A new energy sending end power grid DC fault transient overvoltage influence evaluation method, system and readable storage medium

Abstract

The application discloses a new energy sending end power grid DC fault transient overvoltage influence evaluation method and system and a readable storage medium, and belongs to the technical field of power grid fault detection. The method takes overvoltage amplitude, overvoltage duration, fault type and system impedance as influencing factors, determines the membership degrees of various factors at different influence levels according to the corresponding single-factor fuzzy evaluation membership degree matrix of the factor level query, and performs weighted summation on the membership degrees of various factors at different influence levels and the influence weights of various factors to calculate the final membership degrees of various influence levels, and takes the influence level corresponding to the maximum final membership degree as the evaluation level. Through effective processing of fuzzy information, the application can make accurate evaluation under complex power grid fault conditions, thereby reducing the potential risk of overvoltage to the system and equipment and improving the safety and reliability of the power system.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid fault detection, and particularly relates to a method and system for evaluating the impact of DC fault transient overvoltage in a new energy sending-end power grid, as well as a readable storage medium. Background Art

[0002] UHV power transmission and transformation lines shoulder the heavy responsibility of long-distance and large-scale transmission of new energy. The construction of UHV DC for external transmission supporting the new energy large bases is gradually becoming the main development model. While UHV DC transmission supports the optimal allocation of resources, the coupling between AC and DC grids in the power grid is becoming increasingly tight, and the integrated AC-DC characteristics are gradually emerging, posing many challenges to the safe and stable operation of the high-proportion new energy sending-end power grid. Especially during the operation of UHV DC transmission, after commutation failure or DC blocking fault occurs, the AC grid voltage in the near area of the DC sending end drops and rises suddenly, and in severe cases, the wind farms connected in the near area will experience disconnection accidents. The severity and influence range of the sudden drop and rise of the AC grid voltage in the near area of the DC sending end after DC commutation failure or DC blocking fault are closely related to the new energy generation ratio and DC transmission power, and are mutually coupled. The more new energy is generated, the less DC is transmitted, and the more DC is transmitted, the less new energy can be generated, severely restricting the new energy power generation and DC transmission and consumption capabilities of the new energy sending-end power grid.

[0003] In the high-proportion new energy DC sending-end power grid, due to the tight coupling between AC and DC grids, faults such as DC commutation failure and blocking are likely to cause large-scale transient voltage drops and rises in the sending-end AC grid. The transient voltage is transmitted to the wind farm, causing a large number of wind turbines to trip连锁脱网 (the text seems to be incorrect here, assuming it should be "trip in cascade"). Simulation analysis and actual operation experience show that DC faults will greatly increase the risk of fault expansion and spread caused by the cascade tripping of new energy generating units; since the switching time of the static reactive power compensation device is far from the transient voltage change time, it continues to operate connected to the grid during the fault, and the dynamic reactive power compensation device cannot cooperate in control, resulting in serious shortage or excess of reactive power in the local power grid. In severe cases, it causes large-scale disconnection of new energy units, directly threatening the safe and stable operation of the new energy sending-end power grid, bringing new challenges to the safety and stability of the new energy DC sending-end power grid.

[0004] In the process of promoting the solution to the problem of transient overvoltage caused by DC faults in the new energy sending-end power grid, due to the lack of a complete set of overvoltage impact evaluation methods, the overvoltage impact caused by DC faults in the new energy sending-end power grid cannot be reasonably classified and evaluated, so that the overvoltage problems in the new energy sending-end power grid with different impact degrees cannot be solved targeted. Summary of the Invention

[0005] The purpose of this invention is to provide a method, system, and readable storage medium for assessing the impact of transient overvoltages during DC faults in new energy power grids, so as to accurately assess the degree of impact of overvoltage problems in new energy power grids.

[0006] According to a first aspect of the present invention, a method for assessing the impact of transient overvoltage during DC faults in a new energy power grid is provided, comprising:

[0007] Acquire transient overvoltage data when a DC fault occurs in the new energy sending-end grid. The transient overvoltage data includes overvoltage amplitude, overvoltage duration, fault type that caused the overvoltage, and grid system impedance.

[0008] Based on the overvoltage amplitude, the overvoltage duration, the fault type, and the system impedance, a factor set is queried to determine the factor level to which the overvoltage amplitude, overvoltage duration, fault type, and system impedance belong;

[0009] The membership matrix of the corresponding single-factor fuzzy evaluation is queried according to the factor level to determine the membership degree of the overvoltage amplitude, the overvoltage duration, the fault type, and the system impedance at different influence levels; wherein, the single-factor fuzzy evaluation membership matrix is ​​determined based on expert experience and represents the membership degree of different factor levels of overvoltage amplitude, overvoltage duration, fault type, and system impedance to different influence levels.

