HVDC commutation failure rapid prediction method and system based on commutation failure risk factors

Abstract

The invention relates to a commutation failure risk factor-based HVDC commutation failure rapid prediction method and system, belongs to the technical field of commutation failure defense of a high voltage direct current transmission system, and can rapidly predict whether a commutation failure occurs after an LCC-HVDC system has a fault so as to provide support for subsequent commutation failure defense. In the implementation process, the traditional commutation area and the advanced commutation area are defined, the commutation failure risk factor is calculated by integrating the traditional commutation area and the advanced commutation area, and then commutation failure prediction is carried out. According to the method, the current moment information and the future moment information of the characteristic quantity are comprehensively considered in the prediction process, and the prediction speed of the commutation failure is increased. Meanwhile, the weight coefficients of the traditional commutation area and the advanced commutation area can be adaptively adjusted according to the prediction error of the characteristic quantity, the problem that the commutation failure accuracy is reduced due to the fact that the prediction error of the characteristic quantity is large is avoided, and the reliability of the prediction result is ensured.

Description

Technical Field

[0001] The present invention relates to a method and system for quickly predicting commutation failure of a high-voltage direct current (HVDC) transmission system based on a commutation failure risk factor, and belongs to the technical field of commutation failure prevention of HVDC transmission systems. Background Art

[0002] The line-commutated converter high-voltage direct current (LCC-HVDC) technology has the advantages of large transmission capacity, low loss, and low cost, and can effectively address the problem of uneven distribution of energy centers and load centers in China, ensuring the reliable and stable supply of electric energy. Commutation failure (CF) is one of the most common faults in LCC-HVDC. It occurs during the commutation process, manifested as the thyristor being unable to turn off or on normally, threatening the stability and reliability of the power system supply. After commutation failure occurs, abnormal fluctuations will appear in the AC-DC system, bringing impacts of active and reactive power to the LCC-HVDC system, threatening the stability and reliability of the entire power system. Continuous commutation failures may even trigger serious faults or large-scale power outages. Therefore, the commutation failure prevention technology has become a research hotspot, and the prediction of commutation failure is a key link among them. It is an important basis for the operation of the commutation failure control and protection system. If commutation failure can be predicted in advance, the system operating state can be adjusted before the actual commutation failure occurs to increase the commutation margin, thereby avoiding the occurrence of commutation failure and improving the safety and stability of system operation.

[0003] Regarding the above problems, current domestic and foreign research mostly analyzes the commutation process of the converter based on the commutation voltage-time integral area theory, and predicts commutation failure by comparing the defined commutation demand area and commutation provided area.

[0004] However, the traditional commutation failure prediction method based on the commutation area theory predicts commutation failure at future moments based on the characteristic quantities at the measurement moment. It ignores the transient changes of characteristic quantities such as commutation voltage and DC current from the current measurement moment to future moments, resulting in a limit value for the advance prediction time of commutation failure by this method, that is, the size of half a power frequency cycle. Therefore, the traditional prediction method has deficiencies in prediction speed when predicting commutation failure, and is prone to misjudgment. This problem has become the key to restricting the prediction accuracy of commutation failure in the field of commutation failure prevention of high-voltage direct current transmission. Summary of the Invention

[0005] In view of the deficiencies of the prior art, to solve the technical problems existing in the above-mentioned background art, the present invention provides a rapid prediction method for commutation failure of HVDC based on commutation failure risk factors, which can more rapidly predict the occurrence of commutation failure after a fault in the LCC-HVDC system.

