Sarima and simultaneous rate-based method for calculating openable capacity of transformer area

Abstract

The method for calculating the available capacity of a transformer substation based on SARIMA and simultaneity rate includes the following steps: acquisition and preprocessing of three-phase active power data of the substation; establishment of a SARIMA substation load power prediction model; establishment of a dynamic simultaneity rate database for the substation; load prediction for a given month, and calculation of the available capacity of the substation for that month by selecting the largest simultaneity rate prediction from the dynamic simultaneity rate database. This invention establishes a dynamic simultaneity rate database for the substation to replace the fixed constant configuration coefficient in existing traditional available capacity calculation methods, which can reasonably adjust the configuration coefficient and improve calculation accuracy. Furthermore, by using a SARIMA model for substation power prediction to predict future load peaks in the substation, and using the predicted load peak value for the corresponding year as the data for the available transformer capacity, it effectively avoids the situation of transformer overload or underload caused by directly using historical data as available capacity data in existing algorithms, thereby improving the economy and security of the power system.

Description

Technical Field

[0001] This invention belongs to the field of power technology and relates to distribution system of transformer substations. It is a method for calculating the open capacity of transformer substations based on SARIMA and simultaneity rate. Background Technology

[0002] With the continuous development of the power industry, power companies have been comprehensively analyzing and managing users' electricity supply and demand. They have found that, considering economic factors, the load capacity of distribution transformers cannot be judged solely by the sum of the maximum loads used by all users. Therefore, to ensure a dynamic balance between the reliability of user electricity supply and the economic efficiency of equipment, a reasonable method must be developed to measure the load capacity of distribution transformers. Consequently, the available capacity of distribution transformers in a distribution area has become an important reference indicator for measuring the load capacity of the entire distribution system and for the State Grid Corporation when conducting business expansion and installation applications.

[0003] The calculation of the available capacity of a distribution area involves analyzing and calculating the distribution capacity margin while ensuring the safety of the distribution network load. Reasonable analysis and accurate calculation of the available capacity can optimize the operation of the distribution system, improve line utilization, ensure the safe and economical operation of the distribution transformers in the distribution area, and assist the power company in the steady development of business expansion and installation applications.

[0004] Traditional methods for calculating the available capacity of a distribution transformer area are based on empirical formulas for capacity-to-load ratio. These formulas, based on the concept of capacity-to-load ratio, generalize the transformer capacity and load of the distribution transformer area, while also considering peak-shifting of residential electricity consumption. They use the rated capacity of the distribution transformer at the substation or substation level as the core basis to calculate the available capacity of the distribution transformer from a macroscopic perspective. The formula is as follows:

[0005]

[0006]

[0007] S0 – the transformer's open capacity;

[0008] S—Rated capacity of the transformer, which can be obtained from the production management system by referring to the transformer markings;

[0009] P—The transformer’s historical maximum load is obtained from the electricity information collection system by referring to the meter markings at the corresponding point of the transformer.

[0010] P′ j —Pre-access capacity, refer to transformer identifier, obtained from the marketing system, j represents the pre-access user number;

[0011] α′ i —Pre-access configuration coefficients, query the configuration coefficients based on the pre-access user category;

[0012] α — Configuration coefficient for this application; the configuration coefficient is retrieved based on the user category for this application.

[0013] α′ i And the value of α: 0.6 for residents, 0.9 for non-residents, and the default value is 0.9;

[0014] η——Transformer load rate. If the transformer load rate is not less than 80%, then S0 is set to 0.

[0015] P max —Highest historical load, judging criteria: 80% peak load exceeding 60 minutes or 100% peak load exceeding 15 minutes.

[0016] The above methods often have the following problems:

[0017] (1) The configuration coefficients in the open capacity calculation method are fixed, the calculation accuracy is not high, and the efficiency is low.

[0018] Traditionally, when calculating the available capacity of distribution transformers in a distribution area, the configuration factor typically yields only two values: 0.6 for residential transformers and 0.9 for non-residential transformers. This is designed to satisfy the principle of shared distribution transformer capacity, simply replacing the simultaneity rate of all distribution areas with a fixed constant. However, the simultaneity rate varies when calculated in different units such as hours, days, months, and years. Furthermore, the simultaneity rate of users under the same distribution transformer in a given area can fluctuate dramatically over time, and different types of distribution areas often have different simultaneity rates. Therefore, the traditional method cannot fully reflect the operational changes and electricity consumption characteristics of distribution transformers in different regions.