[0010] The membership degrees of the overvoltage amplitude, overvoltage duration, fault type, and system impedance at different influence levels are weighted and summed with the influence weights of each factor (overvoltage amplitude, overvoltage duration, fault type, and system impedance) to calculate the final membership degree of each influence level. The influence level corresponding to the maximum value of the final membership degree is then used as the evaluation level.

[0011] In one example of the above method, based on historical transient overvoltage data samples, the entropy weight method is used to determine the influence weights of factors such as overvoltage amplitude, overvoltage duration, fault type, and system impedance.

[0012] In one example of the above method, the factor level of the overvoltage amplitude is determined based on the proportion by which the overvoltage amplitude exceeds the device's withstand voltage value.

[0013] According to a second aspect of the present invention, a system for assessing the impact of transient overvoltage during DC faults in a new energy power grid is provided, comprising:

[0014] The storage module is used to store factor sets, single-factor fuzzy evaluation membership matrices, and influence weight data; wherein, the single-factor fuzzy evaluation membership matrix is ​​determined based on expert experience and represents the membership degree of different factor levels of overvoltage amplitude, overvoltage duration, fault type, and system impedance to different influence levels.

[0015] The data acquisition module is configured to acquire transient overvoltage data when a DC fault occurs in the new energy transmission grid, wherein the transient overvoltage data includes the overvoltage amplitude, overvoltage duration, fault type that caused the overvoltage, and system impedance of the grid.

[0016] The first query module is configured to: query a set of factors based on the overvoltage amplitude, the overvoltage duration, the fault type, and the system impedance to determine the factor level to which the overvoltage amplitude, the overvoltage duration, the fault type, and the system impedance belong;

[0017] The second query module is configured to: query the corresponding single-factor fuzzy evaluation membership matrix according to the factor level, so as to determine the membership degree of the overvoltage amplitude, the overvoltage duration, the fault type and the system impedance at different influence levels;

[0018] The calculation module is configured to: calculate the final membership degree of each influence level by weighting the membership degree of the overvoltage amplitude, the overvoltage duration, the fault type, and the system impedance at different influence levels with the influence weights of each factor, in order to calculate the final membership degree of each influence level, and take the influence level corresponding to the maximum value of the final membership degree as the evaluation level.

[0019] In one example of the above system, based on historical transient overvoltage data samples, the entropy weight method is used to determine the influence weights of factors such as overvoltage amplitude, overvoltage duration, fault type, and system impedance.

[0020] In one example of the above system, the factor level of the overvoltage amplitude is determined based on the proportion by which the overvoltage amplitude exceeds the device's withstand voltage value.

[0021] In one example of the above system, the overvoltage amplitude is the peak value of the transient overvoltage when the fault occurs.

[0022] According to a third aspect of the present invention, a readable storage medium is provided, on which a computer program is stored; when the computer program is executed, it implements the method for assessing the impact of transient overvoltage during DC faults in the new energy transmission grid.

[0023] This invention employs the fuzzy comprehensive evaluation method to assess the impact of transient overvoltage during DC faults in the power grid at the new energy sending end. By effectively processing fuzzy information, the fuzzy comprehensive evaluation method provides decision-makers with a scientific basis, helping them make reasonable decisions under complex power grid fault conditions, reducing the potential risks of overvoltage to the system and equipment, and thus improving the safety and reliability of the power system.

[0024] Other features and advantages of the invention will become clear from the following detailed description of exemplary embodiments of the invention with reference to the accompanying drawings. Attached Figure Description

[0025] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0026] Figure 1 This is a schematic diagram of the process for assessing the impact of transient overvoltage during DC faults in the power grid at the new energy sending end, according to an embodiment of the present invention.

[0027] Figure 2 This is a schematic diagram of the composition of a transient overvoltage impact assessment system for DC faults in a new energy power grid according to an embodiment of the present invention. Detailed Implementation

[0028] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0029] Overvoltage impact assessment is a crucial component of power system reliability analysis. Commonly used methods include theoretical analysis, equipment characteristic studies, overvoltage source analysis, field testing, risk assessment, protection measure evaluation, and computer simulation. However, all these methods have limitations. For example, while theoretical analysis can provide preliminary voltage fluctuation predictions, it often neglects nonlinear factors and complex electrical environments, leading to insufficient accuracy. Equipment characteristic studies require extensive experimental data and may not cover equipment behavior under all actual operating conditions. Overvoltage source analysis may not fully consider various complex interactions when identifying potential overvoltage sources. Field testing, while providing real-world operating data, is costly to implement and may pose certain risks to equipment. Risk assessment methods (such as FMEA and FTA) may be subjective in quantitative analysis and rely on the accuracy of historical data. Protection measure evaluation often struggles to account for the impact of emerging technologies or unknown risks when assessing the effectiveness of existing measures. While computer simulation can simulate complex scenarios, its results are highly dependent on the accuracy of input parameters and the rationality of the model, potentially leading to errors.

