Ultra-high voltage direct current system fault early warning method and device and storage medium

Abstract

The invention discloses an ultra-high-voltage direct-current system fault early warning method and device and a storage medium, and the method comprises the steps: arranging an intelligent electric meter and a microenvironment sensor module at an ultra-high-voltage direct-current converter transformer, collecting and synchronizing electrical parameters and environmental parameters at high frequency in real time, carrying out the edge cleaning, and then driving a digital twin model to evolve in real time, thereby achieving the early warning of the fault of an ultra-high-voltage direct-current system. The model parameters are ensured to be consistent with a full life cycle mirror image of physical equipment, a digital twin model is utilized to deduce a full cycle commutation process forwards by taking a current working condition as an initial condition, an alternating current-direct current voltage ratio sequence at each commutation triggering moment is automatically extracted, and a self-adaptive interval discrimination algorithm is utilized to capture potential deviation in a millisecond level; once an abnormal state exists, N cycle instantaneous values before and after the abnormal state are traced back, negative sequence voltage and negative sequence current are generated, negative sequence impedance is estimated, and external faults and internal faults can be distinguished, early warning information is generated, and operators are reminded through double comparison with system short-circuit impedance and transformer leakage impedance.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of transformer monitoring and fault diagnosis, in particular to a fault early warning method and device of an ultra-high voltage direct current system based on digital twin technology and a storage medium. BACKGROUND

[0002] Ultra-high voltage direct current has become the national energy artery, with an annual power transmission capacity of more than 3 trillion kilowatt-hours. Among them, the converter transformer, as the core hub of energy conversion, is subjected to high voltage, large current and complex environmental coupling all year round. Its operating state is directly related to the brightness of millions of families and the roar of factories.

[0003] In the prior art, the fault early warning method of the converter transformer in the ultra-high voltage direct current system usually relies on traditional sensor monitoring and simple data analysis model. These methods have significant deficiencies in real-time, accuracy and comprehensiveness, including the following: Firstly, the traditional method lacks the support of a high-fidelity digital model, and is often based on a static or simplified physical model, which cannot dynamically reflect the actual operating state of the converter transformer, resulting in a large deviation between the model and the real device, thereby affecting the accuracy of fault detection; Secondly, the traditional method is difficult to simulate and predict system behavior in real time, and can only rely on historical data or offline analysis, which makes the identification of abnormal state lag behind the actual fault occurrence, and cannot achieve early warning; Thirdly, the existing technology uses threshold comparison or simple algorithms, which are difficult to distinguish between transient fluctuations and real abnormalities, have a high false positive rate, and are weak in fault location, resulting in misjudgment and delayed response, increasing the operation and maintenance cost and system risk. SUMMARY

[0004] The purpose of the present application is to provide a fault early warning method, device and storage medium for an ultra-high voltage direct current system, which solves the problems of poor real-time performance, inaccurate positioning and high false positive rate in the prior art.

[0005] In order to achieve the above-mentioned purpose, the present application adopts the following technical solutions: A fault early warning method for an ultra-high voltage direct current system, comprising the following steps: Step S1: Real-time and continuous acquisition of electrical parameters and environmental parameters associated with the converter transformer in the ultra-high voltage direct current system, combined with digital twin technology, to construct a digital twin model of the converter transformer, wherein the electrical parameters include power parameters such as alternating voltage, direct current voltage, three-phase voltage and three-phase current, and signal parameters such as hydrogen concentration and methane concentration in transformer oil, and the environmental parameters include temperature parameters, humidity parameters and vibration parameters of the current environment of the converter transformer; Step S2: The constructed converter transformer digital twin model takes the actual converter transformer as a simulation object and performs simulation operation within a preset simulation period, and extracts the AC voltage sequence and DC voltage sequence within the simulation period; Step S3: Analyze the obtained AC voltage sequence and DC voltage sequence to determine whether there is an abnormal state of the converter transformer digital twin model within the simulation period. If there is an abnormal state, perform step S4, otherwise return to step S2; Step S4: Extract the three-phase voltage and three-phase current within several cycles before the occurrence of the abnormal state, and the three-phase voltage and three-phase current within several cycles after the occurrence of the abnormal state of the converter transformer digital twin model, and construct the three-phase voltage sequence and three-phase current sequence corresponding to the commutation trigger moment; Step S5: Based on the obtained three-phase voltage sequence and three-phase current sequence, determine the negative sequence voltage and negative sequence current within several cycles, calculate the negative sequence impedance, and perform double comparison of the negative sequence impedance according to the external system short-circuit impedance and the transformer leakage impedance to obtain the comparison result, and determine the occurrence position of the abnormal state based on the obtained comparison result; Step S6: Output the occurrence position of the abnormal state.

