Embedded LCC-HVDC system commutation failure prediction optimization method, device, equipment and medium

Abstract

The invention relates to the technical field of conventional high-voltage direct-current power transmission LCC-HVDC, in particular to an embedded LCC-HVDC system commutation failure prediction optimization method, device, equipment and medium, and the method comprises the steps: collecting electrical signal parameters of a high-voltage side, a medium-voltage side and a low-voltage side, building a criterion according to the electrical signal parameters of the three sides, and judging whether a commutation failure prediction strategy needs to be started or not; if it is judged that the commutation failure prediction strategy needs to be started, the collected electric signal parameters are converted into a fuzzy set, fuzzy reasoning is conducted through a pre-fuzzy rule base, and a fuzzy control value with the turn-off angle increased is generated; based on the fuzzy control value, through a defuzzification method, a turn-off angle increase amount is obtained, and a control strategy of the direct current system is adjusted; and dynamically optimizing the fuzzy rule base based on the adjusted direct current system state. According to the method, a traditional commutation failure prediction strategy and an advanced fuzzy control algorithm are combined, the commutation failure problem caused by medium-voltage side and low-voltage side faults is effectively solved, and the system stability is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of high - voltage direct - current (HVDC) transmission with line - commutated converters (LCC - HVDC), and particularly to an optimized method, device, equipment and medium for predicting commutation failure in an embedded LCC - HVDC system. Background Art

[0002] With the rapid growth of power supply capacity, power consumption demand, as well as the increasing tension of resources and energy and the urgent requirement of environmental protection, it is necessary to build a large number of new transmission lines or transform existing transmission lines to greatly improve the transmission capacity of the key sections of the power grid. However, restricted by terrain and natural barriers, the development of key channel resources is extremely difficult. Making full use of the transformation of the key transmission channels within the existing regional power grid to build an "embedded" high - voltage direct - current transmission system LCC - HVDC is an economical and feasible solution.

[0003] Different from conventional ultra - high - voltage or extra - high - voltage direct - current transmission systems, additional main transformers are usually not equipped in the substation, so there will be no situation where the voltage drop of the AC bus connected to the DC system is caused by faults on the voltage levels of the main transformer. However, for a DC system embedded in a regional power grid with a voltage level of 220 kV (high - voltage side of the main transformer), the converter station usually is equipped with a main transformer and is connected to the medium - voltage side (110 kV) and the low - voltage side (35 kV, 10 kV) power grids. In order to quickly respond to faults on the medium - voltage side (110 kV) and the low - voltage side (35 kV, 10 kV) of the main transformer and reduce the risk of commutation failure, it is urgent to develop a new commutation - failure prediction strategy suitable for the "embedded" DC transmission system.

[0004] The information disclosed in this background - art section is only intended to deepen the understanding of the overall background technology of the present invention and should not be regarded as an admission or any form of implication that this information constitutes the prior art already known to those skilled in the art. Summary of the Invention

[0005] The present invention provides an optimized method, device, equipment and medium for predicting commutation failure in an embedded LCC - HVDC system, thus effectively solving the problems in the background art.

[0006] In order to achieve the above - mentioned purpose, the technical solution adopted by the present invention is: an optimized method for predicting commutation failure in an embedded LCC - HVDC system, including the following steps:

[0007] Collect the electrical signal parameters of the high - voltage side, medium - voltage side and low - voltage side, and construct a criterion based on the electrical signal parameters of the three sides to judge whether it is necessary to start the commutation - failure prediction strategy;

[0008] If it is determined that the commutation failure prediction strategy needs to be started, the collected electrical signal parameters are converted into fuzzy sets, and fuzzy reasoning is performed using the pre-fuzzy rule base to generate a fuzzy control value for increasing the turn-off angle;

[0009] Based on the fuzzy control value, the amount of increase in the turn-off angle is obtained through a defuzzification method, and the control strategy of the DC system is adjusted;

[0010] Based on the adjusted state of the DC system, the fuzzy rule base is dynamically optimized.

[0011] Further, the electrical signal parameter is the voltage amplitude, and the voltage drop amplitude is calculated based on the voltage amplitude.

[0012] Further, the conversion of the collected electrical signal parameters into fuzzy sets includes:

[0013] Taking the electrical signal parameters of the high-voltage side, medium-voltage side, and low-voltage side as input variables, and using membership functions to describe the fuzzy degree of different input variables;

[0014] The membership functions include triangular, trapezoidal, and Gaussian membership functions.

