CN112510699A - Transformer substation secondary equipment state analysis method and device based on big data - Google Patents
Transformer substation secondary equipment state analysis method and device based on big data Download PDFInfo
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- 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
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- 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]
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- 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
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- 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
- H02J13/00034—Systems characterised by the controlled or operated power network elements or equipment, the power network elements or equipment not otherwise provided for the elements or equipment being or involving an electric power substation
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- 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
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
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- 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
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- 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
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
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- 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
- Y04S10/16—Electric power substations
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Abstract
The invention relates to a substation secondary equipment state analysis method and equipment based on big data, wherein the method comprises the following steps: acquiring historical state data of a plurality of groups of secondary equipment and abnormal state types corresponding to the historical state data from an equipment abnormal state record library; taking a plurality of groups of historical state data and abnormal state types corresponding to the historical state data as a training set, and establishing a deep learning model between the state data and the abnormal state types according to the training set to obtain a state analysis model; and acquiring real-time state data of the secondary equipment, and analyzing the real-time state of the secondary equipment by using the state analysis model. The method solves the problem that the real-time state of the secondary equipment of the intelligent substation is difficult to acquire at present.
Description
Technical Field
The invention relates to the technical field of substation maintenance, in particular to a substation secondary equipment state analysis method and equipment based on big data and a storage medium.
Background
With the large-scale operation of intelligent substations in recent years, the proportion of intelligent substations in the whole power grid system is also increasing, and this will become a mainstream trend of development of the future power grid system. In an intelligent substation, a digital communication transmission mode represented by Ethernet replaces a traditional hard-wired loop, and the mode has the main advantages that: all transmission signals can be exchanged and shared.
At present, an intelligent substation which is continuously developed and built has a three-layer two-network structure and consists of numerous secondary devices (such as an intelligent terminal, a merging unit, a relay protection device, a measurement and control device, a wave recording device and other secondary devices), when a fault occurs, a large amount of fault simulation data, MMS messages and GOOSE messages can be generated, on the other hand, SCD files also contain a lot of useful configuration information, and the numerous data bring great difficulty to the state analysis of the protection function of the protection device.
At present, tasks such as normal operation, accident judgment and processing of secondary equipment of an intelligent substation are mostly finished by operation and inspection personnel, once an emergency accident occurs, the operation and inspection personnel mainly rely on clear mind and rich operation experience, but in the face of information such as SV, GOOSE, MMS warning signals and wave recording files of the intelligent substation, more and more complex equipment and network structures, the operation and inspection personnel analyze and judge abnormal behaviors of the secondary equipment by simply looking up data through experience and manual work, and an accurate fault judgment and analysis result of the abnormal behaviors of the secondary equipment are difficult to obtain.
Disclosure of Invention
In view of the above, it is necessary to provide a substation secondary device state analysis method, a device and a storage medium based on big data, so as to solve the problem that it is difficult to obtain the real-time state of the intelligent substation secondary device at present.
In a first aspect, the invention provides a substation secondary device state analysis method based on big data, which comprises the following steps:
acquiring historical state data of a plurality of groups of secondary equipment and abnormal state types corresponding to the historical state data from an equipment abnormal state record library;
taking a plurality of groups of historical state data and abnormal state types corresponding to the historical state data as a training set, and establishing a deep learning model between the state data and the abnormal state types according to the training set to obtain a state analysis model;
and acquiring real-time state data of the secondary equipment, and analyzing the real-time state of the secondary equipment by using the state analysis model.
Preferably, in the method for analyzing the state of the secondary equipment of the transformer substation based on the big data, the historical state data at least includes self-checking information of the secondary equipment, time setting state information, MMS messages in a communication network, GOOSE messages and SV message monitoring obtained warning information, and the abnormal state types at least include device temperature out-of-limit, device voltage out-of-limit, light intensity out-of-limit and differential flow out-of-limit.
