Power grid transmission line fault identification method and device, medium and equipment

Abstract

The invention discloses a power grid transmission line fault identification method and device, a medium and equipment, and relates to the field of power system safety. The power grid power transmission line fault identification method comprises the steps of sequentially collecting voltage information of a power transmission line at each sampling moment according to a sampling period; calculating a first-order differential voltage value at each sampling moment based on the voltage information, and detecting whether signal fluctuation occurs in the power transmission line or not through the first-order differential voltage value at each sampling moment; and when signal fluctuation occurs in the power transmission line, calculating a voltage energy index at the current moment based on voltage information at multiple continuous sampling moments in a preset sliding time window at the current moment, and determining whether the power transmission line has a fault according to the voltage energy index. The method utilizes voltage information to perform fault detection, and the algorithm is simple and efficient.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power system safety, and particularly relates to a power grid transmission line fault identification method and device, a medium and equipment. BACKGROUND

[0002] As a new generation of power transmission technology, flexible DC transmission technology is widely used. Because the flexible DC grid is a low-inertia network, when a short-circuit fault occurs on a DC line, each converter station feeds short-circuit current to the fault point, causing the fault current to rise rapidly and have a large amplitude, which can cause harm to the equipment and system safety. Therefore, the protection and fault isolation of the flexible DC transmission system are crucial. Generally, the detection of the flexible DC transmission system requires a series of complex processes such as time-frequency transformation, or a deep learning model is used to extract data features, which is not efficient. SUMMARY

[0003] The present application provides a power grid transmission line fault identification method, device, medium and equipment, which can quickly and efficiently identify transmission line faults and improve the safety of the power grid system.

[0004] In a first aspect, the present application provides a power grid transmission line fault identification method, comprising:

[0005] Collecting voltage information of each sampling time of the transmission line in sequence according to a sampling period;

[0006] Based on the voltage information, calculating a first-order differential voltage value of each sampling time, and detecting whether a signal fluctuation occurs in the transmission line through the first-order differential voltage value of each sampling time;

[0007] When the signal fluctuation occurs in the transmission line, calculating a voltage energy index of the current time based on the voltage information of the continuous multiple sampling times within the preset sliding time window of the current time, and determining whether a fault occurs in the transmission line according to the voltage energy index.

[0008] According to the power grid transmission line fault identification method in the present embodiment, the voltage information of the power grid transmission line is collected, and the first-order differential voltage is used to determine whether fault judgment is needed. The calculation of the first-order differential voltage is relatively simple, and does not easily cause excessive burden in the continuous detection of the transmission line. At the same time, unnecessary fault identification can be reduced, and resource waste can be reduced. When a signal fluctuation is detected, the fault identification is performed, and whether a fault occurs is determined through the voltage information of multiple sampling times within the sliding time window. The fault can be quickly and reliably identified, and the reliability and sensitivity of the transmission line fault protection can be improved.

[0009] Further, the detection of whether a signal fluctuation occurs in the transmission line through the first-order differential voltage value of each sampling time comprises:

[0010] The first-order differential voltage value of each sampling time is obtained, the square sum of the first-order differential voltage value accumulated at each sampling time is calculated, and when the accumulated square sum is greater than a starting threshold, it is determined that the power transmission line has signal fluctuation.

[0011] Further, the voltage energy index of the current time is calculated based on the voltage information of the continuous multiple sampling times in the preset sliding time window of the current time, including:

[0012] According to the voltage information, the intermediate differential voltage module value of each sampling time in the sliding time window is calculated;

[0013] The cumulative value of the intermediate differential voltage module value of each sampling time in the sliding time window is determined to obtain the voltage energy index of the sliding time window.

[0014] Further, the intermediate differential voltage module value is calculated based on the voltage information of the current time and the two sampling times before and after, and is expressed as:

[0015] E k = |u k+1 - 2u k + u k-1 |

[0016] Wherein, u k , u k+1 , u k-1 are the voltage information corresponding to the current time k and the sampling times before and after, respectively, and E k is the intermediate differential voltage module value of the current time k.

