a large amount of abnormal data also exist in the collected power equipment state data, so that the accuracy of the power equipment state prediction is greatly influenced.
Therefore, it is desirable to design a power equipment state prediction method with high prediction accuracy, so as to further improve the effectiveness and safety of power equipment management.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a power equipment maintenance method, equipment and medium based on big data, which have high prediction accuracy, and can further improve the effectiveness and safety of power equipment management.
The aim of the invention can be achieved by the following technical scheme:
according to a first aspect of the present invention, there is provided a power equipment status data prediction method, the method comprising:
s1, acquiring power equipment state data detected by on-site operation and maintenance, and filtering and preprocessing invalid abnormal data by adopting a self-adaptive wavelet filtering algorithm;
s2, analyzing small-scale state data in the power equipment by using a statistical analysis data mining method to obtain a short-term trend of the state of the power equipment; analyzing the large-scale state data in the power equipment by using a deep learning data mining method to obtain the long-term trend of the state of the power equipment;
and S3, respectively carrying out weight distribution on the short-term trend and the long-term trend of the power equipment state, and carrying out fusion prediction.
Preferably, the power equipment status data in the step S1 includes category, temperature, weather and age data.
Preferably, the adaptive filtering threshold in the adaptive wavelet filtering algorithm is expressed as:
wherein: delta, N and M are noise mean square error, signal lifting layer number and signal range respectively; t (T) t And T is the current sampling value and the last filtering result respectively.
Preferably, in the step S2, the deep learning data mining method is used to analyze the large-scale state data in the power equipment, so as to obtain a long-term trend of the state of the power equipment, which specifically includes:
according to the quality improvement degree of the data by the adaptive wavelet filtering algorithm, dividing the data into m period segments, and dividing the period segments into the same timeAverage memory is used as standard memory; reset gate r of gate control circulation unit by using standard memory t And (5) selectively memorizing, and predicting to obtain the long-term trend of the state of the power equipment.
Preferably, the gating cycle unit expression is specifically:
wherein: mu (mu) t,m 、r' t And the threshold value of each period data segment at the same time and the standard memory of the period data segment at the same time are obtained.
According to a second aspect of the present invention, there is provided a power equipment state intelligent platform, the platform comprising:
a device state data storage and query module;
the equipment state rule prediction module is used for predicting the equipment state data by adopting any one of the methods according to the power equipment state data acquired by the equipment state data storage and query module;
the visualization module is used for visually displaying the historical state data of the equipment and the predicted state data output by the equipment state rule prediction module;
and the early warning module is used for carrying out active fault early warning according to the abnormal equipment state predicted by the equipment state rule prediction module.
Preferably, the power equipment state data storage and query module performs data storage in a distributed file storage Mysql mode.
Preferably, the visualization module uses an autoregressive method to fit the device history state data.
According to a third aspect of the present invention there is provided an electronic device comprising a memory and a processor, the memory having stored thereon a computer program, the processor implementing the method of any one of the above when executing the program.
According to a fourth aspect of the present invention, there is provided a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the method of any one of the above.
Compared with the prior art, the invention has the following advantages:
1) According to the invention, a statistical analysis data mining method and a deep learning data mining method are adopted to respectively conduct trend prediction on small-scale state data and large-scale state data, and then accurate prediction on the state of the power equipment is realized through weight fusion.
2) According to the invention, invalid abnormal data in the power equipment state data is filtered through the adaptive wavelet filtering algorithm, so that the effectiveness of the power setting state data is further improved.
3) According to the invention, the association relation and the internal change rule among the power equipment state, the power grid operation and the meteorological environment parameters are revealed from the angle of data analysis, the premonitory information of the early failure of the equipment is captured, the failure development process is traced, and the occurrence probability of the failure is predicted, so that the hidden trouble of the failure is timely discovered, rapidly diagnosed and eliminated, and the operation safety of the power equipment is ensured.