[0006] Further, in the step S1, for the construction process of the converter transformer digital twin model, the following steps are included: Step S1.1: Real-time and continuous acquisition of power parameters associated with the converter transformer in the UHVDC system; Step S1.2: Real-time and continuous acquisition of signal parameters associated with the converter transformer in the UHVDC system; Step S1.3: Real-time and continuous acquisition of environmental parameters associated with the converter transformer in the UHVDC system; Step S1.4: Combine the power parameters and signal parameters to form the electrical parameters associated with the converter transformer in the UHVDC system; Step S1.5: Based on the physical structure and working principle of the actual converter transformer, a converter transformer digital twin model is constructed using digital twin technology; Step S1.6: Calibrate the constructed converter transformer digital twin model using continuously collected environmental parameters and electrical parameters until the various state parameters of the converter transformer digital twin model simulation operation match the actual operation of the converter transformer in the UHVDC system.

[0007] Further, in the step S1.1, the power parameters are collected by the intelligent electric meter pre-installed in the UHVDC system.

[0008] Furthermore, in steps S1.2 and S1.3, the environmental parameters and signal parameters are collected by a micro-environment sensor module pre-installed in the UHVDC system.

[0009] Furthermore, in step S2, the extraction process of the AC voltage sequence and DC voltage sequence of the converter transformer digital twin model during the simulation period specifically includes the following steps: Step S2.1: Obtain the preset simulation period and determine the duration of the simulation period, denoted as . ; Step S2.2: Determine the current time, denoted as ; Step S2.3: Determine the actual converter transformer at the current time. The converter transformer digital twin model is then set to this state, and the simulation runs in that state for one simulation cycle. ; Step S2.4: Based on the actual pulsation number of the converter transformer, calculate the actual commutation interval of the converter transformer, and then obtain the digital twin model of the converter transformer in the simulation cycle. The total number of commutations within the range is denoted as ; Step S2.5: Extract the digital twin model of the converter transformer from the first commutation trigger time to the... All AC and DC voltages at the moment of commutation triggering are collected and sorted in chronological order to obtain the AC voltage sequence and DC voltage sequence, respectively. The AC voltage sequence is denoted as... The DC voltage sequence is denoted as ; Step S2.6: For the obtained AC voltage sequence and DC voltage sequence, the DC voltage at any commutation trigger moment... Set to associate with the corresponding AC voltage ,in, This is the count index for the commutation trigger moment. .

[0010] Furthermore, in step S3, the process for determining whether the digital twin model of the converter transformer exhibits an abnormal state during the simulation period specifically includes the following steps: Step S3.1: Based on the obtained AC voltage sequence and DC voltage sequence, extract the DC voltage at each commutation trigger moment. and the corresponding AC voltage And calculate the ratio of the two. The calculation formula is: When the ratio of the DC voltage to the AC voltage at each commutation trigger time is calculated, a ratio sequence is obtained wherein, is a count index of the commutation trigger time, ; Step S3.2: Each ratio in the ratio sequence is compared with the preset conversion ratio interval, to determine whether there is a ratio that does not belong to the conversion ratio interval. If so, it is determined that the digital twin model of the converter transformer has an abnormal state in the simulation period. If not, it is determined that the digital twin model of the converter transformer does not have an abnormal state in the simulation period.

[0011] Further, in the step S4, the construction process of the three-phase voltage sequence and the three-phase current sequence corresponding to the commutation trigger time includes the following steps: Step S4.1: Based on the obtained ratio sequence, all ratios that do not belong to the conversion ratio interval are extracted from the ratio sequence, the commutation trigger time at which each extracted ratio occurs is determined, and cycles are extracted before and after each commutation trigger time, respectively, to form a cycle interval, and finally at least one cycle interval is obtained, wherein, is a preset integer; Step S4.2: Extract all commutation trigger times in each obtained cycle interval, and sort them in chronological order to obtain at least one three-phase voltage sequence, denoted as wherein, is the total number of commutations in a single cycle interval, is a count index of the cycle interval, , and is the number of obtained cycle intervals; Step S4.3: Extract all commutation trigger times in each obtained cycle interval, and sort them in chronological order to obtain at least one three-phase current sequence, denoted as wherein, is the total number of commutations in a single cycle interval, is a count index of the cycle interval, , and is the number of obtained cycle intervals; Step S4.4: For the obtained three-phase voltage sequence and three-phase current sequence, the three-phase voltage at any commutation trigger time is set to be associated with the corresponding three-phase current , and the three-phase voltage includes the three-phase voltage instantaneous value corresponding to the commutation trigger time​ 、 、 , the three-phase current includes a three-phase current instantaneous value corresponding to the commutation trigger moment 、 、 , wherein, is a count index of the commutation trigger moment, .

