Transformer area load management method and system of intelligent distribution transformer terminal based on local demand

Abstract

The invention discloses a local demand-based area load management method and system for an intelligent distribution transformer terminal. The method comprises the following steps: dividing the load prediction time of a selected test distribution transformer district into 12 month time periods every year, and dividing each month time period into 30 day time periods; respectively counting historical data of each monthly time period and each daily time period of the test distribution transformer district; load prediction of the test distribution transformer district is carried out, an early warning index is obtained, and load management of the test distribution transformer district is carried out; based on power, wind speed and illumination data collected by an intelligent distribution transformer terminal, data analysis and integration are carried out by using a mathematical formula, an early warning index is calculated, test distribution transformer district load management is carried out according to the early warning index, and when an E value is lower than a preset threshold, district load prediction is carried out based on a load growth rate, and overload early warning is carried out. By means of the scheme, load prediction is conducted on the operation condition of the transformer area, and then the power supply capacity of the urban power distribution network can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of power distribution technology, and in particular to a method and system for managing loads in a distribution area of ​​an intelligent distribution transformer terminal based on local demand. Background Art

[0002] Intelligent distribution transformer terminal refers to the distribution transformer supervisory terminal unit (TTU), which is a typical representative of the intelligent terminal of the distribution system. Its basic function is to realize real-time information collection and control of the distribution transformer, and to transmit the collected information to the master station or other intelligent devices, providing the data required for the operation control and management of the distribution system.

[0003] At present, most studies only focus on the advantages of intelligent distribution transformer terminals themselves in improving information collection and communication efficiency, without in-depth integration with the operation of the distribution system. After the intelligent distribution transformer terminals are connected, there is a lack of effective management of the substation load. Summary of the invention

[0004] The object of the present invention is to provide a method and system for managing loads in a distribution area of ​​an intelligent distribution transformer terminal based on local demand, so as to improve the effective management of loads in the distribution area.

[0005] To achieve the purpose of the present invention, the technical solution provided by the present invention is specifically as follows:

[0006] First aspect

[0007] The present invention provides a method for managing load of a distribution transformer terminal based on local demand, comprising the following steps:

[0008] Step S1: Divide the load forecast time of the selected test distribution transformer area into 12 monthly time periods each year, and each monthly time period is divided into 30 daily time periods;

[0009] Step S2: Count the historical data of each monthly time period and daily time period of the test distribution transformer area, and the historical data includes the daily average load P t , Photovoltaic meter power P pvs , Rated power P of distributed wind power rated , Cut-in wind speed v ci , Cut-out wind speed v co and rated wind speed v rated , wind speed v, r s represents the rated light intensity, r is the light intensity;

[0010] Step S3: Based on the historical data, load forecasting of the test distribution transformer area is performed to obtain early warning indicators, and load management of the test distribution transformer area is performed according to the early warning indicators; specifically, the steps include:

[0011] Step S3.1: Calculate the total current of the test distribution transformer area, the formula is as follows:

[0012] A ij =∑(a ij ) m

[0013] In the formula, A ij is the total current of the area on the jth day of the ith month, m is the number of lines; a ij is the current of each line, calculated as follows:

[0014]

[0015] Where P pv is the rated power of distributed photovoltaic, P dwg Wind power active output, Ve is the rated voltage;

[0016] Step S3.2: Calculate the early warning index E based on the current of each line, as follows:

[0017]

[0018] In the formula, m is the number of lines and n is the number of contacts;

[0019]

[0020] Where, X oj =max(X ij ), ρ is a number between 0 and 1;

[0021]

[0022] Step S3.3: Perform load management of the test distribution transformer substation according to the early warning index. When the E value is lower than the preset threshold, perform load forecasting of the substation based on the load growth rate and issue an overload warning.

[0023] Among them, the wind power active output P dwg The calculation method is as follows:

[0024]

[0025] Wherein, the rated power P of the distributed photovoltaic pv The calculation method is as follows:

[0026]

[0027] Second aspect

[0028] The present invention provides a load management system for a substation area of ​​an intelligent distribution transformer terminal based on local demand, comprising the following units: a time division unit, a historical data statistics unit, and a load management unit;

[0029] The time division unit is used to divide the load forecast time of the selected test distribution transformer area into 12 monthly time periods each year, and each monthly time period is divided into 30 daily time periods;

[0030] The historical data statistics unit is used to respectively count the historical data of each monthly time period and daily time period of the test distribution transformer area, and the historical data includes the daily average load P t , Photovoltaic meter power P pvs , Rated power P of distributed wind power rated , Cut-in wind speed v ci , Cut-out wind speed v co and rated wind speed v rated , wind speed v, r s represents the rated light intensity, r is the light intensity;

[0031] The load management unit is used to perform load forecasting of the test distribution transformer area based on the historical data, obtain early warning indicators, and perform load management of the test distribution transformer area according to the early warning indicators; specifically includes the following:

[0032] Step S3.1: Calculate the total current of the test distribution transformer area, the formula is as follows:

[0033] A ij =∑(a ij ) m

[0034] In the formula, A ij is the total current of the area on the jth day of the ith month, m is the number of lines; a ij is the current of each line, calculated as follows:

[0035]

[0036] Where P pv is the rated power of distributed photovoltaic, P dwg Wind power active output, Ve is the rated voltage;

[0037] Step S3.2: Calculate the early warning index E based on the current of each line, as follows:

[0038]

[0039] In the formula, m is the number of lines and n is the number of contacts;

[0040]

[0041] Where, X oj =max(X ij ), ρ is a number between 0 and 1;

[0042]

[0043] Step S3.3: Perform load management of the test distribution transformer substation according to the early warning index. When the E value is lower than the preset threshold, perform load forecasting of the substation based on the load growth rate and issue an overload warning.

