Differential protection data compression method, device and equipment for power distribution network and medium

Abstract

The invention provides a power distribution network differential protection data compression method, device and equipment and a medium, and the method comprises the steps: obtaining the sampling data of a power distribution network, carrying out the feature classification of the sampling data at an edge node of the power distribution network according to the protection demands, and obtaining a fundamental current vector, high-frequency transient energy and an accurate timestamp; acquiring a real-time network bandwidth and a fault risk assessment result of the power distribution network, and selecting a compression mode according to the real-time network bandwidth and the fault risk assessment result; according to a compression mode, sequentially carrying out characteristic grading compression on the fundamental wave current vector, the high-frequency transient energy and the precise timestamp to obtain a characteristic data packet of the power distribution network; and transmitting the characteristic data packet to the master station side module through a hybrid communication network. The sampling data is divided into the fundamental component, the high-frequency transient component and the precise timestamp, the compression priority is distributed according to the differential protection requirement, the performance bottleneck caused by the fixed compression rate is broken through, and the redundant data volume during differential data compression is effectively reduced.

Description

Technical Field

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

[0002] In the prior art, a broadband synchronous sampling method is usually adopted in power systems, resulting in dozens of KB of data generated per second at a single node. When multi-terminal transmission is carried out in power systems, the communication bandwidth pressure is relatively large. Moreover, when data compression for differential protection in power systems is performed, traditional compression algorithms may filter out the high-frequency transient fault characteristics of power systems, leading to a decrease in the sensitivity of relay protection. In addition, the compression algorithms in the prior art have a relatively high computational complexity and are difficult to run in real time on low-power hardware (such as an AMR processor) at edge nodes. Summary of the Invention

[0003] The present invention aims to at least solve one of the technical problems existing in the prior art. For this purpose, the present invention provides a data compression method, device, equipment and medium for differential protection of a distribution network, which can divide sampling data, allocate 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 data compression method for differential protection of a distribution network, including: Applied to a data compression system for differential protection of a distribution network, the data compression system for differential protection of a distribution network includes an edge-side module and a master-station-side module, and the edge-side module is connected to the master-station-side module. The method includes: The edge-side module acquires sampling data of the distribution network, and performs feature grading on the sampling data according to protection requirements at an edge node of the distribution network to obtain a fundamental wave current vector, high-frequency transient energy, and an accurate timestamp; The edge-side module acquires the real-time network bandwidth and the fault risk assessment result of the distribution network, and selects compression modes for the fundamental wave current vector, the high-frequency transient energy, and the accurate timestamp according to the real-time network bandwidth and the fault risk assessment result; Performs feature grading compression on the fundamental wave current vector, the high-frequency transient energy, and the accurate timestamp in sequence according to the compression modes to obtain a feature data packet of the distribution network; Transmits the feature data packet to the master-station-side module through a hybrid communication network.

[0005] In some embodiments of the present invention, the selection of the compression modes for the fundamental wave current vector, the high-frequency transient energy, and the accurate 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, control the distribution network to enter the 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, control the distribution network to enter the lossless compression mode to compress the fundamental wave data of the distribution network; Perform differential coding on the precise timestamp to obtain the combined entropy coding of the precise timestamp.

[0006] In some embodiments of the present invention, the obtaining the implementation network bandwidth and fault risk assessment result of the distribution network includes: Obtain the first weight coefficient and the second weight coefficient of the distribution network; Calculate the fault risk assessment result according to the first weight coefficient, the second weight coefficient, the high-frequency transient energy, and the fundamental wave current vector.

[0007] In some embodiments of the present invention, the sequentially performing feature hierarchical compression on the fundamental wave current vector, the high-frequency transient energy, and the precise timestamp according to the compression mode includes: Obtain the amplitude information and phase information of the fundamental wave 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 respectively compress the first real number sequence and the second real number sequence to compress the fundamental wave current vector; Use wavelet packet decomposition to divide the high-frequency transient energy into multiple subbands, retain the subband coefficients with the top 30% of the energy ratio in the multiple subbands, set the remaining subbands to zero, and reduce the data volume of the high-frequency transient energy through Huffman coding; Perform Columbus coding on the precise timestamp to obtain the difference and sampling interval of the precise timestamp, and compress the precise timestamp according to the difference and the sampling interval.

