CN113949060A - Voltage trend prediction system based on voltage change rule - Google Patents
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Abstract
The invention discloses a voltage trend prediction system based on a voltage change rule, which comprises a plurality of voltage acquisition devices and a voltage trend prediction device, wherein the voltage acquisition devices are respectively arranged on different distribution transformers, feeders and substations and are used for acquiring voltage data of the distribution transformers, the feeders and the substations; the voltage trend prediction device is stored with a prediction model, and predicts the power utilization voltage of a future period of time according to the voltage data of the recent period of time; the prediction model includes: the grey prediction model is used for carrying out long-term prediction on voltage deviation of the transformer substation for 3-5 years; the random time sequence model is used for carrying out medium-term prediction on the voltage trend from month to quarter of the feeder line; and the linear regression model is used for performing short-term prediction on the voltage trend from week to month of the power distribution station area. The invention correspondingly selects different prediction models to perform short-term prediction, medium-term prediction and long-term prediction respectively in the prediction time with different lengths, and has better feasibility and practicability, high prediction speed and accurate prediction result.
Description
Technical Field
The invention relates to the technical field of power supply management of a power grid, in particular to a voltage trend prediction system based on a voltage change rule.
Background
In recent years, with the rapid development of economy, household appliances are continuously increased, the power consumption demand is greatly increased, and the problem of low voltage is endless. If the voltage condition at a future time can be predicted according to the existing voltage change trend, and whether low voltage occurs is judged according to the threshold value, measures can be taken in advance to effectively avoid the low voltage, so that the power utilization quality of users is improved. The voltage condition of a distribution network area is influenced by various factors, mainly comprising climate, time, geography, random factors and the like, and the reasons of unreasonable grid structure, insufficient power supply points, insufficient reactive compensation configuration, unbalanced three-phase load, unreasonable transformer gear setting, overlong medium and low voltage power supply lines, undersize line diameters and the like are added, low voltage treatment becomes a comprehensive problem of coexistence of management and technology, a thorough solution and demonstration experience are not obtained at the present stage, the problems of blind investment, poor pertinence of treatment measures, not deep comprehensive coordination treatment technology research, lack of treatment effect evaluation means and the like still exist, voltage prediction is inaccurate, and the condition that normal electricity utilization of users is influenced by low voltage still exists. Knowing and using the change rule of the predicted voltage, and doing the low-voltage precaution and treatment work in advance can possibly achieve the effect of getting twice the result with half the effort. There is therefore a need for an improvement in the voltage trend prediction systems of the prior art.
Disclosure of Invention
The invention aims to provide a voltage trend prediction system based on a voltage change rule, which can solve the problems that in the prior art, voltage prediction is inaccurate, and low voltage still affects normal power utilization of a user.
The purpose of the invention is realized by the following technical scheme:
a voltage trend prediction system based on a voltage change rule comprises a plurality of voltage acquisition devices and a voltage trend prediction device, wherein the voltage acquisition devices are respectively installed on different distribution transformers, feeders and substations and are used for acquiring voltage data of the distribution transformers, the feeders and the substations; the voltage trend prediction device is stored with a prediction model, and predicts the electricity utilization voltage in a future period of time according to the voltage data of the recent period of time; the prediction model includes: the grey prediction model is used for carrying out long-term prediction on voltage deviation of the transformer substation for 3-5 years; the random time sequence model is used for carrying out medium-term prediction on the voltage trend from month to quarter of the feeder line; and the linear regression model is used for performing short-term prediction on the voltage trend from week to month of the power distribution station area.
Further, the gray prediction model includes a first data obtaining module, a first analyzing module, and a first result outputting module, wherein:
the first data acquisition module is used for acquiring voltage data of a certain transformer substation in a recent period of time;
the first analysis module is used for analyzing the voltage data of a certain transformer substation in the recent period of time and predicting the voltage deviation of the transformer substation in the future 3-5 years;
and the first result output module is used for outputting the prediction result of the first analysis module.