[0019] (2) When calculating the available capacity of the power station area, the peak load is generally based on data from previous years, which has a certain lag.

[0020] Traditionally, when calculating the available capacity of distribution transformers in a substation area, the maximum load data from previous years is used, and this load data is approximated as the future peak load data under the current number of users. However, due to rapid national economic development, continuous improvement in living standards, socio-economic reforms and industrial restructuring, residential electricity consumption is constantly increasing. Even with a fixed number of users, the overall electricity load of the substation area will show an upward trend. Furthermore, occasional warm winters or cool summers can cause a decrease in the peak load of the distribution transformers in the substation area. Therefore, selecting historical peak values ​​as data for calculating available capacity is not scientifically accurate and has a certain lag.

[0021] To overcome the various shortcomings of traditional open capacity calculation, this invention proposes a method for predicting the open capacity of distribution transformers in a distribution area based on big data technology. The method calculates the open capacity of distribution transformers by establishing a dynamic simultaneity rate database for different distribution areas and using a seasonal autoregressive integrated moving average (SARIMA) load prediction model. Summary of the Invention

[0022] The technical problem this invention aims to solve is: addressing the issues of fixed configuration coefficients, low calculation accuracy, low efficiency, and load peak lag in traditional openable capacity calculation methods. This invention proposes a method for calculating the openable capacity of a transformer area based on SARIMA and simultaneity rate. Specifically, the SARIMA model is used to determine the load peak within a specific time period, and the configuration coefficients for calculating openable capacity are determined by establishing a dynamic simultaneity rate database.

[0023] The technical solution of this invention is: a method for calculating the available capacity of a transformer area based on SARIMA and concurrency rate, comprising the following steps:

[0024] (1) Obtain the three-phase active power data of the transformer area within one year, and preprocess it in hours to obtain the time series data of active power;

[0025] (2) Establish a transformer area load power prediction model based on SARIMA, using the time series data of each phase of the three-phase active power of the transformer area as input, and output the load prediction value for the prediction month.

[0026] (3) Using the preprocessed three-phase active power time series data of the distribution area, with the month as the time unit, calculate the simultaneity rate data of the distribution area for each time period, and establish a dynamic simultaneity rate library for the distribution area. The distribution system of the distribution area as a whole includes multiple subsystem users. The ratio of the total maximum load of the distribution system to the cumulative value of the maximum load of the subsystem users within a certain time period is recorded as the simultaneity rate, and is calculated as follows:

[0027]

[0028] In the formula, P max To select the maximum load value of the transformer in the distribution area within a time period; To select the maximum load P of various subsystem users in the distribution area within a time period imax The sum, where n represents the number of subsystems;

[0029] (4) Based on the load forecast for the forecast month and the dynamic simultaneity rate database, predict the available capacity of the transformer substation for that month. Take the highest operating load forecast for the forecast month and the maximum simultaneity rate in the dynamic simultaneity rate database to calculate the available capacity of the transformer substation:

[0030]

[0031] Compared with existing technologies, the method of the present invention has the following beneficial effects:

[0032] (1) Considering the practical significance of calculating the open capacity of distribution transformers and the physical meaning of the simultaneity rate, this invention monitors the simultaneity rate of distribution areas in real time, uses load big data to establish a monthly dynamic simultaneity rate database for distribution transformer users belonging to different distribution areas, and selects the largest simultaneity rate from the simultaneity rate database to replace the constant configuration coefficient.

[0033] (2) Some existing technologies also use the simultaneous rate to calculate the open capacity, such as CN114239903 and CN108154255. However, these schemes all use a fixed value calculation method when calculating the simultaneous rate, so the calculation results cannot effectively reflect the fluctuations of the power system. The dynamic simultaneous rate library of the present invention fully considers the changing factors such as time and users when calculating, and can more accurately match the specific situation of the corresponding distribution area.

[0034] (3) A seasonal differential autoregressive moving average (SARIMA) model is used to predict the future peak load of the transformer substation. The predicted peak load for the corresponding year is used instead of the historical peak load for the transformer, which serves as the data for the transformer's available capacity. This method can effectively avoid the situation of transformer overload or underload caused by using historical data, thereby improving the economy and security of the power system. Attached Figure Description

[0035] Figure 1 This is a flowchart illustrating the method of the present invention.

[0036] Figure 2 This is a sequence diagram of the A-phase load power data for a certain transformer substation.

[0037] Figure 3 This is a flowchart of the SARIMA-based load forecasting process for transformer substations according to the present invention.