[0017] Further, the determination of whether the power transmission line has a fault according to the voltage energy index includes:

[0018] When the voltage energy index of the sliding time window is greater than a fault identification threshold, it is determined that the power transmission line has a fault;

[0019] When the voltage energy index of the sliding time window is not greater than the fault identification threshold, the sliding time window is updated, and the voltage energy index of the sliding time window of the next time is calculated.

[0020] Further, it further includes:

[0021] When the power transmission line has a fault, the positive voltage energy index corresponding to the positive voltage and the negative voltage energy index corresponding to the negative voltage in the sliding time window of the current time are determined respectively;

[0022] The positive voltage energy index and the negative voltage energy index are compared to determine the fault type.

[0023] Further, the comparison of the size of the positive electrode voltage energy index and the negative electrode voltage energy index determines the fault type, including:

[0024] The ratio of the positive electrode voltage energy index and the negative electrode voltage energy index is obtained;

[0025] The ratio is:

[0026] k = E p / E n

[0027] Wherein, E p , E n are the positive electrode voltage energy index and the negative electrode voltage energy index, respectively;

[0028] The ratio k is compared with a preset pole selection threshold k set , and when k ≥ k set , it is a positive electrode fault, when 1 / k set < k < k set , it is a bipolar fault, and when k < 1 / k set , it is a negative electrode fault.

[0029] In a second aspect, the present application provides a power grid transmission line fault identification device, comprising:

[0030] A sampling module is configured to sequentially collect voltage information of each sampling time of the transmission line according to a sampling period;

[0031] A signal fluctuation detection module is configured to calculate a first-order differential voltage value of each sampling time based on the voltage information, and detect whether the transmission line has signal fluctuation through the first-order differential voltage value of each sampling time;

[0032] A fault detection module is configured to calculate a voltage energy index of a current time based on voltage information of a plurality of continuous sampling times within a preset sliding time window of the current time when the transmission line has signal fluctuation, and determine whether the transmission line has a fault according to the voltage energy index.

[0033] In a third aspect, the present application provides an electronic device, which comprises a memory and one or more processors. The memory stores one or more computer programs, and the computer programs comprise instructions which, when executed by the processor, cause the electronic device to perform the power grid transmission line fault identification method in the first aspect.

[0034] In a fourth aspect, the present application provides a computer readable storage medium, which stores instructions, and when the instructions are run on an electronic device, the electronic device performs the power grid transmission line fault identification method in the first aspect.

[0035] In a fifth aspect, the present application provides a computer program product, which, when running on an electronic device, causes the electronic device to perform the power grid transmission line fault identification method according to the first aspect.

[0036] It can be understood that the beneficial effects achieved by the power grid transmission line fault identification apparatus, the electronic device, the computer readable storage medium, and the computer program product provided above can refer to the beneficial effects in the first aspect, which will not be described here again. BRIEF DESCRIPTION OF DRAWINGS

[0037] Figure 1 A flowchart of the power grid transmission line fault identification method provided by the embodiments of the present application is shown in the figure.

[0038] Figure 2 A structural diagram of the power grid transmission line fault identification apparatus provided by the embodiments of the present application is shown in the figure.

[0039] Figure 3 A structural diagram of the electronic device provided by the embodiments of the present application is shown in the figure. DETAILED DESCRIPTION

[0040] In order to clearly describe the technical solutions of the embodiments of the present application, in the embodiments of the present application, the same items or similar items with basically the same functions and effects are distinguished by using "first", "second", etc. For example, the first chip and the second chip are only used to distinguish different chips, and do not limit the sequence. Those skilled in the art can understand that "first", "second", etc. do not limit the quantity and execution sequence, and "first", "second", etc. also do not necessarily mean different. It should be noted that in the embodiments of the present application, "exemplary" or "for example" means to serve as an example, illustration or description. Any embodiment or design scheme described as "exemplary" or "for example" in the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. In fact, "exemplary" or "for example" is used to present the relevant concept in a specific way. In the embodiments of the present application, "at least one" means one or more, and "multiple" means two or more.

