CN108052734A - A kind of method and system predicted based on meteorologic parameter amplitude of lightning current - Google Patents
A kind of method and system predicted based on meteorologic parameter amplitude of lightning current Download PDFInfo
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Abstract
本发明公开了基于气象参数对雷电流幅值进行预测的方法：确认与雷电流幅值相关的气象参数，获取气象参数的数值；利用气象参数的数值以及与气象参数的数值对应的雷电流幅值，建立气象参数的数值与雷电流幅值的相关性模型；选取多次输电线路遭受雷击跳闸时气象参数的数值作为相关性模型的输入参数，选取对应的输电线路跳闸时的雷电流幅值为输出参数，对相关性模型进行训练；选取历史气象参数的数值，利用训练模型对历史气象参数的数值进行计算，获取计算雷电流幅值；当计算雷电流幅值与历史气象参数的数值对应的雷电流幅值之间的误差值满足预定的误差阈值时，将训练模型作为确定的预测模型；将实时的气象参数输入预测模型，获取实时雷电流幅值预测值。
The invention discloses a method for predicting the lightning current amplitude based on meteorological parameters: confirming the meteorological parameters related to the lightning current amplitude, obtaining the numerical value of the meteorological parameter; using the numerical value of the meteorological parameter and the lightning current amplitude corresponding to the numerical value of the meteorological parameter value, and establish a correlation model between the value of meteorological parameters and the amplitude of lightning current; select the value of meteorological parameters when multiple transmission lines are tripped by lightning as the input parameters of the correlation model, and select the corresponding lightning current amplitude when the transmission line trips As an output parameter, train the correlation model; select the value of the historical meteorological parameter, use the training model to calculate the value of the historical meteorological parameter, and obtain the calculated lightning current amplitude; when the calculated lightning current amplitude corresponds to the value of the historical meteorological parameter When the error value between the lightning current amplitudes satisfies the predetermined error threshold, the training model is used as the determined prediction model; the real-time meteorological parameters are input into the prediction model to obtain the real-time lightning current amplitude prediction value.
Description
技术领域technical field
本发明涉及输电线路安全技术领域，更具体地，涉及一种基于气象参数对雷电流幅值进行预测的方法及系统。The present invention relates to the technical field of transmission line safety, and more specifically, to a method and system for predicting lightning current amplitude based on meteorological parameters.
背景技术Background technique
电力系统的安全稳定运行是经济健康发展的前提条件之一，然而雷击事故是威胁电力系统正常运行的重要因素。雷击产生的过电压和大电流不仅可以引发绝缘子闪络、断路器击穿、架空线断线等事故，破坏电力设备，更为严重的是，雷击对关键电力设备的破坏会引起区域性停电，导致巨大的直接与间接经济损失。统计数据表明，雷击是造成电力系统线路跳闸的主要原因，以往的研究主要侧重于加强线路绝缘设计与架设雷电防护设施，在针对输电线路的雷电预警方面研究较少，而雷电预测可以协助电力运行部门及时评估输电线路雷击风险，以便采取必要措施减少甚至避免线路跳闸造成的损失，具有重要意义。The safe and stable operation of the power system is one of the prerequisites for the healthy development of the economy, but lightning accidents are an important factor that threatens the normal operation of the power system. The overvoltage and high current generated by lightning strikes can not only cause accidents such as insulator flashover, circuit breaker breakdown, and overhead line disconnection, but also damage power equipment. Lead to huge direct and indirect economic losses. Statistics show that lightning strikes are the main cause of tripping of power system lines. Previous research has mainly focused on strengthening line insulation design and erecting lightning protection facilities. There are few studies on lightning early warning for transmission lines, and lightning prediction can assist power operation. It is of great significance for departments to timely assess the risk of lightning strikes on transmission lines in order to take necessary measures to reduce or even avoid losses caused by line trips.
近年来气象部门通过天气雷达和闪电定位仪等开展了雷电监测预报方法研究，并将其成功应用于实践。然而输电线路雷电预警和气象雷电预警存在较大区别，气象部分主要是针对某一区域内的雷电进行预报，而电力部门需要在区域雷电预报的基础上进一步根据输电线路基本信息、线路结构特征及绝缘配置、线路地形地貌特征等，针对某一条线路或者某一基杆塔给出精细化雷害风险等级或者雷击闪络跳闸概率。由于受气象条件和电场条件等因素影响，不同地区雷电活动特征存在明显差异，即使同一地区，不同季节其电荷结构也可能不同。现有技术没有将特定气象特征参数转化为用于线路雷击跳闸概率计算的关键参数，不能将气象参数作为电力部门对雷击灾害风险评估和防范的条件。In recent years, meteorological departments have carried out research on lightning monitoring and forecasting methods through weather radars and lightning locators, and have successfully applied them in practice. However, there is a big difference between transmission line lightning warning and meteorological lightning warning. The meteorological part mainly forecasts lightning in a certain area, while the power department needs to further base on the basic information of transmission lines, line structure characteristics and Insulation configuration, line terrain features, etc., for a certain line or a certain base tower, a refined lightning risk level or lightning flashover trip probability is given. Due to the influence of factors such as meteorological conditions and electric field conditions, there are obvious differences in the characteristics of lightning activity in different regions. Even in the same region, the charge structure may be different in different seasons. The existing technology does not convert specific meteorological characteristic parameters into key parameters for calculation of line lightning trip probability, and meteorological parameters cannot be used as conditions for power sector risk assessment and prevention of lightning disasters.
