CN116840767A - Electric energy metering device overall error assessment method and device, storage medium and terminal - Google Patents
Electric energy metering device overall error assessment method and device, storage medium and terminal Download PDFInfo
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Abstract
The application discloses an overall error evaluation method and device of an electric energy metering device, a storage medium and a terminal, which can carry out real-time monitoring and evaluation on errors of an electric energy meter, a mutual inductor and a secondary circuit in operation under the condition of no power failure. Firstly, predicting a measurement error of an electric energy metering device by adopting an optimized RBF neural network through analysis of historical data; then, according to the error values of the electric energy meter, the mutual inductor and the secondary circuit obtained by on-line monitoring, integrating the error values into the integral error of the electric energy metering device in operation; the prediction error based on the RBF neural network is compared with the overall error measured value, so that the abnormal error value generated in the metering process is corrected, the error generated by the electric energy metering device is reduced, and the measurement accuracy of the electric energy metering device is improved.
Description
Technical Field
The application relates to the technical field of operation and maintenance of power grids, in particular to an overall error assessment method and device of an electric energy metering device, a storage medium and a terminal.
Background
With the rapid development of social economy and smart grids in China, the overall development level of each industry is continuously improved, and the demand of society for electric power resources is increased. The electric energy metering device is used as an important measuring tool in the electric power resource transaction process, the running error of the electric energy metering device directly influences the fairness and the fairness of electric quantity transaction settlement and the safety and stability of power grid running, so the electric energy metering device is particularly important for the running state evaluation and overall error management work of the electric energy metering device. In addition, in actual operation, the electric energy metering device is easily influenced by environmental temperature, secondary load, electromagnetic field and the like, which directly leads to the reduction of the measurement accuracy of the metering device, thereby influencing the stable operation of the electric power system and the fairness of metering.
Along with the increase of the using time of the power grid device, the negative line loss phenomenon of the construction development of the traditional power grid is more and more, the accuracy and the reliability of the overall error of the electric energy metering device are extremely questioned, and the fairness and fairness of the electric power transaction are further guaranteed. Therefore, in order to ensure fairness and fairness of electric power transaction and accurately measure electric energy, accuracy and reliability of overall errors of the electric energy measuring device are improved, and an overall error assessment method of the traditional electric energy measuring device is required to be improved.
Because the mapping model of each error source of the electric energy metering device for the comprehensive error in operation is multi-element nonlinearity, the accurate modeling is difficult to use a mathematical method, and the radial basis function (Radial Basis Function, RBF for short) neural network algorithm has strong nonlinearity modeling capability, so that the influence degree of each input error source on the output overall error can be effectively determined. In addition, since error data of the electric energy metering device has randomness, the rule relation existing in the electric energy metering device is difficult to find, the RBF neural network has high self-learning and self-adapting capabilities, rules between input data and output data can be automatically extracted through learning in the training process, learning contents are adaptively memorized in the weight of the network, and the characteristics enable the RBF neural network to be very suitable for prediction of the error data. It is noted that the RBF neural network has a strong generalization capability, and after the network is trained by using the error data, if an unseen mode or a noise pollution mode appears, the RBF neural network can correctly classify the network, so that a complex relationship between each error source and the overall error is avoided, which is very important for online evaluation of the variability error data, and therefore, the overall error of the electric energy metering device needs to be analyzed by combining the RBF neural network.
Disclosure of Invention
The application aims to provide a method and a device for evaluating the overall error of an electric energy metering device, a storage medium and a terminal, so as to ensure fairness and fairness of electric power transaction, precisely meter electric energy and improve the accuracy and reliability of the overall error of the electric energy metering device.
In order to achieve the purpose of the application, the technical scheme adopted by the application is as follows:
first aspect
The application provides an overall error evaluation method of an electric energy metering device, which is an online evaluation method and comprises the following steps:
step one: initializing the weight and threshold of the RBF neural network;
step two: based on historical data of the electric energy metering device, the RBF neural network is utilized to predict the overall error of the electric energy metering device, and the overall error prediction value E of the electric energy metering device is obtained pre ；
Step three: error data of an electric energy meter, a voltage transformer, a current transformer and a secondary circuit in the electric energy metering device are collected in real time;
step four: calculating to obtain an overall error value E of the electric energy metering device in operation;
step five: comparing the whole error predicted value E obtained in the step three pre And the overall error value E obtained in the step four; if the error accuracy is reached, entering a step eight; if the error precision is not achieved, entering a step six;
step six: then the training parameters of the RBF neural network are regulated by using an elastic gradient descent method, and the weight of the RBF neural network is modified;
step seven: correcting the generated abnormal error value by using the modified RBF neural network, updating error data of the electric energy metering device, and entering a step four;
step eight: and outputting a result.
The method is applied to a three-phase four-wire central point grounding system with 220kV voltage class.
Wherein, the weight W of the RBF neural network is calculated by the following formula:
wherein ec is min Generating a minimum value after phase angle normalization in the data; ec and ec max Is the maximum value after normalization; i is RBF networkNumber of hidden layer nodes.
