A simulation method for power distribution network faults for optimized configuration of superconducting current limiters

Abstract

This application discloses a power distribution network fault simulation method for optimizing the configuration of superconducting current limiters, relating to the field of data processing technology. The method includes the following steps: acquiring real-time operating parameters of the power distribution network monitored by various sensors; calculating the anomaly degree of an indicator corresponding to each sensor location based on the real-time operating parameters; determining the fault evolution progress of each sensor location based on the anomaly degree; determining the overall fault severity of the power distribution network in the current cycle based on the fault evolution progress of each sensor location; and performing current limiting simulation on the power distribution network based on the fault severity to obtain simulation results. This application improves the actual current limiting effect of the current limiter during faults.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method for simulating power distribution network faults for optimizing the configuration of superconducting current limiters. Background Technology

[0002] Resistive Superconducting Fault Current Limiter (RSFCL) is a new type of power equipment that can rapidly increase its impedance and limit short-circuit current when a fault occurs in the power grid, thereby protecting the power system from damage. During normal operation, RSFCL utilizes the zero-impedance characteristics of superconducting materials to ensure the normal operation of the power grid. When a fault occurs, RSFCL can quickly enter a quench state, providing high impedance to suppress the flow of fault current and ensure that the protection equipment can respond in a timely manner.

[0003] Currently, in applications where current limiters are configured in distribution networks, faults generated in the distribution network are assessed, and current is limited by the current passing through the current limiter and the calibrated limiting rules. However, in the case of multi-point conduction distribution of faults in actual distribution networks, the current passing through the current limiter is actually quite different from the abnormal current environment generated at the fault point, resulting in poor actual current limiting effect of the current limiter on the fault. Summary of the Invention

[0004] The main purpose of this application is to provide a power grid fault simulation method for optimizing the configuration of superconducting current limiters. It aims to solve the technical problem in related technologies where the current passing through the current limiter is limited according to the calibrated limiting rules, but the actual current passing through the current limiter is far from the abnormal current environment generated at the fault point, resulting in poor actual current limiting effect of the current limiter for faults.

[0005] To achieve the above objectives, embodiments of this application provide a method for simulating power distribution network faults for optimizing the configuration of superconducting current limiters, comprising:

[0006] Obtain real-time operating parameters of the power distribution network monitored by various sensors;

[0007] Based on real-time operating parameters, the anomaly degree of the index corresponding to each sensor location is calculated, and based on the anomaly degree of the index, the failure evolution progress of each sensor location is determined.

[0008] Based on the fault evolution progress at each sensor location, the overall fault severity of the distribution network in the current round is determined;

[0009] The current limiting simulation of the distribution network is performed based on the severity of the fault, and the simulation results are obtained.

[0010] In one possible implementation of this application, determining the fault evolution progress at each sensor location based on the index anomaly degree includes:

[0011] Filter out abnormal indicators that exceed preset limits from real-time operating parameters;

[0012] For any sensor location corresponding to an abnormal indicator, the abnormal deviation magnitude of the indicator corresponding to the current sensor location is calculated based on the abnormality degree of the normal indicator and the abnormality degree of the abnormal indicator. The abnormality degree of the indicator corresponding to the abnormal indicator is called the abnormal indicator abnormality degree, and the abnormality degree of the indicator corresponding to the normal indicator other than the abnormal indicator is called the normal indicator abnormality degree.

[0013] Based on the abnormal deviation amplitude corresponding to each sensor location, a deviation comparison sequence is generated, which is used to characterize the fault evolution progress of each sensor location in the current round.

[0014] In one possible implementation of this application, the overall fault severity of the distribution network in the current round is determined based on the fault evolution progress at each sensor location, including:

[0015] The deviation comparison sequence is split to obtain the fault scale data generated in the current round;

[0016] Based on the fault scale data, calculate the overall fault severity of the distribution network in the current cycle.

[0017] In one possible implementation of this application, the deviation comparison sequence is split to obtain fault scale data generated in the current round, including:

[0018] Based on the deviation comparison sequence, the fault data returned from each sensor location is numbered to obtain a number sequence corresponding to multiple fault data.

[0019] The fault data corresponding to each sensor location and belonging to the same number sequence are divided into fault scale data generated in one round to obtain the fault scale data generated in the current round.