Further, the random time series model includes a second data obtaining module, a second analyzing module, and a second result outputting module, where:
the second data acquisition module is used for acquiring voltage data of a certain feeder line within a recent period of time;
the second analysis module is used for analyzing the voltage data of a certain feeder line within a recent period of time and predicting the voltage trend of the feeder line for months in the future;
and the second result output module is used for outputting the analysis result of the second analysis module.
Further, the linear regression model includes a third data obtaining module, a third analyzing module, and a third result outputting module, where:
the third data acquisition module is used for acquiring voltage data of a certain power distribution area in a recent period of time;
the third analysis module is used for analyzing the voltage data of a certain power distribution area in the latest period of time and predicting the voltage trend of the power distribution area in the next weeks;
and the third result output module is used for outputting the prediction result of the third analysis module.
Further, a unary linear regression model y ═ a + bx + epsilon is stored in the linear regression model, wherein x is an independent variable and y is a dependent variable, and unknown parameters a and b are estimated by applying a least square method to obtain a linear regression equation of y to x.
Further, the formula adopted by the first analysis module for predicting the voltage deviation of the transformer substation in the future 3-5 years of the transformer substation is as follows:
wherein the content of the first and second substances,
representing the predicted value of the steady-state index of the electric energy quality of the transformer substation;
represents the discrete time response function of the gray prediction model, k 1, 2.
According to the voltage trend prediction system based on the voltage change rule, different prediction models are correspondingly selected in prediction time of different lengths, and the different prediction models are adopted to perform short-term prediction, medium-term prediction and long-term prediction respectively, so that the voltage trend prediction system based on the voltage change rule has good feasibility and practicability, the prediction speed is high, and the prediction result is accurate.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic structural diagram of a voltage trend prediction apparatus based on voltage variation rules according to the present invention;
FIG. 2 is a schematic diagram of a gray prediction model according to the present invention;
FIG. 3 is a schematic diagram of the structure of the random time series model of the present invention;
FIG. 4 is a structural diagram of a linear regression model according to the present invention.
Detailed Description
The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.
The embodiments of the present disclosure are described below with specific examples, and other advantages and effects of the present disclosure will be readily apparent to those skilled in the art from the disclosure in the specification. It is to be understood that the described embodiments are merely illustrative of some, and not restrictive, of the embodiments of the disclosure. The disclosure may be embodied or carried out in various other specific embodiments, and various modifications and changes may be made in the details within the description without departing from the spirit of the disclosure. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
The invention discloses a voltage trend prediction system based on a voltage change rule, which comprises a plurality of voltage acquisition devices and a voltage trend prediction device, wherein the voltage acquisition devices are respectively arranged on different distribution transformers, feeders and substations and are used for acquiring voltage data of the distribution transformers, the feeders and the substations. The voltage trend prediction device is stored with a prediction model, and predicts the electricity utilization voltage in a future period of time according to the voltage data in the recent period of time. The prediction model includes: the grey prediction model is used for carrying out long-term prediction on voltage deviation of the transformer substation for 3-5 years; the random time sequence model is used for carrying out medium-term prediction on the voltage trend from month to quarter of the feeder line; and the linear regression model is used for performing short-term prediction on the voltage trend from week to month of the power distribution station area.
The prediction model is established according to historical sample data and is used for analyzing result errors.
Before the prediction model is established, historical sample data is selected and preprocessed.
Before selecting historical sample data for analysis and prediction, the selection of the sample data time period can affect the accuracy of prediction. On one hand, the voltage change of each day contains certain disturbance, if the number of selected sample days is small, the prediction result is easily influenced by random disturbance, the general change rule of each day is not easily reflected, and the characteristic of the data can not be reflected. On the other hand, the general change rule of the voltage every day may change with the passage of time, such as seasonal change, increase of users, change of the types of the electric devices, and the like, and if the number of selected sample days is too large, the prediction result may deviate from the general change rule. Therefore, a suitable number of sample days needs to be selected. The voltage deviation of a certain transformer substation for 3-5 years can be selected for long-term prediction; selecting voltage trend data of a certain feeder from month to quarter to predict the middle period; and selecting voltage trend data of a certain distribution area from week to month for short-term prediction.
