Distribution network differential protection data compression method, device, equipment and medium

Abstract

The present invention proposes a distribution network differential protection data compression method, device, equipment and medium, including obtaining sampled data of the distribution network, performing feature classification on the sampled data according to protection requirements at the edge node of the distribution network to obtain fundamental current vector, high-frequency transient energy and precise timestamp; obtaining the real-time network bandwidth and fault risk assessment results of the distribution network, selecting a compression mode according to the real-time network bandwidth and fault risk assessment results; performing feature classification compression on the fundamental current vector, high-frequency transient energy and precise timestamp in turn according to the compression mode to obtain a feature data packet of the distribution network; transmitting the feature data packet to the master station side module through a hybrid communication network. The present application divides the sampled data into three categories: fundamental component, high-frequency transient component and precise timestamp, assigns compression priority according to differential protection requirements, breaks through the performance bottleneck caused by fixed compression rate, and effectively reduces the amount of redundant data during differential data compression.

Description

Technical Field

[0001] The present invention relates to the technical field of power system relay protection, and in particular to a distribution network differential protection data compression method, device, electronic equipment and medium. Background Art

[0002] Existing technologies typically use broadband synchronous sampling, resulting in a single node generating tens of KB of data per second. This places significant pressure on communication bandwidth during multi-terminal transmission within the power system. Furthermore, when compressing differential protection data, traditional compression algorithms can filter out high-frequency transient fault characteristics, reducing relay protection sensitivity. Furthermore, existing compression algorithms are computationally complex, making them difficult to implement in real time on low-power edge node hardware, such as AMR processors. Summary of the Invention

[0003] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention provides a distribution network differential protection data compression method, apparatus, device, and medium, which can divide sampled data, assign compression priorities according to protection requirements, and effectively reduce the amount of redundant data.

[0004] In a first aspect, an embodiment of the present invention provides a method for compressing distribution network differential protection data, comprising:

[0005] Applied to a distribution network differential protection data compression system, the distribution network differential protection data compression system includes an edge side module and a master side module, the edge side module is connected to the master side module, and the method includes:

[0006] The edge-side module acquires sampled data of the distribution network, and performs feature classification on the sampled data according to protection requirements at the edge node of the distribution network to obtain fundamental current vector, high-frequency transient energy and precise timestamp;

[0007] The edge-side module obtains the real-time network bandwidth and fault risk assessment result of the distribution network, and selects a compression mode of the fundamental current vector, the high-frequency transient energy, and the precise timestamp according to the real-time network bandwidth and the fault risk assessment result;

[0008] Performing feature hierarchical compression on the fundamental current vector, the high-frequency transient energy, and the precise timestamp in sequence according to the compression mode to obtain a feature data packet of the distribution network;

[0009] The characteristic data packet is transmitted to the master station side module through the hybrid communication network.

[0010] In some embodiments of the present invention, the selecting of the compression mode of the fundamental current vector, the high-frequency transient energy, and the precise timestamp includes:

[0011] When the real-time network bandwidth is less than a preset first threshold and the fault risk assessment result is greater than a preset second threshold, controlling the distribution network to enter a lossy compression mode to perform wavelet coefficient threshold truncation on the high-frequency transient energy;

[0012] When the real-time network bandwidth is greater than or equal to the first threshold, or the fault risk assessment result is less than or equal to the second threshold, controlling the distribution network to enter a lossless compression mode to compress the fundamental wave data of the distribution network;

[0013] The precise timestamp is differentially encoded to obtain the precise timestamp and entropy encoded.

[0014] In some embodiments of the present invention, obtaining the implementation network bandwidth and fault risk assessment results of the distribution network includes:

[0015] Obtaining a first weight coefficient and a second weight coefficient of the distribution network;

[0016] The fault risk assessment result is calculated according to the first weight coefficient, the second weight coefficient, the high-frequency transient energy, and the fundamental current vector.