[0012] Further, in the step S5, the analysis and determination process of the abnormal state occurrence position specifically includes the following steps: Step S5.1: Based on the obtained three-phase voltage sequence and three-phase current sequence, extract the three-phase voltage containing a three-phase voltage instantaneous value 、 、 and the three-phase current containing a three-phase current instantaneous value 、 、 corresponding to the commutation trigger moment; Step S5.2: Calculate the negative sequence voltage at each commutation trigger moment, the calculation formula is , wherein, is a rotation operator; Step S5.3: Calculate the negative sequence current at each commutation trigger moment, the calculation formula is , wherein, is a rotation operator; Step S5.4: Based on the calculated negative sequence voltage and negative sequence voltage , calculate the negative sequence impedance at each commutation trigger moment, the calculation formula is ; Step S5.5: According to all negative sequence impedances , determine the abnormal state occurrence position based on the following conditions: (1) If the difference rate of the negative sequence impedance and the external system short-circuit impedance is less than or equal to the difference rate threshold , it indicates that the converter transformer digital twin model is in a high impedance scenario at the corresponding commutation trigger moment, and the abnormal state is determined to be a fault originating from the external system; (2) If the difference rate of the negative sequence impedance and the external system short-circuit impedance is greater than the difference rate threshold and the difference rate of the transformer leakage impedance is less than or equal to a difference rate threshold value , it is indicated that the digital twin model of the converter transformer is in a low impedance scenario at the corresponding commutation trigger moment, and the abnormal state is determined to be caused by an internal fault of the converter transformer; (3) if the difference rate of the negative sequence impedance and the external system short-circuit impedance is greater than a difference rate threshold value , and the difference rate of the transformer leakage impedance is greater than a difference rate threshold value , it is indicated that the digital twin model of the converter transformer is in a fault occurrence stage or a transition state stage at the corresponding commutation trigger moment, and the abnormal state is determined to be caused by a fault of the digital twin model of the converter transformer; wherein, the external system short-circuit impedance , the transformer leakage impedance , and the difference rate threshold value are all preset constants; Step S5.6: record the determination result of the abnormal state occurrence position of the digital twin model of the converter transformer at all commutation trigger moments.

[0013] The application further provides an electronic device, which comprises a memory and a processor, the memory is used for storing program code and transmitting the program code to the processor, and the processor is used for executing the UHV DC system fault early warning method according to instructions in the program code.

[0014] The application further provides a computer readable storage medium, which is used for storing program code, and the program code is used for executing the UHV DC system fault early warning method.

[0015] The application has the following beneficial effects:

[0016] 1、The method constructs a digital twin model of the converter transformer through digital twin technology, which serves as a high-fidelity virtual model and can effectively realize real-time synchronization and advanced simulation of the state of the converter transformer, thereby significantly improving the initiative and accuracy of fault warning; the method can not only capture potential abnormal states in advance through simulation running of the digital twin model of the converter transformer, achieve the purpose of active warning and timely maintenance, and change the previous passive maintenance situation; the method can accurately locate the fault position by analyzing the negative sequence voltage and negative sequence current after the abnormal state occurs, thereby greatly shortening the fault diagnosis time, effectively avoiding direct shutdown of the UHVDC system or larger-scale safety accidents caused by faults of the converter transformer, and ensuring the safety and stability of the operation of the key power infrastructure of the UHVDC system.

[0017] 2、The method constructs a high-precision digital twin model of the converter transformer and continuously calibrates it using real-time collected multi-dimensional parameters, ensuring high consistency between the model and the physical equipment; first, its core advantage is that it can accurately simulate the running state and intelligently diagnose abnormalities, and second, it realizes early, automatic and rapid identification of potential faults by automatically comparing the ratio sequence of the DC voltage and the AC voltage with the preset safety interval, effectively improving the real-time performance of the state monitoring of the converter transformer in the UHVDC system, thereby providing technical support for preventing equipment failure.

[0018] 3、The method constructs the three-phase voltage and current sequence at the commutation trigger moment, calculates the negative sequence impedance, compares the negative sequence impedance with the system short-circuit impedance and the transformer leakage impedance, realizes high-precision diagnosis and rapid positioning of the abnormal state of the digital twin model of the converter transformer; its core advantage is that it can effectively distinguish between internal faults and external system faults, improve the accuracy and reliability of fault identification, and avoid misjudgment; at the same time, the method can timely issue an alarm through an automatic monitoring and warning mechanism, guide the targeted maintenance of the operator, greatly reduce the fault troubleshooting time, prevent the expansion of the fault, and thereby ensure the stable operation of the power system. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 is a method flowchart of embodiment 1 of the present application.

[0020] Figure 2 is a method flowchart of embodiment 2 of the present application.

[0021] Figure 3 is a method flowchart of embodiment 3 of the present application. DETAILED DESCRIPTION

[0022] The application will be further described in detail below with reference to the accompanying drawings.