After the historical sample data is selected, the data processing work is the basis and key for accurate prediction, and index monitoring data for prediction is required to be continuous and uninterrupted in a certain time period. The missing data is estimated from the data at the time points before and after the missing data.
Secondly, a prediction model is established, and the result error of the prediction model is analyzed by using historical sample data.
And (3) taking the preprocessed voltage index data as input data, respectively establishing a grey prediction model for long-term prediction, a random time series model for medium-term prediction and a linear regression model for short-term prediction, inputting historical data to predict each model, and analyzing the precision and result error of the prediction model.
Further, the gray prediction model includes a first data obtaining module, a first analyzing module and a first result outputting module, wherein:
the first data acquisition module is used for acquiring voltage data of a certain transformer substation in a recent period of time. The latest period in the present invention refers to a period of time that lasts until the day before the data is acquired. The length of the specific time period is selected according to actual needs, such as the last 1 month, the last 3 years, the last 1 week and the like. The same terms appear below, with the same interpretation.
And the first analysis module is used for analyzing the voltage data of a certain transformer substation in the recent period of time and predicting the voltage deviation of the transformer substation in the future 3-5 years.
And the first result output module is used for outputting the prediction result of the first analysis module.
The grey model prediction algorithm does not need to calculate statistic characteristics during modeling, is still suitable when voltage historical sample data changes nonlinearly, needs a small amount of data, does not need to consider the distribution rule and the change trend, and is high in prediction precision, so that the future electric energy quality index can be predicted.
The grey prediction model is established by the following steps:
1. and selecting voltage monitoring data in a certain time period, and carrying out grade ratio inspection and feasibility analysis to obtain a reference data sequence.
Firstly, selecting voltage monitoring data in a certain time period, and using data sequence x(0)＝(x(0)(1),x(0)(2),…,x(0)(n)) represents, for the data sequence x(0)And calculating the level ratio among the data according to the following calculation formula:
next, the applicability of the gray prediction model is judged and analyzed.
If it is not
In the interval, the steady-state index data of the electric energy quality are suitable for grey prediction; otherwise, the necessary modifications to the data columns need to be madeThat is, a proper constant is added, and the above-mentioned step ratio calculation and interval judgment analysis are made until the requirements are met. Finally, a voltage quality index data column suitable for gray prediction is determined.
2. And performing transformation processing on the reference data sequence.
The new number sequence generated by accumulating the reference data sequence obtained by checking the steady-state index data of the power quality for one time is as follows:
x(1)＝(x(1)(1),x(1)(2),…,x(1)(n))＝(x(1)(1),x(1)(1)+x(1)(2),…,x(1)(n-1)+x(0)(n)) (2)
3. and establishing a differential equation of the gray prediction model and solving.
1) Establishing a first-order linear differential equation:
wherein a is a parameter to be identified, and μ is an endogenous variable to be identified.
2) And solving the parameters to be identified by using a least square method:
defining the vector to be identified as: a ═ muT、
From least squares
By using MATLAB software functions, parameters can be determined
And
the value of (c).
3) Solving a differential equation:
substituting the obtained parameter value to be identified into the original differential equation to obtain the discrete time response function of the gray prediction model:
then, the primary accumulation sequence is pushed back, and the predicted values of the electric energy quality steady-state indexes of the objects at different time can be obtained:
4. and carrying out error analysis on the gray prediction model.
The error analysis includes absolute error and relative error analysis:
the error of the power quality steady-state index prediction result is analyzed by adopting an intuitive absolute error and a visual relative error. The absolute error is calculated as:
the relative error is calculated as:
further, the random time series model comprises a second data acquisition module, a second analysis module and a second result output module, wherein:
and the second data acquisition module is used for acquiring voltage data of a feeder line in a latest period of time.
And the second analysis module is used for analyzing the voltage data of a certain feeder line in the recent period of time and predicting the voltage trend of the feeder line in the future months.
And the second result output module is used for outputting the analysis result of the second analysis module.
The process of analyzing the result error of the random time series model by using the historical sample data comprises the following steps:
1. and randomly selecting a period of time sequence data from the historical sample data according to time.
The time-series analysis method starts from data related to time in historical sample data, and analogizes or extends the training set according to the development process, direction and trend reflected by the training set on the time attribute, so as to predict the possible range and change rule of the training set at the next period of time.