[0017] In some embodiments of the present invention, the step of sequentially compressing the fundamental current vector, the high-frequency transient energy, and the precise timestamp according to the compression mode comprises:

[0018] Acquiring amplitude information and phase information of the fundamental current vector according to a preset compression algorithm, splitting the amplitude information into a first real number sequence, splitting the phase information into a second real number sequence, and compressing the first real number sequence and the second real number sequence respectively to compress the fundamental current vector;

[0019] Using wavelet packet decomposition to divide the high-frequency transient energy into multiple sub-bands, retaining the sub-band coefficients with the top 30% of the energy in the multiple sub-bands, setting the remaining sub-bands to zero, and then reducing the data volume of the high-frequency transient energy through Huffman coding;

[0020] Golomb coding is used on the precise timestamp to obtain a difference value and a sampling interval of the precise timestamp, and the precise timestamp is compressed according to the difference value and the sampling interval.

[0021] In some embodiments of the present invention, after obtaining the precise timestamp and performing entropy coding, the method further includes:

[0022] When the risk assessment result is less than or equal to a preset third threshold, the distribution network enters the lossy compression mode, retaining only the fundamental amplitude of the fundamental current vector and a high frequency sub-band in the first frequency interval;

[0023] When the risk assessment result is greater than the third threshold and less than or equal to the first threshold, the distribution network switches to a hybrid mode, the fundamental amplitude is losslessly compressed, and the high frequency sub-band is expanded from the first frequency interval to a second frequency interval;

[0024] When the risk assessment result is greater than the first threshold, the distribution network is triggered to enter the full-feature transmission mode, the compression of the fundamental current vector, the high-frequency transient energy and the precise timestamp is suspended, and the original sampling data is directly sent to enable the master station side module to obtain a complete fault recording.

[0025] In some embodiments of the present invention, the edge-side module includes a feature classification unit and a dynamic strategy engine. The feature classification unit is used to extract the power frequency of the distribution network and decompose the high-frequency wavelet of the distribution network.

[0026] In some embodiments of the present invention, the master station side module is provided with a data reconstruction unit and a fault-tolerant processing unit. The data reconstruction unit is used to perform wavelet inverse transform and timestamp recovery on the feature data packet. The fault-tolerant processing unit is used to detect the feature data packet. When it is detected that the data of the feature data packet is lost, the missing characteristic value of the feature data packet is estimated by Kalman filtering.

[0027] In a second aspect, an embodiment of the present invention provides a distribution network differential protection data compression device, comprising at least one control processor and a memory for communicating with the at least one control processor; the memory stores instructions that can be executed by the at least one control processor, and the instructions are executed by the at least one control processor to enable the at least one control processor to execute the distribution network differential protection data compression method as described in the first aspect above.

[0028] In a third aspect, an embodiment of the present invention provides an electronic device comprising the distribution network differential protection data compression device as described in the second aspect above.

[0029] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to execute the distribution network differential protection data compression method as described in the first aspect above.

[0030] The distribution network differential protection data compression method according to the embodiment of the present invention has at least the following beneficial effects:

[0031] The edge-side module obtains the sampled data of the distribution network, and performs feature classification on the sampled data according to the protection requirements at the edge node of the distribution network to obtain the fundamental current vector, high-frequency transient energy and precise timestamp; the edge-side module obtains the real-time network bandwidth and fault risk assessment results of the distribution network, and selects the compression mode of the fundamental current vector, the high-frequency transient energy and the precise timestamp according to the real-time network bandwidth and fault risk assessment results; the fundamental current vector, the high-frequency transient energy and the precise timestamp are sequentially feature-classified and compressed according to the compression mode to obtain a feature data packet of the distribution network; the feature data packet is transmitted to the master-side module via the hybrid communication network. According to the technical solution of this embodiment, the sampled data is divided into three categories: fundamental component, high-frequency transient component and precise timestamp, and compression priority is assigned according to the differential protection requirements, thereby taking into account both distribution network data simplification and feature retention, and a dynamic compression strategy for the distribution network is constructed according to the real-time network status of the distribution network and the fault risk assessment results, thereby breaking through the performance bottleneck caused by the fixed compression rate and effectively reducing the amount of redundant data during differential data compression. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is a flow chart of a distribution network differential protection data compression method provided by one embodiment of the present invention;