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention. Example 1

[0024] like Figure 1 As shown, a fault early warning method for an ultra-high voltage direct current system includes the following steps: Step S1: Real-time and continuous acquisition of electrical and environmental parameters associated with the converter transformer in the UHVDC system. Combined with digital twin technology, construct a digital twin model of the converter transformer. The electrical parameters include power parameters such as AC voltage, DC voltage, three-phase voltage, and three-phase current, as well as signal parameters such as hydrogen concentration and methane concentration in the transformer oil. The environmental parameters include temperature, humidity, and vibration parameters of the environment in which the converter transformer is currently located. Step S2: The constructed digital twin model of the converter transformer uses the actual converter transformer as the simulation object and runs the simulation within a preset simulation period to extract the AC voltage sequence and DC voltage sequence within the simulation period. Step S3: Analyze the obtained AC voltage sequence and DC voltage sequence to determine whether there is an abnormal state in the digital twin model of the converter transformer during the simulation period. If there is an abnormal state, proceed to step S4; otherwise, return to step S2. Step S4: Extract the three-phase voltage and three-phase current of the converter transformer digital twin model in several cycles before the occurrence of the abnormal state, and in several cycles after the occurrence of the abnormal state, and construct the three-phase voltage sequence and three-phase current sequence corresponding to the commutation triggering time. Step S5: Based on the obtained three-phase voltage sequence and three-phase current sequence, determine the negative sequence voltage and negative sequence current within a certain number of cycles, calculate the negative sequence impedance, and perform a double comparison of the negative sequence impedance based on the external system short-circuit impedance and transformer leakage impedance to obtain the comparison result. Based on the obtained comparison result, determine the location of the abnormal state. Step S6: Output the location where the abnormal state occurred.

[0025] Furthermore, in step S1, the construction process of the digital twin model of the converter transformer specifically includes the following steps: Step S1.1: Real-time and continuous acquisition of power parameters associated with converter transformers in the UHVDC system; Step S1.2: Real-time and continuous acquisition of signal parameters associated with the converter transformer in the UHVDC system; Step S1.3: Real-time and continuous acquisition of environmental parameters associated with the converter transformer in the UHVDC system; Step S1.4: Combining the aforementioned power parameters and signal parameters, the electrical parameters associated with the converter transformer in the UHVDC system are thus formed; Step S1.5: Based on the actual physical structure and working principle of the converter transformer, construct a digital twin model of the converter transformer using digital twin technology; Step S1.6: Using continuously collected environmental and electrical parameters, calibrate the constructed converter transformer digital twin model until the various state parameters of the converter transformer digital twin model simulated operation match those of the actual converter transformer operating in the UHVDC system.

[0026] Furthermore, in step S1.1, the power parameters are collected by smart meters pre-installed in the UHVDC system.

[0027] Furthermore, in steps S1.2 and S1.3, the environmental parameters and signal parameters are collected by a micro-environment sensor module pre-installed in the UHVDC system. Example 2

[0028] like Figure 2 As shown, this embodiment is based on embodiment 1. Based on steps S2 and S3, it specifically describes the extraction process of AC voltage sequence and DC voltage sequence of the converter transformer digital twin model during the simulation period, as well as the abnormal state determination process realized by the analysis of AC voltage sequence and DC voltage sequence. The purpose of this embodiment is to accurately confirm whether the actual converter transformer has an abnormal state in the future through the simulation operation of the converter transformer digital twin model.

[0029] In step S2, the extraction process of the AC voltage sequence and DC voltage sequence of the converter transformer digital twin model during the simulation period specifically includes the following steps: Step S2.1: Obtain the preset simulation period and determine the duration of the simulation period, denoted as . ; Step S2.2: Determine the current time, denoted as ; Step S2.3: Determine the actual converter transformer at the current time. The converter transformer digital twin model is then set to this state, and the simulation runs in that state for one simulation cycle. ; Step S2.4: According to the actual commutation transformer pulse number, the actual commutation transformer commutation interval is converted, and then the total number of commutations of the commutation transformer digital twin model in the simulation period is obtained, denoted as Step 2.5: Extract all AC voltages and all DC voltages of the commutation transformer digital twin model from the first commutation trigger time to the nth commutation trigger time, and sort them in chronological order respectively to obtain the AC voltage sequence and the DC voltage sequence, denoted as Step S2.6: For the obtained AC voltage sequence and DC voltage sequence, the DC voltage at any commutation trigger time is set to be associated with the corresponding AC voltage , where is the count index of the commutation trigger time, .

[0030] In the step S3, for the determination process of whether the commutation transformer digital twin model has an abnormal state in the simulation period, the following steps are included: Step S3.1: Based on the obtained AC voltage sequence and DC voltage sequence, the DC voltage at each commutation trigger time and the corresponding AC voltage associated therewith are extracted, and the ratio of the two is calculated , and the calculation formula is After the ratio of the DC voltage to the AC voltage at all commutation trigger times is calculated, the ratio sequence is obtained, where is the count index of the commutation trigger time, ; Step S3.2: Each ratio in the ratio sequence is compared with the preset conversion ratio interval to determine whether there is a ratio that does not belong to the conversion ratio interval. If yes, it is determined that the commutation transformer digital twin model has an abnormal state in the simulation period. If not, it is determined that the commutation transformer digital twin model does not have an abnormal state in the simulation period. Example 3

[0031] As Figure 3 ​​​​​As shown, based on steps S4 and S5 described in embodiments 1 and 2, the present embodiment specifically describes the construction process of the three-phase voltage sequence and the three-phase current sequence corresponding to the commutation trigger time in the simulation period of the converter transformer digital twin model, and the abnormal state occurrence position determination process realized according to the analysis and comparison of the three-phase voltage sequence and the three-phase current sequence. The purpose of the present embodiment is to accurately determine the abnormal state occurrence position of the actual converter transformer in the future period of time through the simulation running of the converter transformer digital twin model.