Preferably, in the method for analyzing the state of the secondary equipment of the substation based on the big data, the step of establishing a deep learning model between the state data and the abnormal state type according to the training set by using a plurality of groups of historical state data and the abnormal state types corresponding to the historical state data as the training set to obtain the state analysis model specifically includes:
taking a plurality of groups of historical state data and corresponding abnormal state types thereof as a training set;
determining an input layer and an output layer of the deep learning model, wherein the input layer is state data, and the output layer is an abnormal state type;
and establishing a deep learning model between the state data and the abnormal state type according to the characteristic value and the target value of the training set to obtain a state analysis model, wherein the characteristic value is historical state data, and the target value is the abnormal state type.
Preferably, in the method for analyzing the state of the secondary equipment of the substation based on the big data, the step of establishing a deep learning model between the state data and the abnormal state type according to the characteristic values and the target values of the training set includes:
inputting the characteristic values in the training set into a first layer of self-encoders, training the first layer of self-encoders, and then sequentially training each layer of self-encoders by taking the output of the hidden layer of the previous layer of self-encoders as the input of the next layer of self-encoders to obtain the output of the hidden layer of each layer of network;
the weights and biases of the hidden layers of each layer network are adjusted using a loss function.
Preferably, in the substation secondary device state analysis method based on big data, the output of the hidden layer of the self-encoder is:
Hj＝σ(W1 jXj+b1 j)，
wherein HjFor the output of the jth hidden layer, σ is the non-linear mapping, W1 jFor the jth weight from the input layer to the hidden layer of the encoder, b1 jFor the jth offset from the input layer to the hidden layer of the encoder, XjIs the input of the jth self-encoder.
Preferably, in the substation secondary device state analysis method based on big data, the loss function is as follows:
wherein, yiIs the actual target value, y _ preiIs a predicted target value.
Preferably, in the substation secondary device state analysis method based on big data, the step of acquiring real-time state data of a secondary device and analyzing the real-time state of the secondary device by using the state analysis model specifically includes:
the method comprises the steps of obtaining real-time state data of secondary equipment, inputting the real-time state data of the secondary equipment into a state analysis model as an input layer, obtaining an output layer corresponding to the real-time state data by using the state analysis model, and taking the output layer as the real-time state of the secondary equipment.
Preferably, in the method for analyzing the state of the secondary device of the substation based on big data, after the step of obtaining the real-time state data of the secondary device and analyzing the real-time state of the secondary device by using the state analysis model, the method further includes:
and carrying out early warning prompt according to the real-time state of the secondary equipment.
In a second aspect, the present invention further provides a substation secondary device status analysis device based on big data, including: a processor and a memory;
the memory has stored thereon a computer readable program executable by the processor;
the processor, when executing the computer readable program, implements the steps in the big data based substation secondary equipment status analysis method as described above.
In a third aspect, the present invention also provides a computer readable storage medium storing one or more programs, which are executable by one or more processors to implement the steps in the big data based substation secondary device status analysis method as described above.
[ PROBLEMS ] the present invention
According to the transformer substation secondary equipment state analysis method, the transformer substation secondary equipment state analysis equipment and the storage medium based on the big data, historical state data and corresponding abnormal state types of the transformer substation secondary equipment are obtained from the big data, then an abnormal state analysis model is established by utilizing the historical state data and the corresponding abnormal state types, and further the real-time state of the secondary equipment can be directly obtained according to the real-time monitored state data.
Drawings
Fig. 1 is a flowchart of a method for analyzing a state of a secondary device of a substation based on big data according to a preferred embodiment of the present invention;
fig. 2 is a schematic operating environment diagram of a substation secondary device status analysis program based on big data according to a preferred embodiment of the present invention.
Detailed Description
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate preferred embodiments of the invention and together with the description, serve to explain the principles of the invention and not to limit the scope of the invention.
Referring to fig. 1, a method for analyzing a state of a secondary device of a substation based on big data according to an embodiment of the present invention includes the following steps:
s100, acquiring historical state data of a plurality of groups of secondary equipment and abnormal state types corresponding to the historical state data from an equipment abnormal state recording library.