[0044] Among them, the wind power active output p dwg The calculation method is as follows:

[0045]

[0046] Wherein, the rated power P of the distributed photovoltaic pv The calculation method is as follows:

[0047]

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] This application is based on the power, wind speed and light data collected by the intelligent distribution transformer terminal, and uses a newly created mathematical formula model to analyze and integrate data, calculate early warning indicators, and perform load management of the test distribution transformer area according to the early warning indicators. When the E value is lower than the preset threshold, the load growth rate is used to predict the load of the area and to issue an overload warning. Using the solution of the present invention, the load of the area operation is predicted, which can effectively improve the power supply capacity of the urban distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A schematic diagram of the method flow provided in an embodiment of the present application. DETAILED DESCRIPTION

[0051] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0052] like Figure 1 As shown, this embodiment provides a method for managing load of a smart distribution transformer terminal based on local demand, comprising the following steps:

[0053] Step S1: Divide the load forecast time of the selected test distribution transformer area into 12 monthly time periods each year, and each monthly time period is divided into 30 daily time periods;

[0054] Step S2: Count the historical data of each monthly time period and daily time period of the test distribution transformer area, and the historical data includes the daily average load P t , Photovoltaic meter power P pvs , Rated power P of distributed wind power rated , Cut-in wind speed v ci , Cut-out wind speed v co and rated wind speed v rated , wind speed v, r s represents the rated light intensity, r is the light intensity;

[0055] Step S3: Based on the historical data, load forecasting of the test distribution transformer area is performed to obtain early warning indicators, and load management of the test distribution transformer area is performed according to the early warning indicators; specifically, the steps include:

[0056] Step S3.1: Calculate the total current of the test distribution transformer area, the formula is as follows:

[0057] A ij =Σ(a ij ) m

[0058] In the formula, A ij is the total current of the area on the jth day of the ith month, m is the number of lines; a ij is the current of each line, calculated as follows:

[0059]

[0060] Where P pv is the rated power of distributed photovoltaic, P dwg Wind power active output, Ve is the rated voltage;

[0061] Among them, the wind power active output p dwg The calculation method is as follows:

[0062]

[0063] Among them, the rated power P of the distributed photovoltaic pv The calculation method is as follows:

[0064]

[0065] Step S3.2: Calculate the early warning index E based on the current of each line, as follows:

[0066]

[0067] In the formula, m is the number of lines and n is the number of contacts;

[0068]

[0069] Where, X oj =max(X ij ), ρ is a number between 0 and 1;

[0070]

[0071] Step S3.3: Perform load management of the test distribution transformer substation according to the early warning index. When the E value is lower than the preset threshold, perform load forecasting of the substation based on the load growth rate and issue an overload warning.

[0072] Corresponding to the above method, this embodiment provides a load management system for a substation area of ​​an intelligent distribution transformer terminal based on local demand, comprising the following units: a time division unit, a historical data statistics unit, and a load management unit;

[0073] The time division unit is used to divide the load forecast time of the selected test distribution transformer area into 12 monthly time periods each year, and each monthly time period is divided into 30 daily time periods;

[0074] The historical data statistics unit is used to respectively count the historical data of each monthly time period and daily time period of the test distribution transformer area, and the historical data includes the daily average load P t , Photovoltaic meter power P pvs , Rated power P of distributed wind power rated , Cut-in wind speed v ci , Cut-out wind speed v co and rated wind speed v rated , wind speed v, r s represents the rated light intensity, r is the light intensity;

[0075] The load management unit is used to perform load forecasting of the test distribution transformer area based on the historical data, obtain early warning indicators, and perform load management of the test distribution transformer area according to the early warning indicators; specifically includes the following:

[0076] Step S3.1: Calculate the total current of the test distribution transformer area, the formula is as follows:

[0077] A ij =Σ(a ij ) m

[0078] In the formula, A ij is the total current of the area on the jth day of the ith month, m is the number of lines; a ij is the current of each line, calculated as follows:

[0079]

[0080] Where P pv is the rated power of distributed photovoltaic, P dwg Wind power active output, Ve is the rated voltage;

[0081] Among them, the wind power active output p dwg The calculation method is as follows:

[0082]

[0083] Among them, the rated power P of the distributed photovoltaic pv The calculation method is as follows:

[0084]

[0085] Step S3.2: Calculate the early warning index E based on the current of each line, as follows:

[0086]

[0087] In the formula, m is the number of lines and n is the number of contacts;

[0088]

[0089] Where, X oj =max(X ij ), ρ is a number between 0 and 1;

[0090]

[0091] Step S3.3: Perform load management of the test distribution transformer substation according to the early warning index. When the E value is lower than the preset threshold, perform load forecasting of the substation based on the load growth rate and issue an overload warning.

