CN116317104A - Power transmission line dynamic capacity-increasing prediction system and method based on data center station - Google Patents
Power transmission line dynamic capacity-increasing prediction system and method based on data center station Download PDFInfo
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- H—ELECTRICITY
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Abstract
The invention relates to a dynamic capacity-increasing prediction system and a dynamic capacity-increasing prediction method for a power transmission line based on a data center station, and belongs to the technical field of power transmission line on-line monitoring. The system comprises: the system comprises an online data monitoring unit, a monitoring data transmission unit and a data center station which are connected in sequence; the online data monitoring unit is arranged on the target transmission line; the online data monitoring unit comprises an online monitoring terminal and a sensor subunit; the sensor subunit is used for collecting terminal monitoring data of the power transmission line; the on-line monitoring terminal is used for receiving the terminal monitoring data and transmitting the terminal monitoring data to the monitoring data transmitting unit; the monitoring data transmission unit is used for transmitting the terminal monitoring data to the data center; the data center station is used for storing line account data, line geographic position data and terminal monitoring data and predicting capacity increase of the power transmission line according to the line account data, the line geographic position data and the terminal monitoring data. The invention improves the accuracy of capacity-increasing prediction.
Description
Technical Field
The invention relates to the technical field of online monitoring of power transmission lines, in particular to a dynamic capacity-increasing prediction system and method of a power transmission line based on a data center station.
Background
In recent years, the Chinese economy continues to grow rapidly, and the power transmission situation of the power grid is getting more severe along with the rapid increase of the power consumption. In economically developed areas with intense land and high-rise buildings, the newly-built transmission line has long construction period, large investment and higher realization difficulty. The conveying capacity of the existing circuit is more strictly limited, and the contradiction between electric energy conveying and electric power consumption requirements often exists. Therefore, the method and the system have great practical significance on improving the safe, economic and reliable operation of the power grid, and improving the transmission capacity of the existing power transmission line while accelerating the construction of the smart power grid.
At present, the dynamic capacity increasing technology of the power transmission line is mainly combined with the online monitoring technology of the power transmission line, so that a dynamic capacity increasing and online monitoring system of the load of the power transmission line is realized, and the transmission capacity of the power transmission line is further improved. However, due to the limitation of hardware technologies such as a sensor and a power supply in the online monitoring device at the present stage, the acquired online monitoring data has the problem of low accuracy and reliability, so that the dynamic capacity-increasing calculation of the power transmission line is performed by simply relying on the online monitoring data, and the risk that the current-carrying capacity of the power transmission line is inaccurate can be caused by the calculation of the system. In addition, in the design stage of the power transmission line, the designer ignores the gas aberration variability of the local area and adopts the same harsh weather standard, so that the design value of the line capacity limit and the actual operation limit of the line may have a certain difference.
Disclosure of Invention
The invention aims to provide a power transmission line dynamic capacity-increasing prediction system and method based on a data center station, which are used for solving the problem that in the prior art, the prediction result is inaccurate due to the fact that the power transmission line dynamic capacity-increasing calculation is carried out only by means of online monitoring data.
In order to achieve the above object, the present invention provides the following solutions:
a data center-based transmission line dynamic capacity-increasing prediction system, comprising: the system comprises an online data monitoring unit, a monitoring data transmission unit and a data center station which are connected in sequence; the online data monitoring unit is arranged on the target power transmission line;
the online data monitoring unit comprises an online monitoring terminal and a sensor subunit; the sensor subunit is connected with the online monitoring terminal; the on-line monitoring terminal is connected with the monitoring data transmission unit; the sensor subunit is used for collecting terminal monitoring data of the power transmission line; the terminal monitoring data comprise ambient temperature, wind speed, wind direction, illumination intensity, wire temperature, wire sag and wire current; the on-line monitoring terminal is used for receiving the terminal monitoring data and transmitting the terminal monitoring data to the monitoring data transmitting unit;
the monitoring data transmission unit is used for transmitting the terminal monitoring data to the data center station;
the data center is used for storing line ledger data, line geographic position data and terminal monitoring data, and calculating the current transmission maximum capacity, the current increasable capacity, the 24-hour forecast transmission maximum capacity, the 24-hour forecast increasable capacity, the 72-hour forecast transmission maximum capacity, the 72-hour forecast increasable capacity, the 24-hour forecast conductor sag and the 72-hour forecast conductor sag of the power transmission line according to the line ledger data, the line geographic position data and the terminal monitoring data.
Optionally, the sensor sub-unit includes an ambient temperature sensor, a wire sag sensor, an illumination intensity sensor, a wind speed and direction sensor, and a wire current sensor.