4) Through the equipment state data storage, inquiry and visualization technology of the platform, the historical data of the equipment are analyzed by adopting a historical state curve, so that the state conditions at different moments are known; by means of the equipment state data processing and analyzing technology of the platform, equipment state change rules and possible subsequent state prediction are mastered by adopting a state fitting curve and state prediction curve mode, the power grid safe and reliable operation capacity is effectively improved, the electricity utilization stability of power users in a wide area range is ensured, and therefore remarkable economic benefits and social benefits are generated.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
fig. 2 is a flow chart of the method in example 2.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
Examples
Because the state change and sudden faults of the power equipment occur under the action of a high-voltage electric field, heat, mechanical force, operation working conditions, meteorological environment and other factors, the difficulty of discovering the latent faults generated by the equipment in operation is high in time and accurately, and different characteristic parameter values and the change trend thereof must be comprehensively analyzed at multiple angles to improve the accuracy of equipment state evaluation, diagnosis and prediction. The state change and fault evolution rules of the power equipment are contained in various state information such as electrified detection, on-line monitoring, inspection tests, operation conditions, environmental climate, power grid operation and the like, the equipment detection means are continuously enriched along with the construction and continuous development of the intelligent power grid, the data quantity generated by the power grid operation and the equipment detection is exponentially increased, and the data are fully utilized to be supported by a corresponding power equipment state big data intelligent analysis technology.
The embodiment provides a power equipment state data prediction method, which comprises the following steps:
s1, acquiring power equipment state data detected by on-site operation and maintenance, wherein the power equipment state data comprises category, temperature, weather and age data, and filtering and preprocessing invalid abnormal data by adopting a self-adaptive wavelet filtering algorithm;
the expression of the adaptive filtering threshold in the adaptive wavelet filtering algorithm is as follows:
wherein: delta, N and M are noise mean square error, signal lifting layer number and signal range respectively; t (T) t And T is the current sampling value and the last filtering result respectively.
S2, analyzing small-scale state data in the power equipment by using a statistical analysis data mining method to obtain a short-term trend of the state of the power equipment;
analyzing the large-scale state data in the power equipment by using a deep learning data mining method to obtain the long-term trend of the state of the power equipment, wherein the method specifically comprises the following steps:
dividing data into m period sections according to the quality improvement degree of the data by the adaptive wavelet filtering algorithm, and taking average memory of the period sections at the same time as standard memory; reset gate r of gate control circulation unit by using standard memory t And (5) selectively memorizing, and predicting to obtain the long-term trend of the state of the power equipment. The gating cycle unit expression is specifically as follows:
wherein: mu (mu) t,m 、r' t And the threshold value of each period data segment at the same time and the standard memory of the period data segment at the same time are obtained.
And S3, respectively carrying out weight distribution on the short-term trend and the long-term trend of the power equipment state, and carrying out fusion prediction.
Next, a power equipment state intelligent platform is provided, comprising:
device state data storage and query module for storing data by adopting distributed file storage Mysql mode
The equipment state rule prediction module is used for predicting the equipment state data by adopting the method according to the power equipment state data acquired by the equipment state data storage and query module;
the visualization module is used for visually displaying the historical state data of the equipment and the predicted state data output by the equipment state rule prediction module; furthermore, an autoregressive method is adopted to perform equipment history state data fitting
And the early warning module is used for carrying out active fault early warning according to the abnormal equipment state predicted by the equipment state rule prediction module.
The electronic device of the present invention includes a Central Processing Unit (CPU) that can perform various appropriate actions and processes according to computer program instructions stored in a Read Only Memory (ROM) or computer program instructions loaded from a storage unit into a Random Access Memory (RAM). In the RAM, various programs and data required for the operation of the device can also be stored. The CPU, ROM and RAM are connected to each other by a bus. An input/output (I/O) interface is also connected to the bus.
