CN113220751A - Metering system and evaluation method for multi-source data state quantity - Google Patents
Metering system and evaluation method for multi-source data state quantity Download PDFInfo
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Abstract
The application discloses measurement system and evaluation method of multi-source data state quantity, which comprises the following steps: the system comprises a comprehensive multi-source data device, a metering master station server and a plurality of energy station detection nodes, wherein each energy station detection node is used for acquiring running state quantity information of various devices in a station where the energy station detection node is located, and the acquired information is transmitted to the comprehensive multi-source data device; the comprehensive multi-source data device is used for receiving and preprocessing the running state quantity information of various devices acquired by the energy station detection nodes, and transmitting the preprocessed information to the metering master station server; and the metering master station server is used for receiving the processing information of the comprehensive multi-source data device and carrying out equipment state quantity evaluation and equipment running state prediction. The invention solves the problems of various electric power data, wide sources, non-uniform protocol diversification, disordered data, low quality and the like.
Description
Technical Field
The invention belongs to the technical field of electric power data processing, and relates to a measuring system and an evaluation method for multi-source data state quantity.
Background
With the continuous increase of energy consumption, energy has become a strategic resource in countries and regions. As one of important applications of the ubiquitous power Internet of things for landing, the multi-station integration integrates multiple resources such as a transformer substation, a photovoltaic station, a charging and replacing station and an energy storage station, optimizes urban resource configuration, improves data perception and analysis operation efficiency, and realizes local load consumption. Along with the construction and development of a distributed power grid, the requirements of users on the quality and stability of electric energy are continuously improved, in order to solve the problems of balanced power supply and demand, reliable power grid, efficient energy utilization and the like, information means such as cloud computing, big data, artificial intelligence and the like are adopted, a direct interaction channel of a power generation end and a user end is constructed while a multi-station fusion value-added service is expanded, and multi-station fusion integrated operation is realized. The multi-source heterogeneous data generated by multi-station integration operation has the problems of various data types, wide sources, non-uniform protocol diversification, disordered data, low quality and the like.
Since the multi-source heterogeneous original data contains a large amount of error and redundant data, and the quality of the data directly affects the reliability of the analysis result of the upper-layer application and the real realization of the application target, the data quality of the data needs to be evaluated so as to provide richer data information for the upper-layer application. Based on the modeling, heterogeneous multi-source multi-modal data quality is modeled, and aiming at the problems that the multi-source multi-modal data has various data types, wide sources, non-uniform protocol diversity, disordered data, low quality and the like, a new multi-source data state quantity measuring system and an evaluation method are needed to be designed to realize the collection and processing of the multi-modal data.
Disclosure of Invention
In order to overcome the defects in the prior art, the application provides the measuring system and the evaluation method of the multi-source data state quantity, and the multi-source data state quantity in the measuring system is monitored, collected, stored and evaluated.
In order to achieve the above purpose, the invention adopts the following technical scheme:
the metering system of the multi-source data state quantity comprises a comprehensive multi-source data device, a metering main station server and a plurality of energy station detection nodes;
each energy station detection node is used for acquiring running state quantity information of various devices in the station, and the acquired information is transmitted to the comprehensive multi-source data device;
the comprehensive multi-source data device is used for receiving and preprocessing the running state quantity information of various devices acquired by the energy station detection nodes, and transmitting the preprocessed information to the metering master station server;
and the metering master station server is used for receiving the processing information of the comprehensive multi-source data device and carrying out equipment state quantity screening, performance index determination, state evaluation model construction and equipment running state prediction.
The invention further comprises the following preferred embodiments:
preferably, the energy station detection nodes comprise a transformer substation detection node, an energy storage station detection node, a photovoltaic station detection node and a charging and replacing station detection node, and the operation state quantity information of various devices in the transformer substation, the energy storage station, the photovoltaic station and the charging and replacing station is acquired respectively.
Preferably, each energy station detection node comprises an acquisition module, a microprocessor and a wireless transmission module;
the acquisition module is used for detecting the electric information of the electric energy metering device and the surrounding environment information thereof;
the microprocessor is used for processing the electrical information and the environmental information detected by the acquisition module, performing analog-to-digital conversion on the electrical information, and processing, analyzing and calculating an environmental information signal into electrical information;
and the wireless transmitting module is used for transmitting the electrical information processed by the microprocessor.