[0038] Figure 4 The images show the autocorrelation and partial autocorrelation plots of the A-phase load power data in an embodiment of the present invention.

[0039] Figure 5 This is a diagram showing the prediction results of phase A by the SARIMA load prediction model for the transformer area according to an embodiment of the present invention. Detailed Implementation

[0040] This invention discloses a method for calculating the available capacity of a transformer substation based on SARIMA and simultaneity rate, the process of which is as follows: Figure 1 As shown, the implementation steps are as follows.

[0041] 1. Acquisition and preprocessing of total three-phase active power data for the transformer area

[0042] Obtain the total three-phase (A, B, C) active power data of the transformer area. The sampling frequency of active power data for each phase is once per hour, and the power data time length is one calendar year.

[0043] The collected total three-phase active power data of the transformer area contains a large number of abnormal data, including missing power data, zero values ​​and other abnormal data. The operation of removing abnormal values ​​and filling missing values ​​in the obtained historical load data results in the three-phase active power data of the transformer area for one natural year after data preprocessing. This data is then used to form time series data for subsequent data.

[0044] Figure 2 This is a sequence diagram of phase A load power data for a certain transformer substation over one year. The processed total three-phase active power data for the substation contains 8784 data points per phase, covering 366 days, with 24 data points per day.

[0045] 2. Establish a SARIMA distribution area load power prediction model

[0046] Using time series data of each phase of the total three-phase active power data of the transformer area as input, a transformer area load power prediction model based on SARIMA is established, and the load prediction value for the prediction month is output. Figure 3 This is a flowchart of the SARIMA-based load forecasting process for transformer substations.

[0047] The SARIMA model is a special type of differencing moving autoregressive model. Compared to the standard ARIMA model, SARIMA extends the seasonal component by using a periodic s-parameter, periodic autoregression (AR), differencing (I), and moving average (MA). This allows for ARIMA calculations over periodic intervals to eliminate additive seasonal effects and reduce prediction errors caused by seasonal trends. The seasonal ARIMA model expression is: SARIMA(p,d,q)(P,D,Q)s, where P, D, and Q describe the parameters with seasonal variations, p, d, and q describe the parameters without seasonal variations, and the s-parameter affects P, D, and Q, representing the seasonal period. For example, in a series model with a monthly seasonal period, s is 12.

[0048] The formula for the SARIMA model is shown below:

[0049]

[0050] in,

[0051] θ(B)=1-θ1B-…-θ q B q ;

[0052] Φ(B s )=1-Φ1B s -…-Φ P B Ps ;

[0053] Θ(B s )=1-Θ1B-…-Θ Q B Qs .

[0054] In the formula, Y t It is a time series of transformer area load data, (1-B) d (1-B s ) D Y t This represents the stationary time series after differencing; B denotes the lag operator, and (1-B) denotes the difference operator. For seasonal autoregressive models, This represents a p-th order autoregressive polynomial. For non-seasonal autoregressive parameters; Φ(B) s ) represents the seasonal autoregressive polynomial, Φ1,Φ2,…Φ P Here are the P-order seasonal autoregressive parameters; θ(B)Θ(B) s Let θ(B) represent the seasonal moving average model, where θ(B) represents the q-th order moving average polynomial, θ1, θ2, ..., θ3. q For non-seasonal moving average parameters, Θ(B) s ) represents the seasonal moving average polynomial, Θ1,Θ2,…,Θ Q Let ε be the parameter of the Q-order seasonal moving average. t It is Gaussian noise.

[0055] like Figure 3 As shown, the process of load forecasting using the SARIMA model is as follows:

[0056] First, sequence stabilization is performed:

[0057] 2.1) Stationarity test: The Augmented Dickey-Fuller test is used to determine whether the input time series is a stationary series; if it is a stationary series, then proceed to 2.3) Model identification and parameter order determination; if it is a non-stationary series, then proceed to 2.2) Difference processing.

[0058] 2.2) Difference Processing: The input time series is subjected to d-order difference processing to transform it into a stationary series. The formula for difference operation is shown below:

[0059]

[0060] 2.3) Calculate the autocorrelation and partial autocorrelation coefficients of the stationary time series and plot them to obtain the autocorrelation coefficient plot ACF and the partial autocorrelation coefficient plot PACF, and preliminarily determine the model parameters. Figure 4 The autocorrelation plot and partial autocorrelation plot are for the load power data of phase A.