[0033] Figure 2 This is a flow chart of a compression mode for selecting a fundamental current vector, high-frequency transient energy, and precise timestamps provided by an embodiment of the present invention;

[0034] Figure 3 This is a flowchart of obtaining the implementation network bandwidth and fault risk assessment results of the distribution network provided by an embodiment of the present invention;

[0035] Figure 4 This is a flow chart of performing feature hierarchical compression according to an embodiment of the present invention;

[0036] Figure 5 This is a flowchart of obtaining an accurate timestamp and entropy coding according to an embodiment of the present invention;

[0037] Figure 6 This is a structural block diagram of a distribution network differential protection data compression system provided by one embodiment of the present invention;

[0038] Figure 7 It is a structural diagram of a distribution network differential protection data compression device provided by another embodiment of the present invention. DETAILED DESCRIPTION

[0039] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present invention and are not to be construed as limiting the present invention.

[0040] In the description of the present invention, it should be understood that descriptions involving orientations, such as up, down, front, back, left, right, etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, they cannot be understood as limitations on the present invention.

[0041] In the description of the present invention, "several" means one or more, "many" means more than two, "greater than," "less than," and "exceed" are understood to exclude the number itself, while "above," "below," and "within" are understood to include the number itself. The use of "first" and "second" in the description is solely for the purpose of distinguishing technical features and should not be construed as indicating or implying relative importance, implicitly specifying the number of the indicated technical features, or implicitly specifying the order of the indicated technical features.

[0042] In the description of the present invention, unless otherwise clearly defined, terms such as setting, installing, and connecting should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in the present invention based on the specific content of the technical solution.

[0043] An embodiment of the present invention provides a distribution network differential protection data compression method, which is applied to a distribution network differential protection data compression system. The distribution network differential protection data compression system includes an edge-side module and a master-side module, wherein the edge-side module is connected to the master-side module. The edge-side module includes a feature classification unit and a dynamic strategy engine. The feature classification unit is used to extract the power frequency of the distribution network and classify the high-frequency wavelets of the distribution network. The edge-side module has a dual-buffer structure. The master-side module is provided with a data reconstruction unit and a fault-tolerant processing unit. The data reconstruction unit is used to perform wavelet inverse transformation and timestamp recovery on feature data packets. The fault-tolerant processing unit is used to detect feature data packets. When data loss of a feature data packet is detected, the missing feature value of the feature data packet is estimated through Kalman filtering.

[0044] It should be noted that the edge-side module is used to extract and compress data features at the data source side of the distribution network, thereby reducing the transmission load. The feature classification unit performs frequency domain decomposition on the distribution network's power frequency signal (such as the 50Hz fundamental wave) and classifies the sampled data according to the importance of features such as signal amplitude and phase. This results in the distribution network's fundamental current vector, high-frequency transient components, and precise timestamps. High-frequency transient components are extracted using wavelet transforms and classified by frequency band and energy value to distinguish normal operating signals from fault characteristics. Furthermore, based on the feature classification structure, a compression strategy is dynamically selected to balance compression efficiency and protection reliability.

[0045] It should be noted that the master station side module is used to achieve accurate data reconstruction and fault-tolerant processing of the distribution network to ensure the normal execution of the differential protection of the distribution network. The data reconstruction unit restores the time domain waveform of the high-frequency transient component by performing an inverse transformation on the characteristic data packet compressed on the edge side. And through the precise timestamp synchronized on the edge side, the reconstructed data is aligned to the original sampling timing to ensure the time synchronization of the differential protection algorithm. It can be understood by those skilled in the art that the fault-tolerant processing unit detects whether there is missing characteristic data during the transmission process through a check code (such as a CRC check) or a sequence number. When data missing is detected, the Kalman filter algorithm is used to dynamically estimate the missing characteristic value (such as the wavelet coefficient at a certain moment), and the missing value is predicted based on historical data and system equations, thereby avoiding protection misoperation or refusal due to missing distribution network data.

[0046] The control method of the embodiment of the present invention is further described below based on the accompanying drawings.