[0015] Further, the construction of a criterion based on the electrical signal parameters on the three sides to determine whether the commutation failure prediction strategy needs to be started includes:

[0016] Setting a first set value and a second set value, and determining whether the bus voltages of the high-voltage side, medium-voltage side, and low-voltage side satisfy the set criterion;

[0017] If the criterion is satisfied, the commutation failure prediction strategy needs to be started.

[0018] Further, the criterion includes:

[0019]

[0020] In the formula, U ac高压侧 , U ac中压侧 , U ac低压侧 are the bus voltages of the high-voltage side, medium-voltage side, and low-voltage side respectively, and U set1 , U set2 are the first set value and the second set value respectively.

[0021] Further, the criterion includes:

[0022]

[0023] In the formula, U ac高压侧 , U ac中压侧 , U ac低压侧 are the bus voltages of the high-voltage side, medium-voltage side, and low-voltage side respectively, and U set1 , Uset2 are the first set value and the second set value respectively; ΔU T11 represents the voltage drop from the high-voltage side to the medium-voltage side of the main transformer when the maximum voltage drop level of the high-voltage side busbar voltage is exactly U set1 during a three-phase fault on the medium-voltage side; ΔU T12 represents the voltage drop from the high-voltage side to the medium-voltage side of the main transformer when the maximum voltage drop level of the high-voltage side busbar voltage is exactly U set2 during a three-phase fault on the medium-voltage side; ΔU T2 represents the voltage drop from the high-voltage side to the low-voltage side of the main transformer when the maximum voltage drop level of the high-voltage side busbar voltage is exactly U set2 during a three-phase fault on the low-voltage side.

[0024] Furthermore, the defuzzification method includes the centroid method or the maximum membership degree method to convert the result of fuzzy inference into an actual value.

[0025] The present invention also includes an embedded LCC-HVDC system commutation failure prediction and optimization device, which uses the method as described above. The device includes:

[0026] A criterion construction unit for collecting the electrical signal parameters of the high-voltage side, medium-voltage side, and low-voltage side, and constructing a criterion based on the electrical signal parameters of the three sides to judge whether to start the commutation failure prediction strategy;

[0027] A fuzzy inference unit for converting the collected electrical signal parameters into a fuzzy set and performing fuzzy inference using a pre-fuzzy rule base to generate a fuzzy control value for increasing the turn-off angle if it is judged that the commutation failure prediction strategy needs to be started;

[0028] A defuzzification unit for obtaining the increased amount of the turn-off angle based on the fuzzy control value through a defuzzification method and adjusting the control strategy of the DC system;

[0029] A learning unit for dynamically optimizing the fuzzy rule base based on the adjusted state of the DC system.

[0030] The present invention also includes a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the method as described above is implemented.

[0031] The present invention also includes a storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method as described above is implemented.

[0032] The beneficial effects of the present invention are as follows: By combining the fuzzy control algorithm, the system can flexibly and dynamically adjust the turn-off angle according to different factors, and accurately control the commutation failure prediction. This method not only improves the adaptability of the system to various fault conditions, but also reduces the occurrence probability of commutation failure when facing complex and uncertain power system faults, thereby improving the stability and reliability of the "embedded" LCC-HVDC system. Moreover, by combining the traditional commutation failure prediction strategy and the advanced fuzzy control algorithm, through the fuzzification process and rule reasoning, more accurate turn-off angle adjustment is provided, which can effectively cope with the commutation failure problems caused by medium-voltage side and low-voltage side faults, and optimize the system stability. Brief Description of the Drawings

[0033] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0034] Figure 1 is the flowchart of the method of the present invention;

[0035] Figure 2 is the main circuit topology diagram of the embedded high-voltage DC project;

[0036] Figure 3 is the waveform diagram of the three-phase short-circuit fault on the 110 kV side of the main transformer;

[0037] Figure 4 is the waveform diagram of the three-phase short-circuit fault on the 35 kV side of the main transformer;

[0038] Figure 5 is the first turn-off angle control strategy;

[0039] Figure 6 is the second turn-off angle control strategy;

[0040] Figure 7 is the structural schematic diagram of the device of the present invention;

[0041] Figure 8 is the structural schematic diagram of the computer device of the present invention. Detailed Embodiments

[0042] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments.