[0008] In some embodiments of the present invention, after obtaining the combined entropy coding 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, and only retains the fundamental wave amplitude of the fundamental wave current vector and the high-frequency subbands 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 wave amplitude is losslessly compressed, and the high-frequency subbands are expanded from the first frequency interval to the second frequency interval; When the risk assessment result is greater than the first threshold, trigger the distribution network to enter the full-feature transmission mode, suspend the compression of the fundamental wave current vector, the high-frequency transient energy, and the precise timestamp, and directly send the original sampling data, so that the master station side module can obtain a complete fault recording.

[0009] In some embodiments of the present invention, the edge side module includes a feature classification unit and a dynamic policy engine. The feature classification unit is used to decompose the power frequency of the distribution network and the high-frequency wavelet of the distribution network.

[0010] In some embodiments of the present invention, the master station side module is provided with a data reconstruction unit and a fault tolerance processing unit. The data reconstruction unit is used to perform inverse wavelet transform and timestamp recovery on the feature data packet. The fault tolerance processing unit is used to detect the feature data packet. When data loss of the feature data packet is detected, the missing feature value of the feature data packet is estimated by Kalman filtering.

[0011] In a second aspect, an embodiment of the present invention provides a distribution network differential protection data compression device, including at least one control processor and a memory communicatively connected to the at least one control processor; the memory stores instructions executable by the at least one control processor, and the instructions are executed by the at least one control processor, so that the at least one control processor can execute the distribution network differential protection data compression method as described in the first aspect above.

[0012] In a third aspect, an embodiment of the present invention provides an electronic device, including the distribution network differential protection data compression device as described in the second aspect above.

[0013] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, storing computer-executable instructions for executing the distribution network differential protection data compression method as described in the first aspect above.

[0014] The distribution network differential protection data compression method according to the embodiments of the present invention has at least the following beneficial effects: The edge - side module acquires the sampling data of the distribution network, performs feature classification on the sampling data according to protection requirements at the edge nodes of the distribution network, and obtains the fundamental wave current vector, high - frequency transient energy, and accurate timestamp; the edge - side module acquires the real - time network bandwidth of the distribution network and the fault risk assessment result, and selects the compression mode of the fundamental wave current vector, the high - frequency transient energy, and the accurate timestamp according to the real - time network bandwidth and the fault risk assessment result; performs feature - level compression on the fundamental wave current vector, high - frequency transient energy, and accurate timestamp in sequence according to the compression mode to obtain the feature data packet of the distribution network; and transmits the feature data packet to the master - station - side module through the hybrid communication network. According to the technical solution of this embodiment, the sampling data is divided into three categories: fundamental wave component, high - frequency transient component, and accurate timestamp, and the compression priorities are allocated according to the differential protection requirements, so as to balance data reduction and feature retention of the distribution network, and construct a dynamic compression strategy for the distribution network according to the real - time network state and fault risk assessment result of the distribution network, thereby breaking through the performance bottleneck caused by the fixed compression ratio and effectively reducing the redundant data volume during differential data compression. Brief Description of the Drawings

[0015] Figure 1 is a flowchart of the differential protection data compression method for a distribution network provided by an embodiment of the present invention; Figure 2 is a flowchart of selecting the compression mode of the fundamental wave current vector, high - frequency transient energy, and accurate timestamp provided by an embodiment of the present invention; Figure 3 is a flowchart of acquiring the implementation network bandwidth and fault risk assessment result of the distribution network provided by an embodiment of the present invention; Figure 4 is a flowchart of performing feature - level compression provided by an embodiment of the present invention; Figure 5 is a flowchart of obtaining the entropy - coded result of the accurate timestamp provided by an embodiment of the present invention; Figure 6 is a structural block diagram of the differential protection data compression system for a distribution network provided by an embodiment of the present invention; Figure 7 is a structural diagram of the differential protection data compression device for a distribution network provided by another embodiment of the present invention. Detailed Embodiment

[0016] The embodiments of the present invention will be described in detail below. The examples of the embodiments are shown in the drawings, where the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions from beginning to end. The embodiments described below by referring to the drawings are exemplary and are only used to explain the present invention, and should not be construed as a limitation of the present invention.

[0017] In the description of the present invention, it should be understood that for the orientation description, such as the upper, lower, front, rear, left, right, etc., the orientation or positional relationship indicated is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation to the present invention.