[0047] Reference Figure 1 , Figure 1 A flow chart of a distribution network differential protection data compression method provided by an embodiment of the present invention includes but is not limited to the following steps:

[0048] Step S11: The edge-side module obtains sampled data from the distribution network, and classifies the sampled data according to protection requirements at the edge node of the distribution network to obtain the fundamental current vector, high-frequency transient energy, and accurate timestamp;

[0049] It should be noted that the fundamental current vector is the amplitude and phase of the power frequency (50 Hz) component, reflecting the steady-state operating status of the system. It is the core input of the differential protection judgment criteria (such as current phase difference and amplitude ratio). The high-frequency transient energy is the transient traveling wave energy generated during a fault (such as the 1 kHz to 10 kHz frequency band), which is a key feature for identifying the fault type (such as phase-to-phase short circuit and ground fault) and locating it. The precise timestamp is the time mark of the sampled data (with an accuracy of μs) and is used for multi-borrowing point data synchronization of the master station side module to ensure the timing consistency of the differential protection algorithm.

[0050] It should be noted that the edge-side module feature classification performs multi-level decomposition of the original sampling data through signal processing algorithms (such as fast Fourier transform FFT and wavelet transform WT), converts the redundant original data into the feature vectors required for protection decision-making, and stores and transmits them in a hierarchical manner according to importance.

[0051] Step S12: The edge-side module obtains the real-time network bandwidth and fault risk assessment results of the distribution network, and selects a compression mode for the fundamental current vector, high-frequency transient energy, and precise timestamp based on the real-time network bandwidth and fault risk assessment results.

[0052] It should be noted that when real-time network bandwidth is high, a relatively low compression ratio mode can be selected. This preserves more detailed information about the fundamental current vector, high-frequency transient energy, and precise timestamps, thus meeting the requirements of high-precision fault analysis. When network bandwidth is limited (e.g., due to congestion or link quality degradation), a higher compression ratio mode is switched to. By reducing the amount of data transmitted, packet loss and transmission delays can be effectively avoided. When fault risk assessment results indicate a high probability of failure in the distribution network (e.g., in areas with aging equipment or historically frequent fault locations), lossless or low-distortion compression modes are preferred for high-frequency transient energy data.

[0053] By dynamically selecting the compression mode, the edge-side module can quickly transmit key fault characteristic data (such as high-frequency transient energy and precise timestamps) to the master-side module when a fault occurs. Based on this timely and accurate data, the master-side module can quickly trigger protection devices or initiate fault isolation processes, shortening the fault duration, reducing the impact of the fault on the normal operation of the distribution network, and improving the reliability and stability of the distribution network.

[0054] Step S13, performing feature hierarchical compression on the fundamental current vector, high-frequency transient energy, and precise timestamp in sequence according to the compression mode to obtain a feature data packet of the distribution network;

[0055] It should be noted that encapsulating the fundamental current vector (steady-state characteristics), high-frequency transient energy (transient characteristics), and timestamp (space-time tag) into a feature data packet in a unified format facilitates rapid parsing of the data packet. The feature data packet retains the complete feature chain before and after the fault occurs (such as steady-state data 100ms before the fault, transient waveform 20ms after the fault, and nanosecond time tags), making it easier for operation and maintenance personnel to reproduce the fault process through historical data and analyze the fault development trend and protection device operation behavior.

[0056] It should be noted that the feature classification compression strategy achieves the performance bottleneck caused by the fixed compression rate of distribution network data through the three-layer mechanism of "data classification-accuracy classification-dynamic compression".

[0057] Step S14: Transmit the characteristic data packet to the master station side module via the hybrid communication network.

[0058] It should be noted that this embodiment divides the sampled data into three categories: fundamental component, high-frequency transient component and precise timestamp, and allocates compression priority according to the differential protection requirements, thereby taking into account both distribution network data simplification and feature retention, and constructs a dynamic compression strategy for the distribution network based on the real-time network status and fault risk assessment results of the distribution network, thereby breaking through the performance bottleneck caused by a fixed compression rate and effectively reducing the amount of redundant data during differential data compression.

