Heavy overload prediction method for distribution transformers in substations based on cloud-edge collaboration

Abstract

The present invention proposes a method for predicting heavy overload of distribution transformers in substations based on cloud-edge collaboration, which is completed by the coordinated cooperation of the distribution master station on the cloud computing end and the substation intelligent terminal on the edge computing end; the prediction process includes the following steps: step 1, the distribution master station selects the substation distribution transformer to be predicted based on historical data, calculates the maximum load rate of the distribution transformer in the historical same period of the current time period and the time period to be predicted, and the basic load rate of the substation to be predicted; step 2, the distribution master station transmits the historical load rate of the substation to the substation intelligent terminal, the substation intelligent terminal calculates the maximum load rate of the distribution transformer in the current time period, and calculates the maximum load rate of the distribution transformer in the time period to be predicted; step 3, the substation intelligent terminal determines whether the distribution transformer is heavily overloaded in the time period to be predicted based on the calculation results; the present invention realizes the prediction of heavy overload of distribution transformers in the substation through the coordinated cooperation of the distribution master station and the substation intelligent terminal, provides a reference basis for substation transformation, and improves the power supply reliability of the substation.

Description

Technical Field

[0001] The present invention relates to the field of power grid operation and maintenance technology, and in particular to a method for predicting heavy overload of distribution transformers in a substation based on cloud-edge collaboration. Background Art

[0002] Substation distribution transformers are important equipment for supplying power to ordinary users. Their heavy load and overload operation will not only increase additional losses, but also affect the life of the equipment and may even cause it to burn out. In actual applications, substation distribution transformers should avoid heavy load and overload operation. However, user load will continue to increase with economic development, and it is necessary to predict the substation distribution load. Traditional methods generally take measures to transform the substation only after monitoring that the substation distribution transformer is close to heavy load. At this time, it may not be possible to avoid the occurrence of heavy overload of the substation distribution transformer. Therefore, it is necessary to predict the load rate of the substation distribution transformer before the substation distribution transformer is severely overloaded. This can plan the transformation measures of the substation in advance, avoid the occurrence of heavy overload of the substation distribution transformer, and improve the reliability of the power supply to the substation. Summary of the Invention

[0003] The present invention proposes a method for predicting heavy overload of distribution transformers in substations based on cloud-edge collaboration. Through the coordinated cooperation of the distribution master station and the substation intelligent terminal, the prediction of heavy overload of distribution transformers in substations is realized, which provides a reference basis for substation transformation and improves the power supply reliability of substations.

[0004] The present invention adopts the following technical solutions.

[0005] The prediction method for heavy overload of distribution transformers in substations based on cloud-edge collaboration is completed by the coordinated cooperation of the distribution master station on the cloud computing end and the substation intelligent terminal on the edge computing end; the prediction process includes the following steps:

[0006] Step 1: The distribution master station selects the distribution transformer of the area to be predicted based on historical data, calculates the maximum load rate of the distribution transformer in the current period and the period to be predicted, and the basic load rate of the area to be predicted;

[0007] Step 2: The distribution master station transmits the historical load rate of the substation to the substation intelligent terminal, which calculates the maximum load rate of the distribution transformer in the current period and the maximum load rate of the distribution transformer in the predicted period;

[0008] Step 3: The intelligent terminal in the substation area determines whether the distribution transformer is overloaded during the predicted time period based on the calculation results.

[0009] When the distribution master station selects the substation distribution transformer to be predicted, the selection method adopted is: predict the substation distribution transformer whose historical load rate in the same period of the predicted time period exceeds 60% and lasts for more than 1 hour.

[0010] The selection method includes the following calculation steps:

[0011] Step SA1: The distribution master station calculates the maximum load rate of the distribution transformer that lasted for more than 1 hour during the same period of the current time period based on historical data:

[0012] Z 01 =(P 01 / S 01 )×100% Formula 1,

[0013] Where, P 01 is the maximum load of the distribution transformer in the same period of history (kW), S 01 is the distribution transformer capacity during the same period in history (kVA);

[0014] Step SA2: The distribution master station calculates the maximum load rate of the distribution transformer that lasts for more than 1 hour during the same period of the historical period of the predicted time period based on historical data:

[0015] Z 02 =(P 02 / S 02 )×100% Formula 2,

[0016] Where, P 02 is the maximum load of the distribution transformer during the same period in history (kW), S 02 The distribution transformer capacity (kVA) during the same historical period;

[0017] Step SA3: The distribution master station obtains the basic load rate Z of the distribution transformer in the area based on the average load rate of the distribution transformer in the area when the temperature is 20℃~25℃ in historical years. 03 , and provide it to the smart terminals in the substation area.