A plurality of components in a device are connected to an I/O interface, comprising: an input unit such as a keyboard, a mouse, etc.; an output unit such as various types of displays, speakers, and the like; a storage unit such as a magnetic disk, an optical disk, or the like; and communication units such as network cards, modems, wireless communication transceivers, and the like. The communication unit allows the device to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processing unit performs the respective methods and processes described above, for example, the methods S1 to S3. For example, in some embodiments, methods S1-S3 may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device via the ROM and/or the communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of the methods S1 to S3 described above may be performed. Alternatively, in other embodiments, the CPU may be configured to perform methods S1-S3 in any other suitable manner (e.g., by means of firmware).
The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a load programmable logic device (CPLD), etc.
Program code for carrying out methods of the present invention may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
Example 2
The embodiment provides a power equipment state prediction method, which comprises the following steps:
step 1: the method comprises the steps of adopting data such as the type, temperature, weather, age and the like of equipment detected by on-site operation and maintenance, predicting long-term and short-term development trends of equipment parameters by using a statistical analysis and deep learning fusion method, and evaluating and predicting the running state of the equipment.
Step 2: and aiming at the influence of invalid abnormal data, performing data quality improvement by using an adaptive wavelet filtering algorithm. Lambda is selected from conventional wavelet filtering common threshold t The formula is:
wherein: delta, N and M are noise mean square error, signal boosting layer number and signal range respectively. Since the occurrence of invalid outliers is randomly unpredictable, so that the statistical characteristic priori rules of data and noise cannot be obtained, the embodiment utilizes the data authenticity to define the self-adaptive threshold range mu for the situation of rule distortion t The improved adaptive threshold wavelet filtering is as follows:
wherein: t (T) t And T is the current sampling value and the last filtering result respectively.
Step 3: the GRU as the LSTM improver simplifies the input and output, namely the input, the output and the forgetting gate of the LSTM are simplified into an update gate and a reset gate, but still keeps the advantage of high LSTM prediction accuracy. For standard GRU gating logic, GRU status output h at time t t The method comprises the following steps:
wherein: sigma, W, b and plus are respectively the Sigmoid activation function, the weight matrix, the bias vector and the dot product operation of the corresponding positions of the data.
Step 4: from the gate logic of the GRU, it is known to reset gate r t Determining how to combine new data with previous memories and updating gate z t It is decided how much the previous memorization works. Therefore, the key to improving the robustness of the algorithm to invalid outlier data is how r t And z t . For this reason, the present embodiment improves the GRU. According to the data quality improvement degree of the adaptive wavelet filtering, the data is divided into m period sections, and the average memory of each period section at the same time is used as the standard memory. Reset gate r for GRU using standard memory t And performing selective memory, namely multiple memories with high data quality and fewer memories with poor data quality. The improved GRU is as follows:
wherein: mu (mu) t,m 、r' t And the threshold value of each period data segment at the same time and the standard memory of the period data segment at the same time are obtained.
Step 5: and processing the acquired data to ensure the quality and reliability of the data, and designing an intelligent power equipment state platform based on the big data analysis technology according to the acquired data and the applied analysis technology by using the big data analysis technology.
Next, the embodiment provides an intelligent power equipment state platform based on a big data analysis technology, which comprises the steps of storing, inquiring and visualizing equipment state data through the platform, analyzing historical data of equipment by adopting a historical state curve, and knowing state conditions at different moments; and grasping a change rule of the equipment state and predicting the possible subsequent state by adopting a state fitting curve and a state predicting curve by means of the equipment state data processing and analyzing technology of the platform. And processing the acquired data to ensure the quality and reliability of the data, and designing an intelligent power equipment state platform based on the big data analysis technology according to the acquired data and the applied analysis technology by using the big data analysis technology. Compared with the prior art, the method and the system disclosed by the invention have the advantages that the association relation and the internal change rule among the state of the power equipment, the operation of the power grid and the meteorological environment parameters are revealed from the angle of data analysis, the premonitory information of the early failure of the equipment is captured, the failure development process is traced, and the occurrence probability of the failure is predicted, so that the hidden trouble of the failure is timely discovered, rapidly diagnosed and eliminated, and the operation safety of the power equipment is ensured.
