A data storage method for overhead conductor tree contact fault detection

Abstract

The application relates to a data storage method for overhead conductor tree collision fault detection, a data preprocessing module is used for inputting line electrical quantity real-time measurement data and outputting current time point to-be-stored data to a stored data updating module; after receiving the to-be-stored data from the data preprocessing module every time, the stored data updating module carries out data point discrimination and updating once, the stored data updating module is provided with K data points and a cache array arranged in sequence, the data preprocessing module carries out processing on the input data once according to a set power frequency period, carries out median filtering on the data measured in the period, and outputs the median filtering result to the first data point of the stored data updating module; a few data storage amounts can be used to realize an ultra-wide monitoring time window of hours to days, and the time window width requirement of the conductor tree collision fault detection is met. The method occupies few computing resources, and is beneficial to realizing on a low-cost and low-performance 10kV system line fault monitoring device.

Description

TECHNICAL FIELD

[0001] The present application relates to a data storage method and system for overhead conductor tree collision fault detection, belonging to the technical field of data storage. BACKGROUND

[0002] 10kV overhead conductor tree collision fault is a special high-resistance grounding fault. In addition to the characteristics of large transition resistance of general high-resistance faults, it also has the characteristics of slow decrease of fault transition resistance over time and large difference in duration of fault development process of different trees. The initial transition resistance of general conductor tree collision fault can reach tens to hundreds of kilo-ohms, far exceeding the transition resistance capacity of existing fault detection devices. Therefore, this type of fault is difficult to detect at the initial stage. Only when the fault transition resistance slowly decreases to several kilo-ohms over time, it can be detected. When the conductor tree collision fault is detected, the fault duration can reach tens of minutes to several hours, which has a serious risk of causing forest fires. Therefore, a detection method specifically for conductor tree collision fault is needed to improve the detection rate of this type of fault and reduce the risk of fire.

[0003] Current conductor tree collision fault detection is generally based on traditional high-resistance grounding fault detection technology. This type of technology (such as patents CN114879085A, CN115166418A, etc.) is mostly based on short-time fault electrical quantity characteristics, such as zero-sequence voltage and zero-sequence current within tens of milliseconds to several seconds. The matching wave recorder uses equal time interval sampling and storage (such as patent CN115061014A). The data storage capacity is linearly related to the recording time length. Due to the limitation of the storage capacity of monitoring equipment, the amount of data that can be recorded is small, and the monitoring time window width is small. Under the original technical conditions, due to the short monitoring time, small amount of available data, weak fault electrical characteristics, and poor measurement environment, the signal-to-noise ratio of the conductor tree collision fault signal is difficult to improve to a sufficient confidence level. At the same time, the important characteristics of the long-term slow development of the conductor tree collision fault are lost, making it difficult to estimate the fault occurrence time.

[0004] To realize the detection of conductor tree collision fault and estimate the occurrence time, it is crucial to fully utilize the slow development characteristics of the fault. This requires the fault monitoring device to store and update electrical measurement quantities in real time, and the monitored time window should not be shorter than the longest fault development duration (such as 5 hours). Due to the limited storage capacity and computing resources of 10kV line fault detection devices, it is not practical to store and calculate all 5 hours of data in real time.

[0005] The present application proposes a data storage method based on the slow development characteristics of fault index for the long-time window monitoring data storage problem required by conductor tree collision fault detection. This method can realize the storage of ultra-long time window information with minimal data storage and computing resource occupation, and the stored data is convenient for subsequent fault detection algorithm processing. SUMMARY

[0006] The present application aims to provide a data storage method for overhead conductor tree collision fault detection, to solve the problem of long time window monitoring data storage required for conductor tree collision fault detection with minimal data storage and computing resource occupation.

