CN114169631B - Oil field power load management and control system based on data analysis - Google Patents
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Abstract
The invention discloses the technical field of oilfield power load prediction, which is used for solving the problems that in the existing mode aiming at oilfield power load prediction, correction analysis on abnormal values in load data is omitted, so that the accuracy and precision of oilfield power load prediction results are difficult to ensure, and the management and control work of load prediction on an oilfield power distribution network is greatly influenced, and particularly discloses an oilfield power load management and control system based on data analysis, which comprises a data acquisition unit, an abnormality recognition unit, a repair modeling unit, a data rating unit, an early warning feedback unit and a display terminal; according to the method, the box-line graph method is selected to complete detection and analysis of the abnormal values of the load sequence data, symbolic calibration, formulaic processing and graph comparison are utilized to complete restoration of the abnormal values, and then fusion analysis is conducted on various data signals, so that the accuracy and precision of prediction of the electric load of the oil field are improved, and management and control of the power distribution network of the oil field are promoted.
Description
Technical Field
The invention relates to the technical field of oilfield power load prediction, in particular to an oilfield power load management and control system based on data analysis.
Background
The power load, also called as the power load, is represented as the sum of electric power taken by the power equipment of an electric energy user to a power system at a certain moment, so that the characteristic of power load data of an oil field distribution network is accurately mastered, the accurate prediction of the power load of the oil field is facilitated, and the accurate prediction of the power load is the basis for ensuring the safe operation of the oil field;
however, in the existing mode for predicting the power load of the oil field, the influence of various abnormal values contained in mass load data on the load prediction result is often ignored, so that the accuracy and precision of the power load prediction result of the oil field are difficult to ensure, and the management and control work of the load prediction of the power distribution network of the oil field is greatly influenced;
in order to solve the above-mentioned drawbacks, a technical solution is now provided.
Disclosure of Invention
The invention aims to solve the problems that the correction analysis of abnormal values in load data is omitted in the existing mode for predicting the power load of an oil field, so that the accuracy and precision of the prediction result of the power load of the oil field are difficult to ensure, and the management and control work of the load prediction of a power distribution network of the oil field is greatly influenced.
The purpose of the invention can be realized by the following technical scheme:
an oil field power load management and control system based on data analysis comprises a data acquisition unit, an abnormality identification unit, a repair modeling unit, a data rating unit, an early warning feedback unit and a display terminal;
the data acquisition unit is used for acquiring power grid data information in the oil field power load in unit time period and sending the power grid data information to the abnormality identification unit;
the data acquisition unit is also used for acquiring multi-index associated data information in the oil field power load in unit time period and sending the multi-index associated data information to the data rating unit;
the abnormal recognition unit analyzes and processes the box plot parameters of the received power grid data information in unit time period, generates a signal with large abnormal data interference, a signal with basic abnormal data interference and a signal with small abnormal data interference according to the box plot parameters, sends the signal with basic abnormal data interference to the data rating unit, and sends the signal with large abnormal data interference and the signal with small abnormal data interference to the repair modeling unit;
the restoration modeling unit is used for receiving a signal with large abnormal data interference and a signal with small abnormal data interference, calling an abnormal data sample according to the abnormal data interference to restore the abnormal data sample, generating a prediction feasible signal and a prediction infeasible signal according to the abnormal data sample, and sending the prediction feasible signal and the prediction infeasible signal to the data rating unit;
the data rating unit performs load prediction performance rating analysis processing on the received basic abnormal-free data interference signal, the predicted feasible signal and the predicted infeasible signal, generates a more-accurate-prediction signal, a general-accurate-prediction signal and a fuzzy-prediction signal according to the load prediction performance rating analysis processing, and sends the more-accurate-prediction signal, the general-accurate-prediction signal and the fuzzy-prediction signal to the early warning feedback unit;
the early warning feedback unit is used for carrying out early warning feedback analysis processing on the received more accurate prediction signal, the general accurate prediction signal and the fuzzy prediction signal, generating a high-grade prediction signal, a middle-grade prediction signal and a low-grade prediction signal according to the early warning feedback analysis processing, and sending the high-grade prediction signal, the middle-grade prediction signal and the low-grade prediction signal to a display terminal of the oil field control equipment for analysis and explanation.