[0041] It should be noted that "at the time of" in the embodiments of the present application can be at the moment when a certain condition occurs, or can be within a period of time after a certain condition occurs, which is not limited in the embodiments of the present application.

[0042] The implementation of the embodiments will be described in detail below with reference to the accompanying drawings.

[0043] The embodiment provides a power grid transmission line fault identification method. The power grid transmission line fault identification method can be applied to various electronic devices such as a computer (PC), a tablet computer, a virtual reality / augmented reality device, a wearable device, an industrial computer, and a vehicle machine. The power grid transmission line fault identification method can also be applied to a server, a cloud, and a server cluster, and the embodiment does not specially limit this.

[0044] Figure 1 A flowchart of the power grid transmission line fault identification method provided by the embodiment is shown.

[0045] As shown in Figure 1 , the power grid transmission line fault identification method can include the following steps:

[0046] Step 101: Collect voltage information of each sampling time of a transmission line in sequence according to a sampling period.

[0047] The voltage information of the power grid transmission line is obtained by a monitoring system including a sensor, a measuring device, and a data acquisition system. The voltage information includes a positive voltage value and a negative voltage value. The sampling period can be determined according to actual conditions.

[0048] Step 102: Calculate a first-order differential voltage value of each sampling time based on the voltage information, and detect whether a signal fluctuation of the transmission line occurs by using the first-order differential voltage value of each sampling time.

[0049] The first-order differential voltage value refers to a difference value of voltage information of two adjacent sampling times. The expression of the first-order differential value is obtained according to the voltage information as follows:

[0050] Δu x x x x-1 x-1 (1)

[0051] wherein x is a current sampling time, x-1 is a previous sampling time of the current sampling time, and Δu x is the first-order differential voltage value of the current sampling time.

[0052] When a fault occurs, the voltage drops significantly, and the first-order differential value of the voltage will have a large mutation. The embodiment uses the sum of squares of the first-order differential value of the voltage to start detection protection. Specifically, the first-order differential voltage value of each sampling time is obtained, the sum of squares of the first-order differential voltage value accumulated at each sampling time is calculated, and when the accumulated sum of squares is greater than a starting threshold, it is determined that a signal fluctuation of the transmission line occurs.

[0053] The sum of squares of the first-order differential voltage value accumulated at each sampling time is expressed as:

[0054]

[0055] wherein s(k) represents the sum of squares accumulated at the first k sampling time, since the first-order differential voltage value is the difference between the voltage values at two sampling times, the sum of squares of the first-order differential voltage value is accumulated from 2, that is, x is greater than or equal to 2 and less than or equal to k.

[0056] s(k) increases instantaneously after the line fault, and a judgment basis for starting detection is constructed by using s(k), specifically as follows:

[0057] s(k) > s set

[0058] wherein s set is a starting threshold. Exemplarily, the value of s set is set to 0.05 pu of the rated voltage, when the sum of squares of the first-order differential voltage at the k sampling time is greater than the threshold, the protection starts, and the fault identification is started, which can meet the reliable starting of the line far-end high resistance fault.

[0059] Step 103: when the signal fluctuation occurs in the power transmission line, calculating the voltage energy index of the current time based on the voltage information of the continuous multiple sampling times in the preset sliding time window of the current time, and determining whether the power transmission line has a fault according to the voltage energy index.

[0060] The fault identification is started when the signal fluctuation occurs, the current time is the sampling time when the signal fluctuation occurs, and the current time is updated over time, that is, the step of calculating the voltage energy index of the current time can be repeatedly executed, that is, the voltage energy index of each subsequent sampling time is calculated after the signal fluctuation occurs, so as to detect the occurrence of the fault.