In this embodiment, the historical state data is automatically stored in the device abnormal state recording library in the operation process of the secondary device, and when each secondary device is abnormal, the abnormal state and the state data in the abnormal state are automatically recorded, so that the historical state data and the corresponding historical state type can be learned and trained, the deep learning model can be established, and the data in the historical state data can be updated in real time, so that the accuracy of model establishment is improved. The historical state data at least comprises self-checking information of the secondary equipment, time setting state information, MMS messages in a communication network, GOOSE and SV message monitoring obtained alarm information, and the abnormal state types at least comprise device temperature out-of-limit, device voltage out-of-limit, light intensity out-of-limit and differential flow out-of-limit. Specifically, when a fault occurs, the protection, measurement and control functions of the secondary equipment of the intelligent substation are realized, which are closely related to the health condition of the secondary equipment and cannot avoid the normal transmission of communication messages between the equipment. When a fault occurs, a large amount of alarm information can be generated no matter the intelligent equipment to be monitored or the communication message. Therefore, by collecting various information, the establishment of the state analysis model can be realized.
S200, taking a plurality of groups of historical state data and abnormal state types corresponding to the historical state data as training sets, and establishing a deep learning model between the state data and the abnormal state types according to the training sets to obtain a state analysis model.
In the embodiment, the training set is used for realizing the establishment of a deep learning model, and the state analysis model can be directly utilized to analyze the state of the secondary equipment by establishing the state analysis model, so that a method for analyzing by adopting artificial experience in the prior art is replaced, the fault judgment of the secondary equipment can be accurately carried out, and the method is simple and convenient.
In specific implementation, the step S200 specifically includes:
taking a plurality of groups of historical state data and corresponding abnormal state types thereof as a training set;
determining an input layer and an output layer of the deep learning model, wherein the input layer is state data, and the output layer is an abnormal state type;
and establishing a deep learning model between the state data and the abnormal state type according to the characteristic value and the target value of the training set to obtain a state analysis model, wherein the characteristic value is historical state data, and the target value is the abnormal state type.
In this embodiment, input and output of a model are first determined, where the deep learning model includes an input layer, a hidden layer, and an output layer, data of the input layer is calculated by the hidden layer and then output to the output layer, so that a target value can be obtained by inputting a feature value of a training set, the hidden layer includes a plurality of self-encoders, in the embodiment of the present invention, the feature value is history state data (including at least self-check information of a secondary device, time setting state information, MMS messages in a communication network, GOOSE, and warning information obtained by SV message monitoring), and the target value is an abnormal state type (including at least device temperature out-of-limit, device voltage out-of-limit, light intensity out-of-limit, and differential flow out-of-limit).
The deep learning framework adopts TensorFlow, the establishment of the deep learning model is divided into two steps, specifically, the step of establishing the deep learning model between the state data and the abnormal state type according to the characteristic value and the target value of the training set comprises the following steps:
inputting the characteristic values in the training set into a first layer of self-encoders, training the first layer of self-encoders, and then sequentially training each layer of self-encoders by taking the output of the hidden layer of the previous layer of self-encoders as the input of the next layer of self-encoders to obtain the output of the hidden layer of each layer of network;
the weights and biases of the hidden layers of each layer network are adjusted using a loss function.
Specifically, the first step is to train each self-encoder in sequence to realize unsupervised layer-by-layer training, where the output of the hidden layer of the self-encoder is:
Hj＝σ(W1 jXj+b1 j)，
wherein HjFor the output of the jth hidden layer, σ is the non-linear mapping, W1 jFor the jth weight from the input layer to the hidden layer of the encoder, b1 jFor the jth offset from the input layer to the hidden layer of the encoder, XjIs the input of the jth self-encoder.
Preserving the weight W between the input layer and the hidden layer after the first self-encoder training is completed1 jAnd bias b1 jAnd the hidden layer is inputOut of HjAs input to the (i + 1) th self-encoder. By means of the layer-by-layer training mode, the coding processes of the m self-encoders are obtained.
The second step is to implement supervised fine tuning, where the supervised fine tuning refers to adjusting weights and offsets of each layer of network, and in the embodiment of the present invention, the weights and offsets are adjusted by using a loss function, specifically, the loss function is:
wherein, yiIs the actual target value, y _ preiIs a predicted target value.
Specifically, firstly, the weight W and the bias b of each layer of network are initialized, in the initial process of the stacked self-encoder, the result of the training process is fully utilized, the network weight and the bias obtained in the pre-training process are used as the initial values of the stacked self-encoding neural network, the loss function is solved by using a gradient descent method, namely, the weight and the bias of each layer are adjusted, and thus a state analysis model is obtained. The process of solving the loss function by using the gradient descent method is the prior art, and is not described herein again.