[0020] In one possible implementation of this application, the overall fault severity of the distribution network in the current cycle is calculated based on fault scale data, including:

[0021] The locations of sensors belonging to the same fault cycle in the fault scale data are marked to obtain multiple marked locations;

[0022] Determine the marker sequence and branch sequence of the marker sequence between the location of the current limiter and the marker location of the earliest backhaul fault;

[0023] Based on the label sequence, branch sequence, and abnormal deviation magnitude, the overall fault severity of the distribution network in the current round is calculated.

[0024] In one possible implementation of this application, the overall fault severity of the distribution network in the current round is calculated based on the marker sequence, branch sequence, and abnormal deviation magnitude, including:

[0025] According to the order of fault occurrence at each sensor location, the deviation comparison sequence and the marked sequence are compared with abnormal indicators to obtain the number of abnormal indicators in the marked sequence for any abnormal indicator.

[0026] The extent of fault propagation is determined based on the first length of the marker sequence and the second length of the branch sequence.

[0027] The overall fault severity of the distribution network in the current round is calculated based on the degree of fault propagation, the sum of the abnormal deviation amplitudes of each marked sequence, and the number of abnormalities.

[0028] In one possible implementation of this application, a current-limiting simulation of the distribution network is performed based on the severity of the fault to obtain simulation results, including:

[0029] The target voltage level is calculated based on the severity of the fault and the preset maximum voltage level.

[0030] The current limiting simulation of the distribution network was performed using the target level value, and the simulation results were obtained.

[0031] In one possible implementation of this application, the overall fault severity of the distribution network in the current round is calculated based on the degree of fault propagation, the sum of the abnormal deviation amplitudes of each marker sequence, and the number of abnormalities, including:

[0032] Calculate the first ratio between the sum of the outlier magnitudes of each labeled sequence and the number of outliers;

[0033] The overall fault severity of the distribution network in the current cycle is calculated by multiplying the degree of fault propagation by the first ratio.

[0034] In one possible implementation of this application, the anomaly degree of the index corresponding to each sensor location is calculated based on real-time operating parameters, including:

[0035] Based on real-time operating parameters, determine the sensor's current index detection value and sensor location.

[0036] Based on the sensor location and the index detection value, the index anomaly degree corresponding to each sensor location is calculated.

[0037] In one possible implementation of this application, based on the sensor location and the index detection value, the index anomaly degree corresponding to each sensor location is calculated, including:

[0038] Determine the first distance between the location of each sensor and the location of the current limiter;

[0039] Based on the indicator detection value, the maximum value of the indicator detection value, and the minimum value of the indicator detection value, the degree of abnormal deviation of the indicator detection value at the current moment is calculated.

[0040] Based on the degree of abnormal deviation and the first distance, the degree of abnormality of the index corresponding to each sensor position is calculated.

[0041] This application provides a power distribution network fault simulation method for optimizing the configuration of superconducting current limiters. Compared to related technologies that limit current through the current limiter according to calibrated limiting rules, where the actual current through the current limiter differs significantly from the abnormal current environment generated at the fault point, resulting in poor current limiting effectiveness, this application acquires real-time operating parameters of the power distribution network detected by various sensors. Based on these parameters, the anomaly degree of the index corresponding to each sensor location is calculated. Then, based on the anomaly degree and the fault evolution degree at each sensor location, the overall fault severity of the power distribution network is determined. Current limiting simulation is then performed on the power distribution network based on the fault severity, yielding accurate simulation results. This application determines the overall fault severity of the power distribution network through the anomaly degree of the index at each sensor location and the fault evolution progress, using the fault severity to reflect the abnormal current environment generated at the fault point. Furthermore, current limiting simulation is performed on the power distribution network based on the fault severity, issuing accurate current limiting commands to the current limiter at the fault point, thus improving the accuracy of power distribution network fault simulation and consequently enhancing the actual current limiting effect of the current limiter. Attached Figure Description

[0042] Figure 1 This is a flowchart illustrating the first embodiment of the power grid fault simulation method for optimizing the configuration of superconducting current limiters according to this application.

[0043] Figure 2 This is a schematic diagram of the system architecture involved in the power grid fault simulation method for superconducting current limiter optimization configuration in this application;

[0044] Figure 3 This is a schematic diagram of the device structure of the hardware operating environment involved in the embodiments of this application. Detailed Implementation

[0045] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0046] This application provides a method for simulating power grid faults for optimizing the configuration of superconducting current limiters. In the first embodiment of this method, referring to... Figure 1 The methods include:

[0047] Step S10: Obtain the real-time operating parameters of the power distribution network monitored by each sensor;

[0048] Step S20: Based on real-time operating parameters, calculate the anomaly degree of the index corresponding to each sensor location, and determine the fault evolution progress of each sensor location based on the anomaly degree of the index.