[0018] The method for calculating the load rate of the substation area during the intended prediction period by the substation area intelligent terminal includes the following steps:

[0019] Step SB1: Calculate the maximum load rate of the distribution transformer that lasts for more than 1 hour in the current time period:

[0020] Z 11 =(P 11 / S 11 )×100% Formula 3,

[0021] Where, P 11 is the maximum load of the distribution transformer in the current time period (kW), S 11 The distribution transformer capacity (kVA) for the current time period;

[0022] Step SB2: Calculate the predicted value of the maximum load factor of the distribution transformer for the intended prediction period lasting more than 1 hour:

[0023] Z 12 = (P 11 +(P 02 -P 01)) / S 12 ×100% Formula 4,

[0024] Where S 12 The distribution transformer capacity (kVA) in the current time period.

[0025] The basis for the intelligent computing terminal in the substation area to determine whether the distribution transformer is overloaded includes:

[0026] Criterion 1: Heavy overload during the same period in history and Z 12 >75%, it is determined to be a high probability heavily overloaded distribution transformer;

[0027] Criterion 2: (Z 02 - Z 03 )>15% and Z 12 >75%, it is determined to be a high probability heavily overloaded distribution transformer, where (Z 02 - Z 03 )>15% of the distribution transformer load is temperature sensitive load, Z 03 The basic load rate of the distribution transformer in the substation area;

[0028] Criterion 3: Other Z 12 >80%, it is determined to be a distribution transformer with a high probability of severe overload.

[0029] The intelligent computing terminals in the substation area include intelligent distribution and transformation terminals and intelligent fusion terminals.

[0030] When the load factor of the distribution transformer in the prediction result of the prediction method is greater than 200%, the prediction method checks the rationality of the basic data.

[0031] The substation is a low-voltage distribution substation, and each substation includes a substation intelligent terminal and a distribution transformer; the substation intelligent terminal collects the operating data of the distribution transformer and sends it to the distribution master station. The operating data includes voltage, current, and power. The substation intelligent terminal can save historical data of the distribution transformer for a preset period of time.

[0032] The intelligent computing terminal in the substation will feed back the judgment results to the main distribution station, and the main distribution station will generate a work order to carry out early transformation of the substation where heavy overload may occur to avoid overloading of the distribution transformer.

[0033] The present invention and its preferred embodiments have the following advantages or beneficial effects:

[0034] A) A method for predicting heavy overload of distribution transformers in substations based on cloud-edge collaboration is proposed to realize heavy overload prediction of distribution transformers in substations, which has obvious feasibility and economic benefits.

[0035] B) Provide a reference for substation renovation, avoid heavy overload in the substation, and improve the reliability of power supply in the substation. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:

[0037] Attachment Figure 1 This is a schematic diagram of a low-voltage distribution station area according to an embodiment of the present invention;

[0038] Attachment Figure 2 This is a flow chart of a method for predicting heavy overload of distribution transformers in substations based on cloud-edge collaboration in an embodiment of the present invention. DETAILED DESCRIPTION

[0039] Hereinafter, specific embodiments of the present application will be described in detail with reference to the accompanying drawings. Based on these detailed descriptions, those skilled in the art will be able to clearly understand the present application and implement the present application. Without violating the principles of the present application, the features of different embodiments may be combined to obtain new implementations, or certain features of certain embodiments may be substituted to obtain other preferred implementations.

[0040] To make the features and advantages of this patent more clearly understood, the following embodiments are specifically described in detail with reference to the accompanying drawings:

[0041] As shown in the figure, the prediction method for heavy overload of distribution transformers in substations based on cloud-edge collaboration is completed by the coordinated cooperation of the distribution master station on the cloud computing end and the substation intelligent terminal on the edge computing end; the prediction process includes the following steps:

[0042] Step 1: The distribution master station selects the distribution transformer of the area to be predicted based on historical data, calculates the maximum load rate of the distribution transformer in the current period and the period to be predicted, and the basic load rate of the area to be predicted;

[0043] Step 2: The distribution master station transmits the historical load rate of the substation to the substation intelligent terminal, which calculates the maximum load rate of the distribution transformer in the current period and the maximum load rate of the distribution transformer in the predicted period;

[0044] Step 3: The intelligent terminal in the substation area determines whether the distribution transformer is overloaded during the predicted time period based on the calculation results.

