Method and device for preventing and controlling commutation failure risk of multi-direct-current feed-in system

Abstract

The invention relates to a commutation failure risk prevention and control method and device for a multi-direct-current feed-in system. The method comprises the steps of collecting current operation data and current network side voltage amplitude information of each DC inverter station of the multi-DC feed-in system, and identifying a network side voltage disturbance amplitude value of each DC inverter station; on the basis of the current operation data and the grid-side voltage disturbance amplitude value of each direct-current inverter station, calculating the grid-side voltage drop amplitude value of each direct-current inverter station except the abnormal direct-current inverter station, so as to predict a target direct-current inverter station with a commutation failure risk; generating a voltage disturbance adjustment strategy of the abnormal direct-current inverter station, and adjusting to obtain new operation data of the abnormal direct-current inverter station; and replacing the operation data of the abnormal direct-current inverter station with the new operation data, and returning to the above steps until the target direct-current inverter station with no commutation failure risk exists. By adopting the method, the risk prevention and control effect on the successive commutation failure of the multi-direct-current feed-in system can be improved.

Description

Technical Field

[0001] This application relates to the field of multiple DC feed systems and successive commutation failure risk prediction technology, and in particular to a method and apparatus for preventing and controlling commutation failure risk in a multiple DC feed system. Background Technology

[0002] With the large-scale development of new energy sources and the rapid growth of electricity load, the problem of power imbalance in regional power grids has become increasingly prominent. High-voltage direct current (HVDC) transmission systems, characterized by large transmission capacity, long transmission distance, and high transmission efficiency, have significant advantages in solving power imbalances and cross-regional power transmission problems. Currently, most HVDC transmission systems employ conventional HVDC transmission technology, which offers advantages such as large transmission capacity and low cost, making it one of the main methods for large-scale power transmission. However, current HVDC transmission systems cannot avoid commutation failure. In particular, when multiple conventional HVDC systems have multiple input points in a multi-HVDC system, a commutation failure in a single HVDC line often triggers subsequent commutation failures in multiple input systems, leading to severe power deficits and posing a serious threat to the safe and stable operation of the power grid. Therefore, improving the risk control effect of commutation failure in multi-HVDC systems is a current research focus.

[0003] Traditional risk control methods for commutation failure utilize Bayesian network theory. This involves establishing evidence indicators and successive commutation failure immunity factors based on converter bus voltage drop, DC current change rate, and harmonic distortion rate. Furthermore, a Bayesian network for successive commutation failure is constructed to infer the probability of successive commutation failure in multi-DC-feed systems, thereby controlling the risk of commutation failure. However, this method involves complex modeling and calculation processes, which has significant limitations for engineering applications and is not adaptable to the various forms of multi-DC-feed systems. Consequently, the risk control effect for successive commutation failure in multi-DC-feed systems is poor. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, device, computer equipment, computer-readable storage medium, and computer program product for preventing commutation failure risks in a multi-DC feed system, in response to the above-mentioned technical problems.

[0005] Firstly, this application provides a method for preventing commutation failure risk in a multi-DC feed system, including:

[0006] The system collects the current operating data of each DC inverter station in the multi-DC feed-in system, as well as the current grid-side voltage amplitude information of each DC inverter station, and identifies the grid-side voltage disturbance amplitude value of each DC inverter station based on the current grid-side voltage amplitude information of each DC inverter station.

[0007] When there is an abnormal DC inverter station corresponding to a grid-side voltage disturbance amplitude value greater than the preset grid-side voltage disturbance amplitude threshold, the grid-side voltage drop amplitude value of each DC inverter station other than the abnormal DC inverter station is calculated based on the current operating data of each DC inverter station and the grid-side voltage disturbance amplitude value of each DC inverter station. Based on the grid-side voltage drop amplitude value of each DC inverter station, the target DC inverter station with commutation failure risk is predicted.

[0008] Based on the grid-side voltage disturbance amplitude of the abnormal DC inverter station and the grid-side voltage drop amplitude of the target DC inverter station, a voltage disturbance adjustment strategy for the abnormal DC inverter station is generated, and the abnormal DC inverter station is adjusted based on the voltage disturbance adjustment strategy to obtain new operating data of the abnormal DC inverter station.

[0009] The new operating data replaces the operating data of the abnormal DC inverter station, and the process returns to the step of collecting the current operating data of each DC inverter station in the multi-DC feed-in system and the current grid-side voltage amplitude information of each DC inverter station, until there is no target DC inverter station with commutation failure risk, thus completing the commutation failure risk prevention and control task of the multi-DC feed-in system.

[0010] Optionally, identifying the grid-side voltage disturbance amplitude value of each DC inverter station based on the current grid-side voltage amplitude information of each DC inverter station includes:

[0011] For each DC inverter station, based on the current grid-side voltage amplitude information of the DC inverter station, the grid-side voltage amplitude variation distribution information of the DC inverter station is generated;

[0012] Based on the distribution information of the grid-side voltage amplitude variation of the DC inverter station, the trend of the grid-side voltage amplitude variation of the DC inverter station is identified, and based on the trend of the grid-side voltage amplitude variation, the grid-side voltage disturbance amplitude value of the DC inverter station is calculated.

[0013] Optionally, the step of calculating the grid-side voltage drop amplitude of each DC inverter station (excluding the abnormal DC inverter station) based on the current operating data of each DC inverter station and the grid-side voltage disturbance amplitude of each DC inverter station includes:

[0014] The algorithm for deriving the initial voltage drop magnitude between the abnormal DC inverter station and other DC inverter stations is obtained. Based on the current operating data of the abnormal DC inverter station and the current operating data of each DC inverter station, the algorithm for deriving the initial voltage drop magnitude between the abnormal DC inverter station and each DC inverter station is adjusted to obtain the voltage drop magnitude derivation algorithm corresponding to each DC inverter station.

[0015] Based on the grid-side voltage disturbance amplitude of the abnormal DC inverter station, the grid-side voltage sag amplitude of each DC inverter station is calculated using the voltage sag amplitude derivation algorithm corresponding to each DC inverter station.

[0016] Optionally, the prediction of target DC inverter stations with commutation failure risk based on the grid-side voltage drop amplitude of each of the aforementioned DC inverter stations includes:

[0017] Based on the grid-side voltage drop value of each DC inverter station, calculate the percentage value of the grid-side voltage drop value for each DC inverter station;

[0018] DC inverter stations with a grid-side voltage drop percentage exceeding a certain percentage threshold are considered as target DC inverter stations at risk of commutation failure.

[0019] Optionally, generating a voltage disturbance adjustment strategy for the abnormal DC inverter station based on the grid-side voltage disturbance amplitude value of the abnormal DC inverter station includes:

[0020] Based on the initial voltage drop amplitude derivation algorithm between the abnormal DC inverter station and the target DC inverter station, the correspondence between the grid-side voltage disturbance amplitude value of the abnormal DC inverter station and the grid-side voltage drop amplitude percentage value of the target DC inverter station is identified.