Preferably, the acquisition module comprises a temperature and humidity acquisition module, an electromagnetic field intensity acquisition module, a wiring position pollution degree acquisition module, an electric energy meter acquisition module and a mutual inductor parameter acquisition module;
the temperature and humidity acquisition module is used for acquiring the temperature and the humidity near the electric energy meter and the power transformer;
the electromagnetic field intensity acquisition module is used for acquiring the electromagnetic field intensity near the electric energy meter and the power transformer;
the connection part pollution degree acquisition module is used for acquiring the pollution degree of the connection part near the electric energy meter and the power transformer;
the electric energy meter acquisition module is used for acquiring voltage, current and pulse information of the electric energy meter;
and the transformer parameter acquisition module is used for acquiring voltage and current information of the voltage transformer and the current transformer in the secondary circuit.
Preferably, all energy station detection nodes are configured with the same comprehensive multi-source data device, and the comprehensive multi-source data device and the energy station detection nodes connected with the comprehensive multi-source data device form a star topology structure; alternatively, the first and second electrodes may be,
the same comprehensive multi-source data device is configured on the same type of energy station detection node, the comprehensive multi-source data device and the energy station detection node connected with the comprehensive multi-source data device form a star topology structure, and all the comprehensive multi-source data devices and the metering master station server form a star topology structure.
Preferably, data transmission is carried out between the energy station detection node and the comprehensive multi-source data device in a PLC (programmable logic controller), ZigBee or LoRa (Low elevation) mode;
and data transmission is carried out between the comprehensive multi-source data device and the metering master station server in a 4G, 5G or wired Ethernet mode.
Preferably, the network between the energy station detection node and the integrated multi-source data device is rapidly restored by using network memory during operation.
Preferably, the integrated multi-source data device is internally provided with a satellite signal receiving device which is used for communicating with a GPS or a Beidou satellite and acquiring high-precision time service information.
Preferably, the metering master station server comprises a state quantity screening module, a state quantity performance index determining module, a state evaluation model establishing module and an operation state predicting module;
the state quantity screening module is used for selecting the state quantity which has larger influence on the evaluation result as a state quantity evaluation model input variable set according to the pre-evaluated object;
the state quantity performance index determining module is used for determining the indexes of the normal operation state, the fault state and the early warning state of the gateway metering device;
the state evaluation model establishing module is used for establishing an extreme learning machine model to realize the evaluation of the running state of the metering system;
and the running state prediction module is used for predicting the running state of the equipment according to the evaluation result of the state quantity.
The invention also discloses an evaluation method of the multi-source data state quantity, which comprises the following steps:
step 1: and (3) site data acquisition: the energy station detection node acquires running state quantity information of multiple types of equipment of the corresponding station;
step 2: receiving and preprocessing information of the running state quantities of various devices acquired by the energy station detection nodes, and transmitting the information to the metering master station server after preprocessing;
and step 3: and receiving processing information of the comprehensive multi-source data device, and performing equipment state quantity screening, performance index determination, state evaluation model construction and equipment running state prediction.
Preferably, the pretreatment of step 2 comprises: data stamping, dimension reduction, null value processing and normalization processing.
Preferably, step 3 specifically comprises the following steps:
step 301: screening the state quantity: selecting a state quantity which has a large influence on an evaluation result as a state quantity evaluation model input variable set according to a pre-evaluation object;
step 302: determining the state quantity performance index: the method comprises the following steps of determining indexes of a gateway metering device in a normal operation state, a fault state and an early warning state;
step 303: establishing a state evaluation model: establishing an extreme learning machine model, and evaluating the running state of a metering system;
step 304: and (3) predicting the running state: and predicting the operation state of the equipment according to the evaluation result of the state quantity.
Preferably, in step 301, the correlation between each state quantity and the measurement performance index is measured by using mutual information, and the state quantity evaluation model input variable set having a large influence on the evaluation result is selected, which specifically includes:
step 301.1: data fusion: the metering system of the multisource data state quantity fuses the energy station detection node data acquired in real time to form new characteristic attribute data:
step 301.2: variable coding:
discretizing a threshold index continuous variable in the running state index of the gateway metering device in the data;
and carrying out one-hot coding on discrete variables in the data.