[0030] Fuzzy comprehensive evaluation is a comprehensive evaluation method based on fuzzy mathematics. This method transforms qualitative evaluation into quantitative evaluation based on the membership theory of fuzzy mathematics. That is, it uses fuzzy mathematics to make an overall evaluation of things or objects that are constrained by multiple factors. It has the characteristics of clear results and strong systematicity, and can better solve fuzzy and difficult-to-quantify problems.

[0031] Fuzzy comprehensive evaluation can handle uncertainty and fuzziness in complex decision-making environments. By integrating multiple evaluation indicators, this method can comprehensively reflect the performance and characteristics of a system, thus overcoming the limitations of single-indicator evaluation. Its flexibility lies in its ability to adjust evaluation indicators and their weights according to specific application needs, making the evaluation process more targeted. Furthermore, fuzzy comprehensive evaluation possesses both qualitative and quantitative analysis capabilities. Through the effective processing of fuzzy information, this method not only provides scientific decision support for decision-makers but also enhances the reliability and rationality of the evaluation results.

[0032] The evaluation of the impact of transient overvoltages during DC faults in renewable energy power grids is complex. Numerous evaluation factors must be considered, making the evaluation system difficult to construct. Furthermore, the weights of these factors and the judgment of their thresholds vary from person to person, posing significant challenges to the establishment of the evaluation system, the selection of evaluation indicators, the determination of the relationship between each indicator and the impact assessment results, and the identification of evaluation factors and results. Considering the advantages of fuzzy comprehensive evaluation in handling complexity and uncertainty, this method effectively integrates multi-dimensional evaluation indicators, such as voltage amplitude, duration, and equipment impact, overcoming the limitations of single-indicator evaluation. Simultaneously, fuzzy comprehensive evaluation combines qualitative and quantitative analysis capabilities, integrating expert experience with measured data to improve the comprehensiveness and scientific rigor of the evaluation. Moreover, its flexibility allows for adjustments to the evaluation model and weights based on different fault scenarios and operating conditions, ensuring the applicability and accuracy of the evaluation results.

[0033] (I) Factor Set

[0034] According to embodiments of the present invention, the factors affecting transient overvoltage during DC faults in the new energy sending-end power grid include overvoltage amplitude, overvoltage duration, fault type, and system impedance.

[0035] (1) Overvoltage amplitude

[0036] The peak value of transient overvoltage at the time of a fault is a core indicator for evaluation, directly affecting the insulation strength and safety of the equipment.

[0037] As an example, the factor levels for overvoltage amplitude are divided into the following levels:

[0038] Extremely high overvoltage (Level 1):

[0039] Definition: Overvoltage amplitude far exceeds the insulation withstand level of the equipment, usually exceeding the equipment withstand voltage standard by more than 20%.

[0040] Impact: May cause immediate equipment failure, system crash, or serious security incidents.

[0041] High overvoltage (Level 2):

[0042] Definition: Overvoltage amplitude exceeds the insulation withstand level of the equipment, but is between 10% and 20% of the equipment withstand voltage standard.

[0043] Impact: May cause a significant decline in equipment performance and affect system stability; immediate action is required.

[0044] Moderate overvoltage (Level 3):

[0045] Definition: Overvoltage amplitude is close to the insulation withstand level of the equipment, usually between 0% and 10% of the equipment withstand voltage standard.

[0046] Impact: May cause a short-term decrease in equipment performance, but will not affect the safe operation of the overall system.

[0047] Low overvoltage (Level 4):

[0048] Definition: Overvoltage amplitude is within the safe operating range of the equipment, but close to the normal operating limit of the equipment.

[0049] Impact: It usually does not have a significant impact on the normal operation of the equipment, but monitoring is required.

[0050] Normal range (Level 5):

[0051] Definition: The overvoltage amplitude is completely within the safe operating range of the equipment and is far below the insulation withstand level of the equipment.

[0052] Impact: No significant impact; the system is operating normally and stably.

[0053] For example, suppose the equipment's withstand voltage standard is 10kV:

[0054] Extremely high overvoltage (Level 1): >12kV

[0055] High overvoltage (Level 2): ​​10kV-12kV

[0056] Medium overvoltage (Level 3): 9kV-10kV

[0057] Low overvoltage (Level 4): 8kV-9kV

[0058] Normal range (Level 5): <8kV

[0059] (2) Overvoltage duration

[0060] The duration of overvoltage has a significant impact on the thermal stress and aging of equipment, and its long-term effects on equipment performance need to be assessed.

[0061] As an example, the overvoltage duration factor level is divided into the following levels:

[0062] Extremely long duration (Level 1):

[0063] Definition: Duration exceeding 1 second.