Further, the power grid data information is used for representing the power utilization load condition of the power grid in the power load management of the oil field, and the power grid data information comprises an active power load sequence and a reactive power load sequence, wherein the active power load sequence is used for representing electric power sequence data required for keeping normal operation of oilfield equipment, namely electric power sequence data for converting electric energy into other forms of energy, and the reactive power load sequence is used for representing electric power sequence data for establishing and maintaining a magnetic field in the electric equipment in the power grid working at the oil field;
the multi-index associated data information is used for representing various index factor information influencing power load change in an oil field power grid, and comprises an environmental data index and a spatial data index, wherein the environmental data index is used for representing the ratio of abnormal environmental parameters to normal environmental parameters in a unit time period, and the spatial data index is used for representing the utilization rate of the power grid in a planning space in the unit time period.
Further, the specific operation steps of the boxplot parameter analysis processing are as follows:
s1: obtaining useful power and useless power in the power grid data information of a unit time period, respectively and randomly extracting a group of data items of useful power load sequences or a group of data items of useless power load sequences, and calibrating the data items as P in I = { useful power group, useless power group }, n is represented as a set of useful power load sequences or a set of useless power load sequences P in Taking time as abscissa and power data as ordinate, and calculating P in Converting into a rectangular box body form in a box line graph;
s2: according to the formula
And
respectively obtaining an upper quartile Q3 and a lower quartile Q1, and respectively drawing an upper quartile line and a lower quartile line in a box diagram square box body in a straight line drawing mode;
s3: obtaining a median IQR according to a formula IQR = Q3-Q1, and drawing the median IQR in a box diagram square box body in a straight line drawing mode;
s4: according to the formula Mum max = Q3+1.5 i qr and Mum min = Q1-1.5 iQR, maximum value Mum is obtained max And minimum value Mum min Drawing an upper limit boundary and a lower limit boundary in a boxboard square box body in a straight line drawing mode;
s5: calibrating the data values exceeding the upper limit boundary and the lower limit boundary as abnormal points, displaying the abnormal points in a box line graph square box body in red delta, and calibrating the abnormal points as om;
s6: and summing the data of the abnormal points om in the box line graph, generating an abnormal point number value gs according to the abnormal point number value gs, comparing and analyzing the abnormal point number value gs and a rated threshold eto, generating a signal with large abnormal data interference when the abnormal point number value gs is larger than the maximum value of the rated threshold eto, generating a signal with basically no abnormal data interference when the abnormal point number value gs is smaller than the minimum value of the rated threshold eto, and generating a signal with small abnormal data interference when the abnormal point number value gs is within the rated threshold eto.
Further, the specific operation steps of the abnormal data sample repairing process are as follows:
and (4) SS1: receiving a signal with large abnormal data interference and a signal with small abnormal data interference, calling abnormal value sample data of a unit time period according to the abnormal value sample data, calibrating the abnormal value sample data into I, carrying out authenticity constraint processing on the I, and carrying out authenticity constraint processing according to a formula L r ＝D(G(z；θ (G) )；θ (D) ) Determining the loss value L of the authenticity of the abnormal value sample data r Wherein z represents a noise vector input data value in the WGAN model, G (z) represents a data value generated by a generator in the WGAN model, and D (G (z)) represents an output of a discriminator in the WGAN model for discriminating whether the generated data is true or false;
and (4) SS2: obtaining sample data which is most similar to the abnormal value sample data I measurement, and carrying out context constraint processing on the sample data according to a formula
Solving a similarity loss constraint value L s Wherein, in the step (A),
is a multiplication of matrix elements, I being sample data of outliers, M s Similar original sample data;
and (4) SS3: generating a final optimization objective according to the steps SS1 and SS2
Wherein P (z) represents the complexity score between the real dataCloth relation, z to p z ( z ) Representing the distribution of noise vectors z from between the real data and performing data reconstruction processing based thereon, according to a formula
Obtaining a final restoration reconstruction data sample, wherein the final restoration reconstruction data sample is composed of parts of available parts in the original sample item and parts corresponding to abnormal values in the generated sample;
and (4) SS: and comparing and analyzing the final restored and reconstructed data sample image and the initial original data sample image, if the variation trends of the final restored and reconstructed data sample curve and the initial original data sample curve are basically consistent, generating a prediction feasible signal, otherwise, generating a prediction infeasible signal.