The RBF neural network comprises an RBF neural network, a radial basis function and a control unit, wherein the number of layers of the RBF neural network is fixed to be 3, and the RBF neural network comprises an input layer, an hidden layer and an output layer; the input layer is connected with the hidden layer linearly; the hidden layer is connected with the output layer through the weight value, and the output of the network is expressed as:
wherein O is j Output of the j-th node of the output layer; w (W) ij The connection weight between the ith hidden layer node and the jth output layer node is obtained; m is the node number of the hidden layer; h is a i The output of the ith node of the hidden layer;
the radial basis function has stronger response near the center of the kernel function and weaker response at data points far from the center of the kernel function, taking Gaussian radial basis function as an example, the expression of the function is that
Wherein h is a Gaussian kernel function; x represents an input vector of the radial basis function; c represents the center of the kernel function; b represents the width of the sum function.
Second aspect
Correspondingly to the method, the application also provides an overall error evaluation device of the electric energy metering device, which is used for online evaluation and comprises the following units: the system comprises an initialization unit, an error prediction value calculation unit, an error data acquisition unit, an overall error value calculation unit, a comparison unit, an adjustment modification unit, a correction unit and a result output unit;
the initialization unit is used for initializing the weight and the threshold value of the RBF neural network;
the error prediction value calculation unit is used for carrying out integral error on the electric energy metering device by utilizing the RBF neural network based on historical data of the electric energy metering devicePredicting to obtain the whole error predicted value E of the electric energy metering device pre ；
The error data acquisition unit is used for acquiring error data of an electric energy meter, a voltage transformer, a current transformer and a secondary circuit in the electric energy metering device in real time;
the whole error value calculation unit is used for calculating and obtaining the whole error value E of the electric energy metering device in operation;
the comparison unit is used for obtaining the overall error predicted value E pre Comparing with the obtained overall error value E; if the error precision is reached, an execution result output unit; if the error precision is not reached, executing an adjustment and modification unit;
the adjusting and modifying unit is used for adjusting the RBF neural network training parameters by using an elastic gradient descent method and modifying the weight of the RBF neural network;
the correction unit is used for correcting the generated abnormal error value by utilizing the modified RBF neural network, updating the error data of the electric energy metering device, and then executing the integral error value calculation unit;
the result output unit is used for outputting a result.
The device is applied to a three-phase four-wire central point grounding system with 220kV voltage class.
Wherein, the weight W of the RBF neural network is calculated by the following formula:
wherein ec is min Generating a minimum value after phase angle normalization in the data; ec and ec max Is the maximum value after normalization; i is the number of nodes in the hidden layer of the RBF network.
The RBF neural network comprises an RBF neural network, a radial basis function and a control unit, wherein the number of layers of the RBF neural network is fixed to be 3, and the RBF neural network comprises an input layer, an hidden layer and an output layer; the input layer is connected with the hidden layer linearly; the hidden layer is connected with the output layer through the weight value, and the output of the network is expressed as:
wherein O is j Output of the j-th node of the output layer; w (W) ij The connection weight between the ith hidden layer node and the jth output layer node is obtained; m is the node number of the hidden layer; h is a i The output of the ith node of the hidden layer;
the radial basis function has stronger response near the center of the kernel function and weaker response at data points far from the center of the kernel function, taking Gaussian radial basis function as an example, the expression of the function is that
Wherein h is a Gaussian kernel function; x represents an input vector of the radial basis function; c represents the center of the kernel function; b represents the width of the sum function.
Third aspect of the application
Correspondingly, the application also provides a storage medium, wherein at least one instruction, at least one section of program, code set or instruction set is stored in the storage medium, and the at least one instruction, the at least one section of program, the code set or instruction set is loaded and executed by a processor to realize the method for evaluating the overall error of the electric energy metering device.
Fourth aspect of
Correspondingly, the application also provides a terminal, which comprises a processor and a memory, wherein at least one instruction, at least one section of program, a code set or an instruction set is stored in the memory, and the at least one instruction, the at least one section of program, the code set or the instruction set is loaded and executed by the processor to realize the integral error assessment method of the electric energy metering device.
Compared with the prior art, the application has the beneficial effects that,
the application can realize online real-time evaluation of the overall error of the running electric energy metering device, can realize online monitoring and acquisition of the output errors of the running electric energy meter, the voltage transformer, the current transformer and the secondary circuit under the condition of no power failure, and can screen out an optimal model which is most suitable for the running electric energy metering data by analyzing the historical data of the running electric energy metering device and adopting an optimized RBF neural network algorithm to conduct error prediction. And the error prediction value is utilized to correct the abnormal error value generated in the metering process, so that the error generated by the electric energy metering device is reduced, the accuracy of electric energy metering is improved, the working state of the load electric energy metering device is evaluated in real time, the work efficiency of the metering system in abnormal processing work order is improved, and the work load of on-site inspection personnel is effectively reduced.