[0064] Impact: May cause serious damage or failure of equipment, severely threaten system stability, and require immediate emergency measures.

[0065] Longer duration (Level 2):

[0066] Definition: Duration between 0.5 seconds and 1 second.

[0067] Impact: May have a significant impact on the equipment, reducing its performance and lifespan, requiring monitoring and maintenance.

[0068] Medium duration (Level 3):

[0069] Definition: Duration is between 0.1 seconds and 0.5 seconds.

[0070] Impact: The impact on the equipment is relatively minor and usually will not cause equipment failure, but the situation needs to be recorded and monitored. Shorter duration (Level 4):

[0071] Definition: Duration is between 0.01 seconds and 0.1 seconds.

[0072] Impact: Generally, it will not have a significant impact on the equipment and is within the normal fluctuation range.

[0073] Extremely short duration (Level 5):

[0074] Definition: Duration less than 0.01 seconds.

[0075] Impact: It has no significant impact on equipment and systems and is usually considered a transient phenomenon.

[0076] (3) Fault Types

[0077] Different types of faults induce significantly different overvoltage characteristics, and their impact on power grid performance must be considered. As an example, the fault type factor levels include the following seven categories:

[0078] (a) Short circuit fault

[0079] Single-phase short circuit: Occurs only in one phase of the circuit, usually caused by insulation failure or equipment malfunction.

[0080] Two-phase short circuit: Occurs between two phases and may result in a higher fault current.

[0081] Three-phase short circuit: Occurs in a three-phase circuit, usually the most serious type of fault, and can cause the largest fault current.

[0082] (b) Open circuit fault

[0083] Single-phase open circuit: A single phase of the circuit is disconnected, which may lead to unbalanced loads and equipment damage.

[0084] Two-phase open circuit: Two phases of the circuit are disconnected, which may lead to more serious equipment instability.

[0085] Three-phase open circuit: All three phases of the circuit are disconnected, causing the equipment to malfunction.

[0086] (c) Grounding fault

[0087] Single-phase grounding fault: One phase is connected to ground, which may cause overvoltage and insulation damage to equipment.

[0088] Two-phase grounding fault: Two phases are connected to ground, which may cause more serious equipment damage.

[0089] Three-phase grounding fault: When all three phases are grounded, it usually leads to system failure.

[0090] (d) Overload fault

[0091] Equipment overload: The current carried by the equipment exceeds its rated value, which may cause the equipment to overheat and be damaged.

[0092] Line overload: The current carried by a power line exceeds its rated value, which may cause line damage or fire.

[0093] (e) Equipment failure

[0094] Transformer failure: Internal faults in the transformer can lead to voltage instability and equipment damage.

[0095] Switch failure: Failure of the switching equipment may prevent the circuit from switching normally.

[0096] Relay failure: A faulty protective relay may prevent the fault from being cleared in a timely manner.

[0097] (f) Transient faults

[0098] Transient overvoltage: Transient overvoltage caused by lightning strikes, switching operations, etc.

[0099] Transient current: Fluctuations in transient current caused by power grid disturbances or equipment start-up and shutdown.

[0100] (g) Human error

[0101] Operational error: A malfunction caused by operator error.

[0102] Maintenance error: A malfunction caused by misjudgment or improper operation during equipment maintenance.

[0103] (4) System impedance

[0104] The impedance characteristics of the power grid significantly affect the amplitude and characteristics of voltage fluctuations during faults, and therefore should be included in the evaluation model.

[0105] As an example, the system's factor levels are divided into the following levels:

[0106] Extremely low impedance (Class 1)

[0107] Definition: The system impedance is less than or equal to 0.1Ω.

[0108] Impact: May lead to high fault current, increase the risk of equipment damage, and severely affect system stability.

[0109] Lower impedance (Level 2)

[0110] Definition: The system impedance is between 0.1Ω and 0.5Ω.

[0111] Impact: The fault current remains high, which may put some stress on the equipment and requires monitoring and evaluation.

[0112] Medium impedance (Level 3)

[0113] Definition: The system impedance is between 0.5Ω and 1.0Ω.

[0114] Impact: The fault current is moderate and has a relatively small impact on the equipment, but it still needs to be monitored.

[0115] Higher impedance (Level 4)

[0116] Definition: The system impedance is between 1.0Ω and 5.0Ω.

[0117] Impact: The fault current is low, and the equipment is relatively safe, but it may cause system instability in some cases.

[0118] Extremely high impedance (Level 5)

[0119] Definition: System impedance greater than 5.0Ω.

[0120] Impact: The fault current is very low, and the equipment operates safely, but it may cause slow system response and affect the operation of fault detection and protection devices.