Further, the specific operation steps of the load prediction performance rating analysis processing are as follows:
step1: receiving a basic abnormal data interference signal, a prediction feasible signal and a prediction infeasible signal, calling an environment data index and a spatial data index in multi-index associated data information according to the signals, marking the environment data index as the Hua, marking the spatial data index as the Kon, and marking the environmental data index as the Kon according to a formula
Determining a correlation prediction value Gua, where e 1 And e 2 Weight factor coefficients of the environmental data index Hua and the spatial data index Kon, respectively, and e 1 ＞e 2 ＞0，e 1 +e 2 =0.3642, λ is a correction coefficient, and λ is assigned to 1.2613;
step2: substituting the obtained correlation measured value Gua into a corresponding preset threshold ero, if the correlation measured value Gua is within the preset threshold ero, generating a load stabilization signal, and if the correlation measured value Gua is outside the preset threshold ero, generating a load fluctuation signal;
step3: respectively calibrating a basic abnormal data interference-free signal, a prediction feasible signal and a prediction infeasible signal as Z-1, Z-1+ and Z-2, respectively calibrating a load stable signal and a load fluctuation signal as F-1 and F-2, and performing cross analysis on the signals;
step4: when Z-1 = F-1=1 or Z-1+ - # F-1=1, a predictive accurate signal is generated, when Z-2: -F-2 = -2 is acquired, a predictive fuzzy signal is generated, and when Z-1: -F-2 =3or Z-1+ - # F-2=3 is acquired, a predictive normal accurate signal is generated.
Further, the specific operation steps of the early warning feedback analysis processing are as follows:
when a more accurate prediction signal is received, a high-grade prediction signal is generated, a text word is used for 'the basic data is favorable for accurately predicting the condition of the oil field power load', when a general accurate prediction signal is received, a middle-grade prediction signal is generated, a text word is used for 'the basic data is favorable for predicting the accuracy of the condition of the oil field power load', when a fuzzy prediction signal is received, a low-grade prediction signal is generated, and a text word is used for 'the basic data is unfavorable for accurately predicting the condition of the oil field power load'.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the method, the detection and analysis of the abnormal value of the oil field power load sequence data are completed by selecting a box line graph method, and the abnormal value in the power grid data information is further subjected to signal calibration in a data summation and substitution analysis mode, so that the abnormal value condition existing in the power load sequence is determined, a foundation is further laid for the power load prediction accuracy, and the accuracy of the oil field power load result prediction is promoted;
2. according to the method, the abnormal values in the oil field power load sequence data are repaired through symbolic calibration, formula processing and graph comparison, so that the effectiveness and reliability of the load data are further improved while the abnormal values in the power load sequence are repaired;
3. according to the invention, the collected two types of data are subjected to fusion analysis by means of normalization processing, substitution analysis and matrix cross calibration of multi-index associated data information, so that the accuracy and precision of prediction of the power load of the oil field are improved, meanwhile, the management and control of the power distribution network of the oil field are promoted, and the economy and safety of the load of the oil field are improved.