[0049] Step S30: Determine the overall fault severity of the distribution network in the current round based on the fault evolution progress at each sensor location;

[0050] Step S40: Perform current limiting simulation on the distribution network based on the fault severity to obtain simulation results;

[0051] Step S50: Based on the simulation results, set the current configuration parameters of the current limiter in the distribution network.

[0052] This embodiment aims to: simulate current limiting in the distribution network based on the severity of the fault, and issue accurate current limiting commands to the current limiter at the fault point, thereby improving the actual current limiting effect of the current limiter for the fault.

[0053] The specific steps are as follows:

[0054] Step S10: Obtain the real-time operating parameters of the power distribution network monitored by each sensor.

[0055] As an example, the power grid fault simulation method for optimizing the configuration of superconducting current limiters can be applied to a power grid fault simulation device for optimizing the configuration of superconducting current limiters. The power grid fault simulation device for optimizing the configuration of superconducting current limiters belongs to a power grid fault simulation system for optimizing the configuration of superconducting current limiters. This power grid fault simulation system for optimizing the configuration of superconducting current limiters belongs to a power grid fault simulation equipment for optimizing the configuration of superconducting current limiters.

[0056] As an example, the sensor is located at the node of each circuit in the power distribution network. The sensor can be a current sensor, voltage sensor, temperature sensor, or barometric pressure sensor, etc., and there is no specific limitation.

[0057] As an example, real-time operating parameters can be current, voltage, power, temperature, etc. at various nodes in the circuit. These parameters are used to determine which part of the circuit has a fault. Distribution network faults are often abnormal power behaviors generated locally, such as voltage sags, harmonic distortion, cable temperature rise, frequency changes, etc. Therefore, we first look for the source of the abnormality from the abnormal monitoring values ​​in the real-time operating parameters generated at the abnormal unit location, and determine the abnormal factors of the abnormal state. These factors are used to describe the judgment criteria for the impact of the current abnormal state on the overall stability of the distribution network.

[0058] As an example, a current limiter is configured in the distribution network, and the system architecture of the distribution network is as follows: Figure 2 As shown, the sensing layer consists of various sensors (current sensors, voltage sensors) that collect the operating parameters of the power grid in real time; the control layer is composed of a computing unit that processes the collected data and makes decisions on the processing results; the execution layer is used to issue commands to the current limiter, and then the current limiter responds to the output commands of the computing unit in the execution layer to limit the current.

[0059] Step S20: Based on real-time operating parameters, calculate the anomaly degree of the index corresponding to each sensor location, and determine the fault evolution progress of each sensor location based on the anomaly degree of the index.

[0060] As an example, the anomaly index represents the degree of abnormality of an index in real-time operating parameters relative to normal indexes. The higher the anomaly index, the greater the degree of abnormality.

[0061] As an example, a single fault at a location / node in a circuit can usually cause a chain reaction. That is, the fault has a continuous evolution process. For example, when the current fails, it may cause current overload, voltage overload, etc. When the local temperature is high, the continuous conduction through the circuit will cause the temperature at other locations to also be high. When the indicator monitored by a sensor location shows an abnormality, it will also affect other locations. At this time, there will be a fault evolution progress to indicate the degree of fault spillover at the current location.

[0062] The step S20, which calculates the anomaly degree of the index corresponding to each sensor location based on real-time operating parameters, further includes steps S21 to S22:

[0063] Step S21: Based on real-time operating parameters, determine the sensor's current index detection value and sensor position.

[0064] As an example, the index detection value is the index or operating parameter monitored by the sensor corresponding to each sensor position in the circuit, such as current, voltage, etc. It should be noted that there can be one or more sensors at the same position. For example, a current sensor and a voltage sensor are set at position 1. When performing analysis, they are analyzed separately according to different data types.

[0065] As an example, the sensor location is the location where the sensor is installed. The corresponding sensor location marker can be obtained by using the prior topological location of the sensor installation location stored in the server.

[0066] Step S22: Based on the sensor location and the index detection value, calculate the index anomaly degree corresponding to each sensor location.

[0067] As an example, the degree of anomaly of the index at each sensor location is determined based on the position between the sensor and the current limiter, as well as the index detection value.