Step 301.3: and (3) characteristic correlation analysis:
the information entropies H (X), H (Y) and the joint entropies H (XY) of the arbitrary state quantities (X, Y) are calculated as:
calculating X, Y the size of the shared information quantity, i.e. mutual information I (X; Y):
I(X；Y)＝H(X)+H(Y)-H(XY)
normalization of entropy correlation coefficient of mutual information:
selection of IXYThe small state quantity is input as a state quantity evaluation model.
Preferably, step 302 is specifically:
segmenting the data set screened by the state quantity screening module into a training set and a testing set;
adopting a K-fold cross validation method to randomly divide the data into 10 groups, randomly selecting 9 groups each time as a training set, and taking the remaining 1 group as a test set; and after one round is finished, randomly selecting 9 groups as a training set again, using the remaining 1 group as a test set, training the test set to obtain a hypothesis function, putting the hypothesis function on the test set to obtain a classification rate, and calculating the average value of the classification rate obtained by 10 times as the performance index of the classifier under 10-fold cross validation.
Preferably, step 303 is specifically:
selecting an extreme learning machine: the output equation of the ith hidden layer node is as follows:
hi(x)＝G(ai,bi,x)
wherein, aiAnd biIs a parameter of the ith hidden layer node, hi(x) Is an activation function;
the entire hidden layer output is mapped as:
h(x)＝[G(hi(x),...,hL(x))]
given N training samples, the hidden layer output matrix H of the ELM is:
then, the output of the ELM structure with L hidden nodes is:
wherein, betaiIf it is the output weight of the ith hidden node, the output of the whole hidden layer is:
Hβ
the target matrix T is:
after introducing the regularization coefficient C, the objective function is:
obtaining:
parameters of each part of the ELM network are determined, and therefore a metering system running state evaluation model is established.
Preferably, step 304 is specifically:
according to the established running state evaluation model of the metering system, inputting various sensor data collected in real time and data of a user electric energy meter and a power transformer into the running state evaluation model to obtain a real-time running state grading result of the metering system, judging the running state of equipment according to the grading result, and realizing the state prediction of the gateway metering device;
the method comprises the following steps that a plurality of stations are accessed, and each station independently evaluates the running state of a metering system.
The beneficial effect that this application reached:
the method extracts the monitoring key data of each parameter from the collection resources of a plurality of stations such as a transformer substation, a photovoltaic station, a charging and replacing station, an energy storage station and the like, preprocesses the collected data, screens the state quantity, determines the performance index, calculates the running state index of each parameter, applies the evaluation model to state evaluation, and accordingly determines whether the parameter state of each station is normal or not. The invention solves the problems of various electric power data, wide sources, non-uniform protocol diversification, disordered data, low quality and the like, and also solves the problems of large error rate and the like caused by distinguishing and distinguishing according to subjective expectation of people in the traditional state evaluation technology of each station. The scheme of the invention can sensitively identify the abnormal condition and effectively improve the accuracy and comprehensiveness of state diagnosis.
Drawings
FIG. 1 is a block diagram of a first embodiment of a multi-source data state quantity metering system according to the present invention;
FIG. 2 is a block diagram of a second embodiment of the multi-source data state quantity metering system of the present invention;
FIG. 3 is a diagram of a node structure for detecting each energy station according to the present invention;
FIG. 4 is a flow chart of a multi-source data state quantity evaluation method according to the present invention.
Detailed Description
The present application is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present application is not limited thereby.
As shown in fig. 1 and 2, the system for measuring state quantity of multi-source data of the present invention includes a comprehensive multi-source data device, a measuring master station server and a plurality of energy station detection nodes;
each energy station detection node is used for acquiring running state quantity information of various devices in the station, and the acquired information is transmitted to the comprehensive multi-source data device;
the comprehensive multi-source data device is used for receiving and preprocessing the running state quantity information of various devices acquired by the energy station detection nodes, and transmitting the preprocessed information to the metering master station server;
as shown in fig. 1, all energy station detection nodes are configured with the same integrated multi-source data device, and the integrated multi-source data device and the energy station detection nodes connected with the integrated multi-source data device form a star topology; alternatively, the first and second electrodes may be,
as shown in fig. 2, the same type of energy station detection nodes are configured with the same integrated multi-source data device, the integrated multi-source data device and the energy station detection nodes connected with the integrated multi-source data device form a star topology, and all the integrated multi-source data devices and the metering master station server form a star topology.