Drawings
FIG. 1 is a schematic flow chart of a method according to an embodiment of the present application;
FIG. 2 is a schematic diagram of the operation principle of the device according to the embodiment of the present application;
fig. 3 is a schematic diagram of an operation error analysis principle of a voltage transformer according to an embodiment of the present application;
fig. 4 is a schematic diagram of an operation error analysis principle of a current transformer according to an embodiment of the present application;
Detailed Description
The application is described in further detail below with reference to the drawings and the specific examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
Overall description:
the application relates to an online evaluation method for the overall error of an electric energy metering device based on an RBF neural network. Firstly, predicting a measurement error of an electric energy metering device by adopting an optimized RBF neural network through analysis of historical data; then, according to the error values of the electric energy meter, the mutual inductor and the secondary circuit obtained by on-line monitoring, integrating the error values into the integral error of the electric energy metering device in operation; the prediction error based on the RBF neural network is compared with the overall error measured value, so that the abnormal error value generated in the metering process is corrected, the error generated by the electric energy metering device is reduced, and the measurement accuracy of the electric energy metering device is improved. In addition, the overall error online evaluation method can also be applied to carrying out online monitoring and evaluation work on the metering performance of the voltage transformer and the current transformer, can remotely transmit data according to the needs of users, and is convenient for carrying out the functions of data analysis, data sharing and data depth application of the electric energy metering device in the power grid construction.
As shown in fig. 1, the embodiment of the application provides an overall error evaluation method of an electric energy metering device, wherein the method is an online evaluation method and comprises the following steps:
step one: initializing the weight and threshold of the RBF neural network;
step two: based on historical data of the electric energy metering device, the RBF neural network is utilized to predict the overall error of the electric energy metering device, and the overall error prediction value E of the electric energy metering device is obtained pre ；
Step three: error data of an electric energy meter, a voltage transformer, a current transformer and a secondary circuit in the electric energy metering device are collected in real time;
the absolute value method is adopted for data acquisition, the accuracy grade of the voltage signal acquisition unit is 0.01 grade, and the acquisition range is 57.7V (2% -150%); the accuracy grade of the current signal acquisition unit is 0.05 grade, and the acquisition range is 1A (1% -600%); the accuracy grade of the secondary loop acquisition unit is 0.05 grade, and the acquisition range is 57.7V (2% -150%); the accuracy grade of the whole hardware of the device is 0.1 grade;
step four: calculating to obtain an overall error value E of the electric energy metering device in operation;
step five: comparing the whole error predicted value E obtained in the step three pre And the overall error value E obtained in the step four; if the error accuracy (for example, the deviation is less than 5%) is reached, the step eight is entered; if the error accuracy is not achieved (the deviation is more than 5%), the step six is carried out;
step six: then the training parameters of the RBF neural network are regulated by using an elastic gradient descent method, and the weight of the RBF neural network is modified;
step seven: correcting the generated abnormal error value by using the modified RBF neural network, updating error data of the electric energy metering device, and entering a step four;
step eight: and outputting a result.
It should be noted that, the overall error evaluation method of the electric energy metering device is applied to a three-phase four-wire central point grounding system with 220kV voltage level, and the overall error E formula of the electric energy metering device is calculated as follows:
γ＝γ h +γ d +γ e (3)
wherein, gamma h -mutual inductor global error; gamma ray d -a secondary pressure drop overall error; gamma ray e -a power meter error; f (f) I1 And delta I1 Is the error of the first current transformer, f U1 And delta U1 Is the error of the first voltage transformer; f (f) I2 And delta I2 Is the error of the second current transformer, f U2 And delta U2 Is the error of the second voltage transformer; f (f) I3 And delta I3 Is the error of a third current transformer, f U3 And delta U3 The error is the error of the third voltage transformer;-power factor angle; f (f) 1 And delta 1 The voltage drop error of the secondary lead of the first voltage transformer; f (f) 2 And delta 2 The voltage drop error of the secondary lead of the second voltage transformer; f (f) 3 And delta 3 The voltage drop error of the secondary lead of the third voltage transformer.
In the fourth step, the power factor angle of the transformer substation is checked according to a three-phase four-wire calculation formula, and the power factor angle is brought into the formula to calculate and obtain the overall error value of the electric energy device in operation.
For example: error of the electric energy meter is gamma e=0.15%, and errors of the three current transformers and the three voltage transformers are all 0.2 percent and 10'; the secondary voltage drop ratio difference of the three lines is-0.16%, and the phase difference is 4'; the power factor is 0.8 capacity, calculatedAnd (3) carrying out formula calculation on the integral error of the electric energy metering device:
wherein, mutual-inductor overall error is:
the overall error of the secondary pressure drop is as follows:
to sum up, the overall error is obtained as: gamma = gamma h +γ d +γ e ＝0.004+0.0857+0.0015＝0.0912。
For the above example, the overall error predicted value of the electric energy metering device based on the RBF neural network is 0.0858, and the measured overall error value is compared with the measured overall error value to meet the accuracy error requirement, so that the measurement result can be output.