[0121] (II) Membership Matrix of Single-Factor Fuzzy Evaluation

[0122] For example, the impact levels of transient overvoltages caused by DC faults in the renewable energy sending-end power grid can include the following levels:

[0123] (1) Extremely powerful (Level 1):

[0124] Definition: Fault transient overvoltages have a significant impact on the power grid and its related equipment, and may lead to equipment failure, system collapse or safety accidents.

[0125] Characteristics: The voltage peak far exceeds the insulation withstand level of the equipment, the duration is prolonged, the fault types are complex, and there is a lack of effective protection measures.

[0126] (2) Significant impact (Level 2):

[0127] Definition: Transient overvoltages during faults have a significant impact on the power grid and equipment, potentially causing a significant decline in equipment performance or affecting system stability, but they do not lead to serious accidents.

[0128] Characteristics: The voltage amplitude is higher than the safe operating threshold of the equipment, the duration is moderate, and some protection measures fail to function effectively.

[0129] (3) Moderate impact (Level 3):

[0130] Definition: Transient overvoltages have a moderate impact on the power grid and equipment, which may cause a short-term reduction in equipment performance, but do not affect the safe operation of the overall system.

[0131] Characteristics: The voltage amplitude is close to the safe operating standard of the equipment, the duration is short, and it can usually be controlled by existing protection measures.

[0132] (4) Minor impact (Level 4):

[0133] Definition: Fault transient overvoltages have a relatively small impact on the power grid and equipment, and usually do not have a significant impact on the normal operation of the equipment.

[0134] Features: The voltage amplitude is within the safe operating range of the equipment, the duration is short, and the protection measures are functioning well.

[0135] (5) No effect (Level 5):

[0136] Definition: A transient overvoltage during a fault does not have a significant impact on the power grid and equipment, and the system operates normally and stably.

[0137] Features: The voltage amplitude and duration are both within a safe range, the protection measures are effective, and the equipment is not affected in any way.

[0138] According to an embodiment of the present invention, a single-factor fuzzy evaluation membership matrix is ​​determined based on expert experience. This matrix represents the membership degree of different factor levels belonging to different influence levels for factors such as overvoltage amplitude, overvoltage duration, fault type, and system impedance. The specific process includes:

[0139] The steps to determine the membership matrix are as follows:

[0140] (1) Determine the evaluation indicators

[0141] Determine the overvoltage amplitude level and impact level that need to be assessed. Typically, the overvoltage amplitude level is classified based on the actual operating conditions of the power system and the equipment's ratings, while the impact level is classified based on the equipment's sensitivity to overvoltage and the potential degree of damage.

[0142] (2) Establish an expert group

[0143] Select a team of power system experts with extensive experience and expertise, ensuring that team members have relevant backgrounds in equipment operation, fault analysis, and power system protection.

[0144] (3) Conduct expert evaluation meetings

[0145] An expert panel was organized to discuss and explain the definitions of overvoltage amplitude and impact levels, ensuring that all experts had a consistent understanding of the assessment content.

[0146] (4) Use scoring methods

[0147] Each expert, based on their experience and expertise, scores the relationship between each overvoltage amplitude level and its impact level. Scores are typically on a scale of 0 to 1, where 0 indicates no impact and 1 indicates complete impact.

[0148] (5) Summarize expert opinions

[0149] Collect the scores from each expert and calculate the average or weighted average of each overvoltage amplitude level and impact level to form a preliminary membership matrix.

[0150] (6) Discussion and Revision

[0151] The initial membership matrix is ​​discussed again within the group, allowing experts to adjust the scores to reach a consensus. Multiple rounds of discussion and revision can be conducted using methods such as the Delphi method.

[0152] (7) Form the final matrix

[0153] After reaching a consensus, a final membership matrix is ​​formed, and the source and reason for each score are recorded for future reference and verification.

[0154] As an example, the overvoltage membership matrix is ​​shown in the table below:

[0155]

[0156] As an example, the duration membership matrix is ​​shown in the table below:

[0157]

[0158] As an example, the fault type membership matrix is ​​shown in the table below:

[0159]

[0160]

[0161] As an example, the system impedance matrix is ​​shown in the table below:

[0162]

[0163] (III) Factor Influence Weights

[0164] According to an embodiment of the present invention, in order to increase the reliability of fuzzy comprehensive evaluation, a subjective and objective analysis method is adopted to improve the reliability of the evaluation. Therefore, the entropy weight method is used to determine the influence weight of each factor.

[0165] Entropy weighting is an objective weight allocation method based on information entropy. By quantifying the information content and uncertainty of each indicator, it reasonably reflects their relative importance in decision-making. It can effectively handle multi-indicator decision-making problems, reduce subjective bias, and has a simple and easy calculation process. It can also dynamically adjust the weights and is suitable for use in combination with other decision-making methods to improve the scientificity and reliability of decision-making.