[0018] In the description of the present invention, the meaning of "several" is one or more, the meaning of "multiple" is more than two, and the understanding of "greater than", "less than", "exceeding", etc. does not include the present number, and the understanding of "above", "below", "within", etc. includes the present number. If there is a description of "first" and "second", it is only for the purpose of distinguishing technical features and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features or implicitly indicating the sequence relationship of the indicated technical features.

[0019] In the description of the present invention, unless otherwise clearly defined, words such as "set", "installed", "connected", etc. should be understood in a broad sense, and those skilled in the art can reasonably determine the specific meaning of the above words in the present invention in combination with the specific content of the technical solution.

[0020] The embodiment of the present invention provides a method for compressing differential protection data of a distribution network, which is applied to a differential protection data compression system of a distribution network. The differential protection data compression system of the distribution network includes an edge-side module and a master-station-side module, and the edge-side module is connected to the master-station-side module. Among them, the edge-side module includes a feature classification unit and a dynamic policy engine. The feature classification unit is used to classify the power frequency of the distribution network and the high-frequency wavelet of the distribution network, and the edge-side module has a dual-buffer structure. The master-station-side module is provided with a data reconstruction unit and a fault tolerance processing unit. The data reconstruction unit is used to perform inverse wavelet transform and timestamp recovery on the feature data packet, and the fault tolerance processing unit is used to detect the feature data packet. When it detects that the data of the feature data packet is lost, the missing feature value of the feature data packet is estimated through Kalman filtering.

[0021] It should be noted that the edge-side module is used to complete the extraction and compression of data features on the data source side of the distribution network, so as to reduce the transmission load. The feature classification unit performs frequency-domain decomposition on the power frequency signal (such as 50Hz fundamental wave) of the distribution network, and classifies according to the importance of features such as the signal amplitude and phase of the sampled data, so as to obtain the fundamental wave current vector, high-frequency transient component and accurate timestamp of the distribution network. And the high-frequency transient component is extracted by using wavelet transform and classified according to the frequency band and energy value, so as to distinguish the normal operation signal from the fault feature. Further, according to the feature classification structure, the compression strategy is dynamically selected to balance the compression efficiency and protection reliability.

[0022] It should be noted that the main station side module is used to implement precise data reconstruction and fault tolerance processing of the distribution network to ensure the normal execution of differential protection of the distribution network. The data reconstruction unit performs inverse transformation on the feature data packets compressed on the edge side to restore the time-domain waveform of the high-frequency transient component. And through the accurate timestamps synchronized on the edge side, the reconstructed data is aligned to the original sampling time sequence to ensure the time synchronization of the differential protection algorithm. Those skilled in the art can understand that the fault tolerance processing unit detects whether there is missing feature data during the transmission process through a check code (such as CRC check) or sequence number. When data loss is detected, the Kalman filtering algorithm is used to dynamically estimate the missing feature values (such as wavelet coefficients at a certain moment), and the missing values are predicted based on historical data and system equations, so as to avoid misoperation or refusal of protection caused by missing distribution network data.

[0023] Next, based on the accompanying drawings, the control method of the embodiments of the present invention will be further elaborated.

[0024] Refer to Figure 1 , Figure 1 is a flowchart of a method for compressing differential protection data of a distribution network provided by an embodiment of the present invention. The method for compressing differential protection data of the distribution network includes but is not limited to the following steps: Step S11, the edge side module acquires the sampling data of the distribution network, and performs feature classification on the sampling data according to protection requirements at the edge nodes of the distribution network to obtain the fundamental wave current vector, high-frequency transient energy, and accurate timestamp; It should be noted that the fundamental wave current vector is the amplitude and phase of the power frequency (50Hz) component, which reflects the steady-state operation state of the system and is the core input of differential protection criteria (such as current phase difference, amplitude ratio). The high-frequency transient energy is the transient traveling wave energy generated during a fault (such as in the frequency band of 1kHz to 10kHz), which is a key feature for identifying fault types (such as phase-to-phase short circuit, ground fault) and location. The accurate timestamp is the time mark of the sampling data (with an accuracy of μs level), which is used for multi-point data synchronization of the main station side module to ensure the timing consistency of the differential protection algorithm.

[0025] It should be noted that the edge side module performs multi-level decomposition on the original sampling data through signal processing algorithms (such as fast Fourier transform FFT, wavelet transform WT), converts the redundant original data into feature vectors required for protection decision-making, and stores and transmits them according to importance levels.