During specific implementation, the energy station detection nodes comprise a transformer substation detection node, an energy storage station detection node, a photovoltaic station detection node and a charging and replacing station detection node, and the running state quantity information of various devices in the transformer substation, the energy storage station, the photovoltaic station and the charging and replacing station is acquired respectively.
As shown in fig. 3, each energy station detection node includes an acquisition module, a microprocessor, and a wireless transmission module;
the acquisition module is used for detecting the electric information of the electric energy metering device and the surrounding environment information thereof;
the acquisition module comprises a temperature and humidity acquisition module, an electromagnetic field intensity acquisition module, a wiring position pollution degree acquisition module, an electric energy meter acquisition module and a mutual inductor parameter acquisition module;
the temperature and humidity acquisition module is used for acquiring the temperature and the humidity near the electric energy meter and the power transformer;
the electromagnetic field intensity acquisition module is used for acquiring the electromagnetic field intensity near the electric energy meter and the power transformer;
the connection part pollution degree acquisition module is used for acquiring the pollution degree of the connection part near the electric energy meter and the power transformer;
the electric energy meter acquisition module is used for acquiring voltage, current and pulse information of the electric energy meter;
and the transformer parameter acquisition module is used for acquiring voltage and current information of the voltage transformer and the current transformer in the secondary circuit.
The microprocessor is used for processing the electrical information and the environmental information detected by the acquisition module, performing analog-to-digital conversion on the electrical information, and processing, analyzing and calculating an environmental information signal into electrical information;
and the wireless transmitting module is used for transmitting the electrical information processed by the microprocessor.
Data transmission is carried out between the energy station detection node and the comprehensive multi-source data device in a PLC (power line on-line communication), ZigBee or LoRa (Low elevation line) mode;
and data transmission is carried out between the comprehensive multi-source data device and the metering master station server in a 4G, 5G or wired Ethernet mode.
The 4G mode has the advantages of convenience in construction and low cost, and the 5G mode ensures real-time performance, reliability, mass performance and confidentiality of transmission data.
When the network between the energy station detection node and the comprehensive multi-source data device works, the network memory rapid recovery network is adopted, when the detection node of a certain station is disconnected or restarted, the network can be directly worked without being added into the network again, wherein information, such as information, addresses and the like of the detection node of the corresponding station are recorded in Flash in a chip, the detection node of the corresponding station reads information stored in the Flash after being electrified every time, and if the information is effective, namely is not 0, the network directly works after being monitored and synchronized.
And a satellite signal receiving device is arranged in the comprehensive multi-source data device and is used for communicating with a GPS or a Beidou to obtain high-precision time service information.
And the metering master station server is used for receiving the processing information of the comprehensive multi-source data device and carrying out equipment state quantity screening, performance index determination, state evaluation model construction and equipment running state prediction.
The metering master station server comprises a state quantity screening module, a state quantity performance index determining module, a state evaluation model establishing module and an operation state predicting module;
the state quantity screening module is used for selecting the state quantity which has larger influence on the evaluation result as a state quantity evaluation model input variable set according to the pre-evaluated object;
the state quantity performance index determining module is used for determining the indexes of the normal operation state, the fault state and the early warning state of the gateway metering device;
the state evaluation model establishing module is used for establishing an extreme learning machine model to realize the evaluation of the running state of the metering system;
and the running state prediction module is used for predicting the running state of the equipment according to the evaluation result of the state quantity.
As shown in fig. 4, the method for evaluating the state quantity of multi-source data of the present invention includes the following steps:
step 1: and (3) site data acquisition: the energy station detection node acquires running state quantity information of multiple types of equipment of the corresponding station;
step 2: receiving and preprocessing information of the running state quantities of various devices acquired by the energy station detection nodes, and transmitting the information to the metering master station server after preprocessing;
the pretreatment comprises the following steps: data stamping, dimension reduction, null value processing and normalization processing.
By time stamping the data, different data can be associated and synchronized when in use. The parameter accuracy calculated according to the synchronous data packet is higher, and the condition that the information accuracy calculated according to the data packet is low due to the fact that the time of the data packet is asynchronous is avoided.