[0166] The specific steps for determining the influence weights of each factor using the entropy weight method are as follows:

[0167] (1) Based on m historical transient overvoltage data samples and n (n=4) factors (or indicators), construct the decision matrix X:

[0168]

[0169] (2) Standardize the decision matrix so that the values ​​of each factor are within the same range, for example, by using range standardization:

[0170]

[0171] (3) Calculate the proportion of each sample for each factor:

[0172]

[0173] (4) Calculate the entropy value of each factor:

[0174]

[0175] (5) Calculate the influence weight of each factor:

[0176]

[0177] The obtained factor set, single-factor fuzzy evaluation membership matrix, and influence weight data are stored in the storage module for impact assessment when transient overvoltage occurs during DC faults in the new energy power grid.

[0178] Figure 1 This is a schematic diagram of the process for assessing the impact of transient overvoltage during DC faults in the power grid at the new energy sending end, according to an embodiment of the present invention. Figure 1 As shown, the method includes:

[0179] Step 101: Obtain transient overvoltage data when a DC fault transient overvoltage fault occurs at the new energy sending-end grid. The transient overvoltage data includes overvoltage amplitude, overvoltage duration, fault type that caused the overvoltage, and grid system impedance.

[0180] Step 102: Based on the overvoltage amplitude, overvoltage duration, fault type, and system impedance, query the factor set to determine the factor level to which the overvoltage amplitude, overvoltage duration, fault type, and system impedance belong.

[0181] Step 103: Query the corresponding single-factor fuzzy evaluation membership matrix according to the factor level to determine the membership degree of overvoltage amplitude, overvoltage duration, fault type and system impedance at different influence levels;

[0182] Step 104: The membership degrees of overvoltage amplitude, overvoltage duration, fault type, and system impedance at different influence levels are weighted and summed with the influence weights of each factor, to calculate the final membership degree of each influence level. The influence level corresponding to the maximum value of the final membership degree is taken as the evaluation level.

[0183] This invention uses the membership degree matrix of each single factor to substitute each factor into the corresponding membership degree matrix for different situations of transient overvoltage of DC fault in the new energy sending-end power grid, so as to obtain the membership degree of each factor at different evaluation levels. Then, the final membership degree of different evaluation levels is determined according to the weight of each factor. Finally, the impact of this transient overvoltage of DC fault in the new energy sending-end power grid is evaluated as the level with the highest membership degree.

[0184] Figure 2 This is a schematic diagram of a transient overvoltage impact assessment system for DC faults in a new energy power grid according to an embodiment of the present invention.

[0185] like Figure 2 As shown, the system includes:

[0186] Storage module 201 is used to store factor set, single-factor fuzzy evaluation membership matrix and influence weight data; wherein, the single-factor fuzzy evaluation membership matrix is ​​determined based on expert experience, representing the membership degree of different factor levels of overvoltage amplitude, overvoltage duration, fault type and system impedance to different influence levels.

[0187] In addition, storage module 201 is also used to store historical operating data of the renewable energy transmission grid, including fault data and normal operating data. It can be understood that storage module 201 may consist of one or more memory devices or storage units.

[0188] The data acquisition module 202 is configured to acquire transient overvoltage data when a DC fault transient overvoltage fault occurs at the new energy sending-end grid. The transient overvoltage data includes the overvoltage amplitude, overvoltage duration, fault type that caused the overvoltage, and grid system impedance.

[0189] The first query module 203 is configured to query a set of factors based on overvoltage amplitude, overvoltage duration, fault type, and system impedance to determine the factor level to which the overvoltage amplitude, overvoltage duration, fault type, and system impedance belong.

[0190] The second query module 204 is configured to query the corresponding single-factor fuzzy evaluation membership matrix based on the factor level, so as to determine the membership degree of overvoltage amplitude, overvoltage duration, fault type and system impedance at different influence levels.

[0191] The calculation module 205 is configured to: calculate the final membership degree of each influence level by weighting the membership degree of overvoltage amplitude, overvoltage duration, fault type and system impedance at different influence levels with the influence weights of each factor, and take the influence level corresponding to the maximum value of the final membership degree as the evaluation level.

[0192] As an example, the first query module 203, the second query module 204, and the calculation module 205 can be implemented using different processors, or they can be implemented using the same processor. Alternatively, they can be implemented using software modules.

[0193] The technical solution of the present invention will be further described below through specific examples.

[0194] Assume the collected data sample is as follows:

[0195] Sample / Factor Overvoltage amplitude Duration Fault type System impedance Sample 1 230 0.4 1 0.1 Sample 2 240 1 2 0.3 Sample 3 220 0.7 3 0.5

[0196] Taking Sample 1 as an example, determine the corresponding level of each factor in its factor set. The equipment withstand voltage is 220V, the overvoltage is 230V, and the overvoltage exceeds the limit by about 4.5%, which is a medium overvoltage (level 3); the duration is 0.4 seconds, which is a medium duration (level 3); the fault type is a short circuit fault; the system impedance is 0.1Ω, which is an extremely low impedance (level 1).