[0059] In addition, in one embodiment, referring to Figure 2 ,exist Figure 1 Step S11 of the illustrated embodiment also includes but is not limited to the following steps:

[0060] Step S21: When the real-time network bandwidth is less than a preset first threshold and the fault risk assessment result is greater than a preset second threshold, the distribution network is controlled to enter a lossy compression mode to perform wavelet coefficient threshold truncation on high-frequency transient energy;

[0061] Step S22: When the real-time network bandwidth is greater than or equal to the first threshold, or the fault risk assessment result is less than or equal to the second threshold, controlling the distribution network to enter a lossless compression mode to compress the fundamental wave data of the distribution network;

[0062] Step S23: performing differential encoding on the precise timestamp to obtain the precise timestamp and performing entropy encoding.

[0063] It should be noted that when the real-time network bandwidth B is less than 1Mbps (the first threshold) and the fault risk assessment result R is greater than 0.7 (the second threshold), the distribution network is controlled to enter the lossy compression mode to perform the wavelet coefficient threshold stage on the high-frequency transient component so that the compression rate is ≥50%; when the real-time network bandwidth B is greater than or equal to 1Mbps (the first threshold) or the fault risk assessment result R is less than or equal to 0.7, the distribution network is controlled to enter the lossless compression mode and the improved LZW algorithm is used to compress the fundamental wave data; then the precise timestamp t is differentially encoded to generate And entropy increase code, reducing the number of timestamps by more than 70%.

[0064] It's important to note that the core of dynamically switching compression modes lies in the linkage of data hierarchical compression, network status awareness, and service priority scheduling. This ensures minimal transmission of high-frequency transient components, especially when there's network congestion or a high risk of failure. When the distribution network is transmitting data during idle or low-risk periods, lossless compression is used to improve data quality, providing reliable data for advanced applications such as distribution network status assessment and energy efficiency analysis. Furthermore, differentiated compression strategies minimize communication bandwidth usage and storage costs while still meeting demand.

[0065] In addition, in one embodiment, referring to Figure 3 ,exist Figure 1 Step S11 of the illustrated embodiment also includes but is not limited to the following steps:

[0066] Step S31, obtaining a first weight coefficient and a second weight coefficient of the distribution network;

[0067] Step S32 , calculating a fault risk assessment result according to the first weight coefficient, the second weight coefficient, the high-frequency transient energy, and the fundamental current vector.

[0068] It should be noted that when the first weight coefficient is high, it indicates that more attention is paid to transient faults (such as short circuits and lightning strikes). During compression, the details of high-frequency transient energy will be retained first (such as lossless compression or low-distortion compression) to avoid the loss of key fault characteristics. When the second weight coefficient is high, the focus is on steady-state hidden danger monitoring (such as load abnormalities and equipment aging). During compression, the integrity of the fundamental wave data will be optimized (such as reducing the dimensionality loss of the fundamental wave vector). The fault risk assessment results are bound to the compression strategy through the weight coefficient to achieve dynamic adaptation and avoid distortion of important information caused by excessive compression.

[0069] It should be noted that, in this embodiment, the first weight coefficient is 0.6, the second weight coefficient is 0.4, and the calculated fault risk assessment result is specifically expressed by the following first formula:

[0070] ;

[0071] in, is the first weight coefficient, is the second weight coefficient, is the high-frequency transient energy, is the fundamental current vector.

[0072] In addition, in one embodiment, referring to Figure 4 ,exist Figure 1 Step S11 of the illustrated embodiment also includes but is not limited to the following steps:

[0073] Step S41: Acquire amplitude information and phase information of the fundamental current vector according to a preset compression algorithm, split the amplitude information into a first real number sequence, split the phase information into a second real number sequence, and compress the first real number sequence and the second real number sequence respectively to compress the fundamental current vector;

[0074] Step S42: using wavelet packet decomposition to divide the high-frequency transient energy into multiple sub-bands, retaining the sub-band coefficients with the top 30% of the energy in the multiple sub-bands, setting the remaining sub-bands to zero, and then using Huffman coding to reduce the amount of high-frequency transient energy data;

[0075] Step S43 , using Golomb coding on the precise timestamp to obtain the difference and sampling interval of the precise timestamp, and compressing the precise timestamp according to the difference and sampling interval.