Optionally, the on-line monitoring terminal is further configured to store an on-line monitoring terminal identifier and a sensor identifier, record the acquisition time of the sensor subunit, and upload the on-line monitoring terminal identifier, the sensor identifier and the acquisition time to the data center through the monitoring data transmission unit.
Optionally, the monitoring data transmission unit comprises a VPN dedicated line, a security access device and a database server which are connected in sequence;
the VPN special line is connected with the online monitoring terminal; the database server is connected with the data center.
Optionally, the data center station comprises a mass heterogeneous multi-source data unit and a fusion prediction analysis unit which are sequentially connected;
the mass heterogeneous multi-source data unit is used for storing the line ledger data, the line geographic position data, the online monitoring terminal identification, the sensor identification, the acquisition time and the terminal monitoring data; the line ledger data comprises terminal monitoring historical data, line scheduling current data, line defect data, line overhaul historical data, line fault historical data, line defect historical data, line scheduling current historical data, adjacent terminal monitoring data and adjacent terminal monitoring historical data; the line geographic position data comprise public weather forecast data, public weather history data, geographic terrain information and natural disaster information;
the fusion prediction analysis unit is used for calculating the current transmission maximum capacity, the current increasable capacity, the 24-hour prediction transmission maximum capacity, the 24-hour prediction increasable capacity, the 72-hour prediction transmission maximum capacity, the 72-hour prediction increasable capacity, the 24-hour prediction wire temperature, the 72-hour prediction wire temperature, the 24-hour prediction wire sag and the 72-hour prediction wire sag of the power transmission line according to the line standing account data, the line geographic position data, the online monitoring terminal identifier, the sensor identifier, the acquisition time and the terminal monitoring data.
Optionally, the fusion prediction analysis unit is further configured to perform a security risk analysis according to a wire temperature threshold, a wire sag threshold, the wire temperature, the wire sag, the 24-hour predicted wire temperature, the 72-hour predicted wire temperature, the 24-hour predicted wire sag, and the 72-hour predicted wire sag.
Optionally, the data center further includes the analysis conclusion display unit; the analysis conclusion display unit is connected with the fusion prediction analysis unit;
the analysis conclusion display unit is used for displaying a security risk analysis conclusion, the online terminal monitoring data, massive heterogeneous multi-source data and capacity-increasing prediction data; the capacity-increasing prediction data comprise the current transmission maximum capacity, the current capacity-increasing capacity, the 24-hour forecast transmission maximum capacity, the 24-hour forecast capacity-increasing, the 72-hour forecast transmission maximum capacity and the 72-hour forecast capacity-increasing of the power transmission line; the massive heterogeneous multi-source data comprise the line ledger data and the line geographic position data.
Optionally, the analysis conclusion display unit includes a security risk analysis conclusion module, an online terminal monitoring data module, a massive heterogeneous multi-source data module and a capacity-increasing prediction data module;
the security risk analysis conclusion module is used for inquiring and displaying a security risk analysis conclusion;
the online terminal monitoring data module is used for inquiring and displaying the online terminal monitoring data;
the mass heterogeneous multi-source data module is used for inquiring and displaying the mass heterogeneous multi-source data;
the capacity-increasing prediction data module is used for inquiring and displaying the capacity-increasing prediction data.
The method is applied to the dynamic capacity-increasing prediction system of the power transmission line based on the data center, and comprises the following steps:
acquiring line ledger data, line geographic position data and terminal monitoring data; the line ledger data comprises terminal monitoring historical data, line scheduling current data, line defect data, line overhaul historical data, line fault historical data, line defect historical data, line scheduling current historical data, adjacent terminal monitoring data and adjacent terminal monitoring historical data; the line geographic position data comprise public weather forecast data, public weather history data, geographic terrain information and natural disaster information; the terminal monitoring data comprise ambient temperature, wind speed, wind direction, illumination intensity, wire temperature, wire sag and wire current;
respectively associating the terminal monitoring data with the line ledger data and the line geographic position data to obtain associated terminal monitoring data;
according to the correlated terminal monitoring data, adopting an iterative weighted least square method to correct the terminal monitoring data and the public weather forecast data to obtain corrected terminal monitoring data and corrected public weather forecast data;
according to the corrected terminal monitoring data, correcting the terminal monitoring historical data by adopting an iterative weighted least square method to obtain corrected monitoring data for calculating the molar root current-carrying capacity; the corrected monitoring data for the molar root current-carrying capacity calculation comprises ambient temperature correction data, wind speed correction data, wind direction correction data, illumination intensity correction data, wire temperature correction data, wire current correction data, wire sag correction data, ambient temperature forecast data, wind speed forecast data, wind direction forecast data and illumination intensity forecast data;