[0026] Step S12, the edge side module acquires the real-time network bandwidth and the fault risk assessment result of the distribution network, and selects the compression mode of the fundamental wave current vector, high-frequency transient energy, and accurate timestamp according to the real-time network bandwidth and the fault risk assessment result; It should be noted that when the real-time network bandwidth is at a relatively high level, a mode with a relatively low compression ratio can be selected. This can retain more detailed information of the fundamental current vector, high-frequency transient energy, and accurate timestamps, thus meeting the requirements of high-precision fault analysis. When the network bandwidth is limited (such as in cases of congestion or degraded link quality), the mode with a higher compression ratio is switched. By reducing the amount of data transmitted, data packet loss and transmission delay problems can be effectively avoided. When the fault risk assessment result shows that the distribution network has a relatively high fault probability (such as in areas with aging equipment and frequent historical fault points), the lossless compression or low-distortion compression mode is preferentially adopted for high-frequency transient energy data.

[0027] By dynamically selecting the compression mode, it is ensured that when a fault occurs, the edge-side module can quickly transmit key fault feature data (such as high-frequency transient energy and accurate timestamps) to the master-side module. So that the master-side module can quickly trigger the action of the protection device or start the fault isolation process based on this timely and accurate data, shorten the fault duration, reduce the impact of the fault on the normal operation of the distribution network, and improve the reliability and stability of the distribution network.

[0028] Step S13: Perform feature hierarchical compression on the fundamental current vector, high-frequency transient energy, and accurate timestamps in sequence according to the compression mode to obtain the feature data packets of the distribution network; It should be noted that encapsulating the fundamental current vector (steady-state feature), high-frequency transient energy (transient feature), and timestamp (spatiotemporal marker) into feature data packets in a unified format facilitates the rapid parsing of data packets. Among them, the feature data packets retain the complete feature chain before and after the fault (such as 100ms of steady-state data before the fault, 20ms of transient waveforms after the fault, and nanosecond-level time markers), which is convenient for maintenance personnel to reproduce the fault process through historical data and analyze the fault development trend and the action behavior of the protection device.

[0029] It should be noted that the feature hierarchical compression strategy realizes the performance bottleneck caused by the fixed compression ratio of distribution network data through a three-layer mechanism of "data classification - precision grading - dynamic compression".

[0030] Step S14: Transmit the feature data packets to the master-side module through the hybrid communication network.

[0031] It should be noted that in this embodiment, by dividing the sampled data into three categories: fundamental component, high-frequency transient component, and accurate timestamp, and allocating the compression priority according to the differential protection requirements, the data reduction and feature retention of the distribution network are taken into account, and a dynamic compression strategy for the distribution network is constructed based on the real-time network state and fault risk assessment result of the distribution network, thus breaking through the performance bottleneck caused by the fixed compression ratio and effectively reducing the redundant data volume during differential data compression.

[0032] In addition, in one embodiment, refer toFigure 2 In Figure 1 step S11 of the embodiment shown, there are also the following steps including but not limited to: 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, control the distribution network to enter the lossy compression mode to perform wavelet coefficient threshold truncation on high-frequency transient energy; 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, control the distribution network to enter the lossless compression mode to compress the fundamental wave data of the distribution network; Step S23, perform differential coding on the accurate timestamp to obtain the combined entropy coding of the accurate timestamp.

[0033] It should be noted that when the real-time network bandwidth B < 1 Mbps (the first threshold) and the fault risk assessment result R > 0.7 (the second threshold), control the distribution network to enter the lossy compression mode to perform wavelet coefficient thresholding on the high-frequency transient component, so that the compression ratio ≥ 50%; when the real-time network bandwidth B ≥ 1 Mbps (the first threshold) or the fault risk assessment result R ≤ 0.7, control the distribution network to enter the lossless compression mode, and use the improved LZW algorithm to compress the fundamental wave data; then perform differential coding on the accurate timestamp t to generate combined entropy increment coding, reducing the number of timestamps by more than 70%.

[0034] It should be noted that the core of dynamically switching the compression mode lies in the linkage mechanism of data hierarchical compression, network status perception, and service priority scheduling, so as to ensure the minimum transmission of high-frequency transient components when there is network congestion or high fault risk during the transmission process. When the distribution network data transmission is in an idle network or low-risk period, lossless compression is used to improve the quality of the transmitted data and provide reliable data for advanced applications such as the state assessment and energy efficiency analysis of the distribution network. In addition, through the differential compression strategy, the communication bandwidth occupancy and storage cost are minimized under the premise of meeting the requirements.