[0021] Based on the percentage threshold of the target DC inverter station, the target grid-side voltage disturbance amplitude value of the abnormal DC inverter station is identified through the correspondence. Based on the target grid-side voltage disturbance amplitude value and the grid-side voltage disturbance amplitude value of the abnormal DC inverter station, the operating parameter adjustment information of the abnormal DC inverter station is generated through a preset operating data adjustment strategy.

[0022] The operating parameter adjustment information of the abnormal DC inverter station is used as the voltage disturbance adjustment strategy for the abnormal DC inverter station.

[0023] Optionally, the adjustment process for the abnormal DC inverter station based on the voltage disturbance adjustment strategy to obtain new operating data for the abnormal DC inverter station includes:

[0024] Based on the operating parameter adjustment information of the abnormal DC inverter station, the data adjustment direction of each operating data type of the abnormal DC inverter station and the degree of single data adjustment of each operating data type are identified;

[0025] Based on the data adjustment direction of each type of operating data and the degree of single data adjustment of each type of operating data, the current sub-operating data of each type of operating data of the abnormal DC inverter station is processed by data adjustment to obtain each new sub-operating data of the abnormal DC inverter station.

[0026] The new sub-operational data of the abnormal DC inverter station are used as the new operation data of the abnormal DC inverter station.

[0027] Secondly, this application also provides a commutation failure risk prevention device for a multi-DC feed system, comprising:

[0028] The acquisition module is used to acquire the current operating data of each DC inverter station in the multi-DC feed-in system, as well as the current grid-side voltage amplitude information of each DC inverter station, and to identify the grid-side voltage disturbance amplitude value of each DC inverter station based on the current grid-side voltage amplitude information of each DC inverter station.

[0029] The prediction module is used to calculate the grid-side voltage drop amplitude of each DC inverter station other than the abnormal DC inverter station when there is an abnormal DC inverter station corresponding to a grid-side voltage disturbance amplitude value greater than a preset grid-side voltage disturbance amplitude threshold, based on the current operating data of each DC inverter station and the grid-side voltage disturbance amplitude value of each DC inverter station, and predict the target DC inverter station with commutation failure risk based on the grid-side voltage drop amplitude value of each DC inverter station.

[0030] The adjustment module is used to generate a voltage disturbance adjustment strategy for the abnormal DC inverter station based on the grid-side voltage disturbance amplitude value of the abnormal DC inverter station and the grid-side voltage drop amplitude value of the target DC inverter station, and to perform adjustment processing on the abnormal DC inverter station based on the voltage disturbance adjustment strategy to obtain new operating data of the abnormal DC inverter station.

[0031] The iterative module is used to replace the operating data of the abnormal DC inverter station with the new operating data, and return to the step of collecting the current operating data of each DC inverter station in the multi-DC feed-in system and the current grid-side voltage amplitude information of each DC inverter station, until there is no target DC inverter station with commutation failure risk, thus completing the commutation failure risk prevention and control task of the multi-DC feed-in system.

[0032] Optionally, the acquisition module is specifically used for:

[0033] For each DC inverter station, based on the current grid-side voltage amplitude information of the DC inverter station, the grid-side voltage amplitude variation distribution information of the DC inverter station is generated;

[0034] Based on the distribution information of the grid-side voltage amplitude variation of the DC inverter station, the trend of the grid-side voltage amplitude variation of the DC inverter station is identified, and based on the trend of the grid-side voltage amplitude variation, the grid-side voltage disturbance amplitude value of the DC inverter station is calculated.

[0035] Optionally, the prediction module is specifically used for:

[0036] The algorithm for deriving the initial voltage drop magnitude between the abnormal DC inverter station and other DC inverter stations is obtained. Based on the current operating data of the abnormal DC inverter station and the current operating data of each DC inverter station, the algorithm for deriving the initial voltage drop magnitude between the abnormal DC inverter station and each DC inverter station is adjusted to obtain the voltage drop magnitude derivation algorithm corresponding to each DC inverter station.

[0037] Based on the grid-side voltage disturbance amplitude of the abnormal DC inverter station, the grid-side voltage sag amplitude of each DC inverter station is calculated using the voltage sag amplitude derivation algorithm corresponding to each DC inverter station.

[0038] Optionally, the prediction module is specifically used for:

[0039] Based on the grid-side voltage drop value of each DC inverter station, calculate the percentage value of the grid-side voltage drop value for each DC inverter station;

[0040] DC inverter stations with a grid-side voltage drop percentage exceeding a certain percentage threshold are considered as target DC inverter stations at risk of commutation failure.

[0041] Optionally, the adjustment module is specifically used for:

[0042] Based on the initial voltage drop amplitude derivation algorithm between the abnormal DC inverter station and the target DC inverter station, the correspondence between the grid-side voltage disturbance amplitude value of the abnormal DC inverter station and the grid-side voltage drop amplitude percentage value of the target DC inverter station is identified.

[0043] Based on the percentage threshold of the target DC inverter station, the target grid-side voltage disturbance amplitude value of the abnormal DC inverter station is identified through the correspondence. Based on the target grid-side voltage disturbance amplitude value and the grid-side voltage disturbance amplitude value of the abnormal DC inverter station, the operating parameter adjustment information of the abnormal DC inverter station is generated through a preset operating data adjustment strategy.

[0044] The operating parameter adjustment information of the abnormal DC inverter station is used as the voltage disturbance adjustment strategy for the abnormal DC inverter station.

[0045] Optionally, the adjustment module is specifically used for:

[0046] Based on the operating parameter adjustment information of the abnormal DC inverter station, the data adjustment direction of each operating data type of the abnormal DC inverter station and the degree of single data adjustment of each operating data type are identified;

[0047] Based on the data adjustment direction of each type of operating data and the degree of single data adjustment of each type of operating data, the current sub-operating data of each type of operating data of the abnormal DC inverter station is processed by data adjustment to obtain each new sub-operating data of the abnormal DC inverter station.

[0048] The new sub-operational data of the abnormal DC inverter station are used as the new operating data of the abnormal DC inverter station.

[0049] Thirdly, this application provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method described in any one of the first aspects.

[0050] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the method described in any one of the first aspects.

[0051] Fifthly, this application provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the steps of the method described in any one of the first aspects.