according to the corrected monitoring data for calculating the molar root current-carrying capacity, optimizing the molar root current-carrying capacity calculation parameter to obtain an optimized molar root current-carrying capacity calculation parameter; the optimized molar root current-carrying capacity calculation parameters comprise wire quality, wire diameter, wire resistance at 20 ℃, wire temperature coefficient, wire radiation coefficient, wire heat absorption coefficient and wire comprehensive heat capacity coefficient;
and calculating the current transmission maximum capacity, the current increasable capacity, the 24-hour forecast transmission maximum capacity, the 24-hour forecast increasable capacity, the 72-hour forecast transmission maximum capacity, the 72-hour forecast increasable capacity, the 24-hour forecast wire temperature, the 72-hour forecast wire temperature, the 24-hour forecast wire sag and the 72-hour forecast wire sag of the power transmission line according to the optimized molar root current-carrying capacity calculation parameters, the corrected monitoring data for molar root current-carrying capacity calculation and the corrected public weather forecast data.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention relates to a power transmission line dynamic capacity-increasing prediction system based on a data center station, which comprises an online data monitoring unit, a monitoring data transmission unit and a data center station which are connected in sequence; the online data monitoring unit is arranged on the target power transmission line. The terminal monitoring data of the power transmission line are collected through the online data monitoring unit, the terminal detection data are transmitted to the data center station, the line standing book data and the line geographic position data are stored in the data center station, the line standing book data and the line geographic position data are combined with the terminal monitoring data, and dynamic capacity expansion of the power transmission line is predicted, so that accuracy of a prediction result is improved. In addition, the line ledger data comprises public weather forecast data, and the difference of weather is considered in the calculation of the line capacity limit, so that the accuracy of a forecast result is further improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a block diagram of a dynamic capacity-increasing prediction system of a power transmission line based on a data center station;
FIG. 2 is a schematic diagram of an analysis conclusion display unit according to the present invention;
fig. 3 is a flowchart of a method for predicting dynamic capacity increase of a power transmission line based on a data center station provided by the invention;
fig. 4 is a schematic diagram of data association provided in the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a power transmission line dynamic capacity-increasing prediction system based on a data center station, which aims to solve the problem that in the prior art, the prediction result is inaccurate due to the fact that the power transmission line dynamic capacity-increasing calculation is carried out by simply relying on-line monitoring data.
Aiming at the problems, the invention provides a data center-based power transmission line dynamic capacity-increasing prediction system which combines an online monitoring technology with a data center, not only can effectively utilize intelligent online monitoring terminals deployed on the power transmission line, but also can fully apply a fusion prediction analysis model of a large amount of heterogeneous multi-source data in the data center, thereby realizing high-efficiency and high-accuracy dynamic capacity-increasing prediction analysis of the power transmission line in a regional power grid and ensuring stable operation of the regional power grid.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Fig. 1 is a block diagram of a dynamic capacity-increasing prediction system for a power transmission line based on a data center station, provided by the invention, as shown in fig. 1, the system includes: the system comprises an online data monitoring unit, a monitoring data transmission unit and a data center station which are connected in sequence; the online data monitoring unit is arranged on the target power transmission line. In practical application, the system can comprise a plurality of online data monitoring units, and can monitor each position of the power transmission line.
The online data monitoring unit comprises an online monitoring terminal and a sensor subunit; the sensor subunit is connected with the online monitoring terminal; the on-line monitoring terminal is connected with the monitoring data transmission unit.
The sensor subunit comprises an ambient temperature sensor, a wire sag sensor, an illumination intensity sensor, a wind speed and direction sensor and a wire current sensor. The sensor subunit is used for collecting terminal monitoring data of the power transmission line; the terminal monitoring data comprises ambient temperature, wind speed, wind direction, illumination intensity, wire temperature, wire sag and wire current.
The on-line monitoring terminal is used for receiving the terminal monitoring data and transmitting the terminal monitoring data to the monitoring data transmitting unit. The on-line monitoring terminal mainly realizes the functions of receiving and transmitting the real-time monitoring data of various sensors. The on-line monitoring terminal is also used for storing the on-line monitoring terminal identification and the sensor identification, recording the acquisition time of the sensor subunit and transmitting the on-line monitoring terminal identification, the sensor identification and the acquisition time to the monitoring data transmission unit.
The monitoring data transmission unit comprises a VPN special line, a safety access device and a database server which are connected in sequence; the VPN special line is connected with the online monitoring terminal; the database server is connected with the data center. The monitoring data transmission unit is used for transmitting the terminal monitoring data to the data center station.
In practical application, the main function of the monitoring data transmission unit is to realize that terminal monitoring data acquired by an online monitoring data acquisition module (online data monitoring unit) is transmitted and accessed into a database server of an intranet of a power grid by a VPN private line of the power grid and a safety access device.
