An Optimization Method for Commutation Failure Criteria in High Voltage Direct Current Transmission Systems

Abstract

This invention discloses an optimization method for commutation failure criteria in high-voltage direct current (HVDC) transmission systems, relating to the field of HVDC operation analysis technology. The method includes: continuously acquiring voltage information and calculating fault criteria; establishing a fault criterion data vector based on the calculated fault criteria; estimating the probability that the voltage data corresponding to any element in the data vector is normal data based on the mean and standard deviation, and forming a state vector related to the data vector; calculating the hidden state probability matrix of the data vector based on the calculated state vector; and assessing the probability of fault data existing in the current data vector based on the calculated hidden state probability matrix. This commutation failure criterion optimization method can optimize any threshold criterion based on single-point judgment, improving the stability of the criterion output, ensuring rapid response under real fault conditions, and accelerating the implementation of commutation failure control.

Description

Technical Field

[0001] This invention relates to the field of high-voltage direct current (HVDC) transmission operation analysis technology, specifically to an optimization method for commutation failure criteria in HVDC transmission systems. Background Technology

[0002] my country has a vast territory, with over 80% of its energy resources located in the western and northern regions, while approximately 75% of its electricity consumption is concentrated in the economically developed central and eastern coastal areas, resulting in a supply-demand distance of 800 to 3000 kilometers. High-voltage direct current (HVDC) transmission, with its advantages in long-distance, high-capacity power transmission, plays a crucial role in optimizing energy allocation in my country. Currently, hundreds of millions of kilowatts of power from large energy bases in the "Three Norths" (Northeast, North, and Northwest China) and Southwest China are being transmitted across regions via dozens of HVDC transmission lines to load centers in the central and southeastern coastal areas thousands of kilometers away. The basic principle of HVDC transmission is as follows: at the sending end of the HVDC system, a converter rectifies the three-phase alternating current (AC) into direct current (DC). The electrical energy is then transmitted through the HVDC transmission lines. At the receiving end of the HVDC system, a converter inverts the DC back into three-phase AC, which is then fed into the AC system at the receiving end.

[0003] High-voltage direct current (HVDC) transmission converters typically employ six-pulse bridge converters for rectification and inversion. These converters use thyristor valves without self-turn-off capability as basic circulating current elements, with each valve alternately conducting during normal operation. Due to the presence of reactance parameters in the circuit, the switching of current between two valves cannot be instantaneous and requires a certain amount of time, known as the commutation process. After the commutation process is completed, the valve will conduct again, a phenomenon referred to as commutation failure. Commutation failure is one of the most common fault types in HVDC transmission systems, usually occurring on the inverter side. Commutation failure leads to a sudden increase in DC current, impacting the converter valves and even causing DC blockage, seriously threatening the safe and stable operation of the power grid.

[0004] Currently, commutation failure prevention (CFPREV) is typically incorporated into UHVDC projects to prevent commutation failure under external fault disturbances and accelerate DC recovery after a fault. CFPREV consists of two parallel parts: one part detects single-phase faults based on the zero-sequence voltage U0; the other part transforms the three-phase AC voltages to U0 in a stationary coordinate system using Clark transformation. αβ To detect three-phase faults, U0, U αβ The calculation method is as follows:

[0005] U0=U A +U B +U C

[0006]

[0007]

[0008]

[0009] In the formula: U A U B U C These are the instantaneous values ​​of the AC voltage for phases A, B, and C, respectively.

[0010] During a single-phase fault, the sum of the three-phase voltages is not zero, and the zero-sequence voltage U0 will increase; during a three-phase fault, U αβ The amplitude will decrease. Once the change in either of the two detection indicators exceeds its corresponding threshold, it is determined that an AC fault has occurred, thereby triggering the commutation threshold in advance to prevent commutation failure.

[0011] Currently, some literature discloses improved algorithms for commutation failure control, mainly focusing on improving the discrimination index, but the overall idea is still "to take control action after the index exceeds the threshold." Typical improved algorithms include:

[0012] [1] Liu Yuchao, Guo Chunyi, Xu Weihua, et al. A control method to reduce the probability of commutation failure in DC transmission[J]. Power System Technology. 2015, 39(1), 76-82.