S300, acquiring real-time state data of the secondary equipment, and analyzing the real-time state of the secondary equipment by using the state analysis model.
Specifically, after the state analysis model is obtained, the result may be output only by inputting data, and specifically, the step S300 specifically includes:
the method comprises the steps of obtaining real-time state data of secondary equipment, inputting the real-time state data of the secondary equipment into a state analysis model as an input layer, obtaining an output layer corresponding to the real-time state data by using the state analysis model, and taking the output layer as the real-time state of the secondary equipment.
Preferably, after step S300, the method further includes:
and carrying out early warning prompt according to the real-time state of the secondary equipment.
In other words, after the real-time state of the secondary equipment is analyzed, if the secondary equipment has a fault, a fault alarm is carried out to prompt a worker, so that the worker can conveniently and quickly maintain.
As shown in fig. 2, based on the method for analyzing the state of the secondary equipment of the transformer substation based on the big data, the invention further provides a device for analyzing the state of the secondary equipment of the transformer substation based on the big data, wherein the device for analyzing the state of the secondary equipment of the transformer substation based on the big data can be a mobile terminal, a desktop computer, a notebook computer, a palm computer, a server and other computing devices. The big data-based substation secondary equipment state analysis device comprises a processor 10, a memory 20 and a display 30. Fig. 2 shows only some of the components of the big data based substation secondary equipment status analysis device, but it should be understood that not all of the shown components are required to be implemented, and that more or fewer components may be implemented instead.
The storage 20 may be an internal storage unit of the big data-based substation secondary device status analysis device in some embodiments, for example, a hard disk or a memory of the big data-based substation secondary device status analysis device. In other embodiments, the memory 20 may also be an external storage device of the substation secondary device status analyzing device based on big data, for example, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like, which is equipped on the substation secondary device status analyzing device based on big data. Further, the memory 20 may also include both an internal storage unit and an external storage device of the substation secondary device state analysis device based on big data. The memory 20 is used for storing application software installed in the substation secondary equipment state analysis device based on big data and various types of data, such as program codes of the substation secondary equipment state analysis device based on big data. The memory 20 may also be used to temporarily store data that has been output or is to be output. In an embodiment, the memory 20 stores a big data-based substation secondary device status analysis program 40, and the big data-based substation secondary device status analysis program 40 is executable by the processor 10, so as to implement the big data-based substation secondary device status analysis method according to the embodiments of the present application.
The processor 10 may be, in some embodiments, a Central Processing Unit (CPU), a microprocessor or other data Processing chip, and is configured to run program codes stored in the memory 20 or process data, for example, execute the substation secondary device status analysis method based on big data.
The display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch panel, or the like in some embodiments. The display 30 is used for displaying information of the substation secondary equipment state analysis equipment based on big data and displaying a visual user interface. The components 10-30 of the substation secondary equipment status analysis device based on big data communicate with each other via a system bus.
In an embodiment, when the processor 10 executes the big data-based substation secondary device status analysis program 40 in the memory 20, the steps in the big data-based substation secondary device status analysis method according to the embodiment are implemented, and since the above description has been made in detail on the big data-based substation secondary device status analysis method, details are not described here.
In summary, according to the substation secondary device state analysis method, the equipment and the storage medium based on the big data provided by the invention, the historical state data and the corresponding abnormal state type of the substation secondary device are obtained from the big data, and then the abnormal state analysis model is established by using the historical state data and the corresponding abnormal state type, so that the real-time state of the secondary device can be directly obtained according to the real-time monitored state data, a method of analyzing by adopting manual experience in the prior art is replaced, the fault judgment of the secondary device can be accurately carried out, and the method is simple and convenient.