The main transmitted data types comprise terminal monitoring data and related data monitored by various sensors in real time: the related data comprises an online monitoring terminal identifier, a sensor identifier and acquisition time.
The data center is used for storing line ledger data, line geographic position data and terminal monitoring data, and calculating the current transmission maximum capacity, the current increasable capacity, the 24-hour forecast transmission maximum capacity, the 24-hour forecast increasable capacity, the 72-hour forecast transmission maximum capacity, the 72-hour forecast increasable capacity, the 24-hour forecast conductor sag and the 72-hour forecast conductor sag of the power transmission line according to the line ledger data, the line geographic position data and the terminal monitoring data.
Further, the data center comprises a mass heterogeneous multi-source data unit, a fusion prediction analysis unit and an analysis conclusion display unit which are sequentially connected. The fusion prediction analysis unit mainly realizes the dynamic capacity-increasing fusion prediction analysis function of the transmission line based on the data center, and the analysis conclusion display unit mainly realizes the dynamic capacity-increasing fusion prediction analysis conclusion display function of the transmission line based on the data center.
Specifically, the massive heterogeneous multi-source data unit is used for storing the line ledger data, the line geographic position data, the online monitoring terminal identifier, the sensor identifier, the acquisition time and the terminal monitoring data; the line ledger data comprises terminal monitoring historical data, line scheduling current data, line defect data, line overhaul historical data, line fault historical data, line defect historical data, line scheduling current historical data, adjacent terminal monitoring data and adjacent terminal monitoring historical data; the line geographic location data includes public weather forecast data, public weather history data, geographic terrain information, and natural disaster information.
The fusion prediction analysis unit is used for calculating the current transmission maximum capacity, the current increasable capacity, the 24-hour prediction transmission maximum capacity, the 24-hour prediction increasable capacity, the 72-hour prediction transmission maximum capacity, the 72-hour prediction increasable capacity, the 24-hour prediction wire temperature, the 72-hour prediction wire temperature, the 24-hour prediction wire sag and the 72-hour prediction wire sag of the power transmission line according to the line standing account data, the line geographic position data, the online monitoring terminal identifier, the sensor identifier, the acquisition time and the terminal monitoring data.
In practical applications, the fusion prediction analysis unit is further configured to perform a security risk analysis according to a wire temperature threshold, a wire sag threshold, the wire temperature, the wire sag, the 24-hour predicted wire temperature, the 72-hour predicted wire temperature, the 24-hour predicted wire sag, and the 72-hour predicted wire sag.
In practical application, according to the operation and detection technology [ 2012 ] 390 issued by the national network, the on-line monitoring information alarm rule (trial run) of power transmission and transformation equipment is set, the wire temperature threshold value and the wire sag threshold value of the power transmission line are set, and safety risk analysis is performed on the calculated wire temperature monitoring data, the wire sag monitoring data, the 24-hour predicted wire temperature and the 72-hour predicted wire temperature, the 24-hour predicted wire sag and the 72-hour predicted wire sag, so as to judge whether safety risk alarm exists.
The fusion prediction analysis unit is mainly used for carrying out dynamic capacity-increasing fusion prediction analysis on the power transmission line by utilizing a large data algorithm model of the middle data platform by utilizing a plurality of monitoring data of a large heterogeneous multi-source data and an online data monitoring unit package in the middle data platform according to an optimized molar root current-carrying capacity calculation method to obtain a power transmission line safety risk analysis conclusion, and realizing a power transmission line dynamic capacity-increasing fusion prediction analysis function based on the middle data platform.
The analysis conclusion display unit is used for displaying a security risk analysis conclusion, the online terminal monitoring data, massive heterogeneous multi-source data and capacity-increasing prediction data; the capacity-increasing prediction data comprise the current transmission maximum capacity, the current capacity-increasing capacity, the 24-hour forecast transmission maximum capacity, the 24-hour forecast capacity-increasing, the 72-hour forecast transmission maximum capacity and the 72-hour forecast capacity-increasing of the power transmission line; the massive heterogeneous multi-source data comprise the line ledger data and the line geographic position data.
Further, the analysis conclusion display unit comprises a security risk analysis conclusion module, an online terminal monitoring data module, a massive heterogeneous multi-source data module and a capacity-increasing prediction data module.
The security risk analysis conclusion module is used for inquiring and displaying the security risk analysis conclusion.
The online terminal monitoring data module is used for inquiring and displaying the online terminal monitoring data.
The mass heterogeneous multi-source data module is used for inquiring and displaying the mass heterogeneous multi-source data.
The capacity-increasing prediction data module is used for inquiring and displaying the capacity-increasing prediction data.