[0006] The technical solution of the present invention is as follows: A rapid prediction method for commutation failure of HVDC based on commutation failure risk factors includes the following steps: Step 1: Obtain the commutation voltage amplitude at the current moment, U com.t the direct current at the current moment, I d.t the commutation voltage amplitude at the future moment, U com.c the direct current at the future moment, I d.c and the trigger angle command value α , all of which are per-unit values; Step 2: Based on the commutation voltage amplitude at the current moment U com.t , the direct current at the current moment I d.t and the trigger angle command value α , calculate the commutation demand area and the maximum commutation supply area, and define them as the traditional commutation demand area and the traditional maximum commutation supply area respectively; at the same time, in order to improve the prediction speed of commutation failure and overcome the problem of the limit of the advance prediction time existing in the traditional prediction method, based on the predicted commutation voltage amplitude at the future moment U com.c , the direct current at the future moment I d.c and the trigger angle command value α , calculate the commutation demand area and the maximum commutation supply area, and define them as the advanced commutation demand area and the advanced maximum commutation supply area respectively; Preferably, in step 2, based on the commutation voltage amplitude at the current moment U com.t , the direct current at the current moment I d.t and the trigger angle command value α , calculate the traditional commutation demand area S need.t and the traditional maximum commutation supply area S pro.t respectively according to formulas (2) and (3); Based on the predicted commutation voltage amplitude at the future moment U com.c , the direct current at the future moment Id.c and the trigger angle command value α calculate the leading commutation demand area according to equations (4) and (5) respectively S need.c and the leading maximum commutation supply area S pro.c ; S need.t = 2 X c I d.t (2) (3) S need.c = 2 X c I d.c (4) (5) In the formula, X c is the equivalent commutation reactance, γ min is the minimum turn-off angle, ω 0 is the system angular frequency, with a magnitude of 2π / T , T is the fundamental period of the system; t represents the integration variable.

[0007] Step 3: Considering that there may be errors in the predicted commutation voltage amplitude U com.c at the future moment and the DC current I d.c at the future moment in some cases, calculate the weight coefficients of the traditional commutation demand area and the leading commutation demand area; Preferably, in Step 3, based on the DC current I d.t at the current moment and the predicted value I d.c0 of the current DC current in the historical data, calculate the weight coefficients C 1 and C 2 of the traditional commutation demand area and the leading commutation demand area according to equation (6); (6) In the formula, k i1 is the normalization coefficient of the commutation demand area weight, k i2is the sensitivity coefficient of the area weight of the commutation demand.

[0008] Step 4: Calculate the weight coefficients of the traditional maximum commutation provided area and the leading maximum commutation provided area; Preferably, in Step 4, based on the commutation voltage amplitude at the current moment U com.t and the predicted value of the current commutation voltage amplitude in the historical data U com.c0 Calculate the weight coefficients of the traditional maximum commutation provided area and the leading maximum commutation provided area according to Equation (7) C 3 and C 4 ; (7) In the formula, k u1 is the normalization coefficient of the maximum commutation provided area weight, k u2 is the sensitivity coefficient of the maximum commutation provided area weight.

[0009] Step 5: Weightedly integrate the calculated traditional commutation demand area, traditional maximum commutation provided area, leading commutation demand area, and leading maximum commutation provided area through the weight coefficients to obtain the commutation failure risk factor, and then predict whether commutation failure will occur; and during the prediction process, adaptively adjust the weight coefficients of each commutation area according to the prediction error of the characteristic quantity at the previous moment. The characteristic quantity prediction error is the absolute value part in Equation (6) and Equation (7). The prediction error of the DC current is | I d.t - I d.c0 |, and the prediction error of the commutation voltage is | U com.t - U com.c0 |. The adjustment of the weight coefficients is as shown in Equation (6) and Equation (7). As the system operating state changes, the characteristic quantity prediction error changes in real time, and the weight coefficients of each commutation area are adaptively adjusted according to the real-time prediction error of the characteristic quantity (the process of real-time calculation according to Equation (6) and Equation (7) is the adaptive adjustment). If the commutation voltage prediction error is large, the weight coefficient of the leading maximum commutation provided area is small, while the weight coefficient of the traditional maximum commutation provided area is large. The same is true for the DC current. On the premise of ensuring the prediction accuracy, the prediction speed of commutation failure is improved.