[0052] The aforementioned method and apparatus for preventing commutation failure in multi-DC-infeed systems collects current operating data and current grid-side voltage amplitude information of each DC inverter station in the multi-DC-infeed system. Based on the current grid-side voltage amplitude information of each DC inverter station, it identifies the grid-side voltage disturbance amplitude value of each DC inverter station. When there is an abnormal DC inverter station corresponding to a grid-side voltage disturbance amplitude value greater than a preset grid-side voltage disturbance amplitude threshold, it calculates the grid-side voltage drop amplitude value of each DC inverter station other than the abnormal DC inverter station based on the current operating data and grid-side voltage disturbance amplitude value of each DC inverter station. Based on the grid-side voltage drop amplitude value of each DC inverter station, it predicts the existence of commutation failure. The system identifies the target DC inverter station at risk of commutation failure. Based on the grid-side voltage disturbance amplitude of the abnormal DC inverter station and the grid-side voltage drop amplitude of the target DC inverter station, a voltage disturbance adjustment strategy for the abnormal DC inverter station is generated. Based on this strategy, the abnormal DC inverter station is adjusted to obtain new operating data. This new operating data replaces the operating data of the abnormal DC inverter station. The system then returns to the previous steps of collecting the current operating data of each DC inverter station in the multi-DC feeder system and the current grid-side voltage amplitude information of each DC inverter station. This process continues until there are no target DC inverter stations at risk of commutation failure, thus completing the commutation failure risk prevention and control task for the multi-DC feeder system. This solution analyzes and judges the current operating data of each DC inverter station and the current grid-side voltage amplitude information to screen out abnormal inverter stations with grid-side voltage disturbance amplitude values ​​greater than the preset grid-side voltage disturbance amplitude threshold. Based on the correspondence between the abnormal inverter station and each DC inverter station, the algorithm calculates the grid-side voltage drop amplitude value in each DC inverter station to identify target DC inverter stations that may have commutation failure risks. Since a voltage drop of approximately 17% on the grid side of the converter valve can trigger commutation failure in conventional DC converter valves, this solution calculates the voltage drop on the grid side of each DC inverter station using a reactive power calculation algorithm. This allows for the prediction of target DC inverter stations at risk of commutation failure, avoiding the inefficiencies of complex modeling and simulation training with large amounts of engineering operation data. Furthermore, after predicting the target DC inverter stations at risk of commutation failure, this solution can generate adjustment strategies for abnormal DC inverter stations and adjust their operating parameters to effectively prevent commutation failure. This significantly improves the risk control effect for successive commutation failures in multi-DC-feed systems. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 This is a flowchart illustrating a method for preventing commutation failure risk in a multi-DC-feed system in one embodiment.

[0055] Figure 2 This is a schematic diagram of the network circuit structure of the power grid equivalent network of a multi-DC feed-in system in one embodiment.

[0056] Figure 3 This is a flowchart illustrating an example of commutation failure risk prevention in a multi-DC-feed system in one embodiment.

[0057] Figure 4 This is a structural block diagram of a commutation failure risk prevention device for a multi-DC feed system in one embodiment.

[0058] Figure 5 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0059] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0060] The commutation failure risk prevention method for multi-DC-infeed systems provided in this application embodiment can be applied to the application environment of commutation failure risk prevention for multi-DC-infeed systems. This method can be applied to terminals, servers, or systems including both terminals and servers, and is implemented through interaction between the terminals and servers. The terminals can be, but are not limited to, various personal computers, laptops, mid-range computers, etc. The terminal analyzes and judges the current operating data of each DC inverter station and the current grid-side voltage amplitude information to filter out abnormal inverter stations corresponding to grid-side voltage disturbance amplitude values ​​exceeding a preset grid-side voltage disturbance amplitude threshold. Based on the correspondence between these abnormal inverter stations and each DC inverter station, the terminal calculates the grid-side voltage drop amplitude value in each DC inverter station using an algorithm, thereby identifying target DC inverter stations that may have commutation failure risks. Since a voltage drop of approximately 17% on the grid side of the converter valve can trigger commutation failure in conventional DC converter valves, this solution calculates the voltage drop on the grid side of each DC inverter station using a reactive power calculation algorithm. This allows for the prediction of target DC inverter stations at risk of commutation failure, avoiding the inefficiencies of complex modeling and simulation training with large amounts of engineering operation data. Furthermore, after predicting the target DC inverter stations at risk of commutation failure, this solution can generate adjustment strategies for abnormal DC inverter stations and adjust their operating data to effectively prevent commutation failure. This significantly improves the risk control effect for successive commutation failures in multi-DC-feed systems.

[0061] In one exemplary embodiment, such as Figure 1 As shown, a method for preventing commutation failure risk in a multi-DC feed system is provided. Taking the application of this method to a terminal as an example, the method includes the following steps S101 to S104. Wherein:

[0062] Step S101: Collect the current operating data of each DC inverter station in the multi-DC feed-in system and the current grid-side voltage amplitude information of each DC inverter station, and identify the grid-side voltage disturbance amplitude value of each DC inverter station based on the current grid-side voltage amplitude information of each DC inverter station.

[0063] In this embodiment, the terminal feeds into the grid equivalent network (e.g., the DC inverter station of the multi-DC feedin system) of each DC inverter station. Figure 2As shown in the diagram, operational data for each DC inverter station is collected to obtain the current operational data for each DC inverter station. Then, the terminal collects grid-side voltage change information for each DC inverter station to obtain the current grid-side voltage amplitude information for each DC inverter station. This operational data includes, but is not limited to, the multi-infeed short-circuit ratio for each DC inverter station, the multi-infeed interaction factor between each DC inverter station and other DC inverter stations, DC current value, turn-off angle, and the number of 6-pulse converters. The acquisition method for each operational data is, for example, through methods such as... Figure 2 The equivalent network of the multi-DC feed-in system power grid shown includes four DC inverter stations: S1, S2, S3, and S4. Let the short-circuit capacity of station S1 be S. ac1 S2 station short-circuit capacity S ac2 S3 station short-circuit capacity S ac3 S4 station short-circuit capacity S ac4 Therefore, according to the existing definition of CIGRE multi-infeed short-circuit ratio, the C1 DC multi-infeed short-circuit ratio M1 is:

[0064] (1)

[0065] In the formula P d1 For C1 DC transmission power, P d2 For C2 DC transmission power, P d3 For C3 DC transmission power, P d4 For C4 DC transmission power, F MII21 F is the multi-feedback interaction factor between station S2 and station S1. MII31 F is the multi-feedback interaction factor between station S3 and station S1. MII41 This refers to the multi-feedback interaction factor between stations S4 and S1.

[0066] Similarly, for C2 DC multi-infeed short-circuit ratio M2, it is:

[0067] (2)

[0068] In the formula F MII12 F is the multi-feedback interaction factor between station S1 and station S2. MII32 F is the multi-feedback interaction factor between stations S3 and S2. MII42 This refers to the multi-feedback interaction factor between stations S4 and S2.

[0069] For the C3 DC multi-infeed short-circuit ratio M3, it is:

[0070] (3)

[0071] In the formula F MII13 F is the multi-feedback interaction factor between stations S1 and S3. MII23F is the multi-feedback interaction factor between stations S2 and S3. MII43 This refers to the multi-feedback interaction factor between stations S4 and S3.