[0197] The membership degree of each evaluation result level is determined based on the levels of each factor and the membership degree matrix:

[0198] Rating Level 1 Rating Level 2 Rating level 3 Rating level 4 Rating level 5 Overvoltage amplitude (Level 3) 0 0.6 0.2 0.2 0 Time (Level 3) 0.2 0.4 0.2 0.2 0 Fault type (Type 1) 0.6 0.3 0.1 0 0 Impedance (Level 1) 0.6 0.3 0.1 0 0

[0199] The entropy weight method was used to determine the influence weights of each factor: First, the minimum and maximum values ​​of each indicator were calculated, and then standardized. Overvoltage: minimum = 220, maximum = 240; Duration: minimum = 0.4, maximum = 1; Fault type: minimum = 1, maximum = 3; System impedance: minimum = 0.1, maximum = 0.5. After standardization, the following results were obtained:

[0200] Sample / Factor Overvoltage amplitude Duration Fault type System impedance Sample 1 1.0 0.0 0.0 0.0 Sample 2 0.5 1.0 0.5 0.5 Sample 3 0.0 0.4 1.0 1.0

[0201] Further processing yielded:

[0202]

[0203]

[0204] Calculate the entropy value for each influencing factor:

[0205] factor <![CDATA[Entropy value E j > Overvoltage amplitude 0.4 Duration 0.3 Fault type 0.5 System impedance 0.2

[0206] The weights are calculated based on the entropy values:

[0207] factor <![CDATA[Weight w j > Overvoltage amplitude 0.25 Duration 0.2 Fault type 0.35 System impedance 0.2

[0208] The weights of each factor were calculated using the entropy weight method as follows:

[0209] Overvoltage: 0.25

[0210] Duration: 0.20

[0211] Fault type: 0.35

[0212] System impedance: 0.20

[0213] Substitute the weights of each factor into the membership table to calculate the final membership degree.

[0214] Maximum impact (Level 1): 0.25×0 + 0.2×0.2 + 0.35×0.6 + 0.2×0.6 = 0.37

[0215] Significant impact (Level 2): ​​0.25×0.6 + 0.2×0.4 + 0.35×0.3 + 0.2×0.3 = 0.395

[0216] Moderate impact (Level 3): 0.25×0.2 + 0.2×0.2 + 0.35×0.1 + 0.2×0.1 = 0.145

[0217] Minor impact (Level 4): 0.25×0.2 + 0.2×0.2 + 0.35×0 + 0.2×0 = 0.09

[0218] No effect (Level 5): 0.25×0 + 0.2×0 + 0.35×0 + 0.2×0 = 0

[0219] The sample with the greatest impact has the highest membership degree, therefore, the impact of Sample 1 is assessed as having a significant impact (Level 2), and corresponding countermeasures are taken to ensure the safety and stability of the power grid. As an example, specific countermeasures for each impact level include:

[0220] (1) Extremely powerful (Level 1):

[0221] Emergency Response: Immediately activate the emergency plan, quickly cut off the power supply to the affected area, and ensure personnel safety.

[0222] Equipment maintenance: Conduct a comprehensive inspection and assessment of the affected equipment, and replace or repair it if necessary.

[0223] System Analysis: Conduct detailed fault analysis, identify the causes of the fault, and perform a systemic assessment to prevent similar incidents from recurring.

[0224] Enhance protection measures: Review the settings of existing protection devices, optimize protection strategies, and improve the system's overvoltage resistance.

[0225] (2) Significant impact (Level 2):

[0226] Monitoring and assessment: Strengthen the monitoring of the power grid's operating status and assess the health status of equipment.

[0227] Implement temporary repairs: If minor damage to the equipment is found, perform temporary repairs and develop a long-term repair plan.

[0228] Optimize operating strategies: Adjust power grid operating strategies based on the type and severity of the fault to reduce system load and mitigate the impact.

[0229] Training and Drills: Strengthen the training of operators, conduct regular emergency drills, and improve response capabilities.

[0230] (3) Moderate impact (Level 3):

[0231] Regular inspections: Increase the frequency of regular equipment inspections to ensure that the equipment is operating within safe limits.

[0232] Data analysis: Collect and analyze fault data, assess its long-term impact on the system, and develop corresponding maintenance plans.

[0233] Improve protection settings: Adjust the settings of the protection devices according to the fault conditions to ensure that they can function effectively in future events.

[0234] (4) Minor impact (Level 4):

[0235] Routine maintenance: Perform routine maintenance and inspections on the equipment to ensure its normal operation.