2. And (5) smoothing the time series data.
When the prediction model is built by using the ARMA algorithm, whether a sequence for building the prediction model is a stationary sequence is determined first. If a sequence is stationary, its autocorrelation function decays exponentially or sinusoidally, and the magnitude of the decay is large. Conversely, if the sequence is not stationary, it decays by a small amount. If the sequence is not stationary, the data may be smoothed by a differential transform. Here, the sequence is differentiated several times to obtain a stationary sequence, and the differentiated number is d in ARMA (p, d, q).
3. And identifying and scaling the model.
The identification process of the model is to calculate the autocorrelation function and the partial correlation function of the sequence, and then determine whether to adopt an AR (p) model, an MA (q) model, an ARMA (p, q) model or an ARIMA (p, d, q) model according to the result. The method is characterized in that the basic characteristics of the correlation of AR, MA and ARMA models are used for identifying the condition that the time sequence { x ] is satisfied through ' truncation ' and ' tailingNThe model of. As shown in the following table:
TABLE 1 basic characteristics of the AR, MA, ARMA models
| Model (model) | Autocorrelation function | Partial correlation function |
| AR(p) | Tailing | p-order truncation |
| MA(q) | q-order truncation | Tailing |
| ARMA(p,q) | Tailing | Tailing |
4. And (6) parameter estimation.
The estimation of the time series model parameters generally utilizes the autocorrelation function of the sequence to establish a Yule-Walker equation, and utilizes the autocorrelation function to obtain the estimation value of the model parameters. After the model parameters are determined, the parameters can be accurately solved through a least square method, so that the prediction result of the model is more accurate.
5. And (6) checking the model.
After the time series model is constructed, statistical tests are required to be carried out on the model, and all models and error sequences fitted by the models are tested. The test for testing the ARMA model is carried out herein using an autocorrelation function method (an analytical method of the metric residuals). Firstly, residual errors are calculated to obtain a residual error sequence. Then, an autocorrelation function of the residual sequence is calculated. If the selected model is appropriate, the error between the actual value and the predicted value is to satisfy the requirement of white noise. And performing inverse transformation on the obtained stable time sequence to obtain an inverse transformation sequence, and taking the initially predicted voltage value as the input of the inverse transformation sequence to obtain the existing voltage quality index.
Further, the linear regression model includes a third data obtaining module, a third analyzing module, and a third result outputting module, wherein:
and the third data acquisition module is used for acquiring the voltage data of a certain power distribution area in a latest period of time.
And the third analysis module is used for analyzing the voltage data of a certain power distribution station area in the recent period of time and predicting the voltage trend of the power distribution station area in the next weeks.
And the third result output module is used for outputting the prediction result of the third analysis module.
The prediction algorithm of the linear regression model is to apply a regression analysis method to predict the voltage trend of the power distribution station from week to month in a short term according to historical voltage monitoring data.
Specifically, the prediction algorithm of the linear regression model includes: obtaining n pairs of data according to a voltage monitoring device/system, establishing a univariate linear regression model y of an independent variable x and a dependent variable y as a + bx + epsilon, and estimating unknown parameters a and b by applying a least square method. The values of a and b are substituted into the equation to obtain a linear regression equation of y to x. And then checking whether the obtained regression equation has practical value, namely, checking the unary linear regression model. And finally, analyzing the relative error and the absolute error of the prediction result.
To further illustrate the beneficial effects of the present invention, the following description is made in conjunction with specific test data: the voltage monitoring data of monitoring points in 2018, 1 month, 2019 year to 11 months, which are provided by 10 monitoring points in the distribution transformer area of the power supply station in the north sea, are used as original data for voltage trend prediction, and three prediction models in the step S2 are applied to predict the voltage deviation monitored by each monitoring point in 2019, 12 months, so as to obtain the predicted value of the voltage monitoring point. The voltage effective values of 10 monitoring points are sampled by a voltage monitor, one point is collected every 5min, the voltage of the voltage curve of the maximum load day is used as the true value of the voltage of the transformer area, and the final prediction result is shown in table 2.