The above error data is the overall error data of the weighing apparatus calculated at one time. Assuming that such error data is calculated every 15 minutes of the day, 96 calculated gamma values will appear, with these amounts being gamma, respectively, with one calculation cycle per day 1 、γ 2 ......γ 96 . And calculating the expected sum variance of the overall error through repeated iteration, and finally obtaining the value of the overall error of the electric energy metering device in one day. The error expectation formula is:variance of errorThe formula is: />The final calculated overall error is gamma It is desirable to The variance of the value is gamma Variance of The overall error of the power grid can be estimated on line through the overall error and the variance value obtained through calculation.
It should be noted that, the method further includes a step of constructing an RBF neural network before the first step, specifically including the following steps:
and acquiring historical data of the electric energy metering device in the transformer substation, acquiring relevant parameters of the RBF neural network through offline training, and establishing a corresponding neural network prediction model. And combining the actually acquired data, taking the measurement error of the substation electric energy metering device as an input layer of a network, wherein the error Ti of the electric energy meter, the synthesis error Ta of the transformer and the voltage drop error Tc of the secondary circuit of the voltage transformer form the input of a prediction model, and the node number of an hidden layer is obtained by using a formula (4).
N y ＝N+0.618(N-M) (4)
N is the number of input nodes, M is the number of output nodes, and the output layer is the measurement error of the electric energy metering device obtained through prediction, so that the measurement error can be compared with the actual measurement value of the device, and the error value calibration is realized.
The RBF neural network structure adopted by the embodiment of the application has the advantages that the number of layers of the network is fixed to be 3, and the network is respectively an input layer, an hidden layer and an output layer. Wherein the activation function of the hidden layer uses a radial basis function; the input layer is connected with the hidden layer linearly; the hidden layer is connected with the output layer through a weight. Similar to BP (Back Propagation) neural networks, the output of the network can be expressed as:
wherein O is j Output of the j-th node of the output layer; w (W) ij The connection weight between the ith hidden layer node and the jth output layer node is obtained; m is the node number of the hidden layer; h is a i Is an implicit layerThe output of the i-th node.
In addition, in order to improve the network training speed, the application optimizes the RBF neural network initial weight. The training speed in the initial stage is directly influenced by the size of the initial weight. The optimized network weight parameters can enable the RBF neural network to learn more valuable information and prevent network divergence. Thus, the initial weight W of the RBF network is calculated by:
wherein ec is min Generating a minimum value after phase angle normalization in the data; ec and ec max Is the maximum value after normalization; i is the number of nodes in the hidden layer of the RBF network.
In order to ensure that the neural network can obtain the global optimal solution in the learning process and meet the accuracy requirement of the measurement of the electric energy metering device, the application adopts an elastic gradient descent method to optimize the neural network model so as to realize the prediction of the measurement error of the electric energy metering device of the transformer substation. According to the method, the traditional accumulated weight is reflected by introducing a momentum item, the weight is adjusted and updated, the gradient only influences the adjustment direction of the weight, and the problem that the neural network is slow in convergence caused by the gradient is greatly avoided.
The electric energy metering device adopted by the application predicts that the radial basis function has stronger response near the center of the kernel function and weaker response at data points far from the center of the kernel function. Taking a gaussian radial basis function as an example, the expression of the function is:
wherein h is a Gaussian kernel function; x represents an input vector of the radial basis function; c represents the center of the kernel function; b represents the width of the sum function.
It should be noted that, the operation error of the voltage transformer is calculated by adopting the following modes:
the position of the measuring winding of the voltage transformers of the 220kV I busbar and the II busbar is determined in the transformer substation control room, the signals of the measuring winding of the voltage transformers of the I busbar and the II busbar are connected into the voltage signal acquisition unit of the platform, and the data of the running voltage transformers can be automatically acquired in real time after the analysis platform is operated, and meanwhile, the running errors of the voltage transformers are analyzed. The following provides a specific description of two data methods adopted by the data analysis section of the present platform.
First kind: peer-to-peer alignment
1. The monitored voltage transformer offline error data is obtained as shown in table 1.
Table 1 off-line error data of 220kV voltage transformer of certain transformer substation
2. Error deviation data in operation of two sets of voltage transformers are obtained using the system as shown in table 2.
Table 2 live error data for 220kV voltage transformer of certain transformer substation
3. Data analysis was performed. Firstly, the data of the live monitoring system show that the relative deviation of the two groups of voltage transformers is within +/-0.4% and +/-20', namely the two groups of transformers are free from out-of-tolerance phenomenon. And further analyzing the relative running stability of the two groups of transformers, and comparing the deviation of the two groups of transformers when offline with the deviation of the two groups of transformers when online to obtain data of Table 3.