Meanwhile, for the multidimensional influence quantity, when the neural network model is established, a large amount of computing resources are consumed by the model during training due to the input of multidimensional data, and the training effect of the model is poor due to the existence of redundant data. Therefore, it is necessary to perform a dimension reduction process on the data.
And step 3: the method comprises the following steps of receiving processing information of a comprehensive multi-source data device, screening equipment state quantity, determining performance indexes, constructing a state evaluation model and predicting equipment running states, and specifically comprises the following steps:
step 301: screening the state quantity: selecting a state quantity with large influence on an evaluation result as a state quantity evaluation model input variable set according to a pre-evaluation object:
in specific implementation, mutual information is adopted to measure the correlation between each state quantity and the measurement performance index, and the correlation with larger influence on the evaluation result is selected as a state quantity evaluation model input variable set, specifically:
1) data fusion
And the multi-source data state quantity metering system fuses the energy station detection node data acquired in real time to form new characteristic attribute data.
And for the PT secondary voltage drop synthetic error, calculating a secondary voltage drop characteristic parameter ratio difference f (%) (A, B, C phase) and an angle difference delta (') (A, B, C phase) according to the voltage of the electric energy meter of the secondary circuit and the voltage of the voltage transformer in the multi-source data state quantity metering system operation data, converting the secondary voltage drop characteristic parameter ratio difference f (%) (A, B, C phase) and the angle difference delta (') (A, B, C phase) into the voltage transformer voltage drop synthetic error for measurement, and realizing characteristic parameter fusion processing.
For example:
(a) PT secondary voltage drop synthetic error
For the PT secondary voltage drop synthetic error, according to the operation data of the metering system of the multi-source data state quantity, namely the voltage of an electric energy meter of a secondary circuit and the voltage of a voltage transformer in the data collected by the detection node, calculating a secondary voltage drop characteristic parameter ratio difference f (%) (A, B, C phase) and an angle difference delta (') (A, B, C phase), converting the secondary voltage drop characteristic parameter ratio difference into the voltage transformer voltage drop synthetic error for measurement, and realizing characteristic parameter fusion processing.
For the voltage drop of the secondary circuit, the operation data refers to the voltage of the electric energy meter and the voltage of the voltage transformer in the secondary circuit.
The calculation formula of the synthetic error of the voltage drop of the secondary loop of the gateway electric energy metering device is as follows:
(b) three-phase voltage and current monitoring value variation
A, B, C phase voltage and current values in the metering system operation data of the multi-source data state quantity are converted into three-phase voltage measured values to be worsened, and three-phase current measured values to be worsened. The calculation formula is as follows:
2) variable encoding
And discretizing the threshold class index continuous variable in the gateway metering device operation state index, for example, classifying the error index of the electric energy meter according to the accuracy grade of the electric energy meter.
And carrying out one-hot coding on discrete variables (such as running states: normal, early warning and fault) in the data. And e, calculating the error of the electric energy meter by taking the voltage, current and pulse information of the electric energy meter, which corresponds to the electric energy meter acquired by the corresponding electric energy meter acquisition module.
3) Feature correlation analysis
The information entropy H (X), H (Y), and their joint entropy H (XY) of the random variables (X, Y) are defined as:
mutual information I (X; Y) represents the size of the amount of information shared by variable X, Y:
I(X；Y)＝H(X)+H(Y)-H(XY)
in order to conveniently compare the sizes of mutual information, the entropy correlation coefficient of the mutual information is normalized:
and selecting the state quantity with small mutual information as model input according to the size of the mutual information.
For example, the active error (error, variation) of the electric energy meter, the reactive error (error, variation) of the electric energy meter, the secondary voltage drop (A, B, C phase), the PT secondary load (A, B, C phase), the PT secondary voltage drop composite error, the consistency of the three-phase voltage monitoring values, the consistency of the three-phase current monitoring values and the line loss error index are finally selected as the characteristic attributes. The voltage, current and pulse information of the electric energy meter collected by the corresponding electric energy meter collecting module is exemplified; and the transformer parameter acquisition module acquires voltage and current information of the voltage transformer and the current transformer in the secondary circuit.