[0092] This application is based on the power, wind speed and light data collected by the intelligent distribution transformer terminal, and uses a newly created mathematical formula model to analyze and integrate data, calculate early warning indicators, and perform load management of the test distribution transformer area according to the early warning indicators. When the E value is lower than the preset threshold, the load growth rate is used to predict the load of the area and to issue an overload warning. Using the solution of the present invention, the load of the area operation is predicted, which can effectively improve the power supply capacity of the urban distribution network.

[0093] It should be noted that all solutions not described in detail in this application adopt known technologies.

[0094] The optional implementation modes of the embodiments of the present invention are described in detail above in conjunction with the accompanying drawings. However, the embodiments of the present invention are not limited to the specific details in the above implementation modes. Within the technical concept of the embodiments of the present invention, various simple modifications can be made to the technical scheme of the embodiments of the present invention, and these simple modifications all belong to the protection scope of the embodiments of the present invention.

Claims

1. A method for load management of a smart distribution transformer terminal based on local demand, characterized in that: The following steps are involved: Step S1: Divide the load forecast time of the selected test distribution transformer area into 12 monthly time periods each year, and each monthly time period is divided into 30 daily time periods; Step S2: Count the historical data of each monthly time period and daily time period of the test distribution transformer area, and the historical data includes the daily average load P t , Photovoltaic meter power P pvs , Rated power P of distributed wind power rated , Cut-in wind speed v ci , Cut-out wind speed v co and rated wind speed v rated , wind speed v, r s represents the rated light intensity, r is the light intensity; Step S3: Based on the historical data, load forecasting of the test distribution transformer area is performed, early warning indicators are obtained, and load management of the test distribution transformer area is performed according to the early warning indicators; specifically, the following is included: Step S3.1: Calculate the total current of the test distribution transformer area, the formula is as follows: A ij =Σ(a ij ) m In the formula, A ij is the total current of the area on the jth day of the ith month, m is the number of lines; a ij is the current of each line, calculated as follows: Where P pv is the rated power of distributed photovoltaic, P dwg Wind power active output, Ve is the rated voltage; Step S3.2: Calculate the early warning index E based on the current of each line, as follows: In the formula, m is the number of lines and n is the number of contacts; Where, X oj =max(X ij ), ρ is a number between 0 and 1; Step S3.3: Perform load management of the test distribution transformer substation according to the early warning index. When the E value is lower than the preset threshold, perform load forecasting of the substation based on the load growth rate and issue an overload warning.

2. According to the method for load management of a smart distribution transformer terminal based on local demand in claim 1, it is characterized in that: The wind power active output p dwg The calculation method is as follows:

3. The method for load management of a smart distribution transformer terminal based on local demand according to claim 1 is characterized in that: The rated power P of the distributed photovoltaic pv The calculation method is as follows:

4. A load management system for a smart distribution transformer terminal based on local demand, characterized in that: It includes the following units: time division unit, historical data statistics unit, load management unit; The time division unit is used to divide the load forecast time of the selected test distribution transformer area into 12 monthly time periods each year, and each monthly time period is divided into 30 daily time periods; The historical data statistics unit is used to respectively count the historical data of each monthly time period and daily time period of the test distribution transformer area, and the historical data includes the daily average load P t , Photovoltaic meter power P pvs , Rated power P of distributed wind power rated , Cut-in wind speed v ci , Cut-out wind speed v co and rated wind speed v rated , wind speed v, r s represents the rated light intensity, r is the light intensity; The load management unit is used to perform load forecasting of the test distribution transformer area based on the historical data, obtain early warning indicators, and perform load management of the test distribution transformer area according to the early warning indicators; specifically includes the following: Step S3.1: Calculate the total current of the test distribution transformer area, the formula is as follows: A ij =∑(a ij ) m In the formula, A ij is the total current of the area on the jth day of the ith month, m is the number of lines; a ij is the current of each line, calculated as follows: Where P pv is the rated power of distributed photovoltaic, P dwg Wind power active output, Ve is the rated voltage; Step S3.2: Calculate the early warning index E based on the current of each line, as follows: In the formula, m is the number of lines and n is the number of contacts; Where, X oj =max(X ij ), ρ is a number between 0 and 1; Step S3.3: Perform load management of the test distribution transformer substation according to the early warning index. When the E value is lower than the preset threshold, perform load forecasting of the substation based on the load growth rate and issue an overload warning.

5. A load management system for a distribution transformer area based on local demand according to claim 4, characterized in that: The wind power active output P dwg The calculation method is as follows:

6. A load management system for a distribution transformer area based on local demand according to claim 4, characterized in that: The rated power P of the distributed photovoltaic pv The calculation method is as follows:

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