Table 3 comparison of voltage transformer error relative bias
As can be seen from table 3, the relative error between the two voltage transformers in the off-line state is very close to the deviation of the two voltage transformers in the running state, the ratio deviation of the two voltage transformers is ten-thousandth, and the phase difference deviation is minute. As can be seen from the maximum operational deterioration of the operation of the voltage transformer in the JJG1021-2007 table 5, the two CVT installed in the same substation have the same operating environment temperature and the same operating power frequency, and the maximum operational deviation between the two CVT is 0.1% and 5.4'. The data in Table 3 is far less than this threshold, and therefore, it is determined that the 220kV voltage class CVT in which the station operates is good in operation stability, and the error of the voltage transformer in operation can be represented by an offline error.
Second kind: normal distribution
Assume that the obtained 10 measurement point data of the amplitude of the A phase of the 220kV voltage class I master are respectively: 57.68V, 57.69V, 57.68V, 57.70V, 57.71V, 57.68V, 57.69V, 57.70V, 57.68V; the 10 measurement point data of the B phase amplitude are respectively: 57.70V, 57.69V, 57.72V, 57.70V, 57.69V, 57.69V, 57.70V, 57.68V; the 10 measurement point data of the C phase amplitude are respectively: 57.68V, 57.68V, 57.68V, 57.69V, 57.70V, 57.73V, 57.69V, 57.68V, 57.70V, 57.69V. All measured values are arranged as follows from small to large: x is x 1 、x 2 、┄、x 30 . The acquired data is 30 groups and the dixon criterion is selected. According to the formulaAnd (5) performing calculation. When gamma is ij ＞γ′ ij ,γ ij > D (a, n), x n Is abnormal value when gamma ij ＜γ′ ij ,γ′ ij > D (a, n), x 1 Is an outlier. Using this criterion, outliers can be proposed multiple times, but only one outlier can be culled at a time.
All data were arranged from small to large: 57.68V, 57.68V, 57.68V, 57.68V, 57.68V, 57.68V, 57.68V, 57.68V, 57.68V, 57.69V, 57.69V, 57.69V, 57.69V, 57.69V, 57.69V, 57.69V 57.69V, 57.70V 57.70V, 57.70V 57.70V, 57.71V, 57.72V, 57.73V. n=30, a significant level a=0.05 is selected, and the table look-up results in a threshold D (0.05, 30) =0.412.
γ 11 ＞γ′ 11 ，γ 11 =0.2 < D, so there is no outlier. The monitored set of data is determined to be outlier free, so the operational error can be represented by an off-line error.
In the aspect of voltage transformer error on-line monitoring, the application has the capability of collecting and analyzing analog quantity and data quantity, proposes two calculation models (peer comparison and normal distribution) to perform data analysis, and calculates the expected and variance of data according to the two calculation results to judge whether the metering performance of the voltage transformer is normal in operation.
The real-time acquisition and analysis principle of the voltage transformer error in the application is shown in figure 3, the voltage transformer metering winding signals of analog quantity and digital quantity output can be acquired, the voltage isolation protection unit is protected at the acquisition part during analog signal acquisition, and the digital signal is directly read from the concentrator during digital quantity acquisition; and analyzing the acquired data by adopting two analysis modes of peer comparison and normal distribution calculation, and judging whether the metering performance of the voltage transformer is normal in operation.
The error real-time acquisition and analysis principle of the current transformer is shown in fig. 4, and because the current signals are required to be acquired in series, the high-precision open current sensor is adopted to acquire the output current signals of a plurality of windings of the current transformer; and by combining the offline data and intelligently analyzing differential current variation data among a plurality of winding signals of the current transformer, judging whether the metering performance of the current transformer is normal in operation.
It should be noted that, the operation error of the current transformer is calculated by adopting the following modes:
the transformer substation 220kV voltage class current transformer is provided with 4 secondary windings, and the transformation ratios are 2000A:1A,1000A:1A and 1000A:1A respectively. 2000A:1A is a protection winding, 1000A:1A is a measurement winding and a metering winding respectively. The protection winding is 5P10, the measurement winding is 0.5 level, and the metering winding is 0.2 level.
And carrying out charged monitoring on the metering performance of the current transformer, configuring the number of the current sensors to be 4, and fully connecting all 4 secondary windings. Because the transformation ratio of the protection winding is different from that of the measurement winding and the metering winding, the current sensor needs to adjust the transformation ratio to meet the requirement of the consistency of the secondary signals of 4 windings. The current sensor transformation ratio of the protection winding is 1A to 8V, and the current sensor transformation ratio of the measuring winding is 1A to 4V. After the transformation ratio is adjusted, when primary current is 1000A, the output of the protection winding is 0.5A, and after a current sensor of 1A:8V is overlapped, a subsequent circuit acquisition board obtains voltage of 4V; after the current sensor with the output of the measuring winding being 1A and the current sensor being 1A to 4V is overlapped, the voltage obtained by the follow-up circuit acquisition board is also 4V, so that the follow-up data comparison work is conveniently carried out.