Drawings
In order to facilitate understanding for those skilled in the art, the present invention will be further described with reference to the accompanying drawings;
FIG. 1 is a general block diagram of the system of the present invention;
fig. 2 is a box diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be described below clearly and completely in conjunction with the embodiments, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The first embodiment is as follows:
as shown in fig. 1, an oil field power load management and control system based on data analysis includes a data acquisition unit, an anomaly identification unit, a repair modeling unit, a data rating unit, an early warning feedback unit, and a display terminal;
the data acquisition unit is used for acquiring power grid data information in the oil field power load in a unit time period and sending the power grid data information to the abnormality identification unit, wherein the power grid data information is used for representing the power utilization load condition of a power grid in the oil field power load management, and the power grid data information comprises an active power load sequence and a reactive power load sequence, the active power load sequence is used for representing electric power sequence data required for keeping normal operation of oil field equipment, namely converting electric energy into electric power sequence data of other forms of energy, the reactive power load sequence is used for representing the exchange of an electric field and a magnetic field in the power grid working in the oil field, and is used for establishing and maintaining the electric power sequence data of the magnetic field in the electric equipment;
the data acquisition unit is further used for acquiring multi-index associated data information in the oil field power load in the unit time period and sending the multi-index associated data information to the data rating unit, wherein the multi-index associated data information is used for representing various index factor information influencing the power load change in an oil field power grid and comprises an environmental data index and a spatial data index, the environmental data index is used for representing the ratio of abnormal environmental parameters to normal environmental parameters in the unit time period, and it needs to be noted that the larger the expression value of the environmental data index is, the smaller the factor influencing the power load change is, and meanwhile, the smaller the interference on the power load prediction analysis is represented;
the spatial data index is used for representing the utilization rate of the power grid occupying the planning space in a unit time period, and it needs to be noted that the larger the expression value of the spatial data index is, the smaller the factor influencing the power load change is, and the more optimal the setting of the power grid point is represented;
the abnormal recognition unit analyzes and processes the box plot parameters of the received power grid data information in unit time period, generates a signal with large abnormal data interference, a signal with basically no abnormal data interference and a signal with small abnormal data interference according to the box plot parameters, sends the signal with basically no abnormal data interference to the data rating unit, and sends the signal with large abnormal data interference and the signal with small abnormal data interference to the repair modeling unit;
the restoration modeling unit is used for receiving a signal with large abnormal data interference and a signal with small abnormal data interference, calling an abnormal data sample to restore the abnormal data sample, generating a prediction feasible signal and a prediction infeasible signal according to the abnormal data sample, and sending the prediction feasible signal and the prediction infeasible signal to the data rating unit;
the data rating unit carries out load prediction performance rating analysis processing on the received basic abnormal-free data interference signal, the prediction feasible signal and the prediction infeasible signal, generates a prediction accurate signal, a prediction general accurate signal and a prediction fuzzy signal according to the load prediction performance rating analysis processing, and sends the prediction accurate signal, the prediction general accurate signal and the prediction fuzzy signal to the early warning feedback unit;
the early warning feedback unit is used for carrying out early warning feedback analysis processing on the received prediction accurate signal, the prediction general accurate signal and the prediction fuzzy signal, generating a high-grade prediction signal, a middle-grade prediction signal and a low-grade prediction signal according to the early warning feedback analysis processing, and sending the high-grade prediction signal, the middle-grade prediction signal and the low-grade prediction signal to a display terminal of the oil field control equipment for analysis and explanation.
Example two:
as shown in fig. 1 and fig. 2, when the anomaly identification unit receives the grid data information of a unit time period, and performs the box diagram parameter analysis processing according to the grid data information, the specific operation steps are as follows:
s1: obtaining useful power and useless power in the power grid data information of a unit time period, respectively and randomly extracting a group of data items of useful power load sequences or a group of data items of useless power load sequences, and calibrating the data items as P in I = { useful power group, useless power group }, n is represented as a set of useful power load sequences or a set of useless power load sequences P in The total number of (A) is represented by a time interval with time as abscissa and 24 hours as abscissa, a power interval with power data as ordinate and 10 power as ordinate, and P is represented by in Converting into a rectangular box body form in the box line graph;
s2: according to the formula
And