[0010] Preferably, in Step 5, based on the traditional commutation demand area calculated in Steps 2 - 4 S need.t 、the traditional maximum commutation provided areaS pro.t , the leading commutation demand area S need.c and the leading maximum commutation supply area S pro.c and the weight coefficient C 1 - C 4 Calculate the commutation failure risk factor in real time according to Equation (8) F ; F = C 1 S need.c + C 2 S need.t + C 3 S pro.c + C 4 S pro.t (8) If F ≥0, it is considered that the system state meets the normal commutation condition and commutation failure will not occur; if F <0, it is considered that the system state does not meet the normal commutation condition and commutation failure will occur.

[0011] A computer-readable storage medium, on which a program is stored, and when the program is executed by a processor, the steps in the HVDC commutation failure rapid prediction method based on the commutation failure risk factor are implemented.

[0012] An electronic device, including a memory, a processor, and a program stored on the memory and executable on the processor, and when the processor executes the program, the steps in the HVDC commutation failure rapid prediction method based on the commutation failure risk factor are implemented.

[0013] The beneficial effects of the present invention are as follows: This paper proposes a fast prediction method for HVDC commutation failure based on commutation failure risk factors, which essentially predicts the occurrence of the first commutation failure in advance for subsequent defense. This method can quickly and accurately predict whether commutation failure will occur after a fault in the LCC-HVDC system, providing support for subsequent defense against commutation failure. In the implementation process, this method defines the traditional commutation area and the leading commutation area, and comprehensively uses the traditional commutation area and the leading commutation area to predict commutation failure. The method comprehensively considers the current moment information and future moment information of the characteristic quantities during the prediction process, ensuring the rapidity and accuracy of the prediction and improving the prediction speed of commutation failure. At the same time, it can adaptively adjust the weight coefficients of the traditional commutation area and the leading commutation area according to the prediction error of the characteristic quantities, define the commutation failure risk factor for commutation failure prediction, avoid the reduction of commutation failure accuracy caused by large prediction errors of characteristic quantities, and ensure the reliability of the prediction results. Description of the Drawings

[0014] Figure 1 It is the structure diagram of a 6-pulse converter; Figure 2 It is the flow chart of the commutation failure prediction method based on commutation failure risk factors in this application; Figure 3 It is the main structure diagram of the LCC-HVDC system; Figure 4 It is the waveform diagram of DC current, three-phase AC bus voltage, and valve current under the single-phase 0.17H grounding fault condition; (a) is the DC current waveform diagram near the fault moment, (b) is the three-phase AC bus voltage waveform diagram of the inverter station near the fault moment, and (c) is the valve current waveform diagram of the two valves where commutation failure first occurs; Figure 5 It is the waveform diagram of DC current, three-phase AC bus voltage, and valve current under the three-phase 0.18H symmetrical grounding fault condition; (a) is the DC current waveform diagram near the fault moment, (b) is the three-phase AC bus voltage waveform diagram of the inverter station near the fault moment, and (c) is the valve current waveform diagram of the two valves where commutation failure first occurs. Detailed Embodiment

[0015] In order to make the technical solutions of the present invention clearer, the following further details the fast prediction process of commutation failure in this invention patent in combination with the drawings and examples, but not limited thereto.

[0016] Embodiment 1: The fast prediction method for HVDC commutation failure based on commutation failure risk factors has a process as Figure 2 shown. The steps are as follows: Step 1: During the operation of LCC-HVDC, the commutation voltage amplitude at the current moment of the converter station is collected and calculated in real time U com.t , the DC current at the current moment I d.t , the commutation voltage amplitude at the future moment U com.t , the DC current at the future moment I d.c , and the trigger angle command value α (all are per-unit values).

[0017] The DC current at the current moment I d.t and the trigger angle command value α are obtained by real-time acquisition. The commutation voltage amplitude at the current moment U com.t cannot be directly acquired, but is calculated through the instantaneous value of the commutation voltage collected in real time by a conventional known algorithm. The commutation voltage amplitude at the future moment U com.c , and the DC current at the future moment I d.c are respectively calculated by a conventional prediction algorithm based on the commutation voltage amplitude at the current moment U com.t , and the DC current at the current moment I d.t . Any prediction method that can calculate the predicted value containing future moment information can be selected.

[0018] In this embodiment, the method for calculating the DC current at the future moment I d.t using the DC current at the current moment I d.c is based on the Taylor expansion, as shown in the following formula. In the formula, t 0 is the current moment, t c is the predicted future moment. In the formula, is the approximate first-order differential of the DC current at the current moment, is the approximate second-order differential of the DC current at the current moment. In the present invention, T 1 is taken as 1 ms, T 2 is taken as 8 ms.

[0019] (1) The values of the above characteristic quantities can be obtained by known conventional methods and are regarded as known quantities in Step 1.

[0020] Step 2: Based on the commutation voltage amplitude at the current moment U com.t , the direct current at the current moment I d.t and the trigger angle command value α , calculate the traditional commutation demand area S need.t and the traditional maximum commutation supply area S pro.t respectively according to Equations (2) and (3). Based on the predicted commutation voltage amplitude at the future moment U com.c , the direct current at the future moment I d.c and the trigger angle command value α , calculate the leading commutation demand area S need.c and the leading maximum commutation supply area S pro.c .

[0021] S need.t = 2 X c I d.t (2) (3) S need.c = 2 X c I d.c (4) (5) In the formula, X c is the equivalent commutation reactance, γ min is the minimum turn-off angle, ω 0 is the system angular frequency, with a magnitude of 2π / T , T is the fundamental period of the system; t represents the integration variable.

[0022] Step 3: Based on the direct current at the current moment I d.t and the predicted value of the current direct current in the historical data I d.c0 , calculate the weight coefficients C 1 and C2 .

[0023] (6) Wherein, k i1 is the normalization coefficient of the commutation demand area weight, k i2 is the sensitivity coefficient of the commutation demand area weight.

[0024] Step 4: Based on the commutation voltage amplitude at the current moment U com.t and the predicted value of the current commutation voltage amplitude in the historical data U com.c0 Calculate the weight coefficients of the traditional maximum commutation provided area and the leading maximum commutation provided area according to Equation (7) C 3 and C 4 .

[0025] (7) Wherein, k u1 is the normalization coefficient of the maximum commutation provided area weight, k u2 is the sensitivity coefficient of the maximum commutation provided area weight.

[0026] Step 5: Based on the traditional commutation demand area S need.t , the traditional maximum commutation provided area S pro.t , the leading commutation demand area S need.c and the leading maximum commutation provided area S pro.c calculated in Steps 2 - 4 and the weight coefficients C 1 - C 4 Calculate the commutation failure risk factor F in real time according to Equation (8).

[0027] F = C 1 S need.c + C 2 S need.t + C 3 S pro.c + C 4S pro.t (8) If F ≥ 0, it is considered that the system state meets the normal commutation condition and commutation failure will not occur; if F < 0, it is considered that the system state does not meet the normal commutation condition and commutation failure will occur.

[0028] Experimental example To verify the feasibility of the proposed commutation failure rapid prediction method of the present invention, the following verifies it in combination with the method of Embodiment 1. The parameters of the LCC-HVDC are shown in Table 1.

[0029] Table 1 LCC-HVDC parameters

[0030] In Figure 3 Verification is carried out in the shown monopolar LCC-HVDC standard model, where both the rectifier side and the inverter side converter stations of the LCC-HVDC adopt 12-pulse converters (composed of two series-connected 6-pulse converters). The commutation transformers adopt three-phase three-winding transformers, with the high-voltage side connected in Yg type, and the two low-voltage side windings are connected in Y type and D1 type respectively, and provide commutation voltages for the two 6-pulse converters respectively. The LCC-HVDC parameters are shown in Table 1. In this example, a grounding fault is set at the inverter side AC bus at 0.75 s during the normal operation of the LCC-HVDC, and the commutation failure of the system is predicted based on the traditional commutation failure prediction method and the method proposed by the present invention respectively, and the prediction effects of the two methods are compared to verify the method proposed by the present invention.

[0031] Next, the rapidity of the proposed method is verified by comparing the prediction results of the commutation failure rapid prediction method proposed by the present invention and the existing prediction method based on commutation area. The relevant parameter settings are as follows: the normalization coefficient k i1 = 1.7 of the commutation demand area weight, the sensitivity coefficient k i2 = -20 of the commutation demand area weight, the normalization coefficient k u1 = 1.8 of the maximum commutation supply area weight, and the sensitivity coefficient k u2 = -60 of the maximum commutation supply area weight. It can be seen from Equation (6) that k i1 the role of C 1 and C 2 is to limit the calculated weight coefficients C 1 andC 2 too large or too small, k i2 then the description weight coefficient C 1 and C 2 the sensitivity to the change of the DC current prediction error, k i2 the larger the absolute value of, the greater the weight coefficient C 1 and C 2 is more sensitive to the change of the DC current prediction error. Similarly, from Equation (7), it can be seen that k u1 the role of is to limit the calculated weight coefficients C 3 and C 4 within [0, 0.9], k u2 the description weight coefficient C 3 and C 4 the sensitivity to the change of the commutation voltage amplitude prediction error, k u2 the larger the absolute value of, the greater the weight coefficient C 3 and C 4 is more sensitive to the change of the commutation voltage amplitude prediction error. In the proposed prediction method, k i1 , k i2 , k u1 , k u2 parameters such as directly affect the speed and accuracy of the prediction method. Therefore, these parameters need to be reasonably set before prediction.

[0032] Since commutation failure in the LCC-HVDC operation process is usually caused by a single-phase ground fault, the method is first verified with a single-phase ground fault as an example. Set an A-phase ground fault at the AC bus on the inverter side of the LCC-HVDC system, and the grounding impedance is 0.17 H. The relevant waveforms under this condition are as Figure 4 shown, Figure 4 in which, (a) is the DC current waveform near the fault moment, (b) is the three-phase voltage waveform of the AC bus of the inverter station near the fault moment, and (c) is the valve current waveforms of the two valves when commutation failure occurs for the first time. From Figure 4It can be seen that after a single-phase grounding fault occurs, the DC current fluctuates, the AC voltage of the faulty phase drops significantly, and the first commutation failure occurs during the commutation process of converter valves 4 - 6 corresponding to the Y-connected transformer at 0.8279 s. After the commutation failure occurs, the DC current rises rapidly, the distortion of the AC voltage becomes more serious, and the system becomes unstable. The commutation failure under this condition is predicted based on the traditional commutation area method and the method proposed in the present invention. The prediction results are shown in Case 4 of Table 2. The "×" indicates that the traditional method has a missed judgment, while the method proposed in the present invention successfully predicts at 0.8232 s. Continue to set single-phase grounding faults with different impedance values on the inverter AC side for verification. The impedance values cover 7 different fault impedances ranging from 0.14 H to 0.2 H, with a step size of 0.1 H, and the fault duration is 100 ms for all cases. The prediction effects of the two methods are compared, and the prediction results are shown in Table 2. The first column of data in Table 2 is the fault test condition, the second column is the magnitude of the grounding impedance, the third column is the actual commutation failure occurrence time after the fault, and "not occurred" means that no commutation failure occurs during the fault and system recovery period; the fourth column and the fifth column of data are the times (the time when the criterion is less than 0) for the traditional commutation area method and the method proposed in the present invention to judge commutation failure respectively. The "×" indicates a missed judgment of commutation failure, that is, it fails to successfully predict before the actual commutation failure occurs, and the "√" indicates no false judgment of commutation failure, that is, the criterion is always greater than 0 when the system does not have a commutation failure. It can be seen from the data in Table 2 that in single-phase fault cases 1, 2, and 3, both the traditional method and the proposed method can successfully predict before the actual commutation failure occurs, and the method of the present invention can judge commutation failure earlier and has a faster prediction speed compared with the traditional method; in single-phase fault case 4, the traditional method has a missed judgment of commutation failure, while the method of the present invention successfully predicts before the actual commutation failure occurs; in single-phase fault cases 5, 6, and 7, no commutation failure occurs, and neither the traditional method nor the proposed method has a false judgment.

[0033] Table 2 Prediction Results of Commutation Failure under Single-Phase Fault Conditions

[0034] Note: The data in the fourth column is the time when the criterion of the traditional method is less than 0; the data in the fifth column is the time when the criterion of the proposed method is less than 0; the "×" in Table 2 indicates that it fails to successfully predict before the actual commutation failure occurs, that is, a missed judgment occurs; the "√" in Table 2 indicates that the criterion is always greater than 0 when the system does not have a commutation failure, that is, no false judgment occurs.

[0035] Taking the three-phase symmetrical grounding fault as an example for verification. A three-phase symmetrical grounding fault is set at the AC bus of the system inverter side, and the grounding impedance is 0.18 H. The relevant waveforms under this condition are as Figure 5 shown. Figure 5Among them, (a) is the DC current waveform near the fault moment, (b) is the three-phase voltage waveform of the inverter station AC bus near the fault moment, and (c) is the valve current waveforms of the two valves where commutation failure first occurs. From Figure 5 It can be seen that after the three-phase grounding fault occurs, the DC current fluctuates, the three-phase AC voltages of A, B, and C all drop significantly, and the first commutation failure occurs during the commutation process of valves 2 - 4 of the converter corresponding to the D1 type connection transformer at 0.7628 s. The commutation failure under this condition is predicted based on the traditional commutation area method and the method proposed in the present invention. The prediction results are shown in Case 2 of Table 3. The traditional method successfully predicts at 0.7560 s, while the method proposed in the present invention successfully predicts at 0.7532 s. A three-phase symmetrical grounding fault is continuously set at the AC bus on the inverter side of the system. The impedance values cover 4 different fault impedances ranging from 0.13 H to 0.28 H, with a step size of 0.5 H, and the fault duration is 100 ms for all cases. The prediction effects of the two methods are shown in Table 3. From the data in Table 3, it can be seen that in the three-phase fault condition 1, the traditional method fails to predict the commutation failure, while the method proposed in the present invention successfully predicts in advance; in the three-phase fault condition 2, both the traditional method and the method proposed in the present invention successfully predict before the actual commutation failure occurs, and the method proposed in the present invention can judge the commutation failure earlier than the traditional method; in the three-phase fault conditions 3 and 4, the commutation failure does not occur, and neither the traditional method nor the method proposed in the present invention makes a misjudgment. Thus, it can be seen that the method proposed in the present invention can predict the occurrence of commutation failure faster while ensuring accuracy after a single-phase grounding fault occurs in the AC system on the inverter side of the LCC - HVDC, which is more conducive to the subsequent operation of the commutation failure control and protection system.

[0036] Table 3 Commutation Failure Prediction Results under Three-Phase Symmetrical Fault Conditions

[0037] Note: The data in the 4th column are the moments when the criterion of the traditional method is less than 0; the data in the 5th column are the moments when the criterion of the proposed method is less than 0; the × in Table 3 indicates that the prediction fails to be successful before the actual commutation failure occurs, that is, a missed judgment occurs; the √ in Table 3 indicates that the criterion is always greater than 0 when the commutation failure does not occur in the system, that is, no misjudgment occurs.

Claims

1. A fast prediction method for HVDC commutation failure based on commutation failure risk factor, characterized in that: Based on the current commutation voltage amplitude U com.t 、Current DC current I d.t , the commutation voltage amplitude at the future moment U com.c 、DC current at future time I d.c The traditional commutation area is calculated separately S need.t , S pro.t and leading commutation area S need.c , S pro.c , and then through the adaptive weight coefficient C 1- C 4. Integrate the traditional commutation area and the advanced commutation area to obtain the commutation failure risk factor F , and then judge the occurrence of commutation failure, and realize the rapid prediction of commutation failure after LCC-HVDC fault.

2. The HVDC commutation failure rapid prediction method based on commutation failure risk factor according to claim 1 is characterized in that: The steps include: Step 1: Obtain the current commutation voltage amplitude during the operation of the LCC-HVDC system in real time U com.t 、Current DC current I d.t , the commutation voltage amplitude at the future moment U com.c 、DC current at future time I d.c , trigger angle command value α , are all per unit values; Step 2: Based on the current commutation voltage amplitude U com.t 、Current DC current I d.t and firing angle command value α Calculate the required commutation area and the maximum commutation area, and define them as the traditional commutation required area and the traditional maximum commutation area respectively; at the same time, based on the predicted commutation voltage amplitude at the future moment U com.c 、DC current at future time I d.c and firing angle command value α Calculate the commutation requirement area and the maximum commutation provision area, and define them as the leading commutation requirement area and the leading maximum commutation provision area respectively; Step 3: Calculate the weight coefficients of the traditional commutation required area and the advanced commutation required area; Step 4: Calculate the weight coefficients of the conventional maximum commutation area and the advanced maximum commutation area; Step 5: The calculated traditional commutation requirement area, traditional maximum commutation area, advanced commutation requirement area and advanced maximum commutation area are weighted and integrated through the weight coefficient to obtain the commutation failure risk factor, and then predict whether the commutation failure will occur; and in the prediction process, the weight coefficient of each commutation area is adaptively adjusted according to the prediction error of the characteristic quantity at the previous moment.

3. The HVDC commutation failure rapid prediction method based on commutation failure risk factor according to claim 2 is characterized in that: In step 2, based on the current commutation voltage amplitude U com.t 、Current DC current I d.t and firing angle command value α Calculate the required area of ​​traditional commutation according to formula (2) and formula (3) respectively: S need.t The maximum commutation area provided by the traditional S pro.t ; Based on the predicted commutation voltage amplitude at the future moment U com.c 、DC current at future time I d.c and firing angle command value α Calculate the required area for leading commutation according to equations (4) and (5) respectively: S need.c and leading maximum commutation area S pro.c ; S need.t =2 X c I d.t （2） （3） S need.c =2 X c I d.c （4） （5） In the formula, X c is the equivalent commutation reactance, γ min is the minimum turn-off angle, ω 0 is the system angular frequency, which is 2π / T , T is the fundamental period of the system; t represents the integration variable.

4. The HVDC commutation failure rapid prediction method based on commutation failure risk factor according to claim 2 is characterized in that: In step 3, based on the current DC current I d.t and the predicted value of the current DC current from historical data I d.c0 According to formula (6), the weight coefficient of the traditional commutation required area and the advanced commutation required area is calculated. C 1 and C 2; （6） In the formula, k i1 is the normalized coefficient of the commutation required area weight, k i2 is the sensitivity coefficient of the commutation requirement area weight.

5. The HVDC commutation failure rapid prediction method based on commutation failure risk factor according to claim 2 is characterized in that: In step 4, based on the current commutation voltage amplitude U com.t and the predicted value of the current commutation voltage amplitude from historical data U com.c0 According to formula (7), the weight coefficients of the traditional maximum commutation area and the advanced maximum commutation area are calculated. C 3 and C 4; （7） In the formula, k u1 Provides a normalized coefficient for the area weight for maximum commutation, k u2 Provides area-weighted sensitivity coefficient for maximum commutation.

6. The HVDC commutation failure rapid prediction method based on commutation failure risk factor according to claim 2 is characterized in that: In step 5, based on the traditional commutation required area calculated in steps 2-4 S need.t , the traditional maximum commutation area S pro.t , Advanced commutation required area S need.c and leading maximum commutation area S pro.c and weight coefficient C 1- C 4 Calculate the commutation failure risk factor in real time according to formula (8) F ; F=C 1 S need.c + C 2 S need.t + C 3 S pro.c + C 4 S pro.t （8） like F ≥0, the system state is considered to meet the normal commutation conditions and commutation failure will not occur; if F <0, it is considered that the system state does not meet the normal commutation conditions and commutation failure will occur.

7. A computer-readable storage medium, characterized in that: A program is stored thereon, and when the program is executed by a processor, the steps in the method for quickly predicting HVDC commutation failure based on a commutation failure risk factor as claimed in any one of claims 1 to 6 are implemented.

8. An electronic device, characterized in that: The invention comprises a memory, a processor and a program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the method for quickly predicting HVDC commutation failure based on a commutation failure risk factor as claimed in any one of claims 1 to 6 are implemented.

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