Based on the configuration method, the collection of 4 winding signals is realized, and the difference signals of the protection winding, the measurement winding and the metering winding can be calculated according to the corresponding 4 independent voltage signals, so that the total difference is 3. These 3 difference amounts can be plotted on the time axis using real-time measurements, while theoretically 3 difference signals are about 0 in size. In practice, the measurement windings are all 0.5 level, the measurement windings are 0.2 level, the difference between the two is 0.7% at maximum, and the maximum difference is 4v×0.7% =0.028v for a signal with an output of 4V. When the voltage difference between the measurement winding and the metering winding is less than 0.028V and the variation on the time axis is less than 4v×0.2% =0.008V, it is indicated that the metering performance of the current transformer is normal. When the metering performance of the current transformer is normal, the running error of the current transformer can be calculated by using an off-line error; when the voltage difference between the measuring winding and the metering winding is smaller than 0.028V, but the change on the time axis is larger than 4v×0.2% =0.008V, the metering performance of the current transformer fluctuates greatly, and important observation is recommended; when the voltage difference between the measuring winding and the metering winding is larger than 0.028V, the metering performance of the current transformer is abnormal, and the offline detection is suggested to be carried out as soon as possible.
In the aspect of on-line error monitoring of the current transformer, the application firstly provides a detection mode for obtaining output signals of a plurality of windings of the current transformer under the operating condition through an opening current sensor, and the metering performance state of the current transformer in operation is analyzed through dual monitoring of absolute quantity and difference value. Meanwhile, the process can correct the offline error, and the overall error accuracy of the electric energy metering device is further improved;
it should be noted that, the operation errors of the electric energy meter and the secondary voltage drop are calculated by adopting the following modes:
the running error of the electric energy meter is carried out according to the existing live detection mode, and the method is that a standard electric energy meter with higher accuracy level is additionally arranged in a screen cabinet, and the running error value of the electric energy meter can be known through comparison. However, the conventional live detection is a temporary connection mode, and cannot meet the long-term monitoring requirement. Therefore, in order to realize long-term on-line monitoring, the patent changes temporary connection into long-term cable. Meanwhile, the live detection of the electric energy meter is a conventional technology, and is not described in detail herein.
The secondary voltage drop monitoring is to compare and calculate the error between the secondary voltage of the installation position of the voltage transformer and the voltage in the screen cabinet of the control room. The secondary voltage signal of the voltage transformer installation position is acquired in parallel in a wiring cabinet of primary equipment and is transmitted to a control room in a wireless mode, the voltage in the screen cabinet of the control room is acquired in parallel by adopting a sensor installed in the control room, and the error between the two is considered to be the error introduced by wireless transmission. The secondary voltage drop live detection is a conventional technique and will not be described in detail herein.
The system can realize online real-time evaluation of the overall error of the running electric energy metering device, can realize online monitoring and acquisition of the output errors of the running electric energy meter, the voltage transformer, the current transformer and the secondary circuit under the condition of no power failure, and can screen out an optimal model which is most suitable for the running electric energy metering data by analyzing the historical data of the running electric energy metering device and adopting an optimized RBF neural network algorithm to conduct error prediction. And the error prediction value is utilized to correct the abnormal error value generated in the metering process, so that the error generated by the electric energy metering device is reduced, the accuracy of electric energy metering is improved, the working state of the load electric energy metering device is evaluated in real time, the work efficiency of the metering system in abnormal processing work order is improved, and the work load of on-site inspection personnel is effectively reduced.
Specifically, voltage transformer operation error accessible is connected in parallel in control room screen cabinet gathers voltage signal, and current transformer operation error is through setting up the current signal that gathers different windings of same current transformer in control room screen cabinet, and the secondary voltage drop obtains error signal value through calculating the voltage difference between the voltage transformer secondary terminal that wireless transmission obtained and the control room screen cabinet internal voltage secondary terminal, and the electric energy meter obtains real-time error data through current conventional electrified detection mode. This patent can in time discover the measurement performance state of voltage transformer, current transformer in the operation, can report to the police when the transformer error exceeds the limit value, and suggestion fortune dimension personnel opens further detection, has guaranteed the accuracy of electric energy metering device overall error.
The method can realize on-line monitoring of metering performance of the voltage transformer and the current transformer in operation, establishes a voltage and current long-term supervision data platform on a time axis according to the collection quantity and the difference value, and derives a curve of the variation trend of the error along with the single influence quantity according to the variation of the single quantity. Meanwhile, the patent can draw a change curve with the abscissa being time according to the calculated overall error of the metering device, and remotely transmit the data and the calculation result to a data processing center in a wired or wireless mode.
The whole error database of the operation of the electric energy metering device at the station domain level is established based on the whole error data of the metering devices of different power grid systems, so that the electric energy metering devices operated in the same operation environment and typical substations can be conveniently searched and predicted, risk rating works with different accuracy and reliability can be carried out for different substations, and the safe and reliable operation of the power grid and the fairness and fairness of electric power transaction are ensured.
The system can be independently used for monitoring and evaluating the metering performance of the voltage transformer or the current transformer in operation, has the functions of local display storage and remote data transmission, can locally display and store acquired data and calculation results, and can remotely transmit the results and parameters to a background system in a wired or wireless mode for analysis and display.