[0072] For the C4 DC multi-infeed short-circuit ratio M4, it is:

[0073] (4)

[0074] In the formula F MII14 F is the multi-feedback interaction factor between stations S1 and S4. MII24 F is the multi-feedback interaction factor between stations S2 and S4. MII34 This represents the multi-infeed interaction factor between stations S3 and S4. Based on the above formula, for multi-DC infeed systems, the multi-infeed short-circuit ratio is strongly correlated with the coupling strength of the DC inverter stations. The stronger the coupling strength between a DC inverter station and its adjacent DC inverter stations, the smaller the multi-infeed short-circuit ratio of that DC system. Relatively speaking, commutation failure is more likely to occur when the grid-side voltage is disturbed. Then, the terminal identifies the grid-side voltage disturbance amplitude of each DC inverter station based on the current grid-side voltage amplitude information. The specific identification process will be explained in detail later. The grid-side voltage disturbance amplitude of each DC inverter station characterizes the fluctuation of the grid-side voltage amplitude of that DC inverter station. The greater the voltage amplitude fluctuation, the greater the grid-side voltage disturbance amplitude; the smaller the voltage amplitude fluctuation, the smaller the grid-side voltage disturbance amplitude.

[0075] Step S102: When there is an abnormal DC inverter station corresponding to a grid-side voltage disturbance amplitude value greater than the preset grid-side voltage disturbance amplitude threshold, the grid-side voltage drop amplitude value of each DC inverter station other than the abnormal DC inverter station is calculated based on the current operating data of each DC inverter station and the grid-side voltage disturbance amplitude value of each DC inverter station. Based on the grid-side voltage drop amplitude value of each DC inverter station, the target DC inverter station with commutation failure risk is predicted.

[0076] In this embodiment, the terminal determines whether there is a grid-side voltage disturbance amplitude value greater than a preset grid-side voltage disturbance amplitude threshold. If such a value exists, the DC inverter station corresponding to that value is designated as an abnormal DC inverter station. Then, based on the current operating data and grid-side voltage disturbance amplitude values ​​of each DC inverter station, the terminal calculates the grid-side voltage drop amplitude value for each DC inverter station (excluding the abnormal ones). The specific calculation process will be explained in detail later. Finally, based on the grid-side voltage drop amplitude values ​​of each DC inverter station, the terminal predicts target DC inverter stations with a risk of commutation failure. The percentage value of the grid-side voltage drop amplitude corresponding to the target DC inverter station's grid-side voltage drop amplitude value is greater than a preset percentage threshold. Since a voltage drop of about 17% on the grid side of the converter valve can cause commutation failure in a conventional DC converter valve, the terminal selects target DC inverter stations with a grid side voltage drop percentage greater than 17% as target DC inverter stations with the risk of commutation failure.

[0077] Step S103: Based on the grid-side voltage disturbance amplitude of the abnormal DC inverter station and the grid-side voltage drop amplitude of the target DC inverter station, a voltage disturbance adjustment strategy for the abnormal DC inverter station is generated. Based on the voltage disturbance adjustment strategy, the abnormal DC inverter station is adjusted to obtain new operating data of the abnormal DC inverter station.

[0078] In this embodiment, the terminal generates a voltage disturbance adjustment strategy for the abnormal DC inverter station based on the grid-side voltage disturbance amplitude of the abnormal DC inverter station and the grid-side voltage drop amplitude of the target DC inverter station. Based on this strategy, the terminal adjusts the abnormal DC inverter station to obtain new operating data. Adjusting the grid-side voltage disturbance amplitude of the abnormal DC inverter station reduces the grid-side voltage drop amplitude of each DC inverter station. Since this adjustment is achieved by adjusting the operating data of the abnormal DC inverter station, the voltage disturbance adjustment strategy is an adjustment strategy that modifies the operating parameters of the abnormal DC inverter station. The operating parameters affecting the grid-side voltage disturbance amplitude are primarily the DC current and turn-off angle of the abnormal DC inverter station. The specific adjustment process will be explained in detail later.

[0079] Step S104: Replace the operating data of the abnormal DC inverter station with the new operating data, and return to the step of collecting the current operating data of each DC inverter station in the multi-DC feed-in system and the current grid-side voltage amplitude information of each DC inverter station, until there is no target DC inverter station with commutation failure risk, thus completing the commutation failure risk prevention and control task of the multi-DC feed-in system.

[0080] In this embodiment, the terminal replaces the operating data of the abnormal DC inverter station with the new operating data, and returns to the step of collecting the current operating data of each DC inverter station in the multi-DC feed-in system and the current grid-side voltage amplitude information of each DC inverter station, until there is no target DC inverter station with commutation failure risk, thus completing the commutation failure risk prevention and control task of the multi-DC feed-in system.

[0081] Based on the above scheme, by analyzing and judging the current operating data of each DC inverter station and the current grid-side voltage amplitude information, abnormal inverter stations corresponding to grid-side voltage disturbance amplitude values ​​that are greater than the preset grid-side voltage disturbance amplitude threshold are screened. Based on the correspondence between the abnormal inverter station and each DC inverter station, the grid-side voltage drop amplitude value in each DC inverter station is calculated by combining the algorithm, thereby identifying the target DC inverter station that may have the risk of commutation failure. Since a voltage drop of approximately 17% on the grid side of the converter valve can trigger commutation failure in conventional DC converter valves, this solution calculates the voltage drop on the grid side of each DC inverter station using a reactive power calculation algorithm. This allows for the prediction of target DC inverter stations at risk of commutation failure, avoiding the inefficiencies of complex modeling and simulation training with large amounts of engineering operation data. Furthermore, after predicting the target DC inverter stations at risk of commutation failure, this solution can generate adjustment strategies for abnormal DC inverter stations and adjust their operating parameters to effectively prevent commutation failure. This significantly improves the risk control effect for successive commutation failures in multi-DC-feed systems.

[0082] Optionally, based on the current grid-side voltage amplitude information of each DC inverter station, the grid-side voltage disturbance amplitude value of each DC inverter station is identified, including: for each DC inverter station, generating grid-side voltage amplitude variation distribution information of the DC inverter station based on the current grid-side voltage amplitude information of the DC inverter station; identifying the grid-side voltage amplitude variation trend of the DC inverter station based on the grid-side voltage amplitude variation distribution information of the DC inverter station; and calculating the grid-side voltage disturbance amplitude value of the DC inverter station based on the grid-side voltage amplitude variation trend.

[0083] In this embodiment, the terminal generates grid-side voltage amplitude variation distribution information for each DC inverter station based on the current grid-side voltage amplitude information of the DC inverter station. The grid-side voltage amplitude variation distribution information is the distribution of the grid-side voltage amplitude variation over time.

[0084] Then, based on the distribution information of the grid-side voltage amplitude change of the DC inverter station, the terminal fits the grid-side voltage change curve of the DC inverter station through a linear fitting strategy, and identifies the trend of grid-side voltage amplitude change of the DC inverter station based on the grid-side voltage change curve of the DC inverter station.

[0085] Finally, the terminal calculates the grid-side voltage disturbance amplitude value of the DC inverter station based on the trend of grid-side voltage amplitude change.

[0086] Based on the above scheme, by performing distribution analysis on the current grid-side voltage amplitude information of each DC inverter station and then calculating the grid-side voltage disturbance amplitude value of each DC inverter station, the accuracy of the calculated grid-side voltage disturbance amplitude value of each DC inverter station is improved.