输电线路的防雷性能在工程计算中用耐雷水平和雷击跳闸率来衡量。根据规程，雷击杆塔顶部发生闪络并建立电弧，或者雷绕过避雷线击于导线发生闪络并建立电弧，线路将发生跳闸。输电线路雷击跳闸的前提是雷电流幅值超过线路的反击或绕击耐雷水平，因此，输电线路雷击跳闸概率的计算首先需要获取雷电流幅值。而雷电流与气象条件、地理环境等因素有关，是一个随机变量，雷电探测设备得不到雷电流的预报幅值。The lightning protection performance of transmission lines is measured by the lightning resistance level and lightning tripping rate in engineering calculations. According to the regulations, if the lightning strikes the top of the tower and a flashover occurs and an arc is established, or the lightning bypasses the lightning conductor and strikes a conductor and a flashover occurs and an arc is established, the line will trip. The premise of lightning tripping of transmission lines is that the amplitude of lightning current exceeds the level of counterattack or shielding lightning resistance of the line. Therefore, the calculation of lightning tripping probability of transmission lines first needs to obtain the amplitude of lightning current. The lightning current is related to meteorological conditions, geographical environment and other factors, and is a random variable. The lightning detection equipment cannot obtain the forecasted amplitude of the lightning current.
因此，需要一种技术，以实现基于气象参数对雷电流幅值进行预测。Therefore, a technology is needed to realize the prediction of lightning current amplitude based on meteorological parameters.
发明内容Contents of the invention
本发明提供了一种基于气象参数对雷电流幅值进行预测的方法及系统，以实现如何基于气象参数对雷电流幅值进行预测的技术。The invention provides a method and system for predicting the amplitude of lightning current based on meteorological parameters, so as to realize the technology of how to predict the amplitude of lightning current based on meteorological parameters.
为了解决上述问题，本发明提供了一种基于气象参数对雷电流幅值进行预测的方法，In order to solve the above problems, the present invention provides a method for predicting lightning current amplitude based on meteorological parameters,
确认与雷电流幅值相关的气象参数，获取所述气象参数的数值；Confirming meteorological parameters related to the magnitude of the lightning current, and obtaining the values of the meteorological parameters;
利用所述气象参数的数值以及与所述气象参数的数值对应的雷电流幅值，建立所述气象参数的数值与所述雷电流幅值的相关性模型；Using the numerical value of the meteorological parameter and the lightning current amplitude corresponding to the numerical value of the meteorological parameter, establishing a correlation model between the numerical value of the meteorological parameter and the amplitude of the lightning current;
选取多次输电线路遭受雷击跳闸时气象参数的数值作为所述相关性模型的输入参数，选取与所述气象参数的数值对应的输电线路跳闸时的雷电流幅值为输出参数，对所述相关性模型进行训练，得到经过训练的训练模型；Select the value of meteorological parameters when multiple transmission lines suffer lightning trips as the input parameter of the correlation model, select the lightning current amplitude when the transmission line trips corresponding to the value of the meteorological parameters as the output parameter, and the correlation The sex model is trained to obtain a trained training model;
选取历史气象参数的数值，利用所述训练模型对所述历史气象参数的数值进行计算，获取计算雷电流幅值；当所述计算雷电流幅值与历史气象参数的数值对应的雷电流幅值之间的误差值满足预定的误差阈值时，将所述训练模型作为确定的预测模型，用于对雷电流幅值进行预测；Selecting the numerical values of the historical meteorological parameters, using the training model to calculate the numerical values of the historical meteorological parameters to obtain the calculated lightning current amplitude; when the calculated lightning current amplitude corresponds to the numerical value of the historical meteorological parameters When the error value between satisfies a predetermined error threshold, the training model is used as a determined prediction model for predicting the lightning current amplitude;
将实时的气象参数输入所述预测模型，利用所述预测模型对所述实时的气象参数进行计算，获取实时雷电流幅值预测值。Inputting real-time meteorological parameters into the prediction model, using the prediction model to calculate the real-time meteorological parameters to obtain a real-time lightning current amplitude prediction value.
优选地，所述气象参数包括：回波强度，回波顶高，垂直积累液态水含量，组合反射率因子。Preferably, the meteorological parameters include: echo intensity, echo top height, vertically accumulated liquid water content, and combined reflectivity factor.