In correspondence with the above-described embodiments, the present application also provides an overall error evaluation device for an electric energy metering device, for online evaluation, comprising the following units: the system comprises an initialization unit, an error prediction value calculation unit, an error data acquisition unit, an overall error value calculation unit, a comparison unit, an adjustment modification unit, a correction unit and a result output unit;
the initialization unit is used for initializing the weight and the threshold value of the RBF neural network;
the error prediction value calculation unit is used for predicting the overall error of the electric energy metering device by utilizing the RBF neural network based on the historical data of the electric energy metering device to obtain an overall error prediction value E of the electric energy metering device pre ；
The error data acquisition unit is used for acquiring error data of an electric energy meter, a voltage transformer, a current transformer and a secondary circuit in the electric energy metering device in real time;
the whole error value calculation unit is used for calculating and obtaining the whole error value E of the electric energy metering device in operation;
the comparison unit is used for obtaining the overall error predicted value E pre Comparing with the obtained overall error value E; if the error precision is reached, an execution result output unit; if the error precision is not reached, executing an adjustment and modification unit;
the adjusting and modifying unit is used for adjusting the RBF neural network training parameters by using an elastic gradient descent method and modifying the weight of the RBF neural network;
the correction unit is used for correcting the generated abnormal error value by utilizing the modified RBF neural network, updating the error data of the electric energy metering device, and then executing the integral error value calculation unit;
the result output unit is used for outputting a result.
It should be noted that, as shown in fig. 2, the device provided by the application is installed in a substation control room to monitor the output errors of the electric energy meter, the voltage transformer, the current transformer and the secondary circuit in operation on line, and all the signal acquisition processes do not need power failure matching, so that the normal operation of a circuit system is ensured; only the secondary loop in the acquired signals needs to acquire the signals of the secondary terminal box of the voltage transformer, and other signals are acquired in the screen cabinet of the control room, so that the method has the characteristics of convenience, high efficiency and safety in use; the data acquisition comprises real-time data acquisition of an operation voltage transformer metering winding, an operation current transformer metering winding, a voltage transformer secondary voltage drop and an operation electric energy meter and receiving of B codes in a transformer substation; after data acquisition, error analysis is carried out in each module, data results and acquired characteristic quantities are transmitted to a three-phase four-wire system integral error analysis calculation module, and the module adopts a novel rule learning method to carry out repeated iteration to calculate the operation error of the total electric energy metering device; the local database is established, so that the calculation result can be displayed locally, and the result and the parameters can be remotely transmitted to a background system for analysis and display in a wired or wireless mode; the data display and storage communication module can display error real-time change data of the electric energy meter, the mutual inductor and the secondary circuit and change curves on a time axis respectively, can also display integral error real-time change data after superposition of all parts and change curves on the time axis, and can judge whether the monitored electric energy metering device is abnormal according to integral error limit values of the electric energy metering device given in national relevant standards. When the calculated result exceeds the limit value, the system displays a red warning sign to remind the operation and maintenance personnel to further check.
For the functional functioning of the device and for the implementation, reference is made to the description of the method above, which is not described in detail here.
In addition, the embodiment of the application provides a storage medium, in which at least one instruction, at least one section of program, a code set or an instruction set is stored, and the at least one instruction, the at least one section of program, the code set or the instruction set is loaded and executed by a processor to implement the method for evaluating the overall error of the electric energy metering device.
In addition, the embodiment of the application provides a terminal, which comprises a processor and a memory, wherein at least one instruction, at least one section of program, a code set or an instruction set is stored in the memory, and the at least one instruction, the at least one section of program, the code set or the instruction set is loaded and executed by the processor to realize the overall error assessment method of the electric energy metering device.
Including but not limited to computer devices.
It should be apparent to those skilled in the art that the embodiments of the present application may be provided as a method, an apparatus, a storage medium, a terminal. Accordingly, embodiments of the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the application may take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
The foregoing details of the optional implementation of the embodiment of the present application have been described in detail with reference to the accompanying drawings, but the embodiment of the present application is not limited to the specific details of the foregoing implementation, and various simple modifications may be made to the technical solution of the embodiment of the present application within the scope of the technical concept of the embodiment of the present application, and these simple modifications all fall within the protection scope of the embodiment of the present application.
In addition, the specific features described in the above embodiments may be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, various possible combinations of embodiments of the present application are not described in detail.
Claims (10)
1. The method is characterized by being an online evaluation method and comprises the following steps:
step one: initializing the weight and threshold of the RBF neural network;
step two: history based on electric energy metering deviceThe data are used for predicting the overall error of the electric energy metering device by utilizing the RBF neural network to obtain an overall error prediction value E of the electric energy metering device pre ；
Step three: error data of an electric energy meter, a voltage transformer, a current transformer and a secondary circuit in the electric energy metering device are collected in real time;
step four: calculating to obtain an overall error value E of the electric energy metering device in operation;
step five: comparing the whole error predicted value E obtained in the step three pre And the overall error value E obtained in the step four; if the error accuracy is reached, entering a step eight; if the error precision is not achieved, entering a step six;
step six: then the training parameters of the RBF neural network are regulated by using an elastic gradient descent method, and the weight of the RBF neural network is modified;
step seven: correcting the generated abnormal error value by using the modified RBF neural network, updating error data of the electric energy metering device, and entering a step four;
step eight: and outputting a result.