[0076] It should be noted that a compression algorithm (the LZW algorithm in this embodiment) is applied to the fundamental current vector, splitting its phase and amplitude into a first real number sequence and a second real number sequence, which are then compressed separately, increasing the compression rate to 60%. Wavelet packet decomposition is used to divide the high-frequency transient energy into 16 subbands. The coefficients of the subbands with the top 30% of energy are retained, while the remaining subbands are set to zero and then Huffman encoded, reducing the amount of high-frequency transient energy data by 70%. Golomb coding is used for precise timestamps, leveraging the fact that the difference between adjacent timestamps approaches the sampling interval, improving compression efficiency by 85%.

[0077] In addition, in one embodiment, referring to Figure 5 ,exist Figure 1 Step S11 of the illustrated embodiment also includes but is not limited to the following steps:

[0078] Step S51: When the risk assessment result is less than or equal to a preset third threshold, the distribution network enters a lossy compression mode, retaining only the fundamental amplitude of the fundamental current vector and a high frequency sub-band in the first frequency interval;

[0079] Step S52: When the risk assessment result is greater than the third threshold and less than or equal to the first threshold, the distribution network switches to a hybrid mode, the fundamental amplitude is losslessly compressed, and the high frequency sub-band is expanded from the first frequency interval to the second frequency interval;

[0080] Step S53: When the risk assessment result is greater than the first threshold, the distribution network is triggered to enter the full-feature transmission mode, the compression of the fundamental current vector, high-frequency transient energy and precise timestamp is suspended, and the original sampling data is directly sent to enable the master station side module to obtain a complete fault recording.

[0081] It should be noted that when the risk assessment result R≤0.3 (the third threshold), the distribution network is judged to be in normal operating conditions. At this time, the distribution network is controlled to enter the lossy compression mode, retaining only the fundamental wave amplitude and the high-frequency sub-band of 2-5kHz (the first frequency interval) to reduce the data rate to 40% of the original value; when 0.3 (the third threshold) < the risk assessment result R < 0.7 (the second threshold), the distribution network is judged to be in a fault warning state. At this time, the distribution network is controlled to switch to the hybrid mode, the fundamental wave data is losslessly compressed, and the high-frequency sub-band is expanded to 2-10kHz to increase the data rate to 60% of the original value; when the risk assessment result R>0.7 (the second threshold), the distribution network is judged to have a fault. At this time, the distribution network is controlled to enter the full-feature transmission mode, suspending compression and directly sending the original sampled data to ensure that the master station side module can obtain complete fault recordings.

[0082] The edge side module includes a feature classification unit and a dynamic strategy engine. The feature classification unit is used to extract the power frequency of the distribution network and decompose the high-frequency wavelet of the distribution network. The edge side module has a double buffer structure.

[0083] It should be noted that the power frequency characteristics are extracted through the feature classification unit, and the steady-state parameters of the distribution network (such as three-phase balance and power flow distribution) are monitored in real time to provide a benchmark reference value for the differential protection, so as to avoid false triggering of protection actions due to steady-state fluctuations. The high-frequency transient component usually appears within a few milliseconds after the fault occurs. The initial characteristics of the fault can be captured in advance through wavelet decomposition, shortening the delay of protection action. Furthermore, the high-frequency transient energy distribution and frequency components corresponding to different fault types (such as single-phase grounding and phase-to-phase short circuit) are different. Wavelet decomposition can extract characteristic vectors (such as the energy proportion of each frequency band and the singularity of the waveform) to assist in distinguishing the fault type and avoid misjudgment.

[0084] It should be noted that the dual-buffer structure enables data reading and processing to be performed simultaneously in different buffer areas. When the data in one buffer area is being processed by the feature classification unit, the other buffer area can simultaneously collect and store data, thereby avoiding waiting time in the data processing process, improving the overall efficiency of data processing, and enabling the decomposition of the power frequency and high-frequency wavelets of the distribution network to be completed more quickly. In addition, the dual-buffer structure can make the system more stable when facing emergencies or high loads. For example, when the data acquisition rate suddenly increases or a short communication failure occurs, the dual buffer can serve as a buffer zone to temporarily store excess data to prevent data overflow or system crash. At the same time, when a fault or abnormality occurs in one buffer area, the other buffer area can continue to work to maintain the normal operation of the system, thereby improving the stability and reliability of the entire distribution network differential data protection system.