[0013] [2] Liu Lei, Lin Sheng, He Zhengyou. Continuous commutation failure suppression strategy for HVDC system based on virtual commutation area deficiency[J]. Proceedings of the CSEE, 2018, 38(18): 5361-5368.

[0014] Existing commutation failure suppression control technology uses "fault criteria greater than threshold" as the control start condition. However, the actual acquired voltage signal contains disturbances such as harmonics and noise. In order to avoid unnecessary frequent control actions under normal conditions, the threshold is often designed to be large. However, this is not conducive to the rapid response of control after a fault and affects the commutation failure suppression effect. There is a contradiction between the two. Summary of the Invention

[0015] Based on the problems existing in the prior art, the purpose of this invention is to provide an optimization method for commutation failure criteria in high voltage direct current transmission systems. This method can optimize any threshold criterion based on single-point judgment, improve the stability of the criterion output, ensure rapid response under real fault conditions, and accelerate the implementation of commutation failure control.

[0016] This invention is achieved through the following technical solution:

[0017] In a first aspect, this application provides an optimization method for commutation failure criteria in a high-voltage direct current transmission system, comprising the following steps:

[0018] Step 1: Continuously collect voltage information and calculate the fault criterion L(k);

[0019] Step 2: Based on the fault criterion L(k) calculated in Step 1, establish the fault criterion data vector X. k-Len+1：k ;

[0020] Step 3: Based on the mean and standard deviation obtained in Step 1, and the data vector established in Step 2, estimate the probability E that the voltage data corresponding to any element L(j+k-Len) (j=1,2,…,Len) in the data vector is normal data. j And form a state vector E associated with the data vector. 1×Len = [E1, E2, ..., E Len ];

[0021] Step 4: Calculate the data vector X based on the state vector calculated in Step 3. k-Len+1：k The hidden state probability matrix Y Len×Len ;

[0022] Step 5: Evaluate the current data vector X based on the hidden state probability matrix calculated in Step 4. k-Len+1：k The probability of faulty data in memory is Err(k).

[0023] This method introduces the concept of probability, moving beyond simply focusing on whether the current data (criteria) result exceeds a threshold. Instead, it focuses on the probability of a fault point existing within a series of consecutive data (criteria). First, based on the data values ​​within the data window and the relationships between them, each data point is assigned a fault probability. Then, the fault probabilities of all data within the data window are considered comprehensively, and the probability of faulty data within the data window is calculated based on Bayesian theory. For example, the actual probability of a fault is clearly different for the cases of "the current sampling point criterion is abnormal" and "the current sampling point criterion is abnormal, and previous sampling point data were also abnormal." This invention differentiates between these two scenarios, optimizing existing criteria by mining probabilistic information based on multiple specific values. Using this method, any threshold criterion based on single-point judgment can be optimized, improving the stability of the criterion output, ensuring rapid response in real fault situations, and accelerating the implementation of commutation failure control.

[0024] Furthermore, in step 1, the fault criterion L(k) includes the zero-sequence voltage criterion and the comprehensive voltage criterion drop.

[0025] Furthermore, in step 1, the calculation methods for the mean μ and standard deviation σ of the fault criterion under normal conditions are as follows:

[0026]

[0027] In the above formula, N represents the length of the data used.

[0028] Furthermore, in step 2, the data vector X k-Len+1：k Represented as:

[0029] X k-Len+1：k =[L(k-Len+1),L(k-Len+2),…,L(k)],

[0030] Where Len is the selectable vector length.

[0031] Furthermore, in step 3, the probability E of normal data j The estimation method is as follows:

[0032]

[0033] Furthermore, in step 4, the data vector X is calculated. k-Len+1：k The hidden state probability matrix Y Len×Len The calculation rules are as follows:

[0034] The elements of the first row of the matrix are calculated using the following formula:

[0035]

[0036] The elements of the first column of the matrix are calculated using the following formula:

[0037]

[0038] For the other elements in the matrix, the following recursive formula is used for calculation:

[0039] Y i，j =E j Y i-1，j-1 .

[0040] Furthermore, in step 5, the current data vector X is evaluated. k-Len+1：k The method for evaluating the probability Err(k) of faulty data in memory is as follows:

[0041]

[0042] Secondly, this application provides a computer program that can implement the steps of the method described above.