[0035] In addition, in an embodiment, referring to Figure 3 In Figure 1 step S11 of the embodiment shown, there are also the following steps including but not limited to: Step S31, obtain the first weight coefficient and the second weight coefficient of the distribution network; Step S32, calculate the fault risk assessment result according to the first weight coefficient, the second weight coefficient, the high-frequency transient energy, and the fundamental wave current vector.

[0036] It should be noted that when the first weight coefficient is relatively high, it indicates that more attention is currently paid to transient faults (such as short circuits and lightning strikes). When compressing, the details of high-frequency transient energy will be preferentially retained (such as using lossless compression or low-distortion compression) to avoid the loss of key fault features. When the second weight coefficient is relatively high, it focuses on the monitoring of steady-state hidden dangers (such as abnormal loads and equipment aging). When compressing, the integrity of the fundamental wave data will be optimized (such as reducing the dimensional loss of the fundamental wave vector). By binding the fault risk assessment result with the compression strategy through the weight coefficient, dynamic adaptation is achieved, and the distortion of important information caused by excessive compression is avoided.

[0037] It should be noted that in this embodiment, the first weight coefficient is 0.6 and the second weight coefficient is 0.4. The calculation of the fault risk assessment result is specifically expressed by the following first formula: ; Among them, is the first weight coefficient, is the second weight coefficient, is the high-frequency transient energy, is the fundamental wave current vector.

[0038] In addition, in one embodiment, referring to Figure 4 , in Figure 1 the step S11 of the embodiment shown, it further includes but is not limited to the following steps: Step S41, obtain the amplitude information and phase information of the fundamental wave 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 wave current vector; Step S42, use wavelet packet decomposition to divide the high-frequency transient energy into multiple sub-bands, retain the sub-band coefficients with the top 30% of the energy ratio in the multiple sub-bands, set the remaining sub-bands to zero, and then reduce the data volume of the high-frequency transient energy through Huffman coding; Step S43, use Columbus coding for the accurate timestamp to obtain the difference and sampling interval of the accurate timestamp, and compress the accurate timestamp according to the difference and sampling interval.

[0039] It should be noted that for the fundamental wave current vector, a compression algorithm (LZW algorithm is used in this embodiment) is adopted to split the phase and amplitude of the fundamental wave current vector into a first real number sequence and a second real number sequence, and perform separate compression, increasing the compression ratio to 60%. Use wavelet packet decomposition to divide the high-frequency transient energy into 16 sub-bands, retain the sub-band coefficients with the top 30% of the energy ratio, set the remaining sub-bands to zero, and then use Huffman coding to reduce the data volume of the high-frequency transient energy by 70%. Use Columbus coding for the accurate timestamp, and utilize the characteristic that the difference between adjacent timestamps approaches the sampling interval to increase the compression efficiency by 85%.

[0040] In addition, in one embodiment, referring to Figure 5 , in Figure 1 step S11 of the illustrated embodiment, it further includes but is not limited to the following steps: Step S51, when the risk assessment result is less than or equal to a preset third threshold, the distribution network enters the lossy compression mode, and only the fundamental amplitude of the fundamental current vector and the high-frequency subbands in the first frequency range are retained; 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 the hybrid mode, the fundamental amplitude is losslessly compressed, and the high-frequency subbands are expanded from the first frequency range to the second frequency range; Step S53, when the risk assessment result is greater than the first threshold, trigger the distribution network to enter the full-feature transmission mode, suspend the compression of the fundamental current vector, high-frequency transient energy, and precise timestamp, and directly send the original sampling data so that the master station side module can obtain a complete fault recording.

[0041] It should be noted that when the risk assessment result R ≤ 0.3 (the third threshold), it is determined that the distribution network is in the normal working condition. At this time, the distribution network is controlled to enter the lossy compression mode, and only the fundamental amplitude and the high-frequency subbands of 2 - 5 kHz (the first frequency range) are retained, so that the data rate is reduced to 40% of the original value; when 0.3 (the third threshold) < the risk assessment result R < 0.7 (the second threshold), it is determined that the distribution network is in the fault warning state. At this time, the distribution network is controlled to switch to the hybrid mode, the fundamental data is losslessly compressed, and the high-frequency subbands are extended to 2 - 10 kHz to increase the data rate to 60% of the original value; when the risk assessment result R > 0.7 (the second threshold), it is determined that the distribution network has a fault. At this time, the distribution network is controlled to enter the full-feature transmission mode, suspend compression and directly send the original sampling data to ensure that the master station side module can obtain a complete fault recording.