[0087] Optionally, based on the current operating data of each DC inverter station and the grid-side voltage disturbance amplitude value of each DC inverter station, the grid-side voltage sag amplitude value of each DC inverter station (excluding the abnormal DC inverter station) is calculated. This includes: obtaining the initial voltage sag amplitude derivation algorithm between the abnormal DC inverter station and other DC inverter stations, and adjusting the initial voltage sag amplitude derivation algorithm between the abnormal DC inverter station and other DC inverter stations based on the current operating data of the abnormal DC inverter station and the current operating data of each DC inverter station, to obtain the voltage sag amplitude derivation algorithm corresponding to each DC inverter station; and calculating the grid-side voltage sag amplitude value of each DC inverter station based on the grid-side voltage disturbance amplitude value of the abnormal DC inverter station and the voltage sag amplitude derivation algorithm corresponding to each DC inverter station.

[0088] In this embodiment, the terminal obtains the initial voltage sag magnitude derivation algorithm between the abnormal DC inverter station and other DC inverter stations. Based on the current operating data of the abnormal DC inverter station and the current operating data of each DC inverter station, the terminal adjusts the initial voltage sag magnitude derivation algorithm between the abnormal DC inverter station and each DC inverter station to obtain the voltage sag magnitude derivation algorithm corresponding to each DC inverter station. The algorithm generation process of the initial voltage sag magnitude derivation algorithm between the abnormal DC inverter station and each DC inverter station will be explained in detail later.

[0089] Then, based on the grid-side voltage disturbance amplitude value of the abnormal DC inverter station, the terminal calculates the grid-side voltage drop amplitude value of each DC inverter station through the voltage drop amplitude derivation algorithm corresponding to each DC inverter station.

[0090] Specifically, such as Figure 2As shown, for DC converter station C1, assuming the reactive power absorbed by the converter station during operation is Q1, and the reactive power exchanged between C1 and stations S2, S3, and S4 are Q2, Q3, and Q4 respectively, then for DC inverter station C1, the formula for calculating Q1 is:

[0091] Q1=P d tanφ(5)

[0092] In the formula P d Let φ be the active power transmitted by the C1 DC system, and φ be the power factor angle of the C1 DC inverter station. However, P d The calculation formula is:

[0093] P d =U d ×I d (6)

[0094] In the formula U d For C1, the DC voltage of the DC system is I. d It is a direct current. However, U d The calculation formula is:

[0095] U d =N(1.35U2cosγ-3 / πX r2 I d (7)

[0096] In the formula, U2 is the effective value of the no-load rated line voltage on the valve side of the C1 DC inverter station, γ is the turn-off angle, and X... r2 N represents the commutation reactance per phase, and N is the number of 6-pulse converters.

[0097] but:

[0098] Q1=N(1.35U2cosγ-3 / πX r2 I d )I d tanφ(8)

[0099] again:

[0100] (9)

[0101] but:

[0102] (10)

[0103] so:

[0104] (11)

[0105] Let the reactive power output of the filter in DC inverter station C1 be Q. f The reactive power exchanged between the C1 DC inverter station and the grid (with the input inverter station in the forward direction) is Q.G ,but:

[0106] Q1=Q f +Q G +Q2+Q3+Q4(12)

[0107] The field of the C1 DC filter is located on the grid side, therefore;

[0108] Q f =B 2 nU2 (13)

[0109] In the formula, B is the equivalent reactance of the filter, and n is the commutator ratio.

[0110] When a fault with a depth of F occurs on the grid side of DC inverter station C1 and the grid side voltage of DC inverter station C1 drops by ΔU2 during the fault period, the reactive power absorbed by the inverter station from the grid is:

[0111] (14)

[0112] At this time, the reactive power exchanged between the C2, C3, and C4 DC inverter stations and the receiving-end grid of the C1 DC inverter station are as follows:

[0113] (15)

[0114] (16)

[0115] (17)

[0116] In the formula This represents the effective value of the unloaded line voltage on the valve side of the C1 DC inverter station after the voltage drop. This represents the effective value of the unloaded line voltage on the valve side of the C2 DC inverter station. This represents the effective value of the unloaded line voltage on the valve side of the C3 DC inverter station. This is the effective value of the unloaded line voltage on the valve side of the C4 DC inverter station.

[0117] Substituting into equation (13) and further refining, we get:

[0118] (18)

[0119] When a fault of depth F occurs on the grid side of DC inverter station C1, causing a voltage drop, the other inverter stations simultaneously experience voltage drops of a certain degree on their grid sides. Based on the definition of the multi-infeed interaction factor, the voltage drops on the grid sides of the other inverter stations are calculated and converted back to the voltage amplitude before the drops, resulting in:

[0120] (19)

[0121] In the formula U22 U is the effective value of the no-load rated line voltage on the valve side of the C2 DC inverter station. 23 U is the effective value of the no-load rated line voltage on the valve side of the C3 DC inverter station. 24 This is the effective value of the unloaded rated line voltage on the valve side of the C4 DC inverter station.

[0122] Based on the definition of the multi-feedback interaction factor, we can derive:

[0123] (20)

[0124] (twenty one)

[0125] (twenty two)

[0126] Substituting equations (20), (21), and (22) into equation (19), we get:

[0127] (twenty three)

[0128] According to equation (23), the converted reactive power can also be obtained:

[0129] (twenty four)

[0130] (25)

[0131] (26)

[0132] Based on the quantitative relationship between voltage and reactive power, the grid-side voltage drop of DC inverter station C2 and DC inverter station C3 are further calculated. C4 DC inverter station grid-side voltage drop for:

[0133] (27)

[0134] (28)

[0135] (29)

[0136] Based on the above scheme, by identifying the voltage drop amplitude between abnormal DC inverter stations and each DC inverter station, the grid-side voltage drop amplitude value of each DC inverter station other than the abnormal DC inverter station is calculated, thereby improving the calculation accuracy of each grid-side voltage drop amplitude value.

[0137] Optionally, based on the grid-side voltage drop amplitude of each DC inverter station, the target DC inverter station with commutation failure risk is predicted, including: calculating the grid-side voltage drop amplitude percentage value of each DC inverter station based on the grid-side voltage drop amplitude value of each DC inverter station; and identifying the DC inverter station with a grid-side voltage drop amplitude percentage value greater than the percentage threshold as the target DC inverter station with commutation failure risk.

[0138] In this embodiment, the terminal calculates the percentage value of the grid-side voltage sag for each DC inverter station based on the grid-side voltage sag value of each DC inverter station. The percentage calculation algorithm is as follows:

[0139] (30)

[0140] (31)

[0141] (32)

[0142] Then, the terminal identifies DC inverter stations with grid-side voltage drop percentages exceeding a certain percentage threshold as target DC inverter stations at risk of commutation failure. This percentage threshold is a preset value in the terminal, such as 17%.

[0143] Based on the above scheme, since a voltage drop of about 17% on the grid side of the converter valve can cause commutation failure in a conventional DC converter valve, a preset percentage threshold is used to screen target DC inverter stations with the risk of commutation failure, thereby improving the efficiency and accuracy of the screening.