[0068] The step of calculating the anomaly degree of the indicator corresponding to each sensor location based on the sensor location and the indicator detection value includes:

[0069] Determine the first distance between the location of each sensor and the location of the current limiter.

[0070] As an example, the first distance is the distance between each sensor location and the location of the current limiter.

[0071] Based on the indicator detection value, the maximum value of the indicator detection value, and the minimum value of the indicator detection value, the degree of abnormal deviation of the indicator detection value at the current moment is calculated.

[0072] Based on the degree of abnormal deviation and the first distance, the degree of abnormality of the index corresponding to each sensor position is calculated.

[0073] As an example, the degree of abnormal deviation represents the real-time abnormality of the current indicator detection value. The greater the degree of abnormal deviation, the greater the deviation that occurs when the indicator detection value has an abnormal output.

[0074] As an example, taking the indicator detection value r as an example, the indicator anomaly degree The calculation method can be:

[0075]

[0076] In the formula: Current indicator monitoring value Within the current data type R, the maximum and minimum values ​​monitored by this unit ( , The range is compared to indicate the degree of abnormal deviation, where, The maximum value of the indicator detection value. This represents the minimum value of the indicator detection value, used to determine the real-time anomalies generated by the indicator at the current location. The larger the absolute value of this expression, the greater the deviation when the sensor monitoring value at the current time t shows abnormal output.

[0077] This is the distance from the current sensor location to the current limiter's x location, which is also the first distance (i.e. how many sensor locations are needed to transmit the anomaly at the current location to the current limiter's installation location). The larger this value is, the larger the scale of the anomaly at the current sensor location, and the farther the distance it travels, the more it can affect the current limiter's installation location.

[0078] The step S20, which determines the fault evolution progress of each sensor location based on the anomaly of the indicators, further includes:

[0079] Filter out abnormal indicators that exceed preset limits from real-time operating parameters.

[0080] As an example, the preset limit range is a preset value, which is a safe operating range set for the operating parameters. For example, for current, the preset limit range can be 5-17A. When the current exceeds this range, for example, I=18A, then this current indicator is an abnormal indicator, and vice versa.

[0081] For any sensor location corresponding to an abnormal indicator, the abnormal deviation magnitude of the indicator corresponding to the current sensor location is calculated based on the abnormality degree of the normal indicator and the abnormality degree of the abnormal indicator. The abnormality degree of the indicator corresponding to the abnormal indicator is called the abnormal indicator abnormality degree, and the abnormality degree of the indicator corresponding to the normal indicator other than the abnormal indicator is called the normal indicator abnormality degree.

[0082] As an example, the abnormality of indicators is divided into normal indicator abnormality and abnormal indicator abnormality. Normal indicator abnormality is the abnormality of the indicator corresponding to the normal indicator, and abnormal indicator abnormality is the abnormality of the indicator corresponding to the abnormal indicator.

[0083] As an example, when different index detection values ​​are at the same location, the impact and interference amplitude of different indexes on the same location are different. Moreover, the abnormality of a single abnormal index will cause abnormal synergy of other indexes in the distribution network (such as a sudden increase in current, which generates excessive Joule heat through components, resulting in abnormal cable temperature). Therefore, the significant abnormal index in the overall fault situation at any location is the abnormal deviation amplitude.

[0084] As an example, the magnitude of abnormal deviation can be the significance of the abnormal deviation caused by the corresponding indicator at different locations. The corresponding indicator at different locations can be a normal indicator or an abnormal indicator. The larger the magnitude of abnormal deviation, the greater the impact of the abnormal indicator on the fault.

[0085] As an example, the magnitude of abnormal deviations in normal indicators does not need to be calculated, but for abnormal indicators... In other words, the magnitude of abnormal deviation The calculation method can be:

[0086]

[0087] in, Indicates the degree of abnormality of the abnormal indicators. This represents the mean of the abnormality of normal indicators, where... This indicates a normal indicator. It represents the standard deviation between the abnormality of normal indicators.

[0088] Based on the abnormal deviation amplitude corresponding to each sensor location, a deviation comparison sequence is generated, which is used to characterize the fault evolution progress of each sensor location in the current round.

[0089] As an example, the deviation comparison sequence can be a sequence obtained by arranging the abnormal deviation amplitudes corresponding to each sensor position in order. It is used to reflect the real-time abnormality of the monitored indicators. At different times, when the value of the abnormal deviation amplitude changes, it can also show that the position corresponding to the value change has a fault or has been affected.