[0061] In the embodiment, the intermediate differential method is adopted to extract the high-frequency information of the signal, the intermediate differential algorithm is used to represent the irregular fluctuation between data, that is, the strength of the signal transition change, and the degree of waveform change is related to the signal energy, so the intermediate differential is selected to collect the signal. Specifically: calculating the intermediate differential voltage module value of each sampling time in the sliding time window according to the voltage information; determining the cumulative value of the intermediate differential voltage module value of each sampling time in the sliding time window, to obtain the voltage energy index of the sliding time window.

[0062] If there is a function y = f(x), when x takes a series of discrete values x, x+1, x+2, … at equal intervals, then y x+1 -2y x +y x－1 is the intermediate differential of the function at x. Therefore, the local energy of the voltage is simply represented by three consecutive sampling points, and the corresponding frequency characteristics are calculated to verify the accuracy of the intermediate differential method for extracting the high-frequency energy of the signal.

[0063] The sliding time window is a time period, and the length of the time period can be set according to actual conditions. Considering the requirement of protecting the speed of action, the length of the time window can be determined as 1 ms. Exemplarily, n sampling points are included in the sliding time window, and the number of sliding points is n / 2-1. That is, the intermediate differential voltage is continuously calculated after the current sampling point is slid forward and backward by two sampling distances, the modulus values of the intermediate differential voltages of n / 2-1 points are added, and the voltage energy index at the current moment is obtained. The voltage information of multiple sampling points in the sliding time window is used to calculate the voltage energy index, which can enhance the difference between internal and external faults in the sliding time window, highlight the fault features with less calculation amount, smooth the fluctuations of signals under non-fault, and improve the accuracy of fault identification.

[0064] The modulus value of the intermediate differential voltage is E k , and

[0065] E k = |u k+1 -2u k +u k-1 | (3)

[0066] wherein u k , u k+1 and u k-1 are voltage values at the current moment k (k = 2, 3, …, n-2) and the sampling moments before and after the current moment, which can be positive electrode voltages or negative electrode voltages.

[0067] The voltage energy index E in the sliding time window at the current moment k is:

[0068]

[0069] Since the sliding time window includes multiple sampling moments, the voltage information of each sampling moment is repeatedly used when calculating the voltage energy index of each sampling moment, thereby improving the accuracy of fault detection.

[0070] Compared with the voltage outside the time window, the voltage inside the time window contains more high-frequency components when a fault occurs, and the voltage energy index is also larger. Therefore, when the voltage energy index of the sliding time window is greater than a fault identification threshold, it is determined that the power transmission line has a fault; when the voltage energy index of the sliding time window is not greater than the fault identification threshold, the sliding time window is updated, and the voltage energy index of the sliding time window at the next moment is calculated. This is expressed as follows:

[0071] E > E set

[0072] wherein E setFor fault identification threshold, for intra-zone and external fault discrimination, the maximum value of external fault voltage energy index should be considered when setting the value, i.e. the valve side metallic grounding short circuit.

[0073] The embodiment further comprises: determining the positive voltage energy index corresponding to the positive voltage and the negative voltage energy index corresponding to the negative voltage in the sliding time window at the current moment when the power transmission line is faulty; and determining the fault type by comparing the positive voltage energy index and the negative voltage energy index.

[0074] The comparison of the positive voltage energy index and the negative voltage energy index to determine the fault type comprises: obtaining the ratio of the positive voltage energy index and the negative voltage energy index; and the ratio is:

[0075] k = E p / E n

[0076] E p , E n are the positive and negative voltage energy indexes respectively.

[0077] The comparison of the ratio k and a preset polarity threshold k set comprises: when k ≥ k set , it is a positive pole fault; when 1 / k set < k < k set , it is a bipolar fault; and when k < 1 / k set , it is a negative pole fault.

[0078] In a bipolar DC system, when a pole line is faulty, due to the coupling effect between the lines, the voltage of the healthy pole will also change, which may cause the protection of the healthy line to malfunction. It is of great significance for the safe and stable operation of the system to quickly and reliably select the faulty pole. Since the transient energy of the faulty pole voltage is large under single-pole fault condition, and the transient energy of the positive and negative pole voltages is almost equal under bipolar fault condition, a fault pole selection criterion can be constructed by utilizing the difference between the positive and negative pole voltage energy indexes. The ratio of the positive and negative pole voltage energy indexes is defined as k, when the line is a positive pole fault, k > 1; when it is a negative pole fault, k < 1; and when it is a bipolar fault, k ≈ 1. Then the fault type is determined as:

[0079]

[0080] Exemplarily, in order to ensure the reliability of the fault type determination, a certain margin is considered, and k set = 1.2.