优选地，利用神经网络算法对所述相关性模型进行训练，得到经过训练的训练模型。Preferably, the correlation model is trained using a neural network algorithm to obtain a trained training model.
优选地，包括：利用神经网络算法建立神经网络，设置所述神经网络中各个连接链的权值和阈值。Preferably, the method includes: using a neural network algorithm to establish a neural network, and setting weights and thresholds of each connection chain in the neural network.
优选地，当所述计算雷电流幅值与历史气象参数的数值对应的雷电流幅值之间的误差值满足预定的误差阈值时，对所述神经网络中各个连接链的权值和阈值进行调整。Preferably, when the error value between the calculated lightning current amplitude and the lightning current amplitude corresponding to the value of the historical meteorological parameter satisfies a predetermined error threshold, the weights and thresholds of each connection chain in the neural network are calculated Adjustment.
基于本发明的另一方面，提供一种基于气象参数对雷电流幅值进行预测的系统，According to another aspect of the present invention, a system for predicting lightning current amplitude based on meteorological parameters is provided,
初始单元，用于确认与雷电流幅值相关的气象参数，获取所述气象参数的数值；The initial unit is used to confirm the meteorological parameters related to the magnitude of the lightning current, and obtain the value of the meteorological parameters;
建立单元，利用所述气象参数的数值以及与所述气象参数的数值对应的雷电流幅值，建立所述气象参数的数值与所述雷电流幅值的相关性模型；A building unit that uses the value of the meteorological parameter and the lightning current amplitude corresponding to the value of the meteorological parameter to establish a correlation model between the value of the meteorological parameter and the amplitude of the lightning current;
训练单元，用于选取多次输电线路遭受雷击跳闸时气象参数的数值作为所述相关性模型的输入参数，选取与所述气象参数的数值对应的输电线路跳闸时的雷电流幅值为输出参数，对所述相关性模型进行训练，得到经过训练的训练模型；The training unit is used to select the value of meteorological parameters when multiple transmission lines suffer from lightning strike trips as the input parameter of the correlation model, and select the lightning current amplitude when the transmission line trips corresponding to the value of the meteorological parameters as the output parameter , training the correlation model to obtain a trained training model;
确认单元，用于选取历史气象参数的数值，利用所述训练模型对所述历史气象参数的数值进行计算，获取计算雷电流幅值；当所述计算雷电流幅值与历史气象参数的数值对应的雷电流幅值之间的误差值满足预定的误差阈值时，将所述训练模型作为确定的预测模型，用于对雷电流幅值进行预测；A confirmation unit is used to select the value of the historical meteorological parameter, and use the training model to calculate the value of the historical meteorological parameter to obtain the calculated lightning current amplitude; when the calculated lightning current amplitude corresponds to the value of the historical meteorological parameter When the error value between the lightning current amplitudes satisfies a predetermined error threshold, the training model is used as a determined prediction model for predicting the lightning current amplitude;
预测单元，用于将实时的气象参数输入所述预测模型，利用所述预测模型对所述实时的气象参数进行计算，获取实时雷电流幅值预测值。The prediction unit is configured to input real-time meteorological parameters into the prediction model, use the prediction model to calculate the real-time meteorological parameters, and obtain a real-time lightning current amplitude prediction value.
优选地，所述气象参数包括：回波强度，回波顶高，垂直积累液态水含量，组合反射率因子。Preferably, the meteorological parameters include: echo intensity, echo top height, vertically accumulated liquid water content, and combined reflectivity factor.
优选地，所述训练单元还用于：利用神经网络算法对所述相关性模型进行训练，得到经过训练的训练模型。Preferably, the training unit is further configured to: use a neural network algorithm to train the correlation model to obtain a trained training model.
优选地，所述训练单元还用于：利用神经网络算法建立神经网络，设置所述神经网络中各个连接链的权值和阈值。Preferably, the training unit is further configured to: use a neural network algorithm to establish a neural network, and set weights and thresholds of each connection chain in the neural network.
优选地，所述确认单元还用于：当所述计算雷电流幅值与历史气象参数的数值对应的雷电流幅值之间的误差值满足预定的误差阈值时，对所述神经网络中各个连接链的权值和阈值进行调整。Preferably, the confirmation unit is further configured to: when the error value between the calculated lightning current amplitude and the lightning current amplitude corresponding to the value of the historical meteorological parameter satisfies a predetermined error threshold, each of the neural network The weight and threshold of the connection chain are adjusted.