[0061] By observing the changing trends of the partial autocorrelation plot and the two curves in the autocorrelation plot, such as tailing and truncation, we can determine which model is suitable for the input time series data and preliminarily determine the value range of the SARIMA model parameters p and q.

[0062] Figure 4 In the figure, (a) is the ACF plot and (b) is the PACF plot. Both curves show a tailing state, and the values ​​of p and q need to be further determined according to the AIC criterion.

[0063] AR(p) MA(q) ARMA(p,q) Partial autocorrelation plot p-order truncation Trail Trail Autocorrelation plot Trail q-order truncation Trail

[0064] After the time series data stabilizes, model identification and parameter order determination are performed: The Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) are used to screen and determine model parameters. The AIC criterion is then used to determine parameter values, and the model residual statistics are calculated. Based on the BIC criterion, the residuals are verified to determine the suitability of the model parameters. If suitable, prediction is performed; otherwise, the parameter values ​​are redefined and recalculated. The Akaike Information Criterion (AIC) is a standard for measuring the goodness of fit of a statistical model, and its calculation formula is shown below:

[0065] AIC = -2ln(L) + 2k

[0066] The formula for calculating the Bayesian Information Criterion (BIC) is shown below:

[0067] BIC = -2ln(L) + ln(n) * k

[0068] Where L is the maximum likelihood estimate of the input time series, n is the length of the input stationary time series, and k is the number of variables in the SARIMA model.

[0069] The analysis takes the processed active power of phase A in a certain transformer substation as an example. The parameters of the SARIMA model established based on the historical load data of this substation are p=8, d=0, q=7, P=2, D=0, Q=2, s=24. Figure 5 The graph shows the prediction results of the SARIMA load forecasting model for the distribution area. It displays the curves of the predicted data (solid line) and the original actual data (dashed line). The graph shows that the prediction results are good.

[0070] 3. Calculate the simultaneity rate of transformer substations and establish a dynamic simultaneity rate database for transformer substations.

[0071] Using preprocessed three-phase active power data of the distribution area, and with monthly time units, the simultaneity rate data of the distribution area is calculated, and a dynamic simultaneity rate database for the distribution area is established. The distribution system of the entire distribution area includes multiple subsystem users. Within a certain time period, the ratio of the total maximum load of the distribution area system to the cumulative maximum load of the subsystem users is recorded as the simultaneity rate. The formula for calculating the simultaneity rate is as follows:

[0072]

[0073] In the formula, P max This represents the maximum load value of the transformer in the distribution area during the current time period.

[0074] The maximum load P of various subsystem users in the distribution area during the previous time period. imax The sum, where n represents the number of subsystems.

[0075] 4. Calculate the available capacity of the calculation area.

[0076] Combining the future load change data of the distribution area obtained from the SARIMA-based distribution area load power prediction model in step 2, and the dynamic simultaneity rate database of the distribution area calculated in step 3, the highest operating load value of the predicted month and the maximum simultaneity rate data in the dynamic simultaneity rate database of the distribution area are taken to calculate the available capacity of the distribution area. The calculation formula for available capacity is shown below.

[0077]

[0078] The capacity of the power distribution facility is an inherent property of the transformer itself. The initial rated capacity of this distribution area is 100KW. The maximum simultaneity rate of phase A in the dynamic simultaneity rate database obtained from step 3 is 0.791. The highest operating load value of phase A in the predicted month is 51.732KW obtained from step 2. The open capacity of phase A in the predicted month is calculated to be 61.02KW.

[0079] When acquiring three-phase active power data for a distribution transformer area, the more time-series data points there are, and the closer the acquired year is to the predicted time, the more accurate the predicted available capacity will be. Furthermore, this invention can accommodate the operational changes and power consumption characteristics of distribution transformers in each distribution transformer area, establishing separate load power prediction models and dynamic simultaneity rate libraries for different distribution transformer areas to predict the monthly available capacity for the corresponding areas.