[0032] In step S4, the construction process of the three-phase voltage sequence and the three-phase current sequence corresponding to the commutation trigger time specifically includes the following steps: Step S4.1: Based on the obtained ratio sequence, extract all ratios not belonging to the conversion ratio interval from the ratio sequence, determine the commutation trigger time at which each extracted ratio occurs, and extract cycles before and after each commutation trigger time, respectively, to form a cycle interval, and finally obtain at least one cycle interval, wherein, is a preset integer; Step S4.2: Extract all three-phase voltages at the commutation trigger time in each obtained cycle interval and sort them in chronological order, and finally obtain at least one three-phase voltage sequence, denoted as , wherein, is the total number of commutations in a single cycle interval, is the counting index of the cycle interval, , and is the number of obtained cycle intervals; Step S4.3: Extract all three-phase currents at the commutation trigger time in each obtained cycle interval and sort them in chronological order, and finally obtain at least one three-phase current sequence, denoted as , wherein, is the total number of commutations in a single cycle interval, is the counting index of the cycle interval, , and is the number of obtained cycle intervals; Step S4.4: For the obtained three-phase voltage sequence and three-phase current sequence, the three-phase voltage at any commutation trigger time is set to be associated with the corresponding three-phase current , the three-phase voltage includes the three-phase voltage instantaneous value , , at the commutation trigger time, and the three-phase current The three-phase current instantaneous values corresponding to the commutation trigger time points , , wherein, is a count index of the commutation trigger time point, .

[0033] In the step S5, the analysis and determination process of the abnormal state occurrence position specifically includes the following steps: Step S5.1: Based on the obtained three-phase voltage sequence and three-phase current sequence, the three-phase voltage containing the three-phase voltage instantaneous value , , at each commutation trigger time point and the three-phase current containing the three-phase current instantaneous value , , associated therewith are extracted; Step S5.2: The negative sequence voltage at each commutation trigger time point is calculated, and the calculation formula is wherein, is a rotation operator; Step S5.3: The negative sequence current at each commutation trigger time point is calculated, and the calculation formula is wherein, is a rotation operator; Step S5.4: Based on the calculated negative sequence voltage and the negative sequence voltage , the negative sequence impedance at each commutation trigger time point is calculated, and the calculation formula is ; Step S5.5: According to all the negative sequence impedances , the occurrence position of the abnormal state is determined based on the following conditions: (1) If the difference rate of the negative sequence impedance and the external system short-circuit impedance is less than or equal to the difference rate threshold , it indicates that the converter transformer digital twin model is in a high impedance scenario at the corresponding commutation trigger time point, and it is determined that the abnormal state is caused by a fault in the external system; (2) If the difference rate of the negative sequence impedance and the external system short-circuit impedance is greater than the difference rate threshold , and the difference rate of the transformer leakage impedance is less than or equal to the difference rate threshold , it is indicated that the digital twin model of the converter transformer is in a low impedance scenario at the corresponding commutation trigger moment, and the abnormal state is determined to be an internal fault of the converter transformer; (3) if the difference rate of the negative sequence impedance and the external system short-circuit impedance is greater than the difference rate threshold , and the difference rate of the negative sequence impedance and the transformer leakage impedance is greater than the difference rate threshold , it is indicated that the digital twin model of the converter transformer is in a fault occurrence stage or a transition state stage at the corresponding commutation trigger moment, and the abnormal state is determined to be a fault of the digital twin model of the converter transformer; Wherein, the external system short-circuit impedance , the transformer leakage impedance , the difference rate threshold are all preset constants.

[0034] Step S5.6: Record the determination result of the abnormal state occurrence position of the digital twin model of the converter transformer at all commutation trigger moments.

[0035] It should be understood that the method deeply integrates the converter transformer in the UHVDC system in the physical world with the high-fidelity digital twin model in the virtual space. Intelligent electric meters and micro-environment sensor modules are arranged on the entity side of the converter transformer in the UHVDC system to collect and high-frequency synchronize three-phase voltage, three-phase current, AC / DC side voltage, temperature parameters, humidity parameters, and vibration parameters in real time. After edge cleaning, the digital twin model is driven to evolve in real time, ensuring that the model parameters are consistent with the physical equipment throughout the life cycle. Using the digital twin model, the initial condition is the current working condition, and the whole cycle commutation process is deduced forward. The AC / DC voltage ratio sequence at each commutation trigger moment is automatically extracted, and the adaptive interval difference algorithm is used to capture potential deviations at the millisecond level. Once the limit is exceeded, there is an abnormal state. Backtrack the N-cycle instantaneous values before and after the abnormal state, generate negative sequence voltage and negative sequence current, and estimate the negative sequence impedance. Through double comparison with the external system short-circuit impedance and the transformer leakage impedance, external faults and internal faults can be distinguished according to the comparison results. Warning information is pushed to the operator to remind the operator to repair in time, and the whole process realizes early perception, accurate positioning, and intelligent warning of potential faults.