The front end display page (analysis conclusion display unit) is developed in the data center, and the display content is as follows: heterogeneous multi-source data statistical query (massive heterogeneous multi-source data module), online monitoring data statistical query (online terminal monitoring data module), dynamic capacity-increasing query (capacity-increasing prediction data module) of a power transmission line, safety risk analysis (safety risk analysis conclusion module) of the power transmission line and system management, wherein the heterogeneous multi-source data statistical query comprises heterogeneous multi-source data statistics and heterogeneous multi-source data query as shown in fig. 2; the online monitoring data statistics query comprises online monitoring data statistics and online monitoring data query; the dynamic capacity increasing inquiry of the power transmission line comprises a maximum capacity conveying inquiry and a capacity increasing conveying inquiry; the safety risk analysis of the power transmission line comprises a lead sag prediction alarm and a transmission capacity prediction alarm; system management includes user management and log management.
The invention further provides a power transmission line dynamic capacity-increasing prediction method based on the data center station, as shown in fig. 3, the method comprises the following steps:
step 301: and acquiring line ledger data, line geographic position data and terminal monitoring data. The line ledger data comprises terminal monitoring historical data, line scheduling current data, line defect data, line overhaul historical data, line fault historical data, line defect historical data, line scheduling current historical data, adjacent terminal monitoring data and adjacent terminal monitoring historical data; the line geographic position data comprise public weather forecast data, public weather history data, geographic terrain information and natural disaster information; the terminal monitoring data comprises ambient temperature, wind speed, wind direction, illumination intensity, wire temperature, wire sag and wire current.
Step 302: and respectively associating the terminal monitoring data with the line ledger data and the line geographic position data to obtain associated terminal monitoring data.
The data association diagram is shown in fig. 4:
and (3) standing book information association: and (3) related information of the terminal monitoring data is found in the line ledger data, and terminal monitoring historical data, adjacent terminal monitoring historical data, line scheduling current data, line defect data, line overhaul historical data, line fault historical data, line defect historical data, line scheduling current historical data and power transmission line geographical position data corresponding to each terminal monitoring data are found in a correlated mode.
Geographic weather information association: and (5) associating the line geographic position data to find out public weather forecast data, public weather history data, geographic topography information and natural disaster information corresponding to each terminal monitoring data.
Step 303: and correcting the terminal monitoring data and the public weather forecast data by adopting an iterative weighted least square method according to the correlated terminal monitoring data to obtain corrected terminal monitoring data and corrected public weather forecast data.
Specifically, each terminal monitoring data is associated with corresponding terminal monitoring historical data, adjacent terminal monitoring historical data, line scheduling current data, line defect data, line maintenance historical data, line fault historical data, line defect historical data, line scheduling current historical data, public weather forecast data, public weather historical data, geographical terrain information and natural disaster information, and an iterative weighted least square method is applied to correct each terminal monitoring data and the public weather forecast data of each terminal.
Step 304: and correcting the terminal monitoring historical data by adopting an iterative weighted least square method according to the corrected terminal monitoring data to obtain corrected monitoring data for calculating the molar root current-carrying capacity. The corrected monitoring data for the molar root current-carrying capacity calculation comprises ambient temperature correction data, wind speed correction data, wind direction correction data, illumination intensity correction data, wire temperature correction data, wire current correction data, wire sag correction data, ambient temperature forecast data, wind speed forecast data, wind direction forecast data and illumination intensity forecast data.
Step 305: and optimizing the molar root current-carrying capacity calculation parameters according to the corrected monitoring data for molar root current-carrying capacity calculation, and obtaining the optimized molar root current-carrying capacity calculation parameters. The optimized molar root current-carrying capacity calculation parameters comprise wire quality, wire diameter, wire resistance at 20 ℃, wire temperature coefficient, wire radiation coefficient, wire heat absorption coefficient and wire comprehensive heat capacity coefficient.
Specifically, according to a molar root current-carrying capacity steady-state analysis empirical formula and a transient empirical formula, the optimized monitoring data for molar root current-carrying capacity calculation obtained in the last step is applied, and the optimized molar root current-carrying capacity calculation parameters are iterated and pushed back to obtain the optimized molar root current-carrying capacity calculation parameters.
Step 306: and calculating the current transmission maximum capacity, the current increasable capacity, the 24-hour forecast transmission maximum capacity, the 24-hour forecast increasable capacity, the 72-hour forecast transmission maximum capacity, the 72-hour forecast increasable capacity, the 24-hour forecast wire temperature, the 72-hour forecast wire temperature, the 24-hour forecast wire sag and the 72-hour forecast wire sag of the power transmission line according to the optimized molar root current-carrying capacity calculation parameters, the corrected monitoring data for molar root current-carrying capacity calculation and the corrected public weather forecast data. Specifically, the correlation calculation is performed by using a molar root current-carrying capacity steady-state analysis empirical formula and a transient empirical formula.