respectively solving an upper quartile Q3 and a lower quartile Q1, and respectively drawing an upper quartile line and a lower quartile line in a box diagram square box body in a straight line drawing mode;
s3: obtaining a median IQR according to a formula IQR = Q3-Q1, and drawing the median IQR in a box diagram square box body in a straight line drawing mode;
s4: according to the formula Mum max = Q3+1.5 × iqr and Mum min = Q1-1.5 iQR, maximum value Mum is obtained max And minimum value Mum min Drawing the upper limit boundary and the lower limit boundary in a box diagram square box body in a straight line drawing mode, wherein the maximum value Mum max Means not differentMaximum upper limit value, minimum value Mum in the normal range min A minimum lower limit value in a non-abnormal range;
s5: calibrating the data values exceeding the upper limit boundary and the lower limit boundary as abnormal points, displaying the abnormal points in a box line graph square box body in red delta, and calibrating the abnormal points as om;
s6: summing data of the number of abnormal points om existing in the box plot, generating an abnormal point number value gs according to the abnormal point number value gs, comparing and analyzing the abnormal point number value gs with a rated threshold eto, generating a signal with large abnormal data interference when the abnormal point number value gs is larger than the maximum value of the rated threshold eto, generating a signal with basically no abnormal data interference when the abnormal point number value gs is smaller than the minimum value of the rated threshold eto, and generating a signal with small abnormal data interference when the abnormal point number value gs is within the rated threshold eto;
sending a signal basically free from abnormal data interference to a data rating unit, and sending a signal with large abnormal data interference and a signal with small abnormal data interference to a repair modeling unit;
it should be noted that, as shown in fig. 2, a rectangular box represents a range of data, upper and lower vertical lines represent an upper limit of the data and a lower limit of the data, Q1 is a lower quartile, Q3 is an upper quartile, IQR is a median, a maximum value is a value greater than 1.5 times a difference between the upper quartile and a minimum value is a value less than 1.5 times the difference between the lower quartile, and an abnormal point is defined as a value less than the lower limit or greater than the upper limit;
it should be noted that, the boxplot judges the abnormal process of the data, completely depends on actual data, does not need to assume a data distribution form, truly reflects the original change trend of the data, and the judgment standard is based on quartile and quartile distance, so that the abnormal value does not influence the standard, and the identification result is more accurate;
the position and the range of data distribution are reflected through the box line graph, the identification and the processing of abnormal data are realized, a foundation is provided for the prediction of the power load, and the accuracy and the authenticity of the power load prediction are promoted.
Example three:
as shown in fig. 1, when the repair modeling unit receives a signal with large abnormal data interference and a signal with small abnormal data interference, and calls an abnormal data sample to perform abnormal data sample repair processing according to the signal, the specific operation steps are as follows:
and (4) SS1: receiving a signal with large abnormal data interference and a signal with small abnormal data interference, calling abnormal value sample data of a unit time period according to the abnormal value sample data, calibrating the abnormal value sample data into I, carrying out authenticity constraint processing on the I, and carrying out authenticity constraint processing according to a formula L r ＝D(G(z；θ (G) )；θ (D) ) Determining the loss value L of authenticity of the abnormal value sample data r Wherein z represents a noise vector input data value in the WGAN model, G (z) represents a data value generated by a generator in the WGAN model, and D (G (z)) represents an output of a discriminator in the WGAN model for discriminating whether the generated data is true or false;
and (4) SS2: obtaining sample data which is most similar to the abnormal value sample data I measurement, and carrying out context constraint processing on the sample data according to a formula
Solving a similarity loss constraint value L s Wherein, in the process,
is a multiplication of matrix elements, I being sample data of outliers, M s Similar to the original sample data, it should be noted that the context constraint processing is performed to ensure that the verification model can continuously optimize the input z, so that the abnormal value sample data has a consistent context relationship, thereby realizing the accuracy and effectiveness of repairing the abnormal value sample data, and ensuring that the repaired data has consistent characteristics with the original authentic data;
and (4) SS3: generating a final optimization objective according to the steps SS1 and SS2
Wherein P (z) represents a complex distribution relationship between real data, and z to P z (z) watchRepresenting the distribution of noise vectors z from real data, and performing data reconstruction processing according to the formula
Obtaining a final restoration reconstruction data sample, wherein the final restoration reconstruction data sample is composed of parts of available parts in the original sample item and parts corresponding to abnormal values in the generated sample;
and (4) SS: and comparing and analyzing the final restored and reconstructed data sample image and the initial original data sample image, if the variation trends of the final restored and reconstructed data sample curve and the initial original data sample curve are basically consistent, generating a prediction feasible signal, otherwise, generating a prediction infeasible signal, and sending both the prediction feasible signal and the prediction infeasible signal to a data rating unit.