本发明技术方案提供了一种基于气象参数对雷电流幅值进行预测的方法及系统，本发明基于输电线路历史雷击事件获取雷电流幅值与雷电相关的气象特征参数，利用神经网络算法，建立雷电流幅值计算模型，再基于数值天气预报平台获取的实时气象特征参数得到雷电流幅值的预测值。利用本发明的技术方案，可以分析输电线路雷电性能，计算雷击跳闸的预测概率，进行线路雷害风险实时预警。本发明的技术方案为输电线路雷电预警提供了新的思路，提高了线路雷电防护管理水平，从而提升电网安全性，减少经济损失。The technical solution of the present invention provides a method and system for predicting the amplitude of lightning current based on meteorological parameters. The present invention obtains the amplitude of lightning current and meteorological characteristic parameters related to lightning based on historical lightning strike events of transmission lines, and uses neural network algorithms to establish The lightning current amplitude calculation model, and then based on the real-time meteorological characteristic parameters obtained by the numerical weather forecast platform, the predicted value of the lightning current amplitude is obtained. Utilizing the technical scheme of the invention, it is possible to analyze the lightning performance of the transmission line, calculate the predicted probability of lightning tripping, and perform real-time early warning of the line lightning hazard risk. The technical scheme of the invention provides a new idea for the lightning early warning of the transmission line, improves the line lightning protection management level, thereby improving the safety of the power grid and reducing economic losses.
附图说明Description of drawings
通过参考下面的附图，可以更为完整地理解本发明的示例性实施方式：A more complete understanding of the exemplary embodiments of the present invention can be had by referring to the following drawings:
图1为根据本发明实施方式的基于气象参数对雷电流幅值进行预测的方法流程图；以及Fig. 1 is a flow chart of a method for predicting lightning current amplitude based on meteorological parameters according to an embodiment of the present invention; and
图2为根据本发明实施方式的基于气象参数对雷电流幅值进行预测的系统结构图。Fig. 2 is a structural diagram of a system for predicting lightning current amplitude based on meteorological parameters according to an embodiment of the present invention.
具体实施方式Detailed ways
现在参考附图介绍本发明的示例性实施方式，然而，本发明可以用许多不同的形式来实施，并且不局限于此处描述的实施例，提供这些实施例是为了详尽地且完全地公开本发明，并且向所属技术领域的技术人员充分传达本发明的范围。对于表示在附图中的示例性实施方式中的术语并不是对本发明的限定。在附图中，相同的单元/元件使用相同的附图标记。Exemplary embodiments of the present invention will now be described with reference to the drawings; however, the present invention may be embodied in many different forms and are not limited to the embodiments described herein, which are provided for the purpose of exhaustively and completely disclosing the present invention. invention and fully convey the scope of the invention to those skilled in the art. The terms used in the exemplary embodiments shown in the drawings do not limit the present invention. In the figures, the same units/elements are given the same reference numerals.
除非另有说明，此处使用的术语(包括科技术语)对所属技术领域的技术人员具有通常的理解含义。另外，可以理解的是，以通常使用的词典限定的术语，应当被理解为与其相关领域的语境具有一致的含义，而不应该被理解为理想化的或过于正式的意义。Unless otherwise specified, the terms (including scientific and technical terms) used herein have the commonly understood meanings to those skilled in the art. In addition, it can be understood that terms defined by commonly used dictionaries should be understood to have consistent meanings in the context of their related fields, and should not be understood as idealized or overly formal meanings.
图1为根据本发明实施方式的基于气象参数对雷电流幅值进行预测的方法流程图。本发明实施方式提供了一种基于气象参数对雷电流幅值进行预测的方法，本申请基于输电线路历史雷击事件和事故雷电定位系统获取雷电流幅值与雷电相关的气象特征参数，建立雷电流幅值计算模型，利用神经网络算法对模型进行测算，获取确定的雷电流幅值预测模型。本申请基于数值天气预报平台获取的实时气象特征参数得到雷电流幅值的预测值。本申请提供了一种基于气象参数对雷电流幅值进行预测的方法，为气象数据与输电线路的雷电性能之间提供了数据接口，通过提取气象数据中与雷电流幅值相关的气象参数，利用雷电活动的预测信息，获取雷电流幅值的预测值，进而对输电线路雷电绕击和反击跳闸概率的计算进行预测提供了可行性。如图1所示，一种基于气象参数对雷电流幅值进行预测的方法100：Fig. 1 is a flowchart of a method for predicting lightning current amplitude based on meteorological parameters according to an embodiment of the present invention. The embodiment of the present invention provides a method for predicting the amplitude of lightning current based on meteorological parameters. This application obtains the amplitude of lightning current and meteorological characteristic parameters related to lightning based on the historical lightning strike events of transmission lines and the lightning accident location system, and establishes the lightning current The amplitude calculation model uses the neural network algorithm to measure and calculate the model, and obtains the determined lightning current amplitude prediction model. This application obtains the predicted value of the lightning current amplitude based on the real-time meteorological characteristic parameters obtained by the numerical weather prediction platform. This application provides a method for predicting lightning current amplitude based on meteorological parameters, which provides a data interface between meteorological data and lightning performance of transmission lines, by extracting meteorological parameters related to lightning current amplitude in meteorological data, Using the forecast information of lightning activities to obtain the forecast value of lightning current amplitude, it is feasible to predict the calculation of lightning shielding and counter trip probability of transmission lines. As shown in FIG. 1, a method 100 for predicting lightning current amplitude based on meteorological parameters:
优选地，在步骤101：确认与雷电流幅值相关的气象参数，获取气象参数的数值。Preferably, in step 101: confirm the meteorological parameters related to the magnitude of the lightning current, and acquire the values of the meteorological parameters.