[0090] As an example, different indicators R at various sensor locations are sorted in the same order, among which, the existing abnormal indicators... Abnormal deviation amplitude Placed in the sequence (with other normal indicators set to 0), a bias comparison sequence is obtained. :

[0091]

[0092] Here, q represents the sensor position. For each type of index detection value, there is a corresponding deviation comparison sequence, which is obtained by arranging the sensor positions sequentially. For example, for the deviation comparison sequence of current type... This represents the magnitude of the abnormal deviation at sensor position three. The two zeros at the beginning represent the abnormal deviation magnitudes of sensor position one and sensor position two, respectively. The abnormal deviation amplitude of sensor position six, voltage type, or other type of sequence arrangement are similar.

[0093] Step S30: Determine the overall fault severity of the distribution network in the current round based on the fault evolution progress at each sensor location.

[0094] As an example, the severity of a fault in the current round can be determined based on the fault evolution progress, which is the deviation comparison sequence. In the current round, one fault may cause multiple faults, and there will be a corresponding fault evolution progress. For example, a fault in one location may cause faults in other locations as well. The timing of the faults in each location may be different, but they belong to the fault spillover situation in the same round.

[0095] As an example, the overall fault severity of the distribution network is assessed based on the fault evolution progress at each sensor location, resulting in a fault severity score. This score can accurately reflect the actual fault situation of the distribution network.

[0096] The step S30, which determines the overall fault severity of the distribution network in the current round based on the fault evolution progress at each sensor location, includes:

[0097] Step S31: Split the deviation comparison sequence to obtain the fault scale data generated in the current round.

[0098] As an example, the deviation comparison sequence is divided to classify the fault data in the current round, and fault data that belong to the same round but are of different types are classified into fault scale data in the same round.

[0099] As an example, fault scale data can be a collection of various types of fault data.

[0100] The step S31, which involves splitting the deviation comparison sequence to obtain the fault scale data generated in the current round, includes:

[0101] Based on the deviation comparison sequence, the fault data returned from each sensor location is numbered to obtain a number sequence corresponding to multiple fault data.

[0102] As an example, the central computer counts the fault data transmitted from each sensor location and then numbers each fault data. For example, the fault data transmitted from each location for the first time is integrated into the same number sequence. Since this embodiment uses a local power distribution network, the faults at each location may occur consecutively, and the interval between the occurrence of the faults may be 3ms, 3s, etc. The timing of the faults is different, so the fault data is divided according to the number of transmissions. For example, the first fault at each location belongs to the first round, and the second fault belongs to the second round. Then, the transmitted fault data is divided into multiple number sequences according to the number.

[0103] The fault data corresponding to each sensor location and belonging to the same number sequence are divided into fault scale data generated in one round to obtain the fault scale data generated in the current round.

[0104] As an example, the same number sequence at different sensor locations is classified as the same round of faults, that is, the first report of all sensor locations is recorded as the first fault, and then the fault scale data under the current round is obtained.

[0105] Step S32: Based on the fault scale data, calculate the overall fault severity of the distribution network in the current round.

[0106] As an example, for the current cycle, the data sequence between each sensor location and the current limiter installation location is determined. Each sensor type corresponds to one data sequence, for example, the current fault data detected by the current sensor at each sensor location. After integrating the current fault data, the data sequence corresponding to the current sensor type is obtained. It should be noted that each sensor and the current limiter are continuously connected, and the fault propagation occurs through each sensor and the current limiter. For each data sequence, depending on its fault propagation degree, each data sequence will have a branch sequence that propagates from the current data sequence to other directions. Therefore, based on the fault scale data, each data sequence and the branch sequence are determined, thereby calculating the overall fault severity of the distribution network in the current cycle.

[0107] Step S32, which calculates the overall fault severity of the distribution network in the current round based on fault scale data, includes:

[0108] The locations of sensors belonging to the same fault cycle in the fault scale data are marked to obtain multiple marked locations.

[0109] As an example, the locations of each fault in a single incident are marked in the sensor locations within the network topology, resulting in multiple marked locations.

[0110] Determine the marker sequence and branch sequence of the marker sequence between the location of the current limiter and the marker location of the earliest backhaul fault.

[0111] As an example, based on the marked positions, a marked sequence is determined between the location of the current limiter and the marked position of the earliest back-transmission fault. For each sensor type, there is a corresponding marked sequence. The location of the current limiter and the marked position of the earliest back-transmission fault are continuous. After determining the marked sequence, the marked sequence is the shortest sequence between the location of the current limiter and the marked position of the earliest back-transmission fault. According to the branching of the marked sequence, the sensor positions of multiple faults that have spread from the marked sequence are connected. Then, a branch sequence is constructed based on the fault data at the corresponding positions. In this way, each branch sequence of each marked sequence is determined.