[0043] Such as Figure 1Shown: An optimization method for commutation failure prediction in an embedded LCC-HVDC system, including the following steps:

[0044] Collect the electrical signal parameters of the high-voltage side, medium-voltage side, and low-voltage side, and construct a criterion based on the electrical signal parameters of the three sides to determine whether to initiate the commutation failure prediction strategy;

[0045] If it is determined that the commutation failure prediction strategy needs to be initiated, convert the collected electrical signal parameters into a fuzzy set, and use the pre-fuzzy rule base for fuzzy reasoning to generate a fuzzy control value for increasing the extinction angle;

[0046] Based on the fuzzy control value, obtain the increase amount of the extinction angle through the defuzzification method, and adjust the control strategy of the DC system;

[0047] Dynamically optimize the fuzzy rule base based on the adjusted state of the DC system.

[0048] By combining the fuzzy control algorithm, the system can flexibly and dynamically adjust the extinction angle according to different factors, and precisely control the commutation failure prediction. This method not only improves the system's adaptability to various fault conditions, but also reduces the occurrence probability of commutation failure when facing complex and uncertain power system faults, thereby improving the stability and reliability of the "embedded" LCC-HVDC system. And by combining the traditional commutation failure prediction strategy and the advanced fuzzy control algorithm, through the fuzzification process and rule reasoning, it provides more accurate extinction angle adjustment, can effectively cope with the commutation failure problems caused by medium-voltage side and low-voltage side faults, optimizes the system stability, combines the traditional commutation failure prediction strategy and the advanced fuzzy control algorithm, effectively copes with the commutation failure problems caused by medium-voltage side and low-voltage side faults, and significantly improves the system stability.

[0049] Commutation failure prediction control (CFPRED) is used to prevent commutation failure caused by AC faults. Three-phase faults are detected based on the α / β conversion of the AC voltage. The detection principle is:

[0050] αβ transformation voltage criterion;

[0051] The αβ transformation voltage criterion is mainly for the disturbance conditions of the AC system on the inverter side. The αβ transformation is a transformation method from time phasors to space vectors. The AC voltage is transformed through the abc-αβ transformation to obtain the corresponding two components on the α-axis and β-axis of the space stationary coordinate system. After the three-phase symmetrical components are transformed, a vector rotating at an angular velocity is obtained in the α-β plane.

[0052]

[0053] When a three-phase grounding fault occurs in the AC system on the inverter side, if the α / β conversion voltage is less than the α / β conversion voltage under the steady-state condition and the change exceeds the set value, the commutation failure prediction control function is started.

[0054] ΔU αβ =U αβ (t)(1 - e -t / 2 ) - U αβ ;

[0055] Its operating condition is:

[0056] ΔU αβ >AB_SET_VAL;

[0057] Among them, each voltage quantity takes the per-unit value, and AB_SET_VAL is the starting value of the zero-sequence voltage. Generally, for UHV projects, it takes 0.15.

[0058] The larger value of the zero-sequence voltage criterion and the α / β conversion voltage criterion is taken and multiplied by the gain coefficient (0.075) to obtain the angle output AMIN_CFPREV, which acts on the AMAX controller to increase the turn-off angle and is beneficial to avoiding the occurrence of commutation failure.

[0059] As Figure 2 shown, in the embedded HVDC project, the converter station and the substation are usually built at the same site. There may be different voltage levels on the high, medium, and low voltage sides of the main transformer in the station. When an AC fault occurs on the medium and low voltage sides, due to the time delay in the conduction of the main transformer, the risk of commutation failure of the converter valves on the inverter side may increase.

[0060] In this embodiment, the collected electrical signal parameters are converted into fuzzy sets, including:

[0061] The electrical signal parameters of the high voltage side, medium voltage side, and low voltage side are used as input variables, and membership functions are used to describe the fuzzy degree of different input variables;

[0062] The membership functions include triangular, trapezoidal, and Gaussian membership functions.

[0063] Among them, the electrical signal parameter is the voltage amplitude, and the voltage drop amplitude is calculated based on the voltage amplitude.

[0064] The input variables are converted into fuzzy sets through a fuzzification function. The voltage amplitude and the voltage drop amplitude can be represented by Gaussian membership functions.

[0065] Fuzzy inference is to obtain the output fuzzy set according to the input fuzzy set and the fuzzy rule base. The Mamdani inference method is used to deduce the output in the fuzzy rule base.

[0066] Based on the voltage amplitude and the voltage dip amplitude, the fuzzy rules can combine these conditions to infer the degree of increase in the turn-off angle (Δγ). The inference result will obtain a fuzzy output according to the inference results in the rule base.