Step 302: determining the state quantity performance index: the method is used for determining indexes of a gateway metering device in a normal operation state, a fault state and an early warning state, and specifically comprises the following steps:
segmenting the data set screened by the state quantity screening module into a training set and a testing set;
adopting a K-fold cross validation method to randomly divide the data into 10 groups, randomly selecting 9 groups each time as a training set, and taking the remaining 1 group as a test set; and after one round is finished, randomly selecting 9 groups as a training set again, using the remaining 1 group as a test set, training the test set to obtain a hypothesis function, putting the hypothesis function on the test set to obtain a classification rate, and calculating the average value of the classification rate obtained by 10 times as the performance index of the classifier under 10-fold cross validation.
For example, the gateway metering device health indicators are as follows:
(1) active error (0.2S grade) of the electric energy meter: error: less than or equal to 0.16 percent, variation: less than or equal to 0.10 percent;
(2) reactive error of electric energy meter (2 grade): error: less than or equal to 1.6 percent, variation: less than or equal to 1.0 percent;
(3) secondary pressure drop and synthetic error: error: less than or equal to 0.16 percent, variation: less than or equal to 0.06 percent;
(4) PT secondary load: 2.5 VA-80% rated secondary load (Sn > 10 VA);
(5) CT secondary load: 3.75 to 80% rated secondary load (Sn > 10VA, In 5A);
(6) three-phase voltage monitoring value: and (3) deterioration: less than or equal to 2 percent;
(7) three-phase current monitoring value: and (3) deterioration: less than or equal to 10 percent.
The gateway metering device has the following early warning state indexes:
(1) active error (0.2S grade) of the electric energy meter: error: 0.16% -0.20%, variation: 0.10 to 0.20 percent;
(2) reactive error of electric energy meter (2 grade): error: 1.6% -2%, variation: 1% -2%;
(3) secondary pressure drop and synthetic error: error: 0.16% -0.20%, variation: 0.06 percent to 0.18 percent;
(4) PT secondary load: 80% -100% rated secondary load (Sn > 10 VA);
(5) CT secondary load: 80% -100% rated secondary load (Sn > 10VA, In ═ 5A);
(6) three-phase voltage monitoring value: and (3) deterioration: 2% -4%;
(7) three-phase current monitoring value: and (3) deterioration: 10 to 50 percent.
And the failure state of the gateway metering device, namely the operation data is out of the normal range and the early warning range.
Step 303: establishing a state evaluation model: establishing an extreme learning machine model, and evaluating the running state of a metering system, wherein the method specifically comprises the following steps:
selecting an extreme learning machine:
the output equation of the ith hidden layer node is as follows:
hi(x)＝G(ai,bi,x)
wherein a isiAnd biIs a parameter of the ith hidden layer node, hi(x) Is an activation function. The entire hidden layer output is mapped as:
h(x)＝[G(hi(x),...,hL(x))]
given N training samples, the hidden layer output matrix H of the ELM is:
then, the output of the ELM structure with L hidden nodes is:
wherein, betaiIf it is the output weight of the ith hidden node, the output of the whole hidden layer is:
Hβ
the target matrix T is:
after introducing the regularization coefficient C, the objective function is:
the following can be obtained:
parameters of various portions of the ELM network may be determined to build a metrology system operating state evaluation model.
Step 304: and (3) predicting the running state: predicting the running state of the equipment according to the evaluation result of the state quantity, specifically:
according to the established running state evaluation model of the metering system, inputting various sensor data collected in real time and data of a user electric energy meter and a power transformer into the running state evaluation model to obtain a real-time running state grading result of the metering system, judging the running state of equipment according to the grading result, and realizing the state prediction of the gateway metering device;
the method comprises the following steps that a plurality of stations are accessed, and each station independently evaluates the running state of a metering system.
In specific implementation, the operation state and operation and maintenance policy rules of the device are as shown in table 1:
TABLE 1 operating State and operation and maintenance policy rules for devices
The present applicant has described and illustrated embodiments of the present invention in detail with reference to the accompanying drawings, but it should be understood by those skilled in the art that the above embodiments are merely preferred embodiments of the present invention, and the detailed description is only for the purpose of helping the reader to better understand the spirit of the present invention, and not for limiting the scope of the present invention, and on the contrary, any improvement or modification made based on the spirit of the present invention should fall within the scope of the present invention.