[0043] Thirdly, this application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the above-described method.

[0044] Fourthly, this application provides a computer device including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the above-described method.

[0045] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0046] The commutation failure criterion optimization method of this invention introduces the concept of probability, moving beyond simply focusing on whether the current data (criterion) result is greater than a threshold. Instead, it focuses on the probability of a fault point existing within a series of continuous data (criterions). First, based on the data values ​​within the data window and the correlation between them, each data point is assigned a fault probability. Then, the fault probabilities of all data within the data window are comprehensively considered, and the probability of faulty data within the data window is calculated based on Bayesian theory. For example, the actual probability of a fault is clearly different for the two scenarios: "the current sampling point criterion is abnormal" and "the current sampling point criterion is abnormal, and the data from previous sampling points are also abnormal." This invention differentiates between these scenarios and optimizes existing criteria by mining probabilistic information based on multiple specific values. Using this method, any threshold criterion based on single-point judgment can be optimized, improving the stability of the criterion output, ensuring rapid response in real fault situations, and accelerating the implementation of commutation failure control. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0048] Therefore, the following detailed description of embodiments of the present invention is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0049] The terms “comprising,” “including,” 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 includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0050] Example 1

[0051] This embodiment provides an optimization method for commutation failure criteria in a high-voltage direct current transmission system, which is carried out according to the following steps:

[0052] Step 1: Continuously collect voltage information and calculate the fault criterion L(k);

[0053] The criterion is the zero-sequence voltage criterion;

[0054] The mean μ and standard deviation σ of the fault criterion under normal conditions are calculated using the following formula:

[0055]

[0056] In the above formula, N represents the length of the data used.

[0057] Step 2: Based on the criterion L(k) calculated in Step 1, establish the fault criterion data vector X. k-Len+1：k Where Len is the optional vector length, the vector is represented as:

[0058] X k-Len+1：k =[L(k-Len+1), L(k-Len+2), ..., L(k)].

[0059] Step 3: Based on the mean and standard deviation obtained in Step 1, and the data vector established in Step 2, estimate the probability E that the voltage data corresponding to any element L(j+k-Len) (j=1,2,…,Len) in the data vector is normal data. j And form a state vector E associated with the data vector. 1×Len = [E1, E2, ..., E Len ]. E j The estimation method is as follows:

[0060]

[0061] Step 4: Calculate the data vector X based on the state vector calculated in Step 3. k-Len+1：k The hidden state probability matrix Y Len×Len The calculation rules are as follows:

[0062] The elements of the first row of the matrix are calculated using the following formula:

[0063]

[0064] The elements of the first column of the matrix are calculated using the following formula:

[0065]

[0066] For the other elements in the matrix, the following recursive formula is used for calculation:

[0067] Y i，j =E j Y i-1，j-1 .

[0068] Step 5: Evaluate the current data vector X based on the hidden state probability matrix calculated in Step 4. k-Len+1 The probability Err(k) of faulty data existing in memory xx is evaluated using the following method:

[0069]

[0070] Under normal circumstances, Err(k) = 0. The larger the value, the higher the probability of failure. The maximum value is 1.

[0071] This embodiment of the optimization method for commutation failure criteria in ultra-high voltage direct current (UHVDC) transmission systems introduces the concept of probability, moving beyond simply focusing on whether the current data (criteria) result exceeds a threshold. Instead, it focuses on the probability of a fault point existing within a series of consecutive data (criteria). First, based on the data values ​​within the data window and the correlation between them, each data point is assigned a fault probability. Then, the fault probabilities of all data within the data window are comprehensively considered, and the probability of faulty data within the data window is calculated based on Bayesian theory. For example, the actual probability of a fault is clearly different for the two scenarios: "the current sampling point criterion is abnormal" and "the current sampling point criterion is abnormal, and previous sampling point data were also abnormal." This invention differentiates between these scenarios, optimizing existing criteria by mining probabilistic information based on multiple specific values. Using this method, any threshold criterion based on single-point judgment can be optimized, improving the stability of the criterion output, ensuring rapid response in real fault situations, and accelerating the implementation of commutation failure control.