[0067] As a preference of the above embodiment, a criterion is constructed based on the three-side electrical signal parameters to determine whether to start the commutation failure prediction strategy, including:

[0068] Set a first set value and a second set value, and determine whether the bus voltages of the high-voltage side, medium-voltage side, and low-voltage side meet the set criterion;

[0069] If the criterion is met, it is necessary to start the commutation failure prediction strategy.

[0070] As Figures 3 to 5 shown, in order to enable the DC control system to respond faster when faults occur on the medium- and low-voltage sides of the main transformer and reduce the risk of commutation failure, an auxiliary prediction criterion for commutation failure is constructed. Compared with the conventional commutation failure prediction strategy, the auxiliary criterion increases the turn-off angle after detecting that the medium-voltage and low-voltage side voltages are lower than a certain level. That is, the bus voltages of the high-voltage side (220 kV), medium-voltage side (110 kV), and low-voltage side (35 kV) are collected simultaneously. Among them, when the bus voltage of the high-voltage side (220 kV) is lower than the fixed value U set1 (0.85 p.u. is taken in the project); when the bus voltages of the medium-voltage side (110 kV) and low-voltage side (35 kV, 10 kV) are lower than U set2 (0.8 p.u. is taken in the project), the γ angle of the inverter will increase.

[0071] Among them, the criterion includes:

[0072]

[0073] In the formula, U ac高压侧 , U ac中压侧 , U ac低压侧 are the bus voltages of the high-voltage side, medium-voltage side, and low-voltage side respectively, and U set1 , U set2 are the first set value and the second set value respectively;

[0074] When the high-voltage side is less than the first set value and the medium-voltage side and low-voltage side are less than the second set value, the criterion is met, and it is necessary to start the commutation failure prediction strategy. After fuzzy inference, the turn-off angle increases by 15° - 25° according to the degree of voltage level drop.

[0075] However, as Figure 3 and 4The simulated fault waveforms show that in actual engineering, when a three-phase grounding fault occurs on the medium-voltage side (110 kV) bus, the maximum voltage drop level on the high-voltage side (220 kV) is 84.5%. When a three-phase grounding fault occurs on the low-voltage side (35 kV) bus, the voltage drop level on the medium-voltage side (110 kV) is 64.3%, and the voltage drop level on the high-voltage side (220 kV) is 96%. It can be seen that faults on the low-voltage side (35 kV) and the medium-voltage side (110 kV) have little impact on the voltage on the 220 kV side.

[0076] Or as Figure 6 shown, in order to prevent severe voltage drop faults from occurring at 220 kV and 35 kV, the DC system adjusts the extinction angle, affecting the stability of the system. The supplementary commutation failure criterion is adjusted to the following strategy:

[0077] The criterion includes:

[0078]

[0079] In the formula, U ac高压侧 , U ac中压侧 , U ac低压侧 are the bus voltages of the high-voltage side, medium-voltage side, and low-voltage side respectively, and U set1 , U set2 are the first set value and the second set value respectively; ΔU T11 represents the voltage drop from the high-voltage side to the medium-voltage side on the main transformer when the maximum voltage drop level of the high-voltage side bus is exactly U set1 during a three-phase fault on the medium-voltage side; ΔU T12 represents the voltage drop from the high-voltage side to the medium-voltage side on the main transformer when the maximum voltage drop level of the high-voltage side bus is exactly U set2 during a three-phase fault on the medium-voltage side; ΔU T2 represents the voltage drop from the high-voltage side to the low-voltage side on the main transformer when the maximum voltage drop level of the high-voltage side bus is exactly U set2 during a three-phase fault on the low-voltage side;

[0080] If the high-voltage side is less than the first set value, if the medium-voltage side satisfies U set1 -ΔU T11 when, if the medium-voltage side satisfies U set1 -ΔU T12 <U ac中压侧 <U set2 -ΔU T12 when, if the low-voltage side satisfies U set1 -ΔU T2 when, the criterion is satisfied, and the commutation failure prediction strategy needs to be started. After fuzzy reasoning, the extinction angle should be increased by at least 5°.

[0081] Based on the fuzzy control value obtained by fuzzy inference, through the defuzzification method, the specific value of the increase in the turn-off angle is obtained.

[0082] According to the simulation test, the impact of three-phase faults on the high-voltage side bus voltage drop in the medium-voltage side and the low-voltage side is relatively small. Only when relatively serious three-phase short-circuit faults occur in the medium-voltage side and the low-voltage side will the high-voltage side voltage drop significantly. Therefore, the commutation failure setting values for the medium-voltage side and the low-voltage side should be determined by the voltage drop level of the high-voltage side bus and the voltage drop on the main transformer.