[0144] Optionally, based on the grid-side voltage disturbance amplitude value of the abnormal DC inverter station, a voltage disturbance adjustment strategy for the abnormal DC inverter station is generated, including: based on an initial voltage drop amplitude derivation algorithm between the abnormal DC inverter station and the target DC inverter station, identifying the correspondence between the grid-side voltage disturbance amplitude value of the abnormal DC inverter station and the percentage value of the grid-side voltage drop amplitude of the target DC inverter station; based on the percentage threshold of the target DC inverter station, identifying the target grid-side voltage disturbance amplitude value of the abnormal DC inverter station through the correspondence, and based on the target grid-side voltage disturbance amplitude value and the grid-side voltage disturbance amplitude value of the abnormal DC inverter station, generating operating parameter adjustment information for the abnormal DC inverter station through a preset operating parameter adjustment strategy; and using the operating parameter adjustment information of the abnormal DC inverter station as the voltage disturbance adjustment strategy for the abnormal DC inverter station.

[0145] In this embodiment, the terminal uses an algorithm to derive the initial voltage sag between the abnormal DC inverter station and the target DC inverter station to identify the correspondence between the grid-side voltage disturbance amplitude value of the abnormal DC inverter station and the percentage value of the grid-side voltage sag amplitude of the target DC inverter station. This correspondence is an algorithmic correspondence between the grid-side voltage disturbance amplitude value and the percentage value of the grid-side voltage sag amplitude. The terminal performs reverse algorithmic derivation on the algorithm for calculating the percentage value of the grid-side voltage sag amplitude of the target DC inverter station from the grid-side voltage disturbance amplitude value of the abnormal DC inverter station to obtain this algorithmic correspondence.

[0146] Then, based on the percentage threshold of the target DC inverter station, the terminal calculates the target grid-side voltage disturbance amplitude value of the abnormal DC inverter station using the aforementioned algorithm correspondence. Next, based on the target grid-side voltage disturbance amplitude value and the grid-side voltage disturbance amplitude value of the abnormal DC inverter station, the terminal generates operating parameter adjustment information for the abnormal DC inverter station using a preset operating parameter adjustment strategy. This operating parameter adjustment information includes the direction of operating data adjustment and the degree of adjustment for each instance. The direction of operating data adjustment refers to the direction of increase or decrease in operating data, such as the direction of increase or decrease in DC current or the direction of increase or decrease in the turn-off angle. The degree of adjustment for each instance refers to the amount of adjustment of operating data in a single instance, such as the amount of adjustment in DC current or the amount of adjustment in the turn-off angle. This operating parameter adjustment information includes the amount of adjustment for each instance corresponding to the range of deviations between different target grid-side voltage disturbance amplitude values ​​and grid-side voltage disturbance amplitude values. Based on the target grid-side voltage disturbance amplitude value calculated this time and the deviation value range between the target grid-side voltage disturbance amplitude value and the grid-side voltage disturbance amplitude value of the abnormal DC inverter station, the terminal determines the single adjustment amount corresponding to each operating data.

[0147] Finally, the terminal uses the operating parameter adjustment information of the abnormal DC inverter station as a voltage disturbance adjustment strategy for the abnormal DC inverter station.

[0148] Based on the above scheme, the operating parameter adjustment information of abnormal DC inverter stations is identified by reverse algorithm deduction, which improves the identification efficiency while ensuring the accuracy of the identification of operating parameter adjustment information.

[0149] Optionally, based on the voltage disturbance adjustment strategy, the abnormal DC inverter station is adjusted to obtain new operating data of the abnormal DC inverter station, including: based on the operating parameter adjustment information of the abnormal DC inverter station, identifying the parameter adjustment direction of each operating data type of the abnormal DC inverter station and the degree of single parameter adjustment of each operating data type; based on the parameter adjustment direction of each operating data type and the degree of single parameter adjustment of each operating data type, performing parameter adjustment processing on the current sub-operating data of each operating data type of the abnormal DC inverter station to obtain each new sub-operating data of the abnormal DC inverter station; and using each new sub-operating data of the abnormal DC inverter station as the new operating data of the abnormal DC inverter station.

[0150] In this embodiment, the terminal identifies the parameter adjustment direction and the degree of single data adjustment for each data type of the abnormal DC inverter station based on the operational parameter adjustment information. Then, based on the data adjustment direction and degree of single data adjustment for each data type, the terminal performs data adjustment processing on the current sub-operational data of each data type of the abnormal DC inverter station to obtain new sub-operational data for the abnormal DC inverter station. Finally, the terminal uses this new sub-operational data as the new operational data for the abnormal DC inverter station.

[0151] Based on the above scheme, by iteratively adjusting the data step by step, the accuracy of the adjustment and the stability of each DC inverter station after the adjustment are ensured, thus avoiding the problem of a single large-scale adjustment affecting the operational stability of the multi-DC feed-in system.

[0152] This application also provides an example of risk prevention and control for successive commutation failure in a multi-DC feed system, such as... Figure 3 As shown, the specific processing procedure includes the following steps:

[0153] Step S301: Collect the current operating data of each DC inverter station in the multi-DC feed-in system, as well as the current grid-side voltage amplitude information of each DC inverter station.

[0154] Step S302: For each DC inverter station, generate grid-side voltage amplitude variation distribution information of the DC inverter station based on the current grid-side voltage amplitude information of the DC inverter station.

[0155] Step S303: Based on the distribution information of the grid-side voltage amplitude change of the DC inverter station, identify the trend of the grid-side voltage amplitude change of the DC inverter station, and calculate the grid-side voltage disturbance amplitude value of the DC inverter station based on the trend of the grid-side voltage amplitude change.

[0156] Step S304: Obtain the initial voltage drop magnitude derivation algorithm between the abnormal DC inverter station and other DC inverter stations, and adjust the initial voltage drop magnitude derivation algorithm between the abnormal DC inverter station and other DC inverter stations based on the current operating data of the abnormal DC inverter station and the current operating data of each DC inverter station to obtain the voltage drop magnitude derivation algorithm corresponding to each DC inverter station.

[0157] Step S305: Based on the grid-side voltage disturbance amplitude value of the abnormal DC inverter station, calculate the grid-side voltage drop amplitude value of each DC inverter station using the voltage drop amplitude derivation algorithm corresponding to each DC inverter station.

[0158] Step S306: Calculate the percentage value of the grid-side voltage drop for each DC inverter station based on the grid-side voltage drop value for each DC inverter station.

[0159] Step S307: The DC inverter station corresponding to the percentage value of the grid-side voltage drop that is greater than the percentage threshold is regarded as the target DC inverter station with the risk of commutation failure.

[0160] Step S308: Based on the algorithm for deriving the initial voltage drop amplitude between the abnormal DC inverter station and the target DC inverter station, identify the correspondence between the grid-side voltage disturbance amplitude value of the abnormal DC inverter station and the percentage value of the grid-side voltage drop amplitude of the target DC inverter station.