[0036] It should be noted that the basic principle of the analysis and processing of the alternating voltage sequence and the direct voltage sequence is that: the commutation transformer is the AC-DC coupling point, the valve side winding of the commutation transformer bears complex electrical stress, including power frequency AC component and characteristic harmonic and non-periodic component generated due to commutation process, so any internal fault will first reflect on the voltage waveform, secondly, the abnormal fluctuation of the direct voltage directly reflects the working state of the integrated commutation valve of the commutation transformer, the insulation condition of the direct current line and the strength of the alternating current system, therefore, the correlation analysis of the direct voltage and the alternating voltage is carried out, according to the stable proportional relationship between the direct voltage and the alternating voltage of the commutation transformer in normal operation, if the stable proportional relationship is destroyed, it means that the commutation process as the core of AC-DC energy conversion has an abnormality, so that the potential fault of the UHVDC system can be detected sensitively and reliably, at the same time, through the advanced simulation of the digital twin model of the commutation transformer, the abnormal state that will exist in the actual commutation transformer in the future period of time is determined, and then the abnormality and risk situation can be found in advance.

[0037] It should be noted that the commutation trigger time specifically refers to a duration segment of the commutation process, the commutation process is the most fragile and critical dynamic process in the UHVDC system, and the fault often occurs at this moment, by extracting the three-phase voltage and the three-phase current at this moment, the most likely fault moment is aligned.

[0038] It should be noted that any three-phase voltage is actually a data set, which includes three-phase voltage instantaneous values corresponding to the commutation trigger time 、 、 Similarly, any three-phase current is also a data set, which includes three-phase current instantaneous values corresponding to the commutation trigger time 、 、 Based on the above basic data, the three-phase voltage and the three-phase current can be decomposed into positive sequence component, negative sequence component and zero sequence component by using the symmetrical component method, so that the negative sequence voltage and the negative sequence current can be determined, and then the negative sequence impedance is estimated, and through double comparison with the external system short-circuit impedance and the transformer leakage impedance, the occurrence position of the abnormal state, i.e. the occurrence position of the potential fault, can be directly determined according to the comparison result.

[0039] The embodiment of the application also provides an electronic device, which comprises a memory and a processor, the memory is used to store program code and transmit the program code to the processor, and the processor is used to execute the UHVDC system fault early warning method according to the instructions in the program code.

[0040] The embodiment of the present application further provides a computer readable storage medium for storing program codes, the program codes being used for executing the fault early warning method of the UHV DC system.

[0041] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the system, the device and the unit described above can refer to the corresponding processes in the foregoing method embodiments, and will not be described herein.

[0042] Each of the embodiments in the specification is described in a progressive manner, and each embodiment focuses on the difference from other embodiments, and the same and similar parts between the embodiments can be referred to each other.

[0043] Those skilled in the art can understand that the embodiments of the present application can be provided as a method, a device or a computer program product. Therefore, the embodiments of the present application can adopt a completely hardware embodiment, a completely software embodiment or an embodiment combining software and hardware aspects. Moreover, the embodiments of the present application can adopt a computer program product in the form of being implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program codes.

[0044] The embodiments of the present application are described with reference to flowcharts and / or block diagrams according to the method, the terminal device (system) and the computer program product of the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams and the combination of the flows and / or blocks can be implemented by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing terminal device to produce a machine, so that the instructions executed by the computer or other programmable data processing terminal device produce a device for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks Figure 1 The devices for implementing the functions specified in one or more flows and / or blocks.

[0045] These computer program instructions can also be stored in a computer readable memory capable of guiding the computer or other programmable data processing terminal device to work in a specific manner, so that the instructions stored in the computer readable memory produce a product including instruction devices, which implement the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks Figure 1 The devices for implementing the functions specified in one or more flows and / or blocks.

[0046] These computer program instructions can also be loaded into a computer or other programmable data processing devices, so that a series of operation steps are performed on the computer or other programmable data processing devices to generate a computer implemented process, thus the instructions executed on the computer or other programmable data processing devices provide a process for implementing the functions specified in the flowchart Figure 1 one or more flows and / or blocks Figure 1 one or more blocks or steps of the functions specified in the flowchart

[0047] Although the preferred embodiments of the present application have been described, those skilled in the art will be able to make additional modifications and variations to these embodiments without departing from the spirit of the present application. Accordingly, the appended claims are intended to cover all such modifications and variations as falling within the scope of the present application.