[0081] In this embodiment, the high-frequency component extraction characteristics of fault voltage are utilized using the intermediate difference algorithm. This, combined with the characteristic that line-side measuring points detect more high-frequency components in faults within the transmission line zone while detecting fewer high-frequency components in faults outside the zone, allows for fault segment identification. Information data from only one end of the transmission line is needed for rapid and accurate identification of faults within and outside the transmission line zone. Furthermore, fault identification using the full-time domain quantity of fault voltage eliminates the need for time-frequency transformation, making the algorithm simpler and more intuitive. Fault identification based on the sliding time window calculation results meets the requirements of speed and reliability.

[0082] Furthermore, this embodiment also provides a power grid transmission line fault identification device, which can be used to execute the above-described power grid transmission line fault identification method.

[0083] like Figure 2 As shown, the power grid transmission line fault identification device 200 specifically includes: a sampling module 201, used to collect voltage information of the transmission line at each sampling time according to the sampling period; a signal fluctuation detection module 202, used to calculate the first-order differential voltage value at each sampling time based on the voltage information, and detect whether the transmission line has signal fluctuations through the first-order differential voltage value at each sampling time; and a fault detection module 203, used to calculate the voltage energy index at the current time based on the voltage information of multiple consecutive sampling times within a preset sliding time window when the transmission line has signal fluctuations, and determine whether the transmission line has a fault based on the voltage energy index.

[0084] In one exemplary embodiment, the signal fluctuation detection module 202 is specifically used to acquire the first-order differential voltage value at each sampling time, calculate the sum of squares of the accumulated first-order differential voltage values ​​at each sampling time, and determine that the transmission line has experienced signal fluctuation when the accumulated sum of squares is greater than the activation threshold.

[0085] In one exemplary embodiment, the fault detection module 203 specifically includes a voltage energy index calculation module, which is used to calculate the intermediate differential voltage magnitude at each sampling time within the sliding window based on the voltage information; determine the cumulative value of the intermediate differential voltage magnitude at each sampling time within the sliding window, and obtain the voltage energy index of the sliding window.

[0086] In one exemplary embodiment, the intermediate differential voltage magnitude is calculated based on the voltage information of the current time and the two preceding and following sampling times, and is expressed as:

[0087] E k =|u k+1 -2u k +u k-1 |

[0088] wherein, u k , u k+1 , u k-1 are voltage information corresponding to the current time k and the sampling time before and after the current time k respectively, and E k is the intermediate differential voltage module of the current time k.

[0089] In an example embodiment, the fault detection module 203 is specifically configured to determine that the power transmission line is faulty when the voltage energy index of the sliding time window is greater than a fault identification threshold; and update the sliding time window and calculate the voltage energy index of the sliding time window at the next time when the voltage energy index of the sliding time window is not greater than the fault identification threshold.

[0090] In an example embodiment, the device further comprises a fault type determination module configured to determine a positive voltage energy index corresponding to the positive voltage and a negative voltage energy index corresponding to the negative voltage in the sliding time window at the current time when the power transmission line is faulty; and compare the positive voltage energy index and the negative voltage energy index to determine the fault type.

[0091] In an example embodiment, the fault type determination module is further specifically configured to obtain a ratio of the positive voltage energy index and the negative voltage energy index.

[0092] The ratio is:

[0093] k = E p / E n

[0094] wherein, E p , E n are the positive voltage energy index and the negative voltage energy index respectively.

[0095] Compare the ratio k with a preset polarity threshold k set , when k ≥ k set , it is a positive fault, when 1 / k set < k < k set , it is a bipolar fault, and when k < 1 / k set , it is a negative fault.