优选地，气象参数包括：回波强度，回波顶高，垂直积累液态水含量，组合反射率因子。本申请基于数值天气预报，获取回波强度、回波顶高、垂直积累液态水含量、组合反射率因子等雷电相关的气象特征参数。Preferably, the meteorological parameters include: echo intensity, echo top height, vertically accumulated liquid water content, and combined reflectivity factor. Based on numerical weather prediction, this application obtains lightning-related meteorological characteristic parameters such as echo intensity, echo top height, vertical accumulated liquid water content, and combined reflectivity factor.
优选地，在步骤102：利用气象参数的数值以及与气象参数的数值对应的雷电流幅值，建立气象参数的数值与雷电流幅值的相关性模型。Preferably, in step 102: using the value of the meteorological parameter and the magnitude of the lightning current corresponding to the value of the meteorological parameter, a correlation model between the value of the meteorological parameter and the magnitude of the lightning current is established.
本申请通过历史输电线路雷击跳闸事件和雷电定位系统，获取气象参数的数值作为相关性模型的输入参数，建立雷电流幅值和气象参数之间的相关性模型，如(1)式所示：This application obtains the value of meteorological parameters as the input parameters of the correlation model through historical transmission line lightning tripping events and lightning positioning system, and establishes a correlation model between lightning current amplitude and meteorological parameters, as shown in formula (1):
Im＝f(x1，x2，x3，x4) (1)I m = f(x 1 , x 2 , x 3 , x 4 ) (1)
(1)式中：Im为雷电流幅值；x1为回波强度；x2为回波顶高；x3为垂直积累液态水含量；x4为组合反射率因子。(1) where: I m is the amplitude of lightning current; x 1 is the echo intensity; x 2 is the height of the echo top; x 3 is the vertically accumulated liquid water content; x 4 is the combined reflectivity factor.
优选地，在步骤103：选取多次输电线路遭受雷击跳闸时气象参数的数值作为相关性模型的输入参数，选取与气象参数的数值对应的输电线路跳闸时的雷电流幅值为输出参数，对相关性模型进行训练，得到经过训练的训练模型。Preferably, in step 103: select the value of the meteorological parameter when multiple transmission lines suffer from lightning tripping as the input parameter of the correlation model, and select the lightning current amplitude when the transmission line trips corresponding to the value of the meteorological parameter as the output parameter, for The correlation model is trained to obtain a trained training model.
优选地，利用神经网络算法对相关性模型进行训练，得到经过训练的训练模型。Preferably, a neural network algorithm is used to train the correlation model to obtain a trained training model.
优选地，利用神经网络算法建立神经网络，设置神经网络中各个连接链的权值和阈值。Preferably, a neural network is established using a neural network algorithm, and weights and thresholds of each connection chain in the neural network are set.
优选地，在步骤104：选取历史气象参数的数值，利用训练模型对历史气象参数的数值进行计算，获取计算雷电流幅值；当计算雷电流幅值与历史气象参数的数值对应的雷电流幅值之间的误差值满足预定的误差阈值时，将训练模型作为确定的预测模型，用于对雷电流幅值进行预测。Preferably, in step 104: select the value of the historical meteorological parameter, use the training model to calculate the value of the historical meteorological parameter, and obtain the calculated lightning current amplitude; when calculating the lightning current amplitude corresponding to the value of the historical meteorological parameter When the error value between the values satisfies a predetermined error threshold, the training model is used as a determined prediction model for predicting the lightning current amplitude.
优选地，当计算雷电流幅值与历史气象参数的数值对应的雷电流幅值之间的误差值满足预定的误差阈值时，对神经网络中各个连接链的权值和阈值进行调整。Preferably, when the error value between the calculated lightning current amplitude and the lightning current amplitude corresponding to the value of the historical meteorological parameter satisfies a predetermined error threshold, the weights and thresholds of each connection chain in the neural network are adjusted.