[0048] It should be noted that, in the present application, the relational terms such as "first" and "second", and the like, are used solely to distinguish one entity or action from another, without necessarily requiring or implying any actual relationship or order between or among such entities or actions. Moreover, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises a... " does not, without more constraints, exclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0049] The above description is merely that of the specific embodiments of the present application, making it possible for those skilled in the art to understand and implement the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein can be applied to other embodiments without departing from the spirit or scope of the present application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A fault early warning method for an ultra-high voltage direct current system, characterized in that: Includes the following steps: Step S1: Real-time and continuous acquisition of electrical and environmental parameters associated with the converter transformer in the UHVDC system. Combined with digital twin technology, construct a digital twin model of the converter transformer. The electrical parameters include power parameters such as AC voltage, DC voltage, three-phase voltage, and three-phase current, as well as signal parameters such as hydrogen concentration and methane concentration in the transformer oil. The environmental parameters include temperature, humidity, and vibration parameters of the environment in which the converter transformer is currently located. Step S2: The constructed digital twin model of the converter transformer uses the actual converter transformer as the simulation object and runs the simulation within a preset simulation period to extract the AC voltage sequence and DC voltage sequence within the simulation period. Step S3: Analyze the obtained AC voltage sequence and DC voltage sequence to determine whether there is an abnormal state in the digital twin model of the converter transformer during the simulation period. If there is an abnormal state, proceed to step S4; otherwise, return to step S2. Step S4: Extract the three-phase voltage and three-phase current of the converter transformer digital twin model in several cycles before the occurrence of the abnormal state, and in several cycles after the occurrence of the abnormal state, and construct the three-phase voltage sequence and three-phase current sequence corresponding to the commutation triggering time. Step S5: Based on the obtained three-phase voltage sequence and three-phase current sequence, determine the negative sequence voltage and negative sequence current within a certain number of cycles, calculate the negative sequence impedance, and perform a double comparison of the negative sequence impedance based on the external system short-circuit impedance and transformer leakage impedance to obtain the comparison result. Based on the obtained comparison result, determine the location of the abnormal state. Step S6: Output the location where the abnormal state occurred.

2. The fault early warning method for ultra-high voltage direct current systems according to claim 1, characterized in that: In step S1, the construction process of the digital twin model of the converter transformer specifically includes the following steps: Step S1.1: Real-time and continuous acquisition of power parameters associated with converter transformers in the UHVDC system; Step S1.2: Real-time and continuous acquisition of signal parameters associated with the converter transformer in the UHVDC system; Step S1.3: Real-time and continuous acquisition of environmental and signal parameters associated with the converter transformer in the UHVDC system; Step S1.4: Combining the aforementioned power parameters and signal parameters, the electrical parameters associated with the converter transformer in the UHVDC system are thus formed; Step S1.5: Based on the actual physical structure and working principle of the converter transformer, construct a digital twin model of the converter transformer using digital twin technology; Step S1.6: Using continuously collected environmental and electrical parameters, calibrate the constructed converter transformer digital twin model until the various state parameters of the converter transformer digital twin model simulated operation match those of the actual converter transformer operating in the UHVDC system.

3. The fault early warning method for ultra-high voltage direct current systems according to claim 2, characterized in that: In step S1.1, the power parameters are collected by smart meters pre-installed in the UHVDC system.

4. The fault early warning method for ultra-high voltage direct current systems according to claim 2, characterized in that: In steps S1.2 and S1.3, the environmental parameters and signal parameters are collected by a micro-environment sensor module pre-installed in the UHVDC system.

5. The fault early warning method for ultra-high voltage direct current systems according to claim 2, characterized in that: In step S2, the extraction process of the AC voltage sequence and DC voltage sequence of the converter transformer digital twin model during the simulation period specifically includes the following steps: Step S2.1: Obtain the preset simulation period and determine the duration of the simulation period, denoted as . ; Step S2.2: Determine the current time, denoted as ; Step S2.3: Determine the actual converter transformer at the current time. The converter transformer digital twin model is then set to this state, and the simulation runs in that state for one simulation cycle. ; Step S2.4: Based on the actual pulsation number of the converter transformer, calculate the actual commutation interval of the converter transformer, and then obtain the digital twin model of the converter transformer in the simulation cycle. The total number of commutations within the range is denoted as ; Step S2.5: Extract the digital twin model of the converter transformer from the first commutation trigger time to the... All AC and DC voltages at the moment of commutation triggering are collected and sorted in chronological order to obtain the AC voltage sequence and DC voltage sequence, respectively. The AC voltage sequence is denoted as... The DC voltage sequence is denoted as ; Step S2.6: For the obtained AC voltage sequence and DC voltage sequence, the DC voltage at any commutation trigger moment... Set to associate with the corresponding AC voltage ,in, This is the count index for the commutation trigger moment. .