[0096] The specific details of each module or unit in the power transmission line fault identification device have been described in detail in the corresponding power transmission line fault identification method, and thus will not be described here.

[0097] The embodiments of the present application also provide an electronic device, Figure 3 a structural schematic diagram of an electronic device suitable for implementing the embodiments of the present disclosure is shown. Figure 3The electronic device 600 shown is merely one example, and should not be taken as limiting the scope of functionality or use of embodiments of the disclosure.

[0098] As shown in Figure 3 The electronic device 600 includes a central processing unit (CPU) 601, which can perform various appropriate actions and processes according to programs stored in a read only memory (ROM) 602 or programs loaded from a storage section 608 into a random access memory (RAM) 603. Various programs and data required for system operation are also stored in the RAM 603. The CPU 601, the ROM 602, and the RAM 603 are connected to each other through a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0099] The following components are connected to the I / O interface 605: an input section 606 including a keyboard, a mouse, etc.; an output section 607 including a display such as a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, a modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as necessary. A removable media 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is attached to the drive 610 as necessary, so that a computer program read therefrom is installed into the storage section 608 as necessary.

[0100] In particular, the processes described above with reference to the flowcharts can be implemented as a computer software program according to embodiments of the present disclosure. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer-readable storage medium, the computer program containing program code for executing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network by the communication section 609, and / or installed from the removable media 611. When the computer program is executed by the central processing unit (CPU) 601, the above-described functions defined in embodiments of the present disclosure are performed.

[0101] For example, when the computer program is executed by the central processing unit (CPU) 601, the following can be performed: sequentially collecting voltage information at each sampling time of a power transmission line according to a sampling period; calculating a first-order differential voltage value at each sampling time based on the voltage information, detecting whether a signal fluctuation occurs in the power transmission line through the first-order differential voltage value at each sampling time; when the signal fluctuation occurs in the power transmission line, calculating a voltage energy index at a current time based on voltage information at a plurality of continuous sampling times within a preset sliding time window at the current time, and determining whether a fault occurs in the power transmission line according to the voltage energy index.

[0102] Note that the computer-readable medium shown in the disclosure can be a computer-readable signal medium or a computer-readable storage medium or any combination thereof. The computer-readable storage medium may, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or apparatus, or any combination thereof. More specific examples of the computer-readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the disclosure, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device. In the disclosure, the computer-readable signal medium can include a data signal that propagates in a baseband or as part of a carrier wave, carrying computer-readable program code. Such a propagated data signal can take on many forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium that is not a storage medium and that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wireline, optical fiber, RF, etc., or any suitable combination of the above.

[0103] The flow diagrams and block diagrams in the drawings are illustrations of possible architectures, functions, and operations of systems, methods, and computer program products in accordance with various embodiments of the disclosure. In this regard, each block in the flow diagrams or block diagrams can represent a module, a segment, or a portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or the blocks can sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flow diagrams, and combinations thereof, can be implemented by special purpose hardware-based systems that perform the specified functions or operations, or combinations of special purpose hardware and computer instructions.

[0104] The units described in the embodiments of the present disclosure can be implemented by software, or by hardware, or by a combination of software and hardware. The units described can also be located in a single processor. In some cases, the names of the units do not constitute a limitation on the units themselves.

[0105] As another aspect, the present application also provides a computer readable medium, which can be included in the electronic device described in the above embodiments, or can exist independently without being assembled into the electronic device. The computer readable medium carries one or more programs, which include instructions that, when executed by the electronic device, cause the electronic device to implement the method described in the above embodiments.

[0106] It should be noted that although several modules or units of the device for action execution are mentioned in the above detailed description, such division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units.