[0045] When the distribution master station selects the substation distribution transformer to be predicted, the selection method adopted is: predict the substation distribution transformer whose historical load rate in the same period of the predicted time period exceeds 60% and lasts for more than 1 hour.

[0046] The selection method includes the following calculation steps:

[0047] Step SA1: The distribution master station calculates the maximum load rate of the distribution transformer that lasted for more than 1 hour during the same period of the current time period based on historical data:

[0048] Z 01 =(P 01 / S 01 )×100% Formula 1,

[0049] Where, P 01 is the maximum load of the distribution transformer in the same period of history (kW), S 01 is the distribution transformer capacity during the same period in history (kVA);

[0050] Step SA2: The distribution master station calculates the maximum load rate of the distribution transformer that lasts for more than 1 hour during the same period of the historical period of the predicted time period based on historical data:

[0051] Z 02 =(P 02 / S 02 )×100% Formula 2,

[0052] Where, P 02 is the maximum load of the distribution transformer during the same period in history (kW), S 02 The distribution transformer capacity (kVA) during the same historical period;

[0053] Step SA3: The distribution master station obtains the basic load rate Z of the distribution transformer in the area based on the average load rate of the distribution transformer in the area when the temperature is 20℃~25℃ in historical years. 03 , and provide it to the smart terminals in the substation area.

[0054] The method for calculating the load rate of the substation area during the intended prediction period by the substation area intelligent terminal includes the following steps:

[0055] Step SB1: Calculate the maximum load rate of the distribution transformer that lasts for more than 1 hour in the current time period:

[0056] Z 11 =(P 11 / S 11 )×100% Formula 3,

[0057] Where, P 11 is the maximum load of the distribution transformer in the current time period (kW), S 11 The distribution transformer capacity (kVA) for the current time period;

[0058] Step SB2: Calculate the predicted value of the maximum load factor of the distribution transformer for the intended prediction period lasting more than 1 hour:

[0059] Z 12 = (P 11 +(P 02 -P 01 )) / S 12 ×100% Formula 4,

[0060] Where S 12 The distribution transformer capacity (kVA) in the current time period.

[0061] The basis for the intelligent computing terminal in the substation area to determine whether the distribution transformer is overloaded includes:

[0062] Criterion 1: Heavy overload during the same period in history and Z 12 >75%, it is determined to be a high probability heavily overloaded distribution transformer;

[0063] Criterion 2: (Z 02 - Z 03 )>15% and Z 12 >75%, it is determined to be a high probability heavily overloaded distribution transformer, where (Z 02 - Z 03 )>15% of the distribution transformer load is temperature sensitive load, Z 03 The basic load rate of the distribution transformer in the substation area;

[0064] Criterion 3: Other Z 12 >80%, it is determined to be a distribution transformer with a high probability of severe overload.

[0065] The intelligent computing terminals in the substation area include intelligent distribution and transformation terminals and intelligent fusion terminals.

[0066] When the load factor of the distribution transformer in the prediction result of the prediction method is greater than 200%, the prediction method checks the rationality of the basic data.

[0067] The substation is a low-voltage distribution substation, and each substation includes a substation intelligent terminal and a distribution transformer; the substation intelligent terminal collects the operating data of the distribution transformer and sends it to the distribution master station. The operating data includes voltage, current, and power. The substation intelligent terminal can save historical data of the distribution transformer for a preset period of time.

[0068] The intelligent computing terminal in the substation will feed back the judgment results to the main distribution station, and the main distribution station will generate a work order to carry out early transformation of substations that may be overloaded, avoid overloading of distribution transformers, and improve power supply reliability.

[0069] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

[0070] This patent is not limited to the above-mentioned optimal implementation method. Anyone can derive various other forms of substation distribution transformer overload prediction methods based on cloud-edge collaboration under the inspiration of this patent. All equal changes and modifications made according to the scope of the patent application of this invention should be covered by this patent.