Example four:
as shown in fig. 1, when the data rating unit receives a substantially abnormal-free data interference signal, a predicted feasible signal and a predicted infeasible signal, and performs load prediction performance rating analysis processing according to the received signals, the specific operation steps are as follows:
step1: receiving a basic abnormal data interference signal, a prediction feasible signal and a prediction infeasible signal, calling an environment data index and a spatial data index in multi-index associated data information according to the signals, marking the environment data index as the Hua, marking the spatial data index as the Kon, and marking the environmental data index as the Kon according to a formula
Determining a correlation prediction value Gua, where e 1 And e 2 Weight factor coefficients of the environmental data index Hua and the spatial data index Kon, respectively, and e 1 ＞e 2 ＞0，e 1 +e 2 =0.3642, λ is a correction coefficient, and λ is assigned to 1.2613;
step2: substituting the obtained correlation measured value Gua into a corresponding preset threshold ero, if the correlation measured value Gua is within the preset threshold ero, generating a load stabilization signal, and if the correlation measured value Gua is outside the preset threshold ero, generating a load fluctuation signal;
step3: respectively calibrating a basic abnormal data interference-free signal, a prediction feasible signal and a prediction infeasible signal as Z-1, Z-1+ and Z-2, respectively calibrating a load stable signal and a load fluctuation signal as F-1 and F-2, and performing cross analysis on the signals;
step4: when Z-1 n-U F-1 or Z-1+ n-F-1 is acquired, a more accurate prediction signal is generated, when Z-2 n-U F-2 is acquired, a prediction fuzzy signal is generated, when Z-1 n-U F-2 is acquired, a general accurate prediction signal is generated, and the more accurate prediction signal, the general accurate prediction signal and the prediction fuzzy prediction signal are all sent to an early warning feedback unit;
when the early warning feedback unit receives the prediction accurate signal, the general prediction accurate signal and the prediction fuzzy signal, and performs early warning feedback analysis processing according to the received prediction accurate signal, the specific operation steps are as follows:
when a more accurate prediction signal is received, generating a high-grade prediction signal, and accurately predicting the condition of the oil field power load by using a text word ' the basic data is favorable for accurate prediction of the condition of the oil field power load ', when a general accurate prediction signal is received, generating a medium-grade prediction signal, and accurately predicting the condition of the oil field power load by using the text word ' the basic data is general ', and when a fuzzy prediction signal is received, generating a low-grade prediction signal, and accurately predicting the condition of the oil field power load by using the text word ' the basic data is unfavorable for accurate prediction;
and the high-grade prediction signal, the middle-grade prediction signal and the low-grade prediction signal are all sent to a display terminal of the oilfield control equipment for analysis and explanation.
The formulas are all obtained by acquiring a large amount of data and performing software simulation, and a formula close to a true value is selected, and coefficients in the formulas are set by a person skilled in the art according to actual conditions;
such as the formula:
by a person skilled in the artCollecting multiple groups of sample data and setting corresponding weight factor coefficients for each group of sample data; substituting the set weight factor coefficient and the collected sample data into a formula, forming a linear equation set by any two formulas, screening the calculated coefficients and taking the mean value to obtain e 1 And e 2 The values are respectively 1.0121 and 0.2492;
the size of the coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and the size of the coefficient depends on the number of sample data and a corresponding weight factor coefficient is preliminarily set for each group of sample data by a person skilled in the art; as long as the proportional relationship between the parameters and the quantized values is not affected.
When the method is used, the power grid data information in the power load of the oil field is collected, the box line graph method is selected to complete the detection and analysis of the abnormal value of the power load sequence data of the oil field, and the abnormal value in the power grid data information is further subjected to signal calibration in a data summation and substitution analysis mode, so that the abnormal value condition existing in the power load sequence is determined, meanwhile, a foundation is further laid for the power load prediction accuracy, and the accuracy of the oil field power load result prediction is promoted;
according to the content of signal calibration, load sequence data is called, abnormal data sample repair is carried out on the load sequence data, and a judgment signal for judging whether power load prediction is carried out or not is output through symbolic calibration, formulaic processing and comparison of a curve graph, so that the effectiveness and reliability of the load data are further improved while the repair of abnormal values in the power load sequence is realized;
according to the judgment signal of the power load prediction, multi-index associated data information influencing the power load of the oil field is called, and the collected two types of data are subjected to fusion analysis by means of normalization processing, substitution analysis and matrix cross calibration, so that the accuracy and precision of the power load prediction of the oil field are improved, meanwhile, the management and control of an oil field power distribution network are promoted, and the economy and safety of the oil field load are improved.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (6)
1. An oil field power load management and control system based on data analysis is characterized by comprising a data acquisition unit, an abnormality recognition unit, a repair modeling unit, a data rating unit, an early warning feedback unit and a display terminal;