[0112] Based on the label sequence, branch sequence, and abnormal deviation magnitude, the overall fault severity of the distribution network in the current round is calculated.

[0113] The steps for calculating the overall fault severity of the distribution network in the current round, based on the marker sequence, branch sequence, and abnormal deviation magnitude, include:

[0114] According to the order of fault occurrence at each sensor location, the deviation comparison sequence and the marked sequence are compared for abnormal indicators to obtain the number of abnormal indicators in the marked sequence for any abnormal indicator.

[0115] As an example, based on the fault occurrence time at different sensor locations, the deviation comparison sequence is placed in the column direction and compared step by step with the marked sequence to determine the number of abnormal indicators on the marked sequence l, that is, the number of abnormal indicators.

[0116] The extent of fault propagation is determined based on the first length of the marker sequence and the second length of the branch sequence.

[0117] As an example, the sequence length of all marked sequences is the first length, and the sequence length of all branch sequences is the second length. Based on the ratio between the two, the degree of fault propagation in the current round is determined. It can be assumed that the more branch sequences there are, the larger the ratio between the two, which means that the degree of fault propagation is greater. In this case, the current limiter needs to suppress the current more strongly in order to reduce the overflow of the fault.

[0118] The overall fault severity of the distribution network in the current round is calculated based on the degree of fault propagation, the sum of the abnormal deviation amplitudes of each marked sequence, and the number of abnormalities.

[0119] The steps for calculating the overall fault severity of the distribution network in the current round, based on the degree of fault propagation, the sum of the abnormal deviation amplitudes of each marked sequence, and the number of abnormalities, include:

[0120] Calculate the first ratio between the sum of the outlier magnitudes of each labeled sequence and the number of outliers;

[0121] The overall fault severity of the distribution network in the current cycle is calculated by multiplying the degree of fault propagation by the first ratio.

[0122] As an example, the fault severity y under a single round of fault scale can be calculated as follows:

[0123]

[0124] In the formula: The total length of all the acquired labeled sequences l, which is also the first length. The total length of all branch sequences l', which is also the second length The ratio between the two values ​​indicates the degree of fault propagation. The larger the value, the greater the impact of the current fault propagation. This means that the current limiter needs to exert a stronger suppression to reduce the spillover of the fault. In other words, more and more obvious power distribution faults have occurred around the location where the current limiter is installed. Therefore, the need for adjustment is greater. The addition of +1 is used to avoid the problem of the fraction being 0 when there is a single-line fault.

[0125] To obtain the abnormal indicators of a single sensor type from all the abnormal indicators of all sensor types. Abnormal deviation amplitude of the entire column The sum of The number of abnormal indicators in the marker sequence corresponding to this sensor type that are in an abnormal state. The ratio represents the first ratio value. This formula represents the number of labeled sequences corresponding to various sensor types. A larger value indicates: firstly, abnormal indicators. The continuous anomalies in the sequence demonstrate that the current attributes can be used to assess the anomalies in the marked sequence more in real time; secondly, the scale of the power distribution attribute anomalies caused by the fault is assessed by the sum of the anomaly deviation amplitudes, i.e., the current anomaly index. Is it significant enough to represent the actual fault situation in the current power distribution network?

[0126] Step S40: Perform current limiting simulation on the distribution network based on the fault severity to obtain simulation results;

[0127] As an example, after determining the overall fault severity of the distribution network, the limiting current of the current limiter is adjusted according to the fault severity, and then the current limiting simulation is performed to obtain the simulation results.

[0128] The step S40, which involves simulating the current limitation of the distribution network based on the severity of the fault and obtaining the simulation results, includes:

[0129] The target voltage level is calculated based on the severity of the fault and the preset maximum voltage level.

[0130] The current limiting simulation of the distribution network was performed using the target level value, and the simulation results were obtained.

[0131] As an example, before calculating the target level value based on fault severity, the fault severity needs to be normalized. Specifically, the fault severity y is normalized using the Sigmoid function, mapping it to a normalized fault severity value between (0,1). .

[0132] As an example, the preset maximum level value can be the maximum level value that the distribution network controller can output. Among them, the target level value The calculation formula can be:

[0133]

[0134] in, Indicates the preset maximum level value. This represents the normalized severity of the fault.