Claims (16)
1. The measurement system of multisource data state quantity, including synthesizing multisource data device, measurement main website server and a plurality of energy station detection node, its characterized in that:
each energy station detection node is used for acquiring running state quantity information of various devices in the station, and the acquired information is transmitted to the comprehensive multi-source data device;
the comprehensive multi-source data device is used for receiving and preprocessing the running state quantity information of various devices acquired by the energy station detection nodes, and transmitting the preprocessed information to the metering master station server;
and the metering master station server is used for receiving the processing information of the comprehensive multi-source data device and carrying out equipment state quantity screening, performance index determination, state evaluation model construction and equipment running state prediction.
2. The multi-source data state quantity metering system of claim 1, wherein:
the energy station detection nodes comprise transformer substation detection nodes, energy storage station detection nodes, photovoltaic station detection nodes and charging and replacing station detection nodes, and are used for respectively collecting running state quantity information of various devices in the transformer substation, the energy storage station, the photovoltaic station and the charging and replacing station.
3. The multi-source data state quantity metering system of claim 1, wherein:
each energy station detection node comprises an acquisition module, a microprocessor and a wireless transmission module;
the acquisition module is used for detecting the electric information of the electric energy metering device and the surrounding environment information thereof;
the microprocessor is used for processing the electrical information and the environmental information detected by the acquisition module, performing analog-to-digital conversion on the electrical information, and processing, analyzing and calculating an environmental information signal into electrical information;
and the wireless transmitting module is used for transmitting the electrical information processed by the microprocessor.
4. The multi-source data state quantity metering system of claim 3, wherein:
the acquisition module comprises a temperature and humidity acquisition module, an electromagnetic field intensity acquisition module, a wiring position pollution degree acquisition module, an electric energy meter acquisition module and a mutual inductor parameter acquisition module;
the temperature and humidity acquisition module is used for acquiring the temperature and the humidity near the electric energy meter and the power transformer;
the electromagnetic field intensity acquisition module is used for acquiring the electromagnetic field intensity near the electric energy meter and the power transformer;
the connection part pollution degree acquisition module is used for acquiring the pollution degree of the connection part near the electric energy meter and the power transformer;
the electric energy meter acquisition module is used for acquiring voltage, current and pulse information of the electric energy meter;
and the transformer parameter acquisition module is used for acquiring voltage and current information of the voltage transformer and the current transformer in the secondary circuit.
5. The multi-source data state quantity metering system of claim 1, wherein:
all energy station detection nodes are configured with the same comprehensive multi-source data device, and the comprehensive multi-source data device and the energy station detection nodes connected with the comprehensive multi-source data device form a star topology structure; alternatively, the first and second electrodes may be,
the same comprehensive multi-source data device is configured on the same type of energy station detection node, the comprehensive multi-source data device and the energy station detection node connected with the comprehensive multi-source data device form a star topology structure, and all the comprehensive multi-source data devices and the metering master station server form a star topology structure.
6. The multi-source data state quantity metering system of claim 1, wherein:
data transmission is carried out between the energy station detection node and the comprehensive multi-source data device in a PLC (programmable logic controller), ZigBee or LoRa (Low elevation) mode;
and data transmission is carried out between the comprehensive multi-source data device and the metering master station server in a 4G, 5G or wired Ethernet mode.
7. The multi-source data state quantity metering system of claim 1, wherein:
and when the network between the energy station detection node and the comprehensive multi-source data device works, the network is quickly restored by adopting network memory.
8. The multi-source data state quantity metering system of claim 1, wherein:
and a satellite signal receiving device is arranged in the comprehensive multi-source data device and is used for communicating with a GPS or a Beidou to obtain high-precision time service information.
9. The multi-source data state quantity metering system of claim 1, wherein:
the metering master station server comprises a state quantity screening module, a state quantity performance index determining module, a state evaluation model establishing module and an operation state predicting module;
the state quantity screening module is used for selecting the state quantity which has larger influence on the evaluation result as a state quantity evaluation model input variable set according to the pre-evaluated object;
the state quantity performance index determining module is used for determining the indexes of the normal operation state, the fault state and the early warning state of the gateway metering device;
the state evaluation model establishing module is used for establishing an extreme learning machine model to realize the evaluation of the running state of the metering system;
and the running state prediction module is used for predicting the running state of the equipment according to the evaluation result of the state quantity.