[0007] The technical solution of the present application is as follows:

[0008] A data storage method for overhead conductor tree collision fault detection, comprising:

[0009] A data preprocessing module, configured to input line electrical quantity real-time measurement data and output the to-be-stored data at the current time to the stored data updating module;

[0010] The stored data updating module is configured to perform data point discrimination and updating once after receiving the to-be-stored data from the data preprocessing module each time (the stored data is saved in the device memory and can be called by the rear algorithm); the stored data updating module is provided with K data points (1st data point-nth data point) and cache arrays (cache array 1-cache array n) arranged in sequence; the data points and the cache arrays correspond to each other (nth data point corresponds to cache array n), and the basic principle is shown in Figure 1 ;

[0011] The data preprocessing module performs one processing on the input data according to the set power frequency period, performs median filtering on the data measured in the period, and outputs the median filtering result to the 1st data point of the stored data updating module; then the 2nd to Kth data points are updated in turn;

[0012] The update process of the nth data point in the stored data updating module is as follows: the original data is moved to the cache array n; it is judged whether the nth-1 data point has completed 2 updates, if yes, the data in the cache array n-1 is stored in the nth data point, if not, it remains unchanged, wherein, 1<n<=K.

[0013] Preferably, the to-be-stored data input by the data preprocessing module is a numerical value or a waveform data.

[0014] Preferably, when the data is called, the nth to 1st points are extracted in order, and the obtained data is approximately exponentially spaced at the corresponding time. If the minimum data update time interval is ΔT, the monitored time range changes between ΔT*2^(n-1) and ΔT*2^n with the data update progress.

[0015] An electronic device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements a data storage method for overhead conductor tree collision fault detection when executing the program.

[0016] A computer readable storage medium, having stored thereon a computer program, which, when executed by a processor, implements a data storage method for overhead conductor tree collision fault detection.

[0017] The present application has the following advantages:

[0018] The present application stores data points in non-equidistant approximate logarithmic time series, and can realize an ultra-wide monitoring time window of hours to days with a small amount of data storage, thereby meeting the time window width requirement of conductor tree collision fault detection.

[0019] The data update algorithm of the data storage method is simple, occupies less computing resources, and is conducive to implementation on a low-cost, low-performance 10kV system line fault monitoring device.

[0020] The conductor tree collision fault grounding current rises exponentially over time. After the measured data are stored according to the method described in the present application, the fault current-time curve changes from exponential to approximately linear when the data are processed at equal intervals, which facilitates subsequent fault detection algorithm and fault occurrence time estimation algorithm processing. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 The present application is a schematic diagram;

[0022] Figure 2 The present application is a stored data update process. DETAILED DESCRIPTION

[0023] The present application will be described in detail below in conjunction with the drawings and specific embodiments.

[0024] Embodiment one:

[0025] Suppose the maximum time range T to be monitored is 18000s, the minimum time interval ΔT of each updated data is 1s, and the stored data is zero sequence current effective value. tot

[0026] First, the maximum number of data points K is determined as follows:

[0027]

[0028] In this example, K is 16, the minimum monitoring time range is 32768s, and the maximum monitoring time range is 65536s.

[0029] ​The 16-bit binary array m is used as the data update monitoring array, the nth bit from the low bit to the high bit (denoted as m(n)) is the detection bit of the nth data point, and sum(m, n) represents the sum of the low n bits of m. Each time the data is updated (interval ΔT), the value of m is increased by 1.

[0030] The data preprocessing process of the data preprocessing module: calculate the zero sequence current sampling data or call the zero sequence current effective value of each power frequency cycle within the last 1 second from other measurement software, take the median of 50 zero sequence current effective values as the current value, and output the value to the stored data update module.

[0031] Stored data update process:

[0032] 1. If m(1) = 0, store the original first data point data in the cache array 1; otherwise, proceed to the next step.

[0033] 2. Store the current value from the data preprocessing module in the first data point, and increase the value of m by 1.

[0034] 3. Let n = 2.