[0161] Step S309: Based on the percentage threshold of the target DC inverter station, the target grid-side voltage disturbance amplitude value of the abnormal DC inverter station is identified through the correspondence. Based on the target grid-side voltage disturbance amplitude value and the grid-side voltage disturbance amplitude value of the abnormal DC inverter station, the operating parameter adjustment information of the abnormal DC inverter station is generated through a preset operating parameter adjustment strategy.

[0162] Step S310: The operating parameter adjustment information of the abnormal DC inverter station is used as the voltage disturbance adjustment strategy for the abnormal DC inverter station.

[0163] Step S311: Based on the operating parameter adjustment information of the abnormal DC inverter station, identify the data adjustment direction of each operating data type of the abnormal DC inverter station, as well as the degree of single data adjustment of each operating data type.

[0164] Step S312: Based on the data adjustment direction of each operating data type and the degree of single data adjustment of each operating data type, perform data adjustment processing on the current sub-operating data of each operating data type of the abnormal DC inverter station to obtain the new sub-operating data of the abnormal DC inverter station.

[0165] Step S313: Use the new sub-operational data of each abnormal DC inverter station as the new operation data of the abnormal DC inverter station.

[0166] Step S314: Replace the operating data of the abnormal DC inverter station with the new operating data, and return to the step of collecting the current operating data of each DC inverter station in the multi-DC feed-in system and the current grid-side voltage amplitude information of each DC inverter station, until there is no risk of commutation failure.

[0167] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0168] Based on the same inventive concept, this application also provides a commutation failure risk prevention and control device for a multi-DC feeder system, used to implement the commutation failure risk prevention and control method for the multi-DC feeder system described above. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations in the one or more embodiments of the commutation failure risk prevention and control device for a multi-DC feeder system provided below can be found in the limitations of the commutation failure risk prevention and control method for multi-DC feeder systems described above, and will not be repeated here.

[0169] In one exemplary embodiment, such as Figure 4 As shown, a commutation failure risk prevention device for a multi-DC feed system is provided, comprising: a data acquisition module 410, a prediction module 420, an adjustment module 430, and an iteration module 440, wherein:

[0170] The acquisition module 410 is used to acquire the current operating data of each DC inverter station in the multi-DC feed-in system and the current grid-side voltage amplitude information of each DC inverter station, and to identify the grid-side voltage disturbance amplitude value of each DC inverter station based on the current grid-side voltage amplitude information of each DC inverter station.

[0171] The prediction module 420 is used to calculate the grid-side voltage drop amplitude of each DC inverter station other than the abnormal DC inverter station when there is an abnormal DC inverter station corresponding to a grid-side voltage disturbance amplitude value greater than a preset grid-side voltage disturbance amplitude threshold, based on the current operating data of each DC inverter station and the grid-side voltage disturbance amplitude value of each DC inverter station, and predict the target DC inverter station with commutation failure risk based on the grid-side voltage drop amplitude value of each DC inverter station.

[0172] The adjustment module 430 is used to generate a voltage disturbance adjustment strategy for the abnormal DC inverter station based on the grid-side voltage disturbance amplitude value of the abnormal DC inverter station and the grid-side voltage drop amplitude value of the target DC inverter station, and to perform adjustment processing on the abnormal DC inverter station based on the voltage disturbance adjustment strategy to obtain new operating data of the abnormal DC inverter station.

[0173] The iteration module 440 is used to replace the operating data of the abnormal DC inverter station with the new operating data, and return to the step of collecting the current operating data of each DC inverter station in the multi-DC feed-in system and the current grid-side voltage amplitude information of each DC inverter station, until there is no target DC inverter station with commutation failure risk, thus completing the commutation failure risk prevention and control task of the multi-DC feed-in system.

[0174] Optionally, the acquisition module 410 is specifically used for:

[0175] For each DC inverter station, based on the current grid-side voltage amplitude information of the DC inverter station, the grid-side voltage amplitude variation distribution information of the DC inverter station is generated;

[0176] Based on the distribution information of the grid-side voltage amplitude variation of the DC inverter station, the trend of the grid-side voltage amplitude variation of the DC inverter station is identified, and based on the trend of the grid-side voltage amplitude variation, the grid-side voltage disturbance amplitude value of the DC inverter station is calculated.

[0177] Optionally, the prediction module 420 is specifically used for:

[0178] The algorithm for deriving the initial voltage drop magnitude between the abnormal DC inverter station and other DC inverter stations is obtained. Based on the current operating data of the abnormal DC inverter station and the current operating data of each DC inverter station, the algorithm for deriving the initial voltage drop magnitude between the abnormal DC inverter station and each DC inverter station is adjusted to obtain the voltage drop magnitude derivation algorithm corresponding to each DC inverter station.

[0179] Based on the grid-side voltage disturbance amplitude of the abnormal DC inverter station, the grid-side voltage sag amplitude of each DC inverter station is calculated using the voltage sag amplitude derivation algorithm corresponding to each DC inverter station.

[0180] Optionally, the prediction module 420 is specifically used for:

[0181] Based on the grid-side voltage drop value of each DC inverter station, calculate the percentage value of the grid-side voltage drop value for each DC inverter station;

[0182] DC inverter stations with a grid-side voltage drop percentage exceeding a certain percentage threshold are considered as target DC inverter stations at risk of commutation failure.

[0183] Optionally, the adjustment module 430 is specifically used for:

[0184] Based on the initial voltage drop amplitude derivation algorithm between the abnormal DC inverter station and the target DC inverter station, the correspondence between the grid-side voltage disturbance amplitude value of the abnormal DC inverter station and the grid-side voltage drop amplitude percentage value of the target DC inverter station is identified.

[0185] Based on the percentage threshold of the target DC inverter station, the target grid-side voltage disturbance amplitude value of the abnormal DC inverter station is identified through the correspondence. Based on the target grid-side voltage disturbance amplitude value and the grid-side voltage disturbance amplitude value of the abnormal DC inverter station, the operating parameter adjustment information of the abnormal DC inverter station is generated through a preset operating data adjustment strategy.

[0186] The operating parameter adjustment information of the abnormal DC inverter station is used as the voltage disturbance adjustment strategy for the abnormal DC inverter station.

[0187] Optionally, the adjustment module 430 is specifically used for:

[0188] Based on the operating parameter adjustment information of the abnormal DC inverter station, the data adjustment direction of each operating data type of the abnormal DC inverter station and the degree of single data adjustment of each operating data type are identified;

[0189] Based on the data adjustment direction of each type of operating data and the degree of single data adjustment of each type of operating data, the current sub-operating data of each type of operating data of the abnormal DC inverter station is processed by data adjustment to obtain each new sub-operating data of the abnormal DC inverter station.

[0190] The new sub-operational data of the abnormal DC inverter station are used as the new operating data of the abnormal DC inverter station.

[0191] Each module in the aforementioned commutation failure risk prevention device for multi-DC feed systems can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the computer device's memory as software, so that the processor can call and execute the corresponding operations of each module.