[0085] The master station side module is provided with a data reconstruction unit and a fault-tolerant processing unit. The data reconstruction unit is used to perform wavelet inverse transform and timestamp recovery on the feature data packet. The fault-tolerant processing unit is used to detect the feature data packet. When it is detected that the data of the feature data packet is lost, the missing characteristic value of the feature data packet is estimated by Kalman filtering.

[0086] It should be noted that the key features of high-frequency transient signals (such as the arrival time of traveling waves and the energy distribution in each frequency band) can be restored through inverse transformation to ensure the calculation accuracy of the differential protection algorithm and avoid feature distortion caused by compression. The differential protection of the distribution network relies on the strict synchronization of data at each node. The restored timestamp can ensure that the multi-terminal data is aligned on the time axis to avoid the failure of the differential judgment due to timing deviation. If the missing data is a key feature (such as the high-frequency energy peak at the initial moment of the fault), direct discarding may cause the protection to refuse to operate. The Kalman filter can fill the missing values through interpolation estimation to maintain the continuity of the judgment. In scenarios with unstable communication quality (such as mountain distribution networks and temporary construction areas), fault-tolerant processing can enable the protection system to maintain basic functions when the data is incomplete, avoiding protection failure caused by "single point failure".

[0087] like Figure 7 As shown, Figure 7 This is a structural diagram of a distribution network differential protection data compression device provided by an embodiment of the present invention. The present invention also provides a distribution network differential protection data compression device, comprising:

[0088] The processor 701 may be implemented as a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is configured to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0089] The memory 702 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 702 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 702 and is called by the processor 701 to execute the distribution network differential protection data compression method of the embodiments of this application.

[0090] Input / output interface 703, used to implement information input and output;

[0091] Communication interface 704, used to implement communication interaction between the apparatus and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WiFi, Bluetooth, etc.);

[0092] Bus 705 , which transmits information between various components of the device (e.g., processor 701 , memory 702 , input / output interface 703 , and communication interface 704 );

[0093] The processor 701 , the memory 702 , the input / output interface 703 and the communication interface 704 are connected to each other in communication within the device via a bus 705 .

[0094] An embodiment of the present application further provides an electronic device, comprising the distribution network differential protection data compression device as described above.

[0095] An embodiment of the present application further provides a storage medium, which is a computer-readable storage medium and stores a computer program. When the computer program is executed by a processor, the above-mentioned distribution network differential protection data compression method is implemented.

[0096] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory optionally includes a memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of the above-mentioned networks include but are not limited to the Internet, an intranet, a local area network, a mobile communication network and a combination thereof. The device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and are located in one place, or may be distributed to multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.

[0097] Those skilled in the art will appreciate that all or some of the steps and systems disclosed above can be implemented as software, firmware, hardware, or any suitable combination thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on computer-readable media, which may include computer storage media (or non-transitory media) and communication media (or transient media). As is well known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disks (DVDs) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. Furthermore, as is well known to those skilled in the art, communication media typically includes computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0098] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the above implementation. Those skilled in the art can also make various equivalent modifications or substitutions under the shared conditions that do not violate the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present invention.