[0072] Example 2

[0073] This embodiment provides a computer device, including a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor performs the steps of the optimization method for commutation failure criteria in the ultra-high voltage direct current transmission system described in Embodiment 1 above.

[0074] The computer device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device can interact with the user via a keyboard, mouse, remote control, touchpad, or voice control.

[0075] The storage device includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or D-interface display memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the storage device may be an internal storage unit of the computer device, such as the hard disk or RAM of the computer device. In other embodiments, the storage device may also be an external storage device of the computer device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device. Of course, the storage device may include both internal storage units and external storage devices of the computer device. In this embodiment, the storage device is often used to store the operating system and various application software installed on the computer device, such as the program code for running the optimization method for the commutation failure criterion of the ultra-high voltage direct current transmission system. In addition, the memory can also be used to temporarily store various types of data that have been output or will be output.

[0076] In some embodiments, the processor may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor is typically used to control the overall operation of the computer device. In this embodiment, the processor is used to run program code stored in the memory or process data, for example, to run the program code for the optimization method of the commutation failure criterion in the ultra-high voltage direct current transmission system.

[0077] Example 3

[0078] This embodiment provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it causes the processor to perform the steps of the above-described optimization method for commutation failure criteria in ultra-high voltage direct current transmission systems.

[0079] The computer-readable storage medium stores an interface display program, which can be executed by at least one processor to cause the at least one processor to perform the steps of the method for optimizing the commutation failure criterion of an ultra-high voltage direct current transmission system.

[0080] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0081] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention. It is obvious to those skilled in the art that this application is not limited to the details of the above exemplary embodiments, and that the present application can be implemented in other specific forms without departing from the spirit or basic characteristics of the present application. Therefore, the embodiments should be considered exemplary and non-limiting in all respects. The scope of this application is defined by the appended claims rather than the foregoing description, and therefore all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included within this application.

Claims

1. An optimization method for commutation failure criteria in a high-voltage direct current transmission system, characterized in that, Includes the following steps: Step 1: Continuously collect voltage information and calculate the fault criterion L(k); Step 2: Based on the fault criterion L(k) calculated in Step 1, establish the fault criterion data vector. ; Step 3: Based on the mean and standard deviation of the fault criterion L(k) obtained in Step 1, and the data vector established in Step 2, estimate any element in the data vector. The probability that the corresponding voltage data is normal. And form a state vector associated with the data vector. ; Step 4: Calculate the data vector based on the state vector calculated in Step 3. Hidden state probability matrix ; Step 5: Evaluate the current data vector based on the hidden state probability matrix calculated in Step 4. The probability of faulty data in memory ; In step 4, the data vector is calculated. Hidden state probability matrix The calculation rules are as follows: The elements of the first row of the matrix are calculated using the following formula: ， The elements of the first column of the matrix are calculated using the following formula: ， For the other elements in the matrix, the following recursive formula is used for calculation: ； In step 5, the current data vector is evaluated. The probability of faulty data in memory The evaluation method is as follows: ； Where Len is the selectable vector length.

2. The optimization method for commutation failure criteria in a high-voltage direct current transmission system according to claim 1, characterized in that, In step 1, the fault criterion L(k) includes the zero-sequence voltage criterion and the comprehensive voltage criterion drop.

3. The optimization method for commutation failure criteria in a high-voltage direct current transmission system according to claim 1, characterized in that, In step 1, the mean μ and standard deviation σ of the fault criterion under normal conditions are calculated using the following formula: ， In the above formula, N represents the length of the data used.

4. The optimization method for commutation failure criteria in a high-voltage direct current transmission system according to claim 1, characterized in that, In step 2, the data vector Represented as: ， Where Len is the selectable vector length.

5. The optimization method for commutation failure criteria in a high-voltage direct current transmission system according to claim 1, characterized in that, In step 3, the probability of normal data The estimation method is as follows: 。 6. A computer program, characterized in that, The computer program can implement the steps of the method described in any one of claims 1 to 5.

7. A computer-readable storage medium, characterized in that, The device contains a computer program that, when executed by a processor, causes the processor to perform the steps of the method according to any one of claims 1 to 5.

8. A computer device, characterized in that, The method includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor causes the processor to perform the steps of the method according to any one of claims 1 to 5.

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