The foregoing is a detailed description of the big data analysis technology of the present invention, and the following is a detailed description of the implementation of an intelligent analysis platform for power equipment status big data of the present invention.
The invention provides an intelligent power equipment state platform based on a big data analysis technology, which is realized based on the big data analysis technology and comprises the following components:
device state storage and inquiry are realized. The invention adopts a distributed file storage (Mysql) mode to store data, and can meet the requirements of high reliability, large capacity, quick storage and the like of state data storage and inquiry. And a compound row key structure is designed through a query method and a secondary index technology of parallel connection of multiple data sources, so that the requirement of flexible multi-condition space-time query is met.
Device history state visualization is implemented. The historical data tracing is presented in a mode of a historical curve, and the process of the state change of the equipment is intuitively reflected, so that any operation and maintenance personnel can master the historical state information of the current equipment.
And realizing the regular fitting of the equipment state. According to the invention, the change rule of the historical state of the equipment is fitted in an autoregressive mode, so that operation and maintenance personnel can grasp the change rule and development direction of the state of the equipment intuitively and conveniently.
And the device state rule prediction is realized. Analyzing the small-scale state data in the power equipment by using a statistical analysis data mining method to obtain a short-term trend of the equipment state; analyzing the large-scale above detection data in the power equipment by using a deep learning data mining method to obtain a long-term trend of the equipment state; and on the basis of the steps, carrying out weight a distribution on the short-term trend, carrying out weight b distribution on the long-term trend, and finally carrying out fusion analysis on the short-term trend and the long-term trend. The rapid prediction of the subsequent state of the equipment is realized, and the efficiency and the intelligent level of the active fault early warning of the equipment are improved.
While the invention has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (10)
1. A method for predicting power equipment status data, the method comprising:
s1, acquiring power equipment state data detected by on-site operation and maintenance, and filtering and preprocessing invalid abnormal data by adopting a self-adaptive wavelet filtering algorithm;
s2, analyzing small-scale state data in the power equipment by using a statistical analysis data mining method to obtain a short-term trend of the state of the power equipment; analyzing the large-scale state data in the power equipment by using a deep learning data mining method to obtain the long-term trend of the state of the power equipment;
and S3, respectively carrying out weight distribution on the short-term trend and the long-term trend of the power equipment state, and carrying out fusion prediction.
2. The power equipment status data prediction method according to claim 1, wherein the power equipment status data in step S1 includes category, temperature, weather and age data.
3. The method for predicting state data of electrical equipment according to claim 1, wherein the adaptive filtering threshold in the adaptive wavelet filtering algorithm is expressed as:
wherein: delta, N and M are noise mean square error, signal lifting layer number and signal range respectively; t (T) t And T is the current sampling value and the last filtering result respectively.
4. The method for predicting state data of electrical equipment according to claim 1, wherein in step S2, the large-scale state data in the electrical equipment is analyzed by using a deep learning data mining method to obtain a long-term trend of the state of the electrical equipment, specifically:
dividing data into m period sections according to the quality improvement degree of the data by the adaptive wavelet filtering algorithm, and taking average memory of the period sections at the same time as standard memory; reset gate r of gate control circulation unit by using standard memory t And (5) selectively memorizing, and predicting to obtain the long-term trend of the state of the power equipment.
5. The method for predicting state data of electrical equipment according to claim 4, wherein the gating cycle unit expression is specifically:
wherein: mu (mu) t,m 、r′ t And the threshold value of each period data segment at the same time and the standard memory of the period data segment at the same time are obtained.
6. An intelligent platform for power equipment status, the platform comprising:
a device state data storage and query module;
the device state rule prediction module is used for predicting the device state data by adopting the method of any one of claims 1-5 according to the power device state data acquired by the device state data storage and query module;
the visualization module is used for visually displaying the historical state data of the equipment and the predicted state data output by the equipment state rule prediction module;
and the early warning module is used for carrying out active fault early warning according to the abnormal equipment state predicted by the equipment state rule prediction module.