the data acquisition unit is used for acquiring power grid data information in the oil field power load in unit time period and sending the power grid data information to the abnormality identification unit;
the data acquisition unit is also used for acquiring multi-index associated data information in the oil field power load in unit time period and sending the multi-index associated data information to the data rating unit;
the abnormal recognition unit analyzes and processes the received power grid data information in unit time period by using box line graph parameters, generates a signal with large abnormal data interference, a signal with basic abnormal data interference and a signal with small abnormal data interference according to the box line graph parameters, sends the signal with basic abnormal data interference to the data rating unit, and sends the signal with large abnormal data interference and the signal with small abnormal data interference to the repair modeling unit;
the restoration modeling unit is used for receiving a signal with large abnormal data interference and a signal with small abnormal data interference, calling an abnormal data sample according to the abnormal data interference to restore the abnormal data sample, generating a prediction feasible signal and a prediction infeasible signal according to the abnormal data sample, and sending the prediction feasible signal and the prediction infeasible signal to the data rating unit;
the data rating unit performs load prediction performance rating analysis processing on the received basic abnormal-free data interference signal, the predicted feasible signal and the predicted infeasible signal, generates a more-accurate-prediction signal, a general-accurate-prediction signal and a fuzzy-prediction signal according to the load prediction performance rating analysis processing, and sends the more-accurate-prediction signal, the general-accurate-prediction signal and the fuzzy-prediction signal to the early warning feedback unit;
the early warning feedback unit is used for carrying out early warning feedback analysis processing on the received more accurate prediction signal, the general accurate prediction signal and the fuzzy prediction signal, generating a high-grade prediction signal, a middle-grade prediction signal and a low-grade prediction signal according to the early warning feedback analysis processing, and sending the high-grade prediction signal, the middle-grade prediction signal and the low-grade prediction signal to a display terminal of the oil field control equipment for analysis and explanation.
2. The oilfield power load management and control system based on data analysis according to claim 1, wherein the grid data information is used for representing the power load situation of the power grid in oilfield power load management, and the grid data information includes an active power load sequence and a reactive power load sequence, wherein the active power load sequence is used for representing electric power sequence data required for keeping normal operation of oilfield equipment, namely converting electric energy into electric power sequence data of other forms of energy, and the reactive power load sequence is used for representing the exchange of electric fields and magnetic fields in circuits in the power grid in oilfield operation and is used for establishing and maintaining the electric power sequence data of the magnetic fields in the electric equipment;
the multi-index associated data information is used for representing various index factor information influencing power load change in an oil field power grid, and comprises an environmental data index and a spatial data index, wherein the environmental data index is used for representing the ratio of abnormal environmental parameters to normal environmental parameters in a unit time period, and the spatial data index is used for representing the utilization rate of the power grid in a planning space in the unit time period.
3. The oil field power load management and control system based on data analysis according to claim 1, wherein the specific operation steps of the boxplot parameter analysis processing are as follows:
s1: acquiring useful power and useless power in power grid data information of unit time period, and respectively randomlyExtracting data items of a group of useful power load sequences or a group of useless power load sequences, and marking the data items as P in I = { useful power group, useless power group }, n is expressed as a set of useful power load sequences or a set of useless power load sequences P in Taking time as abscissa and power data as ordinate, and calculating P in Converting into a rectangular box body form in a box line graph;
s2: according to the formula
And
respectively solving an upper quartile Q3 and a lower quartile Q1, and respectively drawing an upper quartile line and a lower quartile line in a box diagram square box body in a straight line drawing mode;
s3: according to a formula IQR = Q3-Q1, obtaining a median IQR, and drawing the median IQR in a box line graph square box body in a straight line drawing mode;
s4: according to the formula Mum max = Q3+1.5 × iqr and Mum min = Q1-1.5 iQR, maximum value Mum is obtained max And minimum value of Mum min Drawing an upper limit boundary and a lower limit boundary in a boxboard square box body in a straight line drawing mode;
s5: calibrating the data values exceeding the upper limit boundary and the lower limit boundary as abnormal points, displaying the abnormal points in a box line graph square box body in red delta, and calibrating the abnormal points as om;
s6: and performing data summation on the number of the abnormal points om in the box plot, generating an abnormal point number value gs according to the abnormal point number value gs, comparing and analyzing the abnormal point number value gs and a rated threshold eto, generating a signal with large abnormal data interference when the abnormal point number value gs is larger than the maximum value of the rated threshold eto, generating a signal with basically no abnormal data interference when the abnormal point number value gs is smaller than the minimum value of the rated threshold eto, and generating a signal with small abnormal data interference when the abnormal point number value gs is within the rated threshold eto.