[0135] As an example, after determining the target voltage level, the controller sends the voltage level signal corresponding to the target voltage level to the current limiter via a communication link (such as a hard-wired power line). This voltage level signal carries the weight information of the current fault, corresponding to the severity of the fault. The control unit inside the current limiter parses this voltage level signal and converts it into a specific control command value, namely, the current limiting value. For example, weight values (Range 0% to 100%), the fault current needs to be limited to a weighted value of the rated current. This is several times the fault current, at which point there is a limit. Where I is the system preset value, which is not limited in specific terms. After obtaining the current limiting value, the current limiting current of the current limiter is adjusted to the value corresponding to the current limiting value, thus completing the simulation of the distribution network.

[0136] As an example, compared to the current limitation method in the prior art which limits the current through the current limiter and the calibrated limiting rules, this embodiment can simulate the actual abnormal current environment of the distribution network by calculating the overall fault severity of the distribution network, thereby generating accurate simulation results and issuing accurate current limiting commands to the current limiter.

[0137] This application provides a power distribution network fault simulation method for optimizing the configuration of superconducting current limiters. Compared to related technologies that limit current through the current limiter according to calibrated limiting rules, where the actual current through the current limiter differs significantly from the abnormal current environment generated at the fault point, resulting in poor current limiting effectiveness, this application acquires real-time operating parameters of the power distribution network detected by various sensors. Based on these parameters, the anomaly degree of the index corresponding to each sensor location is calculated. Then, based on the anomaly degree and the fault evolution degree at each sensor location, the overall fault severity of the power distribution network is determined. Current limiting simulation is then performed on the power distribution network based on the fault severity, yielding accurate simulation results. This application determines the overall fault severity of the power distribution network through the anomaly degree of the index at each sensor location and the fault evolution progress, using the fault severity to reflect the abnormal current environment generated at the fault point. Furthermore, current limiting simulation is performed on the power distribution network based on the fault severity, issuing accurate current limiting commands to the current limiter at the fault point, thus improving the accuracy of power distribution network fault simulation and consequently enhancing the actual current limiting effect of the current limiter.

[0138] Reference Figure 3 , Figure 3 This is a schematic diagram of the device structure of the hardware operating environment involved in the embodiments of this application.

[0139] like Figure 3 As shown, the power grid fault simulation device for optimizing the configuration of superconducting current limiters may include: a processor 1001, a memory 1005, and a communication bus 1002. The communication bus 1002 is used to realize the connection and communication between the processor 1001 and the memory 1005.

[0140] Optionally, the power grid fault simulation device for optimizing the configuration of superconducting current limiters may also include a user interface, a network interface, a camera, RF (Radio Frequency) circuitry, sensors, a WiFi module, etc. The user interface may include a display screen and an input submodule such as a keyboard; optional user interfaces may also include standard wired or wireless interfaces. The network interface may include standard wired or wireless interfaces (such as a Wi-Fi interface).

[0141] Those skilled in the art will understand that Figure 3 The structure of the power grid fault simulation device for superconducting current limiter optimization configuration shown in the figure does not constitute a limitation on the power grid fault simulation device for superconducting current limiter optimization configuration. It may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0142] like Figure 3As shown, the memory 1005, serving as a storage medium, may include an operating system, a network communication module, and a power grid fault simulation program for optimizing the configuration of the superconducting current limiter. The operating system is a program that manages and controls the hardware and software resources of the power grid fault simulation device for optimizing the configuration of the superconducting current limiter, supporting the operation of the power grid fault simulation program for optimizing the configuration of the superconducting current limiter, as well as other software and / or programs. The network communication module is used to enable communication between the various components within the memory 1005, and communication with other hardware and software in the power grid fault simulation system for optimizing the configuration of the superconducting current limiter.

[0143] exist Figure 3 In the power grid fault simulation device for superconducting current limiter optimization configuration shown, the processor 1001 is used to execute the power grid fault simulation program for superconducting current limiter optimization configuration stored in the memory 1005, and implement the steps of the power grid fault simulation method for superconducting current limiter optimization configuration described above.

[0144] The specific implementation method of the power grid fault simulation device for the optimized configuration of superconducting current limiters in this application is basically the same as the various embodiments of the power grid fault simulation method for the optimized configuration of superconducting current limiters described above, and will not be repeated here.

[0145] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0146] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0147] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods of the various embodiments of this application.