The heat exchange between the wires and the external environment occurs at all times, and if the power transmission line and the external environment are not changed, the system reaches steady-state balance. The steady state equilibrium equation (steady state analytical empirical formula) is as follows:
Q c +Q r ＝Q s +I 2 R T
wherein: q (Q) c Represents the heat dissipation of the wire convection, W/m,
Q r represents the radiation heat dissipation of the wire, W/m, Q r ＝πεSD[(θ+t a +273) 4 -(t a +273) 4 ]。Q s Represents the sunlight heat absorption, W/m and Q of the lead s ＝a s I s D。R T Representing the alternating current resistance value, omega/m and R of the lead T ＝βR d Wherein beta is the alternating current resistance ratio, R d Is a direct current resistor. Typically, the wire will give a wire DC resistance at 20 ℃. According to the direct current resistance of the lead at 20 ℃, calculating the direct current resistance of other temperatures of the lead, wherein the calculation formula is as follows: r is R d ＝R 20 [1+α(T c -20)]. Wherein R is 20 Is the direct current resistance of the lead at 20 ℃, alpha is the temperature coefficient of the lead, T c The temperature of the wire is the temperature of the wire, and the temperature is lower than the temperature; t (T) c ＝t α +θ. The alternating current resistance ratio of the conductor at different temperatures can be obtained by looking up a table. I is wire current, A; θ is the current carrying temperature rise of the wire, and is at DEG C; />
The absolute value of the included angle between the wind direction and the wire; v is wind speed, m/s; d is the diameter of the wire, m; epsilon is the emissivity of the surface of the wire, and the emissivity of the surface of the wire of the bright new wire is 0.23-0.46; s represents the Stefan-Bao Erci Mannich coefficient, S=5.67×10 -8 W/m 2 The method comprises the steps of carrying out a first treatment on the surface of the t represents the ambient temperature, DEG C; a, a s The heat absorption coefficient of the wire is 0.23-0.46, and the heat absorption coefficient of the wire of the blackened old wire is 0.90-0.95; i s For sunlight to the wireIntensity, W/m.
Substituting the parameters into a formula, and sorting to obtain
The current capacity at the current steady state can be calculated according to the real-time meteorological conditions. In order to increase the capacity of the line, the temperature and time of the wire when the current capacity is transited to the maximum current capacity (corresponding steady current capacity at 80 ℃) need to be calculated.
When the line current value changes, the wire temperature is a dynamic change process before reaching a steady state, and the change rule is expressed by a transient heat capacity equation (transient empirical formula):
wherein: m is the wire mass per unit length, kg/m; c (C) p The heat capacity coefficient is the comprehensive heat capacity coefficient of the lead, J/kg is x ℃; a is the calculated heat area of the surface of the wire in unit length, m 2 /m，A＝πD。
The formula term transfer is finished to obtain:
the equation is a standard unitary linear differential equation which is solved as
When t=0, there are
Then the first time period of the first time period,
at line transition, the transient heat capacity equation can be expressed as:
through the formula, when the current-carrying capacity is suddenly changed from the current value to a certain value, the current-carrying capacity after the sudden change is substituted, a temperature time curve is recorded, and the required time can be calculated.
The invention has the following advantages:
(1) The accuracy is high: aiming at the on-line monitoring data, the classification fusion algorithm model of the large-scale heterogeneous multi-source data in the data center is applied by combining the power transmission line account data and the power transmission line geographical position data, the validity judgment and treatment of the on-line monitoring data are carried out, and the problem that the accuracy and the reliability of the on-line monitoring data are low is effectively solved.