6. The fault early warning method for ultra-high voltage direct current systems according to claim 5, characterized in that: In step S3, the process for determining whether the digital twin model of the converter transformer exhibits an abnormal state during the simulation period specifically includes the following steps: Step S3.1: Based on the obtained AC voltage sequence and DC voltage sequence, extract the DC voltage at each commutation trigger moment. and the corresponding AC voltage And calculate the ratio of the two. The calculation formula is: Once the ratio of DC voltage to AC voltage at all commutation trigger moments has been calculated, a ratio sequence is obtained. ,in, This is the count index for the commutation trigger moment. ; Step S3.2: Traverse each ratio in the ratio sequence and compare each ratio with the preset conversion ratio range. The comparison is performed to determine whether there are any ratios that do not fall within the conversion ratio range. If so, it is determined that the converter transformer digital twin model has an abnormal state during the simulation period. If not, it is determined that the converter transformer digital twin model does not have an abnormal state during the simulation period.

7. The fault early warning method for ultra-high voltage direct current systems according to claim 6, characterized in that: In step S4, the construction process for the three-phase voltage sequence and three-phase current sequence corresponding to the commutation trigger moment specifically includes the following steps: Step S4.1: Based on the obtained ratio sequence, extract all ratios that do not belong to the transition ratio interval from the ratio sequence, determine the commutation trigger time of each extracted ratio, and extract ratios before and after each commutation trigger time. Each cycle is defined by the cycles extracted before and after a single commutation trigger moment, forming a cycle interval, ultimately yielding at least one cycle interval, where... The value is a preset integer; Step S4.2: Extract the three-phase voltages at all commutation trigger times within each obtained cycle interval, and sort them in chronological order to finally obtain at least one three-phase voltage sequence, denoted as... ,in, This represents the total number of commutations within a single cycle interval. This is the counting index for the frequency interval. ,and The number of cycles obtained; Step S4.3: Extract the three-phase currents at all commutation trigger times within each obtained cycle interval, and sort them in chronological order to finally obtain at least one three-phase current sequence, denoted as... ,in, This represents the total number of commutations within a single cycle interval. This is the counting index for the frequency interval. ,and The number of cycles obtained; Step S4.4: For the obtained three-phase voltage sequence and three-phase current sequence, the three-phase voltage at any commutation trigger moment... Set to associate with the corresponding three-phase current The three-phase voltage Including the instantaneous values ​​of the three-phase voltages corresponding to the commutation trigger moment. , , The three-phase current Including the instantaneous values ​​of the three-phase currents corresponding to the commutation trigger moment. , , ,in, This is the count index for the commutation trigger moment. .

8. The fault early warning method for ultra-high voltage direct current systems according to claim 7, characterized in that: In step S5, the analysis and determination process for the location of the abnormal state specifically includes the following steps: Step S5.1: Based on the obtained three-phase voltage sequence and three-phase current sequence, extract the three-phase voltage at each commutation trigger moment. The included instantaneous values ​​of three-phase voltage , , and the corresponding three-phase current The instantaneous values ​​of the three-phase currents contained therein , , ; Step S5.2: Calculate the negative sequence voltage at each commutation trigger moment. The calculation formula is: ,in, For rotation operators; Step S5.3: Calculate the negative sequence current at each commutation trigger moment. The calculation formula is: ,in, For rotation operators; Step S5.4: Based on the calculated negative sequence voltage and negative sequence voltage Calculate the negative sequence impedance at each commutation trigger moment. The calculation formula is: ; Step S5.5: Based on all negative sequence impedances The location of the abnormal state is determined based on the following conditions: (1) If the negative sequence impedance is Short-circuit impedance of external system The difference rate is less than or equal to the difference rate threshold. If , it means that the digital twin model of the converter transformer is in a high impedance scenario at the corresponding commutation trigger moment, and the abnormal state is determined to be a fault originating from an external system. (2) If the negative sequence impedance is Short-circuit impedance of external system The difference rate is greater than the difference rate threshold And with the transformer leakage impedance The difference rate is less than or equal to the difference rate threshold. If , it means that the digital twin model of the converter transformer is in a low impedance scenario at the corresponding commutation trigger moment, and the abnormal state is determined to be caused by an internal fault of the converter transformer. (3) If the negative sequence impedance is Short-circuit impedance of external system The difference rate is greater than the difference rate threshold And with the transformer leakage impedance The difference rate is greater than the difference rate threshold If , it means that the digital twin model of the converter transformer is in the fault occurrence stage or the transition stage at the corresponding commutation trigger time, and the abnormal state is determined to be a fault originating from the digital twin model of the converter transformer. Wherein, the external system short-circuit impedance Transformer leakage impedance Difference rate threshold All are preset constants; Step S5.6: Record the determination results of the abnormal state occurrence location of the digital twin model of the converter transformer at all commutation trigger times.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor. The memory is used to store program code and transmit the program code to the processor. The processor is used to execute the ultra-high voltage direct current system fault early warning method according to any one of claims 1 to 8 according to the instructions in the program code.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store program code, which is used to execute the ultra-high voltage direct current system fault early warning method according to any one of claims 1 to 8.

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