[0148] The above are merely preferred embodiments of this application and do not limit the scope of this application. Any equivalent structural or procedural transformations made based on the description and drawings of this application, or direct or indirect applications in other related technical fields, are similarly included within the scope of protection of this application.

[0149] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0150] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for simulating power distribution network faults for optimizing the configuration of superconducting current limiters, characterized in that, The method includes: Obtain real-time operating parameters of the power distribution network monitored by various sensors; Based on the real-time operating parameters, the anomaly degree of the index corresponding to each sensor location is calculated, and based on the anomaly degree of the index, the fault evolution progress of each sensor location is determined. The step of calculating the anomaly degree of the index corresponding to each sensor location based on the real-time operating parameters includes: Based on the real-time operating parameters, determine the sensor's current index detection value and sensor position. Determine the first distance between the location of each sensor and the location of the current limiter; Based on the indicator detection value, the maximum value of the indicator detection value, and the minimum value of the indicator detection value, the degree of abnormal deviation of the indicator detection value at the current moment is calculated. Based on the degree of abnormal deviation and the first distance, the degree of index abnormality corresponding to each sensor position is calculated; The step of determining the fault evolution progress of each sensor location based on the anomaly degree of the indicator includes: Filter out abnormal indicators that exceed the preset limit range from the real-time operating parameters; For any sensor location corresponding to an abnormal indicator, the abnormal deviation magnitude of the indicator corresponding to the current sensor location is calculated based on the abnormality degree of the normal indicator and the abnormality degree of the abnormal indicator. The abnormality degree of the indicator corresponding to the abnormal indicator is the abnormal indicator abnormality degree, and the abnormality degree of the indicator corresponding to the normal indicator other than the abnormal indicator is the normal indicator abnormality degree. Based on the abnormal deviation amplitude corresponding to each sensor location, a deviation comparison sequence is generated, wherein the deviation comparison sequence is used to characterize the fault evolution progress of each sensor location in the current round. Based on the fault evolution progress at each of the sensor locations, the overall fault severity of the distribution network in the current round is determined; The step of determining the overall fault severity of the distribution network in the current round based on the fault evolution progress of each sensor location includes: Based on the aforementioned deviation comparison sequence, the fault data transmitted back from each sensor location is numbered to obtain a number sequence corresponding to multiple fault data. The fault data returned from each sensor location that belong to the same number sequence are divided into the fault scale data generated in a round, and the fault scale data generated in the current round is obtained. The sensor locations belonging to the same round of faults in the fault scale data are marked to obtain multiple marked locations; Determine the marker sequence between the location of the current limiter and the marker location of the earliest backhaul fault, as well as the branch sequence of the marker sequence; Based on the marker sequence, the branch sequence, and the abnormal deviation magnitude, the overall fault severity of the distribution network in the current round is calculated; Based on the severity of the fault, a current limiting simulation of the distribution network was performed, and the simulation results were obtained.

2. The power grid fault simulation method for optimized configuration of superconducting current limiters as described in claim 1, characterized in that, The calculation of the overall fault severity of the distribution network in the current round based on the marker sequence, the branch sequence, and the abnormal deviation amplitude includes: According to the order of fault occurrence at each sensor location, the deviation comparison sequence and the marker sequence are compared for abnormal indicators to obtain the number of abnormal indicators in the marker sequence. The degree of fault propagation is determined based on the first length of the marker sequence and the second length of the branch sequence; Based on the degree of fault propagation, the sum of the abnormal deviation amplitudes of each of the marked sequences, and the number of abnormalities, the overall fault severity of the distribution network in the current round is calculated.

3. The power grid fault simulation method for optimized configuration of superconducting current limiters as described in claim 2, characterized in that, The calculation of the overall fault severity of the distribution network in the current round, based on the degree of fault propagation, the sum of the abnormal deviation amplitudes of each of the marked sequences, and the number of abnormalities, includes: Calculate a first ratio between the sum of the magnitudes of the outliers in each of the labeled sequences and the number of outliers; The overall fault severity of the distribution network in the current cycle is calculated based on the product of the fault propagation degree and the first ratio.

4. The power grid fault simulation method for optimized configuration of superconducting current limiters as described in claim 1, characterized in that, The simulation of current limiting in the distribution network based on the severity of the fault yields simulation results, including: Based on the severity of the fault and the preset maximum level value, the target level value is calculated. The current limiting simulation of the distribution network was performed using the target level value, and the simulation results were obtained.

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