本申请中，选取历史N次输电线路雷击事件作为样本，结合事故雷电定位系统的雷电流幅值、雷电流波形以及雷电相关的气象参数等进行分析研究，输入变量为输电线路跳闸时数值天气预报平台记录的回波强度、回波顶高、垂直积累液态水含量、组合反射率因子四个气象参数值，输出为输电线路跳闸时雷电流幅值。本发明选用反向传播(BP)神经网络算法对雷电流幅值与回波强度、回波顶高、垂直积累液态水含量、组合反射率因子之间的关系进行辨识，具体步骤如下：In this application, the history of N transmission line lightning strike events is selected as a sample, and the lightning current amplitude, lightning current waveform, and lightning-related meteorological parameters of the accident lightning location system are used for analysis and research. The input variable is the numerical weather forecast when the transmission line trips The four meteorological parameter values of echo intensity, echo top height, vertical accumulated liquid water content, and combined reflectivity factor recorded by the platform are output as the lightning current amplitude when the transmission line trips. The present invention selects the backpropagation (BP) neural network algorithm to identify the relationship between the lightning current amplitude and the echo intensity, the echo top height, the vertically accumulated liquid water content, and the combined reflectivity factor. The specific steps are as follows:
a)获取N次输电线路雷击事件发生时的气象参数信息和雷电流幅值作为计算样本数据；a) Obtain the meteorological parameter information and the lightning current amplitude when the lightning strike event of the transmission line occurs for N times as the calculation sample data;
b)应用神经网络函数建立BP神经网络，并初始化神经网络各个连接链的权值和阈值。b) Apply the neural network function to establish the BP neural network, and initialize the weights and thresholds of each connection chain of the neural network.
c)利用BP神经网络算法对神经网络进行计算，输入计算样本数据，正向计算神经网络隐含层和输出层的输出值，反向计算各层神经元的等效误差值，然后调整各层的连接权值和阈值，直至误差满足要求，最后保存计算结果。c) Use the BP neural network algorithm to calculate the neural network, input calculation sample data, forward calculate the output value of the hidden layer and output layer of the neural network, reverse calculate the equivalent error value of each layer of neurons, and then adjust each layer Connection weights and thresholds until the error meets the requirements, and finally save the calculation results.
d)比较计算结果和历史数据，验证所计算神经网络的正确性。d) Compare the calculation results with historical data to verify the correctness of the calculated neural network.
优选地，在步骤105：将实时的气象参数输入预测模型，利用预测模型对实时的气象参数进行计算，获取实时雷电流幅值预测值。本申请通过数值天气预报平台获取实时雷电相关的气象特征参数，利用步骤102中雷电流幅值计算模型，得到雷电流幅值的预测值。Preferably, in step 105: input real-time meteorological parameters into the prediction model, use the prediction model to calculate the real-time meteorological parameters, and obtain a real-time predicted value of lightning current amplitude. The present application obtains meteorological characteristic parameters related to lightning in real time through the numerical weather forecast platform, and uses the lightning current amplitude calculation model in step 102 to obtain the predicted value of the lightning current amplitude.
本发明实施方式公开了一种基于气象参数对雷电流幅值进行预测的方法，基于输电线路历史雷击事件和事故雷电定位系统获取雷电流幅值与雷电特征参数，利用神经网络算法，建立并确定雷电流幅值计算模型。本发明实施方式实现了基于数值天气预报平台获取的实时气象特征参数得到雷电流幅值的预测值。由此可以分析输电线路雷电性能，计算雷击跳闸的预测概率，进行线路雷害风险实时预警。本发明实施方式充分利用了数值天气预报的气象参数数据，并且将气象雷电预测与电网雷电预测相结合，为输电线路雷电预警提供计算依据，进一步提高了电网运行的安全性。The embodiment of the present invention discloses a method for predicting the lightning current amplitude based on meteorological parameters. The lightning current amplitude and lightning characteristic parameters are obtained based on the historical lightning strike events of the transmission line and the lightning accident location system, and the neural network algorithm is used to establish and determine Calculation model of lightning current amplitude. The embodiment of the present invention realizes obtaining the predicted value of the lightning current amplitude based on the real-time meteorological characteristic parameters acquired by the numerical weather forecast platform. In this way, the lightning performance of the transmission line can be analyzed, the predicted probability of lightning tripping can be calculated, and the real-time warning of the lightning risk of the line can be carried out. The embodiment of the present invention makes full use of the meteorological parameter data of the numerical weather forecast, and combines the meteorological lightning forecast with the power grid lightning forecast to provide a calculation basis for the lightning warning of the transmission line, and further improves the safety of the power grid operation.
图2为根据本发明实施方式的基于气象参数对雷电流幅值进行预测的系统结构图。如图2所示，一种基于气象参数对雷电流幅值进行预测的系统200，包括：Fig. 2 is a structural diagram of a system for predicting lightning current amplitude based on meteorological parameters according to an embodiment of the present invention. As shown in FIG. 2, a system 200 for predicting lightning current amplitude based on meteorological parameters includes:
初始单元201，用于确认与雷电流幅值相关的气象参数，获取气象参数的数值。The initial unit 201 is configured to confirm the meteorological parameters related to the magnitude of the lightning current and acquire the values of the meteorological parameters.
优选地，气象参数包括：回波强度，回波顶高，垂直积累液态水含量，组合反射率因子。Preferably, the meteorological parameters include: echo intensity, echo top height, vertically accumulated liquid water content, and combined reflectivity factor.
建立单元202，利用气象参数的数值以及与气象参数的数值对应的雷电流幅值，建立气象参数的数值与雷电流幅值的相关性模型。The establishment unit 202 uses the value of the meteorological parameter and the magnitude of the lightning current corresponding to the value of the meteorological parameter to establish a correlation model between the value of the meteorological parameter and the magnitude of the lightning current.
本申请通过历史输电线路雷击跳闸事件和雷电定位系统，获取气象参数的数值作为相关性模型的输入参数，建立雷电流幅值和气象参数之间的相关性模型，如(1)式所示：This application obtains the value of meteorological parameters as the input parameters of the correlation model through historical transmission line lightning tripping events and lightning positioning system, and establishes a correlation model between lightning current amplitude and meteorological parameters, as shown in formula (1):
Im＝f(x1，x2，x3，x4) (1)I m = f(x 1 , x 2 , x 3 , x 4 ) (1)
(1)式中：Im为雷电流幅值；x1为回波强度；x2为回波顶高；x3为垂直积累液态水含量；x4为组合反射率因子。(1) where: I m is the amplitude of lightning current; x 1 is the echo intensity; x 2 is the height of the echo top; x 3 is the vertically accumulated liquid water content; x 4 is the combined reflectivity factor.
训练单元203，用于选取多次输电线路遭受雷击跳闸时气象参数的数值作为相关性模型的输入参数，选取与气象参数的数值对应的输电线路跳闸时的雷电流幅值为输出参数，对相关性模型进行训练，得到经过训练的训练模型；The training unit 203 is used to select the value of meteorological parameters when multiple transmission lines suffer from lightning strikes and trips as the input parameter of the correlation model, and selects the lightning current amplitude when the transmission line trips corresponding to the value of the meteorological parameters as the output parameter. The sex model is trained to obtain a trained training model;
优选地，训练单元还用于：利用神经网络算法对相关性模型进行训练，得到经过训练的训练模型。Preferably, the training unit is further configured to: use a neural network algorithm to train the correlation model to obtain a trained training model.
优选地，训练单元还用于：利用神经网络算法建立神经网络，设置神经网络中各个连接链的权值和阈值。Preferably, the training unit is also used for: establishing a neural network using a neural network algorithm, and setting weights and thresholds of each connection chain in the neural network.
确认单元204，用于选取历史气象参数的数值，利用训练模型对历史气象参数的数值进行计算，获取计算雷电流幅值；当计算雷电流幅值与历史气象参数的数值对应的雷电流幅值之间的误差值满足预定的误差阈值时，将训练模型作为确定的预测模型，用于对雷电流幅值进行预测。The confirmation unit 204 is used to select the numerical value of the historical meteorological parameter, and utilizes the training model to calculate the numerical value of the historical meteorological parameter to obtain the calculated lightning current amplitude; when the lightning current amplitude corresponding to the numerical value of the calculated lightning current amplitude and the historical meteorological parameter When the error value between satisfies the predetermined error threshold, the training model is used as the determined prediction model for predicting the lightning current amplitude.
优选地，确认单元还用于：当计算雷电流幅值与历史气象参数的数值对应的雷电流幅值之间的误差值满足预定的误差阈值时，对神经网络中各个连接链的权值和阈值进行调整。Preferably, the confirmation unit is also used for: when the error value between the calculated lightning current amplitude and the lightning current amplitude corresponding to the value of the historical meteorological parameter satisfies a predetermined error threshold, the weight and sum of each connection chain in the neural network The threshold is adjusted.
本申请中，选取历史N次输电线路雷击事件作为样本，结合事故雷电定位系统的雷电流幅值、雷电流波形以及雷电相关的气象参数等进行分析研究，输入变量为输电线路跳闸时数值天气预报平台记录的回波强度、回波顶高、垂直积累液态水含量、组合反射率因子四个气象参数值，输出为输电线路跳闸时雷电流幅值。本发明选用反向传播(BP)神经网络算法对雷电流幅值与回波强度、回波顶高、垂直积累液态水含量、组合反射率因子之间的关系进行辨识，具体步骤如下：In this application, the history of N transmission line lightning strike events is selected as a sample, and the lightning current amplitude, lightning current waveform, and lightning-related meteorological parameters of the accident lightning location system are used for analysis and research. The input variable is the numerical weather forecast when the transmission line trips The four meteorological parameter values of echo intensity, echo top height, vertical accumulated liquid water content, and combined reflectivity factor recorded by the platform are output as the lightning current amplitude when the transmission line trips. The present invention selects the backpropagation (BP) neural network algorithm to identify the relationship between the lightning current amplitude and the echo intensity, the echo top height, the vertically accumulated liquid water content, and the combined reflectivity factor. The specific steps are as follows:
a)获取N次输电线路雷击事件发生时的气象参数信息和雷电流幅值作为计算样本数据；a) Obtain the meteorological parameter information and the lightning current amplitude when the lightning strike event of the transmission line occurs for N times as the calculation sample data;
b)应用神经网络函数建立BP神经网络，并初始化神经网络各个连接链的权值和阈值。b) Apply the neural network function to establish the BP neural network, and initialize the weights and thresholds of each connection chain of the neural network.