4. The oil field power load management and control system based on data analysis according to claim 1, wherein the specific operation steps of the abnormal data sample restoration processing are as follows:
and (4) SS1: receiving a signal with large abnormal data interference and a signal with small abnormal data interference, calling abnormal value sample data of a unit time period according to the abnormal value sample data, calibrating the abnormal value sample data into I, carrying out authenticity constraint processing on the I, and carrying out authenticity constraint processing according to a formula L r ＝D(G(z；θ (G) )；θ (D) ) Determining the loss value L of the authenticity of the abnormal value sample data r Wherein z represents a noise vector input data value in the WGAN model, G (z) represents a data value generated by a generator in the WGAN model, and D (G (z)) represents an output of a discriminator in the WGAN model for discriminating whether the generated data is true or false;
and SS2: obtaining sample data which is most similar to the abnormal value sample data I measurement, and carrying out context constraint processing on the sample data according to a formula
Solving a similarity loss constraint value L s Wherein, in the step (A),
is a multiplication of matrix elements, I being sample data of outliers, M s Similar original sample data;
and SS3: generating a final optimization objective according to the steps SS1 and SS2
Wherein P (z) represents a complex distribution relationship between real data, and z to P z (z) represents the distribution of the noise vector z from the real data and performs the data reconstruction process based on this, according to the formula
Solving final repair reconstruction data samplesThe final repair reconstruction data sample is composed of a part of the available part in the original sample item and a part corresponding to the abnormal value in the generated sample;
and (4) SS: and comparing and analyzing the final restored and reconstructed data sample image and the initial original data sample image, if the variation trends of the final restored and reconstructed data sample curve and the initial original data sample curve are basically consistent, generating a prediction feasible signal, otherwise, generating a prediction infeasible signal.
5. The oil field power load management and control system based on data analysis according to claim 1, wherein the specific operation steps of the load prediction performance rating analysis processing are as follows:
step1: receiving a basic abnormal data interference signal, a prediction feasible signal and a prediction infeasible signal, calling an environment data index and a spatial data index in multi-index associated data information according to the signals, marking the environment data index as the Hua, marking the spatial data index as the Kon, and marking the environmental data index as the Kon according to a formula
Determining a correlation prediction value Gua, e 1 And e 2 Weight factor coefficients of the environmental data index Hua and the spatial data index Kon, respectively, and e 1 ＞e 2 ＞0，e 1 +e 2 =0.3642, λ is a correction coefficient, and λ is assigned to 1.2613;
step2: substituting the obtained correlation measured value Gua into a corresponding preset threshold ero, if the correlation measured value Gua is within the preset threshold ero, generating a load stabilization signal, and if the correlation measured value Gua is outside the preset threshold ero, generating a load fluctuation signal;
step3: respectively calibrating a basic abnormal data interference-free signal, a prediction feasible signal and a prediction infeasible signal as Z-1, Z-1+ and Z-2, respectively calibrating a load stable signal and a load fluctuation signal as F-1 and F-2, and performing cross analysis on the signals;
step4: when Z-1 ≈ F-1 or Z-1+ = F-1, a more accurate prediction signal is generated, when Z-2 = F-2 is acquired, a prediction blur signal is generated, and when Z-1 = F-2 or Z-1+ = F-2 is acquired, a general accurate prediction signal is generated.
6. The oil field power load management and control system based on data analysis according to claim 1, wherein the specific operation steps of the early warning feedback analysis processing are as follows:
when a more accurate prediction signal is received, a high-grade prediction signal is generated, a text word 'the basic data is favorable for accurately predicting the condition of the oil field power load', when a general accurate prediction signal is received, a medium-grade prediction signal is generated, a text word 'the basic data is favorable for predicting the accuracy of the condition of the oil field power load', when a fuzzy prediction signal is received, a low-grade prediction signal is generated, and a text word 'the basic data is unfavorable for accurately predicting the condition of the oil field power load'.
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