7. The platform of claim 6, wherein the power device status data storage and query module uses a distributed file storage Mysql approach to data storage.
8. The platform of claim 6, wherein the visualization module uses an autoregressive method to fit the device history state data.
9. An electronic device comprising a memory and a processor, the memory having stored thereon a computer program, characterized in that the processor, when executing the program, implements the method according to any of claims 1-5.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any one of claims 1-5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311553100.2A CN117591994A (en) | 2023-11-20 | 2023-11-20 | Power equipment state data prediction method, intelligent platform, equipment and medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311553100.2A CN117591994A (en) | 2023-11-20 | 2023-11-20 | Power equipment state data prediction method, intelligent platform, equipment and medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117591994A true CN117591994A (en) | 2024-02-23 |
Family
ID=89909358
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311553100.2A Pending CN117591994A (en) | 2023-11-20 | 2023-11-20 | Power equipment state data prediction method, intelligent platform, equipment and medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117591994A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118152970A (en) * | 2024-05-11 | 2024-06-07 | 国网山东省电力公司烟台供电公司 | Equipment state trend sensing method based on edge calculation algorithm |
-
2023
- 2023-11-20 CN CN202311553100.2A patent/CN117591994A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118152970A (en) * | 2024-05-11 | 2024-06-07 | 国网山东省电力公司烟台供电公司 | Equipment state trend sensing method based on edge calculation algorithm |
| CN118152970B (en) * | 2024-05-11 | 2024-07-26 | 国网山东省电力公司烟台供电公司 | Equipment state trend sensing method based on edge calculation algorithm |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110046182A (en) | A kind of huge hydroelectric power plant's intelligent alarm threshold setting method and system | |
| CN107561997A (en) | A kind of power equipment state monitoring method based on big data decision tree | |
| CN117591994A (en) | Power equipment state data prediction method, intelligent platform, equipment and medium | |
| CN118035814A (en) | State fault early warning method and system based on digital twin field data analysis equipment | |
| CN116539994A (en) | Substation main equipment operation state detection method based on multi-source time sequence data | |
| CN114997342B (en) | SCR fault diagnosis method, device, equipment and storage medium | |
| CN118091406B (en) | Motor detection and repair method and device, electronic equipment and storage medium | |
| CN118408583B (en) | Encoder fault diagnosis method and system | |
| CN111314110B (en) | Fault early warning method for distributed system | |
| CN116720983A (en) | Power supply equipment abnormality detection method and system based on big data analysis | |
| CN111934903A (en) | Docker container fault intelligent prediction method based on time sequence evolution genes | |
| CN111614504A (en) | Power grid regulation and control data center service characteristic fault positioning method and system based on time sequence and fault tree analysis | |
| JP7062505B2 (en) | Equipment management support system | |
| CN114492636B (en) | Transformer winding state signal acquisition system | |
| CN117149565A (en) | State detection method, device, equipment and medium for key performance indexes of cloud platform | |
| CN116863664A (en) | Real-time monitoring method and system for gas equipment | |
| CN117705178A (en) | Wind power bolt information detection method and device, electronic equipment and storage medium | |
| Tao et al. | A state and fault prediction method based on RBF neural networks | |
| CN109558258B (en) | Method and device for positioning root fault of distributed system | |
| CN117521084B (en) | Active safety early warning method for complex system | |
| CN118193954B (en) | Power distribution network abnormal data detection method and system based on edge calculation | |
| CN116720324B (en) | Traction substation key equipment fault early warning method and system based on prediction model | |
| CN118410485B (en) | Multi-system collaborative security testing method for open source operating system environment | |
| CN118568439A (en) | Equipment state diagnosis method, system and medium based on multi-source data mining | |
| Menaouer | Anomaly Detection of Aeolian Wind Speed Using Deep Learning Techniques |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |