Research on online monitoring and early warning system of transmission line galloping based on multi-source data

2.1. Spectrum analysis of galloping displacement sequence

In general, the discrete Fourier transform method is used to convert the time-domain data obtained from the galloping monitoring of transmission lines into frequency-domain data, as shown in Eq. (1), laying a foundation for the extraction of the galloping frequency and assigned characteristic parameters:

where, the input discrete signal with length $N$ is represented by $Z_n$, and the discrete Fourier transform result is represented by $Z_k$. The fast Fourier transform method of discrete Fourier transform is mainly used in the calculation. This method has the characteristics of low complexity and fast calculation, and the result of the calculated modulus can represent the amplitude distribution of the signal spectrum component expressed in the form of frequency function. In the amplitude spectrum, the first frequency = the amplitude of the 0 component/the DC component of the sampling points, and the corresponding amplitude of the other frequencies *2/ the sampling points = the true amplitude spectrum. In addition, the peak value in the amplitude spectrum can represent the main frequency component in the time series. In order to detect the displacement sequence with stable frequency as a whole, an additional short-time Fourier transform is needed, i.e., discrete Fourier transform is processed for time-domain windowing in the sliding window, and then the relationship between input signal frequency and time is determined according to the power density of each window.

2.2. Function model construction of galloping displacement sequence

In order to better deal with the displacement time series of the wire spacer's centroid in the $X$ and $Y$ directions, the trigonometric function of Eq. (2) can be used here to process, i.e., to fit in the time domain:

where, the reference offset is represented by $A_0$, the amplitude corresponding to $f_k$ is represented by $A_k$, and the phase corresponding to $f_k$ is represented by ${\phi }_k$. In the fitting process, the nonlinear least square method is used for parameter estimation. In addition, the original data can also be fitted by extracting the galloping eigenvalues, which can avoid the interference of non-integral period truncation. The advantages of these two methods are as follows: First, the error of extracting displacement frequency by these two methods is relatively small, and the spectral analysis method is simple to calculate, without an iterative solution. Second, is obtained by the two methods to extract harmonic amplitude will always be less than half of the actual maximum differential displacement value, angle of disaster monitoring and early warning on see, frequency response can be obtained by means of spectral analysis of $f_k$, and then calculate the maximal displacement difference in sliding window (peak-to-peak value), choose the half wave amplitude can satisfy the actual security needs. Thirdly, the time-domain displacement fitting can be used for trajectory simulation and characteristic analysis of the galloping plane, and the harmonic amplitude obtained from spectrum analysis can be used as the initial value of displacement fitting to accelerate convergence.

3.1. Galloping warning criteria based on meteorological conditions

The structural parameters of transmission lines, as internal factors affecting the movement, usually remain unchanged, so the early warning mainly focuses on the influence of changing meteorological conditions on the line. Since most of the power transmission lines have not installed galloping online monitoring devices, it is considered to use the time data of national ground stations shared by the China Meteorological Data Network for macro meteorological warnings. The data set includes hourly observation values of temperature, wind speed and direction, relative humidity, etc., and the site can basically cover all administrative units at the county level. Early warning based on meteorological elements is the most commonly used model for monitoring and early warning of power grid meteorological disasters. However, meteorological elements are not the direct index to measure micrometeorological power disasters, so a targeted early warning model needs to be established based on relevant knowledge. Therefore, at present, there is no universal galloping classification warning standard for transmission lines, and there are certain differences in indicators and grade classification used in different galloping warning methods, so there is no direct comparison between different kinds of warning standards. Generally, meteorological disaster warning in China is distinguished by four levels (blue), three levels (yellow), two levels (orange), and one level (red). This paper refers to a similar meteorological disaster warning system, combined with the weather conditions of transmission lines galloping and the possible hazards brought by galloping. It decided to adopt the model of “meteorological warning + interphase safety factor warning” to construct a two-level warning system of the occurrence possibility and intensity of galloping. Specifically, before the occurrence of galloping, the possibility of galloping with different combinations of meteorological elements is determined by referring to the warning levels of meteorological departments, and the regions exceeding the threshold are given early warning. For the area with the galloping phenomenon, the key monitoring is carried out, the galloping track of the conductor is obtained by online detection means, and the galloping amplitude is extracted, which is used to calculate the minimum interphase distance. Finally, according to the safety factor of interphase, the warning level is divided to determine the galloping strength of each wire and the existing electrical threat to give early warning to transmission lines exceeding the threshold value.

3.2. Galloping warning criteria based on the same safety factor

Online monitoring data extracted from the wave amplitude is a key indicator to determine the intensity of transmission line galloping. When all the phase conductors have different wave behaviors, and the wave amplitude decreases with the increase of distance, flashover and trip problems may occur due to the small phase spacing after the wave reaches a certain strength. Therefore, it is necessary to set up early warning standards for real-time warning to avoid the occurrence of such problems. According to the safety factor of phase, four warning standards can be divided, and the corresponding values are determined comprehensively according to the systematic error and reservation margin of the measurement. When the minimum interphase distance during galloping is 1.5-2 times of the minimum safety distance, a four-level warning can be issued, indicating that the strength of galloping of transmission lines is small and the line operation is relatively safe. In this environment, flashover problems generally do not occur, but bolts loosening or metal wear may still occur. Galloping down to a safe distance and minimum distance of 1.2 to 1.5 times can be regarded as a level 3 alert, showing that the strength of the transmission line galloping is large, the operation has a threat, and the wave amplitude and duration should be closely concerned. In addition, the maximum dynamic tension can be estimated based on the feature of galloping value caused by transmission line galloping. When the minimum galloping phase spacing is 1.0-1.2 times the safety distance, it can be regarded as a second-level warning, indicating that there is serious galloping behavior on the transmission line, which poses a major threat to the safety of its operation. Therefore, it is necessary to strengthen inspection and make emergency plans. If necessary, the transmission line can be de-iced to destroy the mechanical conditions of galloping. When the minimum interphase distance of galloping is less than the safe distance, it can be regarded as a first-level warning, indicating that tripping and flashover problems may occur on the gravel of transmission lines, and corresponding intervention measures should be taken to formulate emergency treatment plans, as shown in Table 1. The discriminant method of interphase safety factor has the advantages of simple calculation, effectiveness, and accuracy, so it is feasible to use the discriminant method of interphase safety factor in online monitoring and warning of transmission line galloping state in the galloping monitoring and warning system.

4.1. System function design

Aiming at the problem of transmission line galloping, this paper took YN province as an example to design and study its online monitoring and early warning system here, so that the system has a number of functions, such as data management, data analysis, system early warning, and visualization. The design steps are as follows. First, ArcGISDesktop software was used to construct the research object database and two-dimensional scene, and GIS development technology was used for secondary development. Secondly, the geographically weighted regression (GWR) software was used to batch process the trend modeling of meteorological elements. Finally, Matlab software was used to process the images of the galloping video to facilitate the subsequent platform invocation. The system is shown in Fig. 1.

It is seen that the system includes modules such as information management, meteorological element interpolation, meteorological warning, and interphase safety factor warning. In the system management module, it includes the storage, query and management of geographic data, meteorological data, and galloping test data. In the difference value module of meteorological elements, it contains the query function of the minimum temperature, average relative temperature, maximum wind speed, corresponding wind direction, and other key parameters. In the meteorological warning module, the function of hourly meteorological difference and the warning of galloping intensity are given. In the warning module of interphase safety factor, feature parameter extraction function and galloping intensity warning function are included.

Fig. 1System function design

4.2. System framework design

The system framework is mainly composed of the presentation layer, business layer, driver layer, and data layer, as shown in Fig. 2.

Fig. 2System framework design

As can be seen from Fig. 2, the presentation layer provides users with a system visualization module, i.e., users can intuitively understand the results after parameter processing at this level. In addition, users can invoke tasks at the business layer and display the results of tasks in different ways on this interface. As for the business layer, it is a key link in the system framework and plays a bridging role. The driver layer is composed of a series of data access, processing, spatial analysis, and visualization components, which can provide editing, query, add and delete operations for the data layer downward, and provide specific functions for the performance layer and business layer upward. The data layer is mainly composed of two modules, Geodatabase and Access, which are mainly responsible for storing corresponding information or data. Among them, image base map, administrative division, power transmission line, and micro-terrain factors are stored in Geodatabase for management, while meteorological data and galloping data are stored in Access for management.

4.3. Database design

Database design is divided into geographic database design and relational database design. Although there are two types of data in the geographic database (grid data and vector data), these data need to be uniformly projected to the same projection coordinate system and follow the EGM96 (geoid height, geoidal undulation) elevation standard. The source data of grid type includes satellite image, digital elevation model (DEM), land cover type, spatial distributions of meteorological elements, and other information. Through further calculation of DEM, information of relief degree of land surface (RDLS), angle between urban direction and covered area can be obtained. Through calculation of land cover type, regional water area, and area forest area of active research target can be obtained. Through calculation and analysis of spatial distribution of meteorological elements, regional information of time warning can be obtained. Vector type of source data include administrative divisions, weather stations, transmission line and warning lines layer data. The research target of provincial boundary and prefecture level information can be understood through the analysis of the administrative division data calculation. Through the study of target calculation and analysis of data for transmission lines, transmission tower, and power line information can be obtained. The specific design of the geographic database is shown in Fig. 3.

Fig. 3Geographical database framework design

For the relational database, it is responsible for storing the site location information corresponding to the galloping monitoring data and meteorological monitoring data, and the information need to be associated query during the visualization. The maximum number of stations with meteorological time data of the research target is 119, and all stations are stored in the form of table. The format of table name is “site ID+ name”, as shown in Table 2.

In Table 2, ObsTime indicates the observation time, and the fixed string format is YYYYMMDDHH. The maximum wind speed per hour is represented by WindSmax. Hourly precipitation was represented by Pre, hourly minimum temperature and average temperature were represented by Tmin and Tmean respectively. The wind direction Angle corresponding to the maximum wind speed is represented by WindD. When there is still wind, it can be marked with a serial number of 999017. The hourly relative humidity is denoted by RH. The galloping monitoring data involve different galloping monitoring positions and need to be collected from multiple galloping monitoring positions. The monitoring scope of all monitoring points belongs to the first wire, such as N3_N4. When storing these galloping monitoring data, it also needs to be processed in the form of tables, as shown in Table 3.

In Table 3, the sampling frequency of galloping monitoring data is set at 25 Hz, i.e., data will be refreshed every 40 mm to generate new galloping monitoring data. The content of Table 3 is set as follows. The observation time is ObsTime, and the fixed string format is yyyymmddhhMMssmmm. The galloping displacement in the Y direction is represented by L_dY, which is the measurement result of the spacer bar centroid plane in each frame image. The amplitude is represented by L_AmpY, which is half of the peak value of the periodic sliding window. The frequency is represented by L_FreY, which is the result of spectrum analysis. L, C, and R in the table represent the spacer positions of amplitude values on the left, middle, and right three-phase wires, respectively. Columns in the table can be expanded to store multiple $x$-direction data.