[0083] The impact of the low-voltage side bus fault on the high-voltage side bus voltage is lower than that of the medium-voltage side bus fault; among them, a serious fault in the medium-voltage side may cause the high-voltage side bus voltage to drop to U set1 ; a serious fault in the low-voltage side will not cause the high-voltage side bus voltage to drop to U set1 (0.85 p.u.), but can cause the high-voltage side bus voltage to drop by U set2 (0.95 p.u.). Therefore, different criteria are set for the medium-voltage side and the low-voltage side. When the low-voltage side fault criterion is activated, the increase in the turn-off angle is at least 5°

[0084] When a fault in the medium-voltage side causes the high-voltage side bus voltage to drop below U set1 (0.85 p.u.), the increase in the turn-off angle (at least 15°) is determined according to the degree of drop. However, since a fault in the medium-voltage side will not cause the high-voltage side bus fault to be 0, the increase in the turn-off angle will not reach 25°. If a fault in the medium-voltage side causes the high-voltage side bus voltage to drop between U set1 (0.85 p.u.) and U set2 (0.95 p.u.), the commutation failure prediction criterion can also be activated, but the increase in the turn-off angle is at least 5°.

[0085] As an optimization of the above embodiment, the defuzzification method includes the centroid method or the maximum membership degree method, which converts the result of fuzzy inference into an actual value.

[0086] As Figure 7 shown, this embodiment also includes an optimized device for predicting commutation failure in an embedded LCC-HVDC system. Using the method as described above, the device includes:

[0087] A criterion construction unit, used to collect the electrical signal parameters of the high-voltage side, the medium-voltage side, and the low-voltage side, and construct a criterion based on the electrical signal parameters of the three sides to judge whether to start the commutation failure prediction strategy;

[0088] A fuzzy inference unit, used to convert the collected electrical signal parameters into a fuzzy set if it is judged that the commutation failure prediction strategy needs to be started, and perform fuzzy inference using a pre-fuzzy rule base to generate a fuzzy control value for increasing the turn-off angle;

[0089] A defuzzification unit, configured to obtain an increased value of the turn-off angle based on a fuzzy control value through a defuzzification method, and adjust a control strategy of a DC system;

[0090] A learning unit, configured to dynamically optimize a fuzzy rule base based on an adjusted state of the DC system.

[0091] Please refer to Figure 8 the structural schematic diagram of the computer device provided in the embodiment of the present application shown in. A computer device 400 provided in the embodiment of the present application includes: a processor 410 and a memory 420. The memory 420 stores a computer program executable by the processor 410. When the computer program is executed by the processor 410, the above method is executed.

[0092] The embodiment of the present application further provides a storage medium 430. A computer program is stored on the storage medium 430. When the computer program is run by the processor 410, the above method is executed.

[0093] Among them, the storage medium 430 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (Static Random Access Memory, abbreviated as SRAM), an electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, abbreviated as EEPROM), an erasable programmable read-only memory (Erasable Programmable Read Only Memory, abbreviated as EPROM), a programmable read-only memory (Programmable Red-Only Memory, abbreviated as PROM), a read-only memory (Read-Only Memory, abbreviated as ROM), a magnetic memory, a flash memory, a magnetic disk or an optical disc.

[0094] In the description of the present invention, the terms "first" and "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. The meaning of "plurality" is two or more, unless otherwise specifically defined.

[0095] In the present invention, unless otherwise clearly defined or limited, terms such as "installed", "connected", "coupled", "fixed", etc. shall be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral body; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the internal communication of two components or the interaction relationship between two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0096] In the description of this specification, the descriptions with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic descriptions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0097] Any process or method description shown in the flowchart or described in other ways herein can be understood as representing a module, segment, or part of code including one or more executable instructions for implementing a specific logical function or process. And the scope of the preferred embodiments of the present invention includes additional implementations, where the functions can be executed in a substantially simultaneous manner or in the reverse order according to the functions involved, rather than in the order shown or discussed, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0098] The logic and / or steps represented in the flowchart or otherwise described herein can be considered, for example, a definitional sequence list of executable instructions for implementing logical functions, and can be embodied specifically in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or used in conjunction with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer diskette case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, a computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing as necessary, and then stored in a computer memory.

[0099] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), and the like.