(2) The efficiency is high: according to the optimized molar root current-carrying capacity calculation method, a data center large data algorithm model is applied, dynamic capacity-increasing fusion prediction analysis of the power transmission line is timely carried out by utilizing the high-performance calculation rate of the data center, the power transmission line safety risk analysis conclusion is obtained efficiently, and the power transmission line dynamic capacity-increasing fusion prediction analysis function based on the data center is realized.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (9)
1. The utility model provides a transmission line dynamic capacity-increasing prediction system based on data center station which characterized in that includes: the system comprises an online data monitoring unit, a monitoring data transmission unit and a data center station which are connected in sequence; the online data monitoring unit is arranged on the target power transmission line;
the online data monitoring unit comprises an online monitoring terminal and a sensor subunit; the sensor subunit is connected with the online monitoring terminal; the on-line monitoring terminal is connected with the monitoring data transmission unit; the sensor subunit is used for collecting terminal monitoring data of the power transmission line; the terminal monitoring data comprise ambient temperature, wind speed, wind direction, illumination intensity, wire temperature, wire sag and wire current; the on-line monitoring terminal is used for receiving the terminal monitoring data and transmitting the terminal monitoring data to the monitoring data transmitting unit;
the monitoring data transmission unit is used for transmitting the terminal monitoring data to the data center station;
the data center is used for storing line ledger data, line geographic position data and terminal monitoring data, and calculating the current transmission maximum capacity, the current increasable capacity, the 24-hour forecast transmission maximum capacity, the 24-hour forecast increasable capacity, the 72-hour forecast transmission maximum capacity, the 72-hour forecast increasable capacity, the 24-hour forecast conductor sag and the 72-hour forecast conductor sag of the power transmission line according to the line ledger data, the line geographic position data and the terminal monitoring data.
2. The data center based transmission line dynamic capacity-increasing prediction system according to claim 1, wherein the sensor sub-unit comprises an ambient temperature sensor, a wire sag sensor, an illumination intensity sensor, a wind speed and direction sensor, and a wire current sensor.
3. The data center-based transmission line dynamic capacity-increasing prediction system according to claim 2, wherein the on-line monitoring terminal is further configured to store an on-line monitoring terminal identifier and a sensor identifier, record a collection time of the sensor subunit, and upload the on-line monitoring terminal identifier, the sensor identifier, and the collection time to the data center through the monitoring data transmission unit.
4. The data center-based transmission line dynamic capacity-increasing prediction system according to claim 1, wherein the monitoring data transmission unit comprises a VPN dedicated line, a security access device and a database server which are connected in sequence;
the VPN special line is connected with the online monitoring terminal; the database server is connected with the data center.
5. The power transmission line dynamic capacity-increasing prediction system based on the data center station as claimed in claim 3, wherein the data center station comprises a mass heterogeneous multi-source data unit and a fusion prediction analysis unit which are sequentially connected;
the mass heterogeneous multi-source data unit is used for storing the line ledger data, the line geographic position data, the online monitoring terminal identification, the sensor identification, the acquisition time and the terminal monitoring data; the line ledger data comprises terminal monitoring historical data, line scheduling current data, line defect data, line overhaul historical data, line fault historical data, line defect historical data, line scheduling current historical data, adjacent terminal monitoring data and adjacent terminal monitoring historical data; the line geographic position data comprise public weather forecast data, public weather history data, geographic terrain information and natural disaster information;
the fusion prediction analysis unit is used for calculating the current transmission maximum capacity, the current increasable capacity, the 24-hour prediction transmission maximum capacity, the 24-hour prediction increasable capacity, the 72-hour prediction transmission maximum capacity, the 72-hour prediction increasable capacity, the 24-hour prediction wire temperature, the 72-hour prediction wire temperature, the 24-hour prediction wire sag and the 72-hour prediction wire sag of the power transmission line according to the line standing account data, the line geographic position data, the online monitoring terminal identifier, the sensor identifier, the acquisition time and the terminal monitoring data.
6. The data center based transmission line dynamic capacity expansion prediction system according to claim 5, wherein the fusion prediction analysis unit is further configured to perform a security risk analysis according to a wire temperature threshold, a wire sag threshold, the wire temperature, the wire sag, the 24-hour predicted wire temperature, the 72-hour predicted wire temperature, the 24-hour predicted wire sag, and the 72-hour predicted wire sag.
7. The data center based transmission line dynamic capacity-increasing prediction system according to claim 6, wherein the data center further comprises the analysis conclusion display unit; the analysis conclusion display unit is connected with the fusion prediction analysis unit;
the analysis conclusion display unit is used for displaying a security risk analysis conclusion, the online terminal monitoring data, massive heterogeneous multi-source data and capacity-increasing prediction data; the capacity-increasing prediction data comprise the current transmission maximum capacity, the current capacity-increasing capacity, the 24-hour forecast transmission maximum capacity, the 24-hour forecast capacity-increasing, the 72-hour forecast transmission maximum capacity and the 72-hour forecast capacity-increasing of the power transmission line; the massive heterogeneous multi-source data comprise the line ledger data and the line geographic position data.
8. The data center-based power transmission line dynamic capacity-increasing prediction system according to claim 7, wherein the analysis conclusion display unit comprises a security risk analysis conclusion module, an online terminal monitoring data module, a mass heterogeneous multi-source data module and a capacity-increasing prediction data module;
the security risk analysis conclusion module is used for inquiring and displaying a security risk analysis conclusion;
the online terminal monitoring data module is used for inquiring and displaying the online terminal monitoring data;
the mass heterogeneous multi-source data module is used for inquiring and displaying the mass heterogeneous multi-source data;
the capacity-increasing prediction data module is used for inquiring and displaying the capacity-increasing prediction data.