[0192] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 5 As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When executed by the processor, the computer program implements a method for mitigating commutation failure risks in a multi-DC-feed system. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0193] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0194] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described in any one of the first aspects.

[0195] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any one of the first aspects.

[0196] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the method described in any one of the first aspects.

[0197] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0198] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0199] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0200] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for preventing commutation failure risk in a multi-DC feed system, characterized in that, The method includes: The system collects the current operating data of each DC inverter station in the multi-DC feed-in system, as well as the current grid-side voltage amplitude information of each DC inverter station, and identifies the grid-side voltage disturbance amplitude value of each DC inverter station based on the current grid-side voltage amplitude information of each DC inverter station. When there is an abnormal DC inverter station corresponding to a grid-side voltage disturbance amplitude value greater than the preset grid-side voltage disturbance amplitude threshold, the grid-side voltage drop amplitude value of each DC inverter station other than the abnormal DC inverter station is calculated based on the current operating data of each DC inverter station and the grid-side voltage disturbance amplitude value of each DC inverter station. Based on the grid-side voltage drop amplitude value of each DC inverter station, the target DC inverter station with commutation failure risk is predicted. Based on the grid-side voltage disturbance amplitude of the abnormal DC inverter station and the grid-side voltage drop amplitude of the target DC inverter station, a voltage disturbance adjustment strategy for the abnormal DC inverter station is generated, and the abnormal DC inverter station is adjusted based on the voltage disturbance adjustment strategy to obtain new operating data of the abnormal DC inverter station. The new operating data replaces the operating data of the abnormal DC inverter station, and the process returns to the step of collecting the current operating data of each DC inverter station in the multi-DC feed-in system and the current grid-side voltage amplitude information of each DC inverter station, until there is no target DC inverter station with commutation failure risk, thus completing the commutation failure risk prevention and control task of the multi-DC feed-in system.

2. The method according to claim 1, characterized in that, The step of identifying the grid-side voltage disturbance amplitude value of each DC inverter station based on the current grid-side voltage amplitude information of each DC inverter station includes: For each DC inverter station, based on the current grid-side voltage amplitude information of the DC inverter station, the grid-side voltage amplitude variation distribution information of the DC inverter station is generated; Based on the distribution information of the grid-side voltage amplitude variation of the DC inverter station, the trend of the grid-side voltage amplitude variation of the DC inverter station is identified, and based on the trend of the grid-side voltage amplitude variation, the grid-side voltage disturbance amplitude value of the DC inverter station is calculated.

3. The method according to claim 2, characterized in that, The calculation of the grid-side voltage drop amplitude for each DC inverter station (excluding the abnormal DC inverter station) based on the current operating data and the grid-side voltage disturbance amplitude of each DC inverter station includes: The algorithm for deriving the initial voltage drop magnitude between the abnormal DC inverter station and other DC inverter stations is obtained. Based on the current operating data of the abnormal DC inverter station and the current operating data of each DC inverter station, the algorithm for deriving the initial voltage drop magnitude between the abnormal DC inverter station and each DC inverter station is adjusted to obtain the voltage drop magnitude derivation algorithm corresponding to each DC inverter station. Based on the grid-side voltage disturbance amplitude of the abnormal DC inverter station, the grid-side voltage sag amplitude of each DC inverter station is calculated using the voltage sag amplitude derivation algorithm corresponding to each DC inverter station.

4. The method according to claim 1, characterized in that, The target DC inverter stations predicted to have commutation failure risk based on the grid-side voltage drop amplitude of each of the aforementioned DC inverter stations include: Based on the grid-side voltage drop value of each DC inverter station, calculate the percentage value of the grid-side voltage drop value for each DC inverter station; DC inverter stations with a grid-side voltage drop percentage exceeding a certain percentage threshold are considered as target DC inverter stations at risk of commutation failure.

5. The method according to claim 3, characterized in that, The step of generating a voltage disturbance adjustment strategy for the abnormal DC inverter station based on the grid-side voltage disturbance amplitude value includes: Based on the initial voltage drop amplitude derivation algorithm between the abnormal DC inverter station and the target DC inverter station, the correspondence between the grid-side voltage disturbance amplitude value of the abnormal DC inverter station and the grid-side voltage drop amplitude percentage value of the target DC inverter station is identified. Based on the percentage threshold of the target DC inverter station, the target grid-side voltage disturbance amplitude value of the abnormal DC inverter station is identified through the correspondence. Based on the target grid-side voltage disturbance amplitude value and the grid-side voltage disturbance amplitude value of the abnormal DC inverter station, the operating parameter adjustment information of the abnormal DC inverter station is generated through a preset operating data adjustment strategy. The operating parameter adjustment information of the abnormal DC inverter station is used as the voltage disturbance adjustment strategy for the abnormal DC inverter station.

6. The method according to claim 5, characterized in that, The adjustment process based on the voltage disturbance adjustment strategy for the abnormal DC inverter station, to obtain new operating data for the abnormal DC inverter station, includes: Based on the operating parameter adjustment information of the abnormal DC inverter station, the data adjustment direction of each operating data type of the abnormal DC inverter station and the degree of single data adjustment of each operating data type are identified; Based on the data adjustment direction of each type of operating data and the degree of single data adjustment of each type of operating data, the current sub-operating data of each type of operating data of the abnormal DC inverter station is processed by data adjustment to obtain each new sub-operating data of the abnormal DC inverter station. The new sub-operational data of the abnormal DC inverter station are used as the new operation data of the abnormal DC inverter station.

7. A commutation failure risk prevention and control device for a multi-DC feed system, characterized in that, The device includes: The acquisition module is used to acquire the current operating data of each DC inverter station in the multi-DC feed-in system, as well as the current grid-side voltage amplitude information of each DC inverter station, and to identify the grid-side voltage disturbance amplitude value of each DC inverter station based on the current grid-side voltage amplitude information of each DC inverter station. The prediction module is used to calculate the grid-side voltage drop amplitude of each DC inverter station other than the abnormal DC inverter station when there is an abnormal DC inverter station corresponding to a grid-side voltage disturbance amplitude value greater than a preset grid-side voltage disturbance amplitude threshold, based on the current operating data of each DC inverter station and the grid-side voltage disturbance amplitude value of each DC inverter station, and predict the target DC inverter station with commutation failure risk based on the grid-side voltage drop amplitude value of each DC inverter station. The adjustment module is used to generate a voltage disturbance adjustment strategy for the abnormal DC inverter station based on the grid-side voltage disturbance amplitude value of the abnormal DC inverter station and the grid-side voltage drop amplitude value of the target DC inverter station, and to perform adjustment processing on the abnormal DC inverter station based on the voltage disturbance adjustment strategy to obtain new operating data of the abnormal DC inverter station. The iterative module is used to replace the operating data of the abnormal DC inverter station with the new operating data, and return to the step of collecting the current operating data of each DC inverter station in the multi-DC feed-in system and the current grid-side voltage amplitude information of each DC inverter station, until there is no target DC inverter station with commutation failure risk, thus completing the commutation failure risk prevention and control task of the multi-DC feed-in system.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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