[0100] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the methods of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0101] The storage medium mentioned above can be a read-only memory, a magnetic disk, an optical disc, or the like. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. An optimization method for predicting commutation failure in an embedded LCC-HVDC system, characterized in that It includes the following steps: Collect the electrical signal parameters of the high-voltage side, medium-voltage side, and low-voltage side, and construct a criterion based on the three-side electrical signal parameters to determine whether to start the commutation failure prediction strategy; If it is determined that the commutation failure prediction strategy needs to be started, convert the collected electrical signal parameters into fuzzy sets, and use the pre-fuzzy rule base for fuzzy reasoning to generate a fuzzy control value for increasing the turn-off angle; Based on the fuzzy control value, obtain the increase amount of the turn-off angle through a defuzzification method, and adjust the control strategy of the DC system; Dynamically optimize the fuzzy rule base based on the adjusted DC system state.

2. The commutation failure prediction optimization method for the embedded LCC-HVDC system according to claim 1, wherein The electrical signal parameter is the voltage amplitude, and the voltage drop amplitude is calculated based on the voltage amplitude.

3. The commutation failure prediction optimization method for the embedded LCC-HVDC system according to claim 1, characterized in that The conversion of the collected electrical signal parameters into fuzzy sets includes: Take the electrical signal parameters of the high-voltage side, medium-voltage side, and low-voltage side as input variables, and use membership functions to describe the fuzziness of different input variables; The membership functions include triangular, trapezoidal, and Gaussian membership functions.

4. The commutation failure prediction optimization method for the embedded LCC-HVDC system according to claim 1, wherein The construction of the criterion based on the three-side electrical signal parameters to determine whether to start the commutation failure prediction strategy includes: Set a first set value and a second set value, and determine whether the bus voltages of the high-voltage side, medium-voltage side, and low-voltage side meet the set criterion; If the criterion is met, the commutation failure prediction strategy needs to be started.

5. The commutation failure prediction criterion optimization method for the embedded LCC-HVDC system according to claim 4, characterized in that The criterion includes: Where, U ac高压侧 , U ac中压侧 , U ac低压侧 are the bus voltages of the high-voltage side, medium-voltage side and low-voltage side respectively, and U set1 , U set2 are the first set value and the second set value respectively.

6. The commutation failure prediction optimization method for the embedded LCC-HVDC system according to claim 4, characterized in that The criterion includes: Wherein, U ac高压侧 , U ac中压侧 , U ac低压侧 are the bus voltages of the high-voltage side, medium-voltage side and low-voltage side respectively, U set1 , U set2 are the first set value and the second set value respectively; ΔU T11 represents the voltage drop from the high-voltage side to the medium-voltage side of the main transformer when the maximum voltage drop level of the high-voltage side bus voltage is exactly U set1 during a three-phase fault on the medium-voltage side; ΔU T12 represents the voltage drop from the high-voltage side to the medium-voltage side of the main transformer when the maximum voltage drop level of the high-voltage side bus voltage is exactly U set2 during a three-phase fault on the medium-voltage side; ΔU T2 represents the voltage drop from the high-voltage side to the low-voltage side of the main transformer when the maximum voltage drop level of the high-voltage side bus voltage is exactly U set2 during a three-phase fault on the low-voltage side.

7. The commutation failure prediction optimization method for the embedded LCC-HVDC system according to claim 1, wherein The defuzzification method includes the centroid method or the maximum membership degree method, which converts the result of fuzzy reasoning into an actual value.

8. An optimization device for predicting commutation failure of an embedded LCC-HVDC system, characterized in that, When the processor executes the computer program, it implements the method described in any one of claims 1 to 7. A criterion construction unit, configured to collect the electrical signal parameters of the high-voltage side, medium-voltage side, and low-voltage side, and construct a criterion based on the three-side electrical signal parameters to determine whether to start the commutation failure prediction strategy; A fuzzy reasoning unit, configured to, if it is determined that the commutation failure prediction strategy needs to be started, convert the collected electrical signal parameters into fuzzy sets, and use the pre-fuzzy rule base for fuzzy reasoning to generate a fuzzy control value for increasing the turn-off angle; A defuzzification unit, configured to obtain the increase amount of the turn-off angle through a defuzzification method based on the fuzzy control value, and adjust the control strategy of the DC system; A learning unit, configured to dynamically optimize the fuzzy rule base based on the adjusted DC system state.

9. A computer device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method described in any one of claims 1-7.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method described in any one of claims 1-7.

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