9. A method for predicting dynamic capacity expansion of a power transmission line based on a data center, which is characterized in that the method is applied to the power transmission line dynamic capacity expansion prediction system based on the data center as claimed in any one of claims 1 to 8, and the method comprises the following steps:
acquiring line ledger data, line geographic position data and terminal monitoring data; the line ledger data comprises terminal monitoring historical data, line scheduling current data, line defect data, line overhaul historical data, line fault historical data, line defect historical data, line scheduling current historical data, adjacent terminal monitoring data and adjacent terminal monitoring historical data; the line geographic position data comprise public weather forecast data, public weather history data, geographic terrain information and natural disaster information; the terminal monitoring data comprise ambient temperature, wind speed, wind direction, illumination intensity, wire temperature, wire sag and wire current;
respectively associating the terminal monitoring data with the line ledger data and the line geographic position data to obtain associated terminal monitoring data;
according to the correlated terminal monitoring data, adopting an iterative weighted least square method to correct the terminal monitoring data and the public weather forecast data to obtain corrected terminal monitoring data and corrected public weather forecast data;
according to the corrected terminal monitoring data, correcting the terminal monitoring historical data by adopting an iterative weighted least square method to obtain corrected monitoring data for calculating the molar root current-carrying capacity; the corrected monitoring data for the molar root current-carrying capacity calculation comprises ambient temperature correction data, wind speed correction data, wind direction correction data, illumination intensity correction data, wire temperature correction data, wire current correction data, wire sag correction data, ambient temperature forecast data, wind speed forecast data, wind direction forecast data and illumination intensity forecast data;
according to the corrected monitoring data for calculating the molar root current-carrying capacity, optimizing the molar root current-carrying capacity calculation parameter to obtain an optimized molar root current-carrying capacity calculation parameter; the optimized molar root current-carrying capacity calculation parameters comprise wire quality, wire diameter, wire resistance at 20 ℃, wire temperature coefficient, wire radiation coefficient, wire heat absorption coefficient and wire comprehensive heat capacity coefficient;
and calculating the current transmission maximum capacity, the current increasable capacity, the 24-hour forecast transmission maximum capacity, the 24-hour forecast increasable capacity, the 72-hour forecast transmission maximum capacity, the 72-hour forecast increasable capacity, the 24-hour forecast wire temperature, the 72-hour forecast wire temperature, the 24-hour forecast wire sag and the 72-hour forecast wire sag of the power transmission line according to the optimized molar root current-carrying capacity calculation parameters, the corrected monitoring data for molar root current-carrying capacity calculation and the corrected public weather forecast data.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116896167A (en) * | 2023-09-11 | 2023-10-17 | 山东和兑智能科技有限公司 | Power transmission line dynamic capacity-increasing monitoring and early warning method based on artificial intelligence |
| CN117060575A (en) * | 2023-07-06 | 2023-11-14 | 珠海市深瑞智联科技有限公司 | Remote-upgrading power transmission line dynamic capacity-increasing on-line monitoring system and upgrading method thereof |
| CN117767578A (en) * | 2024-02-22 | 2024-03-26 | 国网江苏省电力有限公司 | Abnormal data discrimination method for dynamic capacity-increasing monitoring terminal of power transmission line |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117060575A (en) * | 2023-07-06 | 2023-11-14 | 珠海市深瑞智联科技有限公司 | Remote-upgrading power transmission line dynamic capacity-increasing on-line monitoring system and upgrading method thereof |
| CN117060575B (en) * | 2023-07-06 | 2024-03-29 | 珠海市深瑞智联科技有限公司 | Remote-upgrading power transmission line dynamic capacity-increasing on-line monitoring system and upgrading method thereof |
| CN116896167A (en) * | 2023-09-11 | 2023-10-17 | 山东和兑智能科技有限公司 | Power transmission line dynamic capacity-increasing monitoring and early warning method based on artificial intelligence |
| CN116896167B (en) * | 2023-09-11 | 2023-12-15 | 山东和兑智能科技有限公司 | Power transmission line dynamic capacity-increasing monitoring and early warning method based on artificial intelligence |
| CN117767578A (en) * | 2024-02-22 | 2024-03-26 | 国网江苏省电力有限公司 | Abnormal data discrimination method for dynamic capacity-increasing monitoring terminal of power transmission line |
| CN117767578B (en) * | 2024-02-22 | 2024-04-30 | 国网江苏省电力有限公司 | Abnormal data discrimination method for dynamic capacity-increasing monitoring terminal of power transmission line |
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