[0236] Recording and Reporting: Record malfunctions and report them to management regularly to ensure transparency.

[0237] Optimize operating conditions: Based on operating data, optimize the operating conditions of the power grid as much as possible to reduce potential risks.

[0238] (5) No effect (Level 5):

[0239] Continuous monitoring: Continue routine monitoring of the power grid to ensure system safety and stability.

[0240] Data accumulation: Collect operational data to provide a reference for future fault analysis and system optimization.

[0241] Lessons learned: Summarize the experience gained in handling failures and improve the team's response capabilities to address potential future risks.

[0242] The above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them; although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications can still be made to the specific implementation of the present invention or equivalent substitutions can be made to some technical features without departing from the spirit of the technical solutions of the present invention, and all such modifications and substitutions should be covered within the scope of the technical solutions claimed in the present invention.

Claims

1. A method for assessing the impact of transient overvoltage during DC faults in a new energy power grid, characterized in that, include: Acquire transient overvoltage data when a DC fault occurs in the new energy sending-end grid. The transient overvoltage data includes overvoltage amplitude, overvoltage duration, fault type that caused the overvoltage, and grid system impedance. Based on the overvoltage amplitude, the overvoltage duration, the fault type, and the system impedance, a factor set is queried to determine the factor level to which the overvoltage amplitude, overvoltage duration, fault type, and system impedance belong; The membership matrix of the corresponding single-factor fuzzy evaluation is queried according to the factor level to determine the membership degree of the overvoltage amplitude, the overvoltage duration, the fault type, and the system impedance at different influence levels; wherein, the single-factor fuzzy evaluation membership matrix is ​​determined based on expert experience and represents the membership degree of different factor levels of overvoltage amplitude, overvoltage duration, fault type, and system impedance to different influence levels. The membership degrees of the overvoltage amplitude, overvoltage duration, fault type, and system impedance at different influence levels are weighted and summed with the influence weights of each factor (overvoltage amplitude, overvoltage duration, fault type, and system impedance) to calculate the final membership degree of each influence level. The influence level corresponding to the maximum value of the final membership degree is then used as the evaluation level.

2. The method according to claim 1, characterized in that, Based on historical transient overvoltage data samples, the entropy weight method is used to determine the influence weights of overvoltage amplitude, overvoltage duration, fault type and system impedance.

3. The method according to claim 1, characterized in that, The factor level of overvoltage amplitude is determined based on the proportion by which the overvoltage amplitude exceeds the equipment's withstand voltage value.

4. The method according to claim 1, characterized in that, The overvoltage amplitude is the peak value of the transient overvoltage when the fault occurs.

5. A system for assessing the impact of transient overvoltage during DC faults in a new energy power grid, characterized in that, include: The storage module is used to store factor sets, single-factor fuzzy evaluation membership matrices, and influence weight data; wherein, the single-factor fuzzy evaluation membership matrix is ​​determined based on expert experience and represents the membership degree of different factor levels of overvoltage amplitude, overvoltage duration, fault type, and system impedance to different influence levels. The data acquisition module is configured to acquire transient overvoltage data when a DC fault occurs in the new energy transmission grid, wherein the transient overvoltage data includes the overvoltage amplitude, overvoltage duration, fault type that caused the overvoltage, and system impedance of the grid. The first query module is configured to: query a set of factors based on the overvoltage amplitude, the overvoltage duration, the fault type, and the system impedance to determine the factor level to which the overvoltage amplitude, the overvoltage duration, the fault type, and the system impedance belong; The second query module is configured to: query the corresponding single-factor fuzzy evaluation membership matrix according to the factor level, so as to determine the membership degree of the overvoltage amplitude, the overvoltage duration, the fault type and the system impedance at different influence levels; The calculation module is configured to: calculate the final membership degree of each influence level by weighting the membership degree of the overvoltage amplitude, the overvoltage duration, the fault type, and the system impedance at different influence levels with the influence weights of each factor, in order to calculate the final membership degree of each influence level, and take the influence level corresponding to the maximum value of the final membership degree as the evaluation level.

6. The system according to claim 5, characterized in that, Based on historical transient overvoltage data samples, the entropy weight method is used to determine the influence weights of overvoltage amplitude, overvoltage duration, fault type and system impedance.

7. The system according to claim 5, characterized in that, The factor level of overvoltage amplitude is determined based on the proportion by which the overvoltage amplitude exceeds the equipment's withstand voltage value.

8. The system according to claim 5, characterized in that, The overvoltage amplitude is the peak value of the transient overvoltage when the fault occurs.

9. A readable storage medium, characterized in that, The readable storage medium stores a computer program; when the computer program is executed, it implements the method for assessing the impact of transient overvoltage during DC faults in the new energy power grid as described in any one of claims 1-4.

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