[0035] 4. If sum(m, n) = 0, store the original nth data point data in the cache array n, and store the cache array n-1 data in the nth data point; otherwise, proceed to the next step.

[0036] 5. Increase the value of n by 1.

[0037] 6. If n > K, jump to step 1; otherwise, jump to step 4.

[0038] When the stored data is called, it is called from the nth data point to the first data point in sequence, and the obtained data sequence corresponds to the positive direction of the time axis, and the time interval is approximately logarithmic.

[0039] Example 2

[0040] Assume that the maximum time range T to be monitored is 4096s tot 2048s, the minimum time interval ΔT for updating data is 1s, and the stored data is the zero sequence current waveform.

[0041] In this example, K is 12, the minimum monitoring time range is 2048s, and the maximum monitoring time range is 4096s.

[0042] Data preprocessing process: calculate or call from other measurement software zero sequence current waveform data and A-phase voltage waveform data of each power frequency cycle within 1 second, align the zero sequence current waveform data with A-phase voltage phase and translate to the same cycle, then each measurement time in the cycle corresponds to 50 measurement values, median filter the 50 measurement values of each measurement time, reorganize the median filtered data into a complete cycle, process the cycle according to the required characteristics of the subsequent algorithm, and obtain the data as the output of the data preprocessing module.

[0043] The stored data updating process is shown in Figure 2 The difference from example 1 is that:

[0044] 1. The difference is that each stored data point no longer stores a numerical value, but a waveform data.

[0045] 2. The number of times of updating each data point is changed from a K-bit binary array to K independent monitoring variables.

[0046] The above is only an embodiment of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent flow transformation using the content of the specification and drawings, or direct or indirect application in other related technical fields, is also included in the patent protection scope of the present application.

Claims

1. A data storage method for overhead conductor tree-encounter fault detection, characterized by: The application relates to a data storage method for detecting tree collision of overhead conductor. The data preprocessing module is used for inputting line electrical quantity real-time measurement data and outputting current time point to-be-stored data to the stored data updating module. The stored data updating module is provided with K data points and a cache array arranged in sequence. The data preprocessing module processes the input data according to a set power frequency period, carries out median filtering on the data measured in the period, and outputs the median filtering result to the first data point of the stored data updating module. The update process of the nth data point in the stored data updating module is as follows: the original data is moved to the cache array n; it is judged whether the (n-1)th data point is completed twice, if yes, the data in the cache array n-1 is stored in the nth data point, if not, the data is unchanged, wherein, 1 The maximum storage number K of the data point is determined according to the following formula: ； Wherein, Delta T is the minimum time interval of each update data; T tot Maximum time range for monitoring; The binary array m is used as a data update monitoring array, the nth bit from the low bit to the high bit is the detection bit of the nth data point, and is recorded as m (n); Sum (m, n) represents the sum of the low n bits of m, the value of m is increased by 1 each time the data is updated. The stored data updating process is as follows: Step 1, if m (1) = 0, the original first data point data is stored in the cache array 1; otherwise, the next step; Step 2, the current value from the data preprocessing module is stored in the first data point, and the value of m is increased by 1; Step 3, n = 2; Step 4, if sum (m, n) = 0, the original nth data point data is stored in the cache array n, and the data in the cache array n-1 is stored in the nth data point; otherwise, the next step; Step 5, the value of n is increased by 1; Step 6, if n > K, jump to step 1; otherwise, jump to step 4.

2. A data storage method for tree-fault detection of overhead conductors as recited in claim 1, characterized by: The to-be-stored data input by the data preprocessing module is a numerical value or a waveform data.

3. A data storage method for tree-fault detection of overhead conductors as recited in claim 1, characterized by: When the data is called, the nth to first points are extracted in sequence.

4. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the program to realize the data storage method for detecting tree collision of overhead conductor according to any one of claims 1-3.

5. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to realize the data storage method for detecting tree collision of overhead conductor according to any one of claims 1-3.

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