Claims

1. A method for predicting heavy overload of distribution transformers in substations based on cloud-edge collaboration, characterized by: The prediction method is completed by the coordination of the power distribution master station on the cloud computing end and the intelligent terminal in the substation area on the edge computing end; the prediction process includes the following steps: Step 1: The distribution master station selects the distribution transformer of the area to be predicted based on historical data, calculates the maximum load rate of the distribution transformer in the current period and the period to be predicted, and the basic load rate of the area to be predicted; Step 2: The distribution master station transmits the historical load rate of the substation to the substation intelligent terminal, which calculates the maximum load rate of the distribution transformer in the current period and the maximum load rate of the distribution transformer in the predicted period; Step 3: The intelligent terminal in the substation area determines whether the distribution transformer is severely overloaded during the predicted time period based on the calculation results; When the distribution master station selects the distribution transformers for the planned forecast, the selection method is as follows: forecast the distribution transformers for the planned forecast period where the historical load rate exceeds 60% and lasts for more than 1 hour; The selection method includes the following calculation steps: Step SA1: The distribution master station calculates the maximum load rate of the distribution transformer that lasted for more than 1 hour during the same period of the current time period based on historical data: Z 01 =(P 01 / S 01 )×100% Formula 1, Where, P 01 is the maximum load of the distribution transformer in the same period of history, unit is kW; S 01 The distribution transformer capacity in the same period of history, unit is kVA; Step SA2: The distribution master station calculates the maximum load rate of the distribution transformer that lasts for more than 1 hour during the same period of the historical period of the predicted time period based on historical data: Z 02 =(P 02 / S 02 )×100% Formula 2, Where, P 02 The maximum load of the distribution transformer in the same period of history, unit: kW; S 02 The distribution transformer capacity during the same historical period, in kVA; Step SA3: The distribution master station obtains the basic load rate Z of the distribution transformer in the area based on the average load rate of the distribution transformer in the area when the temperature is 20℃~25℃ in historical years. 03 , and provide it to the intelligent terminal in the substation area; The method for calculating the load rate of the substation area during the intended prediction period by the substation area intelligent terminal includes the following steps: Step SB1: Calculate the maximum load rate of the distribution transformer that lasts for more than 1 hour in the current time period: Z 11 =(P 11 / S 11 )×100% Formula 3, Where, P 11 The maximum load of the distribution transformer in the current time period, in kW; S 11 The distribution transformer capacity in the current time period, in kVA; Step SB2: Calculate the predicted value of the maximum load factor of the distribution transformer for the intended prediction period lasting more than 1 hour: Z 12 =(P 11 +(P 02 -P 01 )) / S 12 ×100% Formula 4, Where S 12 The distribution transformer capacity in the current time period, in kVA; The intelligent computing terminal in the substation area determines whether the distribution transformer is overloaded based on the following criteria: Criterion 1: Heavy overload during the same period in history and Z 12 >75%, it is determined to be a high probability heavily overloaded distribution transformer; Criterion 2: (Z 02 -Z 03 )>15% and Z 12 >75%, it is determined to be a high probability heavily overloaded distribution transformer, where (Z 02 -Z 03 )>15% of the distribution transformer load is temperature sensitive load, Z 03 The basic load rate of the distribution transformer in the substation area; Criterion 3: Other Z 12 >80%, it is judged as a high probability heavily overloaded distribution transformer.

2. The method for predicting heavy overload of distribution transformers in a substation area based on cloud-edge collaboration according to claim 1 is characterized by: The intelligent computing terminal in the substation area includes an intelligent distribution transformer terminal or an intelligent fusion terminal.

3. The method for predicting heavy overload of distribution transformers in substations based on cloud-edge collaboration according to claim 1 is characterized by: When the load factor of the distribution transformer in the prediction result of the prediction method is greater than 200%, the prediction method checks the rationality of the basic data.

4. The method for predicting heavy overload of distribution transformers in a substation area based on cloud-edge collaboration according to claim 1 is characterized by: The substation is a low-voltage distribution substation, and each substation includes a substation intelligent terminal and a distribution transformer; the substation intelligent terminal collects the operating data of the distribution transformer and sends it to the distribution master station. The operating data includes voltage, current and power. The substation intelligent terminal saves the historical data of the distribution transformer for a preset period of time.

5. The method for predicting heavy overload of distribution transformers in substations based on cloud-edge collaboration according to claim 4 is characterized by: The intelligent computing terminal in the substation will feed back the judgment results to the main distribution station, and the main distribution station will generate a work order to carry out early transformation of the substation where heavy overload may occur to avoid overloading of the distribution transformer.

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