[0042] The edge side module includes a feature classification unit and a dynamic policy 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. Among them, the edge side module is a dual-buffer structure.

[0043] It should be noted that power frequency features are extracted by the feature grading unit to monitor the steady-state parameters of the distribution network (such as three-phase balance degree and power flow distribution) in real time, providing a reference value for differential protection to avoid mis-triggering of protection actions due to steady-state fluctuations. High-frequency transient components usually appear within a few milliseconds after a fault occurs. Through wavelet decomposition, the initial fault features can be captured in advance, shortening the protection action delay. 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 feature vectors (such as the energy ratio of each frequency band and waveform singularity) to assist in differentiating fault types and avoiding misjudgment.

[0044] It should be noted that the double-buffer structure enables the reading and processing of data to occur simultaneously in different buffer areas. When the data in one buffer area is being processed by the feature grading unit, the other buffer area can simultaneously collect and store data, thus avoiding waiting time during the data processing process and improving the overall efficiency of data processing, enabling the decomposition of power frequency and high-frequency wavelets of the distribution network to be completed more quickly. In addition, the double-buffer structure can make the system more stable in the face of emergencies or high loads. Exemplarily, when the data acquisition rate suddenly increases or a temporary communication failure occurs, the double buffer can serve as a buffer zone to temporarily store excess data to prevent data overflow or system crashes. At the same time, when a failure or abnormality occurs in one buffer area, the other buffer area can continue to operate to maintain the normal operation of the system, improving the stability and reliability of the entire distribution network differential data protection system.

[0045] The master station side module is provided with a data reconstruction unit and a fault tolerance processing unit. The data reconstruction unit is used to perform inverse wavelet transform and timestamp recovery on the feature data packet. The fault tolerance processing unit is used to detect the feature data packet. When data loss in the feature data packet is detected, the missing feature values of the feature data packet are estimated through Kalman filtering.

[0046] It should be noted that through inverse transform, the key features of high-frequency transient signals (such as the arrival time of traveling waves and the energy distribution of each frequency band) can be restored 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 the alignment of multi-terminal data on the time axis and avoid the failure of the differential criterion 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), directly discarding it may lead to protection refusal, while Kalman filtering can fill in the missing values through interpolation estimation to maintain the continuity of the criterion. In scenarios with unstable communication quality (such as mountainous distribution networks and temporary construction areas), fault tolerance processing can enable the protection system to still maintain basic functions when the data is incomplete and avoid protection failure caused by "single-point failures".

[0047] As Figure 7 shownFigure 7 This is the structural diagram of the data compression device for distribution network differential protection provided by an embodiment of the present invention. The present invention also provides a data compression device for distribution network differential protection, including: A processor 701, which can be implemented in ways such as a general-purpose central processing unit (CPU), a microprocessor, an application specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided by the embodiments of the present application; A memory 702, which can be implemented in forms such as 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 implementing the technical solutions provided by the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 702 and are called by the processor 701 to execute the data compression method for distribution network differential protection of the embodiments of the present application; An input / output interface 703, which is used to implement information input and output; A communication interface 704, which is used to implement communication interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or through wireless means (such as mobile network, WIFI, Bluetooth, etc.); A bus 705, which transmits information between various components of the device (such as the processor 701, the memory 702, the input / output interface 703, and the communication interface 704); Among them, the processor 701, the memory 702, the input / output interface 703, and the communication interface 704 achieve communication connections with each other inside the device through the bus 705.

[0048] The embodiments of the present application also provide an electronic device, including the data compression device for distribution network differential protection as described above.

[0049] The embodiments of the present application also provide a storage medium, which is a computer-readable storage medium. The storage medium stores a computer program, and when the computer program is executed by a processor, it implements the above-mentioned data compression method for distribution network differential protection.