Comparison result of predicted value and actual value of voltage qualification rate of 12 months in 22019 years in table
As can be seen from table 2: and (I) the monitoring point 4 has low-voltage early warning, which shows that the lower limit of the power supply voltage of the monitoring point is continuously 1h, the early warning is successful. And (II) the relative error of the prediction results of part of the monitoring points (such as the monitoring point 9) is relatively high, and the relative error of the prediction results of the rest of the monitoring points is relatively low. The reason for this prediction may be that the monitor point voltage does not fluctuate according to the original change law. And analyzing the electricity utilization characteristics of the user of the monitoring point 9, and finding that the electricity utilization user with large electricity consumption is newly added to the monitoring point 9, so that the voltage change trend changes along with the electricity utilization characteristics of the user. The predicted result of the 12-month voltage deviation is caused to be more different than the actual value. But can still be the basis for analysis.
In conclusion, the prediction result obtained by prediction is relatively accurate, and the relative error is small. In general, this example demonstrates the effectiveness of the prediction model and the accuracy of the prediction method herein.
The above description is for the purpose of illustrating embodiments of the invention and is not intended to limit the invention, and it will be apparent to those skilled in the art that any modification, equivalent replacement, or improvement made without departing from the spirit and principle of the invention shall fall within the protection scope of the invention.
Claims (6)
1. A voltage trend prediction system based on a voltage change rule is characterized by comprising a plurality of voltage acquisition devices and a voltage trend prediction device, wherein the voltage acquisition devices are respectively installed on different distribution transformers, feeders and substations and are used for acquiring voltage data of the distribution transformers, the feeders and the substations; the voltage trend prediction device is stored with a prediction model, and predicts the electricity utilization voltage in a future period of time according to the voltage data of the recent period of time; the prediction model includes: the grey prediction model is used for carrying out long-term prediction on voltage deviation of the transformer substation for 3-5 years; the random time sequence model is used for carrying out medium-term prediction on the voltage trend from month to quarter of the feeder line; and the linear regression model is used for performing short-term prediction on the voltage trend from week to month of the power distribution station area.
2. The voltage trend prediction system based on voltage variation law according to claim 1, wherein the gray prediction model comprises a first data acquisition module, a first analysis module and a first result output module, wherein:
the first data acquisition module is used for acquiring voltage data of a certain transformer substation in a recent period of time;
the first analysis module is used for analyzing the voltage data of a certain transformer substation in the recent period of time and predicting the voltage deviation of the transformer substation in the future 3-5 years;
and the first result output module is used for outputting the prediction result of the first analysis module.
3. The voltage trend prediction system based on the voltage change rule is characterized in that the random time series model comprises a second data acquisition module, a second analysis module and a second result output module, wherein:
the second data acquisition module is used for acquiring voltage data of a certain feeder line within a recent period of time;
the second analysis module is used for analyzing the voltage data of a certain feeder line within a recent period of time and predicting the voltage trend of the feeder line for months in the future;
and the second result output module is used for outputting the analysis result of the second analysis module.
4. The system of claim 1, wherein the linear regression model comprises a third data obtaining module, a third analyzing module, and a third result outputting module, and wherein:
the third data acquisition module is used for acquiring voltage data of a certain power distribution area in a recent period of time;
the third analysis module is used for analyzing the voltage data of a certain power distribution area in the latest period of time and predicting the voltage trend of the power distribution area in the next weeks;
and the third result output module is used for outputting the prediction result of the third analysis module.
5. The voltage trend prediction system based on the voltage variation law according to claim 4, wherein the linear regression model stores a unary linear regression model y ═ a + bx + epsilon, where x is an independent variable and y is a dependent variable, and a least square method is applied to estimate unknown parameters a and b to obtain a linear regression equation of y to x.
6. The voltage trend prediction system based on the voltage change rule is characterized in that the formula adopted by the first analysis module for predicting the voltage deviation of the transformer substation in the future 3-5 years is as follows:
wherein the content of the first and second substances,
representing the predicted value of the steady-state index of the electric energy quality of the transformer substation;
represents the discrete time response function of the gray prediction model, k 1, 2.
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