[0107] The above is merely specific embodiments of the present application, but the protection scope of the present application is not limited thereto, any changes or replacements within the technical scope disclosed in the present application should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for identifying faults in power grid transmission lines, characterized in that, include: Voltage information of the transmission line at each sampling time is collected in sequence according to the sampling period; Based on the voltage information, the first-order differential voltage value at each sampling time is calculated, and the signal fluctuation of the transmission line is detected by the first-order differential voltage value at each sampling time. When a signal fluctuation occurs in the transmission line, the voltage energy index at the current moment is calculated based on the voltage information of multiple consecutive sampling moments within a preset sliding time window at the current moment, and the transmission line is determined to have a fault based on the voltage energy index.

2. The method for identifying faults in power grid transmission lines according to claim 1, characterized in that, The step of detecting whether the transmission line experiences signal fluctuations by using the first-order differential voltage value at each sampling time includes: The first-order differential voltage value is obtained at each sampling time, and the sum of squares of the accumulated first-order differential voltage values ​​at each sampling time is calculated. When the accumulated sum of squares is greater than the start-up threshold, it is determined that the transmission line has experienced signal fluctuation.

3. The method for identifying faults in power grid transmission lines according to claim 1, characterized in that, The calculation of the voltage energy index at the current moment based on voltage information from multiple consecutive sampling moments within a preset sliding window at the current moment includes: The intermediate differential voltage magnitude value at each sampling time within the sliding window is calculated based on the voltage information. The cumulative value of the intermediate differential voltage magnitude at each sampling time within the sliding time window is determined to obtain the voltage energy index of the sliding time window.

4. The power grid transmission line fault identification method according to claim 3, characterized in that, The intermediate differential voltage magnitude is calculated based on the voltage information at the current time and the two sampling times before and after it, and is expressed as: E k =|in k+1 -2u k +in k-1 | Among them, u k u k+1 u k-1 These represent the voltage information at the current time k and the times before and after sampling, E. k This represents the intermediate differential voltage magnitude at the current time k.

5. The method for identifying faults in power grid transmission lines according to claim 1, characterized in that, The step of determining whether the transmission line has experienced a fault based on the voltage energy index includes: When the voltage energy index of the sliding window is greater than the fault identification threshold, it is determined that the transmission line has a fault. When the voltage energy index of the sliding window is not greater than the fault identification threshold, the sliding window is updated, and the voltage energy index of the sliding window at the next moment is calculated.

6. The method for identifying faults in power grid transmission lines according to claim 1, characterized in that, Also includes: When a fault occurs in the transmission line, the positive voltage energy index corresponding to the positive voltage and the negative voltage energy index corresponding to the negative voltage are determined respectively within the sliding time window at the current moment. The fault type is determined by comparing the magnitudes of the positive electrode voltage energy index and the negative electrode voltage energy index.

7. The method for identifying faults in power grid transmission lines according to claim 6, characterized in that, The process of comparing the positive electrode voltage energy index with the negative electrode voltage energy index to determine the fault type includes: Obtain the ratio of the positive electrode voltage energy index to the negative electrode voltage energy index; The ratio is: k=E p / E n Among them, E p E n These are the positive and negative voltage energy indices, respectively. Compare the ratio k with the preset selection threshold k set The size when k≥k set This is a positive electrode fault, when 1 / k is satisfied. set <k<k set This is a bipolar fault, when k < 1 / k set This indicates a negative electrode fault.

8. A fault identification device for power grid transmission lines, characterized in that, include: The sampling module is used to collect voltage information of the transmission line at each sampling moment in sequence according to the sampling period; The signal fluctuation detection module is used to calculate the first-order differential voltage value at each sampling time based on the voltage information, and to detect whether the transmission line has signal fluctuations by using the first-order differential voltage value at each sampling time. The fault detection module is used to calculate the voltage energy index at the current moment based on the voltage information of multiple consecutive sampling moments within a preset sliding time window when the signal fluctuation occurs in the transmission line, and to determine whether the transmission line has a fault based on the voltage energy index.

9. A computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the power grid transmission line fault identification method as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, The device includes a processor and a memory, the memory storing one or more computer programs, the one or more computer programs including instructions that, when executed by the electronic device, cause the electronic device to perform the power grid transmission line fault identification method according to any one of claims 1 to 7.

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