10. The method for evaluating a multisource data state quantity of a multisource data state quantity metering system of any one of claims 1 to 9, wherein:
the method comprises the following steps:
step 1: and (3) site data acquisition: the energy station detection node acquires running state quantity information of multiple types of equipment of the corresponding station;
step 2: receiving and preprocessing information of the running state quantities of various devices acquired by the energy station detection nodes, and transmitting the information to the metering master station server after preprocessing;
and step 3: and receiving processing information of the comprehensive multi-source data device, and performing equipment state quantity screening, performance index determination, state evaluation model construction and equipment running state prediction.
11. The multi-source data state quantity evaluation method according to claim 10, characterized in that:
step 2, the pretreatment comprises the following steps: data stamping, dimension reduction, null value processing and normalization processing.
12. The multi-source data state quantity evaluation method according to claim 10, characterized in that:
the step 3 specifically comprises the following steps:
step 301: screening the state quantity: selecting a state quantity which has a large influence on an evaluation result as a state quantity evaluation model input variable set according to a pre-evaluation object;
step 302: determining the state quantity performance index: the method comprises the following steps of determining indexes of a gateway metering device in a normal operation state, a fault state and an early warning state;
step 303: establishing a state evaluation model: establishing an extreme learning machine model, and evaluating the running state of a metering system;
step 304: and (3) predicting the running state: and predicting the operation state of the equipment according to the evaluation result of the state quantity.
13. The multi-source data state quantity evaluation method according to claim 12, characterized in that:
in step 301, measuring the correlation between each state quantity and the measurement performance index by using mutual information, and selecting a state quantity evaluation model input variable set having a large influence on an evaluation result, specifically including:
step 301.1: data fusion: the metering system of the multisource data state quantity fuses the energy station detection node data acquired in real time to form new characteristic attribute data:
step 301.2: variable coding:
discretizing a threshold index continuous variable in the running state index of the gateway metering device in the data;
performing one-hot coding on discrete variables in data;
step 301.3: and (3) characteristic correlation analysis:
the information entropies H (X), H (Y) and the joint entropies H (XY) of the arbitrary state quantities (X, Y) are calculated as:
calculating X, Y the size of the shared information quantity, i.e. mutual information I (X; Y):
I(X；Y)＝H(X)+H(Y)-H(XY)
normalization of entropy correlation coefficient of mutual information:
selection of IXYThe small state quantity is input as a state quantity evaluation model.
14. The multi-source data state quantity evaluation method according to claim 12, characterized in that:
step 302 specifically comprises:
segmenting the data set screened by the state quantity screening module into a training set and a testing set;
adopting a K-fold cross validation method to randomly divide the data into 10 groups, randomly selecting 9 groups each time as a training set, and taking the remaining 1 group as a test set; and after one round is finished, randomly selecting 9 groups as a training set again, using the remaining 1 group as a test set, training the test set to obtain a hypothesis function, putting the hypothesis function on the test set to obtain a classification rate, and calculating the average value of the classification rate obtained by 10 times as the performance index of the classifier under 10-fold cross validation.
15. The multi-source data state quantity evaluation method according to claim 12, characterized in that:
step 303 specifically includes:
selecting an extreme learning machine: the output equation of the ith hidden layer node is as follows:
hi(x)＝G(ai,bi,x)
wherein, aiAnd biIs a parameter of the ith hidden layer node, hi(x) Is an activation function;
the entire hidden layer output is mapped as:
h(x)＝[G(hi(x),...,hL(x))]
given N training samples, the hidden layer output matrix H of the ELM is:
then, the output of the ELM structure with L hidden nodes is:
wherein, betaiIf it is the output weight of the ith hidden node, the output of the whole hidden layer is:
Hβ
the target matrix T is:
after introducing the regularization coefficient C, the objective function is:
obtaining:
parameters of each part of the ELM network are determined, and therefore a metering system running state evaluation model is established.
16. The multi-source data state quantity evaluation method according to claim 12, characterized in that:
step 304 specifically includes:
according to the established running state evaluation model of the metering system, inputting various sensor data collected in real time and data of a user electric energy meter and a power transformer into the running state evaluation model to obtain a real-time running state grading result of the metering system, judging the running state of equipment according to the grading result, and realizing the state prediction of the gateway metering device;
the method comprises the following steps that a plurality of stations are accessed, and each station independently evaluates the running state of a metering system.
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