c)利用BP神经网络算法对神经网络进行计算，输入计算样本数据，正向计算神经网络隐含层和输出层的输出值，反向计算各层神经元的等效误差值，然后调整各层的连接权值和阈值，直至误差满足要求，最后保存计算结果。c) Use the BP neural network algorithm to calculate the neural network, input calculation sample data, forward calculate the output value of the hidden layer and output layer of the neural network, reverse calculate the equivalent error value of each layer of neurons, and then adjust each layer Connection weights and thresholds until the error meets the requirements, and finally save the calculation results.
d)比较计算结果和历史数据，验证所计算神经网络的正确性。d) Compare the calculation results with historical data to verify the correctness of the calculated neural network.
预测单元205，用于将实时的气象参数输入预测模型，利用预测模型对实时的气象参数进行计算，获取实时雷电流幅值预测值。The forecasting unit 205 is configured to input real-time meteorological parameters into the forecasting model, use the forecasting model to calculate the real-time meteorological parameters, and obtain real-time lightning current amplitude forecast values.
本申请通过数值天气预报平台获取实时雷电相关的气象特征参数，利用步骤建立单元202中雷电流幅值计算模型，得到雷电流幅值的预测值。This application obtains real-time lightning-related meteorological characteristic parameters through the numerical weather forecast platform, and uses the steps to establish the lightning current amplitude calculation model in unit 202 to obtain the predicted value of the lightning current amplitude.
已经通过参考少量实施方式描述了本发明。然而，本领域技术人员所公知的，正如附带的专利权利要求所限定的，除了本发明以上公开的其他的实施例等同地落在本发明的范围内。The invention has been described with reference to a small number of embodiments. However, it is clear to a person skilled in the art that other embodiments than the invention disclosed above are equally within the scope of the invention, as defined by the appended patent claims.
通常地，在权利要求中使用的所有术语都根据他们在技术领域的通常含义被解释，除非在其中被另外明确地定义。所有的参考“一个/所述/该[装置、组件等]”都被开放地解释为所述装置、组件等中的至少一个实例，除非另外明确地说明。这里公开的任何方法的步骤都没必要以公开的准确的顺序运行，除非明确地说明。Generally, all terms used in the claims are to be interpreted according to their ordinary meaning in the technical field, unless explicitly defined otherwise therein. All references to "a/the/the [means, component, etc.]" are openly construed to mean at least one instance of said means, component, etc., unless expressly stated otherwise. The steps of any method disclosed herein do not have to be performed in the exact order disclosed, unless explicitly stated.
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| CN109829407A (en) * | 2019-01-23 | 2019-05-31 | 中国科学技术大学 | Intelligent lightening recognition method based on convolutional neural networks |
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| CN113376711A (en) * | 2020-08-19 | 2021-09-10 | 兰州大学 | Lightning frequency forecasting method based on convolutional neural network |
| CN113376711B (en) * | 2020-08-19 | 2022-11-04 | 兰州大学 | Lightning Frequency Forecast Method Based on Convolutional Neural Network |
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| CN112147718A (en) * | 2020-09-23 | 2020-12-29 | 广东电网有限责任公司 | Lightning stroke prediction method and device for overhead transmission line and storage medium |
| CN112378056A (en) * | 2020-11-18 | 2021-02-19 | 珠海格力电器股份有限公司 | Intelligent air conditioner control method and device, computer equipment and storage medium |
| CN113447724A (en) * | 2021-03-09 | 2021-09-28 | 中国气象科学研究院 | Lightning activity judging method and system |
| CN113447724B (en) * | 2021-03-09 | 2022-07-29 | 中国气象科学研究院 | Lightning activity discrimination method and system |
| CN113222260A (en) * | 2021-05-19 | 2021-08-06 | 南方电网数字电网研究院有限公司 | Lightning trip-out rate prediction model of power transmission line |
| CN113449913A (en) * | 2021-06-25 | 2021-09-28 | 上海电机学院 | Rail transit line tripping intelligent early warning system based on SCADA |
| CN113447998A (en) * | 2021-06-29 | 2021-09-28 | 国网山西省电力公司电力科学研究院 | Power transmission line severe weather monitoring and early warning method and monitoring and early warning device |
| CN115524538A (en) * | 2022-11-02 | 2022-12-27 | 国网湖南省电力有限公司 | A lightning forecast correction method, system and storage medium for a power distribution network |
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