[0050] A memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory optionally includes a memory remotely disposed relative to the processor, and these remote memories can be connected to the processor through a network. Examples of the above networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof. The device embodiments described above are merely illustrative, where the units described as separate components may or may not be physically separated, and may be located in one place, or may be distributed to multiple network units. Part or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0051] Those of ordinary skill in the art can understand that all or some of the steps and systems disclosed above can be implemented as software, firmware, hardware, and appropriate combinations thereof. Some physical components or all physical components can be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or can be implemented as hardware, or can be implemented as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include a computer storage medium (or non-transitory medium) and a communication medium (or transitory medium). As is well known to those of ordinary skill in the art, the term computer storage medium 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 disk (DVD) or other optical disk storage, magnetic cassettes, tapes, 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. In addition, as is well known to those of ordinary skill in the art, a communication medium generally includes computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transmission mechanism, and can include any information delivery medium.

[0052] The above is a specific description of the preferred embodiments of the present invention, but the present invention is not limited to the above embodiments. Those skilled in the art can also make various equivalent deformations or substitutions without departing from the spirit of the present invention, and these equivalent deformations or substitutions are all included within the scope defined by the claims of the present invention.

Claims

1. A data compression method for differential protection of a distribution network, characterized in that, Applied to a data compression system for distribution network differential protection, the distribution network differential protection data compression system includes an edge-side module and a master station-side module, and the edge-side module is connected to the master station-side module. The method includes: The edge-side module acquires sampling data of the distribution network, and performs feature grading on the sampling data according to protection requirements at the edge nodes of the distribution network to obtain a fundamental wave current vector, high-frequency transient energy, and an accurate timestamp; The edge-side module acquires the real-time network bandwidth and the fault risk assessment result of the distribution network, and selects the compression modes of the fundamental wave current vector, the high-frequency transient energy, and the accurate timestamp according to the real-time network bandwidth and the fault risk assessment result; Perform feature grading compression on the fundamental wave current vector, the high-frequency transient energy, and the accurate timestamp in sequence according to the compression mode to obtain a feature data packet of the distribution network; Transmit the feature data packet to the master station-side module through a hybrid communication network.

2. The data compression method for distribution network differential protection according to claim 1, wherein The selection of the compression modes of the fundamental wave current vector, the high-frequency transient energy, and the accurate 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, control 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, control the distribution network to enter a lossless compression mode to compress the fundamental wave data of the distribution network; Perform difference coding on the accurate timestamp to obtain the combined entropy coding of the accurate timestamp.

3. The data compression method for distribution network differential protection according to claim 1, wherein The acquisition of the real-time network bandwidth and the fault risk assessment result of the distribution network includes: Acquire the first weight coefficient and the second weight coefficient of the distribution network; Calculate the fault risk assessment result according to the first weight coefficient, the second weight coefficient, the high-frequency transient energy, and the fundamental wave current vector.

4. The data compression method for distribution network differential protection according to claim 1, wherein The sequential feature grading compression of the fundamental wave current vector, the high-frequency transient energy, and the accurate timestamp according to the compression mode includes: Obtain the amplitude information and phase information of the fundamental wave 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 wave current vector; Use wavelet packet decomposition to divide the high-frequency transient energy into multiple subbands, retain the subband coefficients with the top 30% energy proportion in the multiple subbands, set the remaining subbands to zero, and reduce the data volume of the high-frequency transient energy through Huffman coding; Perform Columbus coding on the accurate timestamp to obtain the difference and sampling interval of the accurate timestamp, and compress the accurate timestamp according to the difference and the sampling interval.

5. The differential protection data compression method for a distribution network according to claim 2, characterized in that, After obtaining the combined entropy coding of the accurate 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, and only the fundamental amplitude of the fundamental current vector and the high-frequency subbands in the first frequency range are retained. 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 subbands are expanded from the first frequency range to the second frequency range. 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 so that the master station side module can obtain a complete fault recording.

6. The data compression method for distribution network differential protection according to claim 1, characterized in that The edge side module includes a feature classification unit and a dynamic policy 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.

7. The data compression method for distribution network differential protection according to claim 1, characterized in that The master station side module is provided with a data reconstruction unit and a fault tolerance processing unit. The data reconstruction unit is used to perform inverse wavelet transform and timestamp recovery on the feature data packet. The fault tolerance processing unit is used to detect the feature data packet. When data loss of the feature data packet is detected, the missing feature values of the feature data packet are estimated by Kalman filtering.

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

9. An electronic device, characterized in that, It includes the distribution network differential protection data compression device according to claim 8.

10. 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 cause a computer to execute the distribution network differential protection data compression method according to any one of claims 1 to 7.

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