2. The method for evaluating the overall error of an electric energy metering device according to claim 1, wherein the method is applied to a system with 220kV voltage class of three-phase four-wire central point grounding.
3. The method for evaluating the overall error of an electric energy metering device according to claim 1, wherein the weight W of the RBF neural network is calculated by the following formula:
wherein ec is min Generating a minimum value after phase angle normalization in the data; ec and ec max Is the maximum value after normalization; i is the number of nodes in the hidden layer of the RBF network.
4. The method for evaluating the overall error of an electric energy metering device according to claim 1, wherein the number of layers of the RBF neural network is fixed to 3, namely an input layer, an hidden layer and an output layer, respectively, and wherein an activation function of the hidden layer uses a radial basis function; the input layer is connected with the hidden layer linearly; the hidden layer is connected with the output layer through the weight value, and the output of the network is expressed as:
wherein O is j Output of the j-th node of the output layer; w (W) ij The connection weight between the ith hidden layer node and the jth output layer node is obtained; m is the node number of the hidden layer; h is a i The output of the ith node of the hidden layer;
the radial basis function has stronger response near the center of the kernel function and weaker response at data points far from the center of the kernel function, taking Gaussian radial basis function as an example, the expression of the function is that
Wherein h is a Gaussian kernel function; x represents an input vector of the radial basis function; c represents the center of the kernel function; b represents the width of the sum function.
5. An overall error assessment device of an electric energy metering device, which is used for online assessment and comprises the following units: the system comprises an initialization unit, an error prediction value calculation unit, an error data acquisition unit, an overall error value calculation unit, a comparison unit, an adjustment modification unit, a correction unit and a result output unit;
the initialization unit is used for initializing the weight and the threshold value of the RBF neural network;
the error prediction value calculation unit is used for predicting the overall error of the electric energy metering device by utilizing the RBF neural network based on the historical data of the electric energy metering device,obtaining the overall error prediction value E of the electric energy metering device pre ；
The error data acquisition unit is used for acquiring error data of an electric energy meter, a voltage transformer, a current transformer and a secondary circuit in the electric energy metering device in real time;
the whole error value calculation unit is used for calculating and obtaining the whole error value E of the electric energy metering device in operation;
the comparison unit is used for obtaining the overall error predicted value E pre Comparing with the obtained overall error value E; if the error precision is reached, an execution result output unit; if the error precision is not reached, executing an adjustment and modification unit;
the adjusting and modifying unit is used for adjusting the RBF neural network training parameters by using an elastic gradient descent method and modifying the weight of the RBF neural network;
the correction unit is used for correcting the generated abnormal error value by utilizing the modified RBF neural network, updating the error data of the electric energy metering device, and then executing the integral error value calculation unit;
the result output unit is used for outputting a result.
6. The device for evaluating the overall error of an electric energy metering device according to claim 5, wherein the device is applied to a system with 220kV voltage class of three-phase four-wire center point grounding.
7. The device for estimating an overall error of an electric energy meter according to claim 5, wherein the weight W of the RBF neural network is calculated by:
wherein ec is min Generating a minimum value after phase angle normalization in the data; ec and ec max Is the maximum value after normalization; i is the number of nodes in the hidden layer of the RBF network.
8. The device for evaluating the overall error of an electric energy metering device according to claim 5, wherein the number of layers of the RBF neural network is fixed to 3, which are an input layer, an hidden layer and an output layer, respectively, and wherein an activation function of the hidden layer uses a radial basis function; the input layer is connected with the hidden layer linearly; the hidden layer is connected with the output layer through the weight value, and the output of the network is expressed as:
wherein O is j Output of the j-th node of the output layer; w (W) ij The connection weight between the ith hidden layer node and the jth output layer node is obtained; m is the node number of the hidden layer; h is a i The output of the ith node of the hidden layer;
the radial basis function has stronger response near the center of the kernel function and weaker response at data points far from the center of the kernel function, taking Gaussian radial basis function as an example, the expression of the function is that
Wherein h is a Gaussian kernel function; x represents an input vector of the radial basis function; c represents the center of the kernel function; b represents the width of the sum function.
9. A storage medium having stored therein at least one instruction, at least one program, code set, or instruction set, the at least one instruction, the at least one program, the code set, or instruction set being loaded and executed by a processor to implement the method of overall error assessment of an electric energy metering device of any one of claims 1 to 4.
10. A terminal comprising a processor and a memory, wherein the memory stores at least one instruction, at least one program, a set of codes, or a set of instructions, the at least one instruction, the at least one program, the set of codes, or the set of instructions being loaded and executed by the processor to implement the method of overall error assessment of an electric energy metering device according to any one of claims 1 to 4.
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