Claims

1. A method for calculating the available capacity of a transformer substation based on SARIMA and simultaneity rate, characterized by: Includes the following steps: (1) Obtain the three-phase active power data of the transformer area within a natural year, and preprocess it to obtain the time series data of active power; (2) Establish a transformer area load power prediction model based on SARIMA, using the time series data of each phase of the three-phase active power of the transformer area as input, and output the load prediction value for the prediction month. (3) Using the preprocessed three-phase active power time series data of the distribution area, with the month as the time unit, calculate the simultaneity rate data of the distribution area for each time period, and establish a dynamic simultaneity rate library for the distribution area. The distribution system of the distribution area as a whole includes multiple subsystem users. The ratio of the total maximum load of the distribution system to the cumulative value of the maximum load of the subsystem users within a certain time period is recorded as the simultaneity rate, and is calculated as follows: In the formula, P max To select the maximum load value of the transformer in the distribution area within a time period; To select the maximum load P of various subsystem users in the distribution area within a time period imax The sum, where n represents the number of subsystems; (4) Based on the load forecast for the forecast month and the dynamic simultaneity rate database, predict the available capacity of the transformer substation for that month. Take the highest operating load forecast for the forecast month and the maximum simultaneity rate in the dynamic simultaneity rate database to calculate the available capacity of the transformer substation:

2. The method for calculating the openable capacity of a transformer area based on SARIMA and simultaneity rate according to claim 1, characterized in that: When acquiring three-phase active power data for a transformer substation, the more time series data points there are, and the closer the acquired year is to the predicted time, the more accurate the predicted available capacity will be.

3. The method for calculating the openable capacity of a transformer area based on SARIMA and simultaneity rate according to claim 1 or 2, characterized in that: For different transformer substations, separate load power prediction models and dynamic simultaneity rate databases are established to predict the monthly available capacity of the corresponding transformer substations.

4. The method for calculating the openable capacity of a transformer area based on SARIMA and simultaneity rate according to claim 1, characterized in that the formula for the SARIMA model is as follows: in, θ(B)=1-θ1B-…-θ q B q Φ(B s )=1-Φ1B s -…-F P B Ps Θ(B s )=1-Θ1B-…-Θ Q B Qs In the formula, Y t It is the time series of the transformer area load data, that is, the time series data of active power, (1-B) d (1-B s ) D Y t This represents the stationary time series after differencing; B denotes the lag operator, and (1-B) denotes the difference operator. For seasonal autoregressive models, This represents a p-th order autoregressive polynomial. For non-seasonal autoregressive parameters; Φ(B) s ) represents the seasonal autoregressive polynomial, Φ1,Φ2,…Φ P Here are the P-order seasonal autoregressive parameters; θ(B)Θ(B) s Let θ(B) represent the seasonal moving average model, where θ(B) represents the q-th order moving average polynomial, θ1, θ2, ..., θ3. q For non-seasonal moving average parameters, Θ(B) s ) represents the seasonal moving average polynomial, Θ1,Θ2,…,Θ Q Let ε be the parameter of the Q-order seasonal moving average. t The noise is Gaussian, and the s-parameter represents the seasonal period.

5. The method for calculating the openable capacity of a transformer area based on SARIMA and simultaneity rate according to claim 4. Its characteristic is that the prediction process of the SARIMA model is as follows: First, perform sequence stabilization: 2.1) Stationarity test: The Augmented Dickey-Fuller test is used to determine whether the input time series is a stationary series; if it is a stationary series, then proceed to 2.3) Model identification and parameter order determination; if it is a non-stationary series, then proceed to 2.2) Difference processing. 2.2) Difference Processing: The input time series is subjected to d-order difference processing to transform it into a stationary series. The formula for difference operation is shown below: ▽ d AND t =▽ d-1 AND t -▽ d-2 AND t-1 Y t This represents the input time series; 2.3) Calculate the autocorrelation and partial autocorrelation coefficients of the stationary time series and plot them to obtain the autocorrelation coefficient plot ACF and the partial autocorrelation coefficient plot PACF, and preliminarily determine the model parameters; After sequence stabilization, model identification and parameter order determination were performed based on the autocorrelation coefficient plot (ACF) and partial autocorrelation coefficient plot (PACF). The model parameters were then selected and determined using the Akaike information criterion and the Bayesian information criterion. The formula for calculating the Akaike Information Content Criterion is as follows: AIC = -2ln(L) + 2k The formula for calculating the Bayesian information criterion is as follows: BIC = -2ln(L) + ln(n) * k Where L is the maximum likelihood estimate of the input time series, n is the length of the input stationary time series, and k is the number of variables in the SARIMA model; The parameters of the SARIMA model are determined by combining the AIC criterion and the residual statistics of the model are calculated. The model parameters are then checked for suitability by the residuals according to the BIC criterion. If they are suitable, prediction is performed. If they are not suitable, the parameter values ​​are re-determined and the BIC is recalculated until the SARIMA model parameters suitable for the current input sequence are obtained, and prediction is then performed.

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