Of course, it will be understood by those skilled in the art that all or part of the processes of the methods of the above embodiments may be implemented by a computer program instructing relevant hardware (such as a processor, a controller, etc.), and the program may be stored in a computer readable storage medium, and when executed, the program may include the processes of the above method embodiments. The storage medium may be a memory, a magnetic disk, an optical disk, etc.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Claims (10)
1. A transformer substation secondary equipment state analysis method based on big data is characterized by comprising the following steps:
acquiring historical state data of a plurality of groups of secondary equipment and abnormal state types corresponding to the historical state data from an equipment abnormal state record library;
taking a plurality of groups of historical state data and abnormal state types corresponding to the historical state data as a training set, and establishing a deep learning model between the state data and the abnormal state types according to the training set to obtain a state analysis model;
and acquiring real-time state data of the secondary equipment, and analyzing the real-time state of the secondary equipment by using the state analysis model.
2. The substation secondary equipment state analysis method based on big data according to claim 1, wherein the historical state data at least includes self-checking information, time setting state information, MMS messages, GOOSE and SV message monitoring alarm information of the secondary equipment in a communication network, and the abnormal state types at least include device temperature out-of-limit, device voltage out-of-limit, light intensity out-of-limit and differential flow out-of-limit.
3. The substation secondary equipment state analysis method based on big data according to claim 1, wherein the step of establishing a deep learning model between the state data and the abnormal state type according to a training set by using a plurality of groups of historical state data and the abnormal state types corresponding to the historical state data as the training set to obtain the state analysis model specifically comprises:
taking a plurality of groups of historical state data and corresponding abnormal state types thereof as a training set;
determining an input layer and an output layer of the deep learning model, wherein the input layer is state data, and the output layer is an abnormal state type;
and establishing a deep learning model between the state data and the abnormal state type according to the characteristic value and the target value of the training set to obtain a state analysis model, wherein the characteristic value is historical state data, and the target value is the abnormal state type.
4. The big-data-based substation secondary equipment state analysis method according to claim 1, wherein the step of building a deep learning model between state data and the abnormal state type according to the eigenvalues and target values of the training set comprises:
inputting the characteristic values in the training set into a first layer of self-encoders, training the first layer of self-encoders, and then sequentially training each layer of self-encoders by taking the output of the hidden layer of the previous layer of self-encoders as the input of the next layer of self-encoders to obtain the output of the hidden layer of each layer of network;
the weights and biases of the hidden layers of each layer network are adjusted using a loss function.
5. The big-data-based substation secondary equipment state analysis method according to claim 4, wherein the output of the hidden layer of the self-encoder is:
Hj＝σ(W1 jXj+b1 j)，
wherein HjFor the output of the jth hidden layer, σ is a non-linear mapping,W1 jfor the jth weight from the input layer to the hidden layer of the encoder, b1 jFor the jth offset from the input layer to the hidden layer of the encoder, XjIs the input of the jth self-encoder.
6. The big-data-based substation secondary equipment state analysis method according to claim 5, wherein the loss function is:
wherein, yiIs the actual target value, y _ preiIs a predicted target value.
7. The substation secondary device state analysis method based on big data according to claim 1, wherein the step of acquiring real-time state data of the secondary device and analyzing the real-time state of the secondary device by using the state analysis model specifically comprises:
the method comprises the steps of obtaining real-time state data of secondary equipment, inputting the real-time state data of the secondary equipment into a state analysis model as an input layer, obtaining an output layer corresponding to the real-time state data by using the state analysis model, and taking the output layer as the real-time state of the secondary equipment.
8. The big-data-based substation secondary equipment state analysis method according to claim 1, wherein the step of obtaining real-time state data of secondary equipment and analyzing the real-time state of the secondary equipment by using the state analysis model further comprises:
and carrying out early warning prompt according to the real-time state of the secondary equipment.
9. The utility model provides a transformer substation secondary equipment state analytical equipment based on big data which characterized in that includes: a processor and a memory;
the memory has stored thereon a computer readable program executable by the processor;
the processor, when executing the computer readable program, implements the steps in the big data based substation secondary equipment status analysis method of any of claims 1-8.
10. A computer readable storage medium, characterized in that the computer readable storage medium stores one or more programs which are executable by one or more processors to implement the steps in the big data based substation secondary device status analysis method according to any one of claims 1-8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011341411.9A CN112510699A (en) | 2020-11-25 | 2020-11-25 | Transformer substation secondary equipment state analysis method and device based on big data |
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