Claims

1. A distribution network differential protection data compression method, characterized in that: Applied to a distribution network differential protection data compression system, the distribution network differential protection data compression system includes an edge side module and a master side module, the edge side module is connected to the master side module, and the method includes: The edge-side module acquires sampled data of the distribution network, and performs feature classification on the sampled data according to protection requirements at the edge node of the distribution network to obtain fundamental current vector, high-frequency transient energy and precise timestamp; The edge-side module obtains the real-time network bandwidth and fault risk assessment result of the distribution network, and selects a compression mode of the fundamental current vector, the high-frequency transient energy, and the precise timestamp according to the real-time network bandwidth and the fault risk assessment result; Performing feature hierarchical compression on the fundamental current vector, the high-frequency transient energy, and the precise timestamp in sequence according to the compression mode to obtain a feature data packet of the distribution network; Transmitting the characteristic data packet to the master station side module via a hybrid communication network; The method includes sequentially performing feature hierarchical compression on the fundamental current vector, the high-frequency transient energy, and the precise timestamp according to the compression mode, including: Acquiring amplitude information and phase information of the fundamental current vector according to a preset compression algorithm, splitting the amplitude information into a first real number sequence, splitting the phase information into a second real number sequence, and compressing the first real number sequence and the second real number sequence respectively to compress the fundamental current vector; Using wavelet packet decomposition to divide the high-frequency transient energy into multiple sub-bands, retaining the sub-band coefficients with the top 30% of the energy in the multiple sub-bands, setting the remaining sub-bands to zero, and then reducing the data volume of the high-frequency transient energy through Huffman coding; Golomb coding is used on the precise timestamp to obtain a difference value and a sampling interval of the precise timestamp, and the precise timestamp is compressed according to the difference value and the sampling interval.

2. The distribution network differential protection data compression method according to claim 1, characterized in that: The selecting of the compression mode of the fundamental current vector, the high-frequency transient energy, and the precise timestamp includes: When the real-time network bandwidth is less than a preset first threshold and the fault risk assessment result is greater than a preset second threshold, controlling the distribution network to enter a lossy compression mode to perform wavelet coefficient threshold truncation on the high-frequency transient energy; When the real-time network bandwidth is greater than or equal to the first threshold, or the fault risk assessment result is less than or equal to the second threshold, controlling the distribution network to enter a lossless compression mode to compress the fundamental wave data of the distribution network; Perform differential coding on the precise timestamp to obtain entropy coding of the precise timestamp.

3. The distribution network differential protection data compression method according to claim 1, characterized in that: The obtaining of the real-time network bandwidth and fault risk assessment results of the distribution network includes: Obtaining a first weight coefficient and a second weight coefficient of the distribution network; The fault risk assessment result is calculated according to the first weight coefficient, the second weight coefficient, the high-frequency transient energy, and the fundamental current vector.

4. The distribution network differential protection data compression method according to claim 2, characterized in that: After obtaining the entropy encoding of the precise timestamp, the method further includes: When the risk assessment result is less than or equal to a preset third threshold, the distribution network enters the lossy compression mode, retaining only the fundamental amplitude of the fundamental current vector and a high frequency sub-band in the first frequency interval; When the risk assessment result is greater than the third threshold and less than or equal to the first threshold, the distribution network switches to the hybrid mode, the fundamental amplitude is losslessly compressed, and the high frequency sub-band is expanded from the first frequency interval to the second frequency interval; When the risk assessment result is greater than the first threshold, the distribution network is triggered to enter the full-feature transmission mode, the compression of the fundamental current vector, the high-frequency transient energy and the precise timestamp is suspended, and the original sampling data is directly sent to enable the master station side module to obtain a complete fault recording.

5. The distribution network differential protection data compression method according to claim 1, characterized in that: The edge side module includes a feature classification unit and a dynamic strategy engine. The feature classification unit is used to extract the power frequency of the distribution network and decompose the high-frequency wavelet of the distribution network.

6. The distribution network differential protection data compression method according to claim 1, characterized in that: The master station side module is provided with a data reconstruction unit and a fault-tolerant processing unit. The data reconstruction unit is used to perform wavelet inverse transform and timestamp recovery on the feature data packet. The fault-tolerant processing unit is used to detect the feature data packet. When it is detected that the data of the feature data packet is lost, the missing characteristic value of the feature data packet is estimated by Kalman filtering.

7. A distribution network differential protection data compression device, characterized in that: It includes at least one control processor and a memory for communicating with the at least one control processor; the memory stores instructions that can be executed by the at least one control processor, and the instructions are executed by the at least one control processor to enable the at least one control processor to execute the distribution network differential protection data compression method according to any one of claims 1 to 6.

8. An electronic device, characterized in that: It includes the distribution network differential protection data compression device as described in claim 7.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the distribution network differential protection data compression method according to any one of claims 1 to 6.

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