Fault diagnosis method and device, computer device, storage medium and program product

Abstract

This application relates to a fault diagnosis method, apparatus, computer equipment, storage medium, and program product. The method includes: in response to determining an anomaly in the distribution network based on first electrical data corresponding to at least one first cycle, acquiring second electrical data corresponding to at least one second cycle of the distribution network; wherein the at least one first cycle includes the current cycle, and the at least one second cycle is a cycle following the current cycle; the electrical data includes zero-sequence voltage data and zero-sequence current data; determining an average zero-sequence impedance angle based on the first electrical data corresponding to the current cycle and the second electrical data corresponding to at least one second cycle; and determining a fault diagnosis result for the distribution network based on the average zero-sequence impedance angle. This method, based on the average zero-sequence impedance angle, can effectively distinguish high-resistance faults within a region from other fault types, enabling accurate fault diagnosis of the distribution network.

Description

Technical Field

[0001] This application relates to the field of power distribution network fault diagnosis technology, and in particular to a fault diagnosis method, device, computer equipment, storage medium and program product. Background Technology

[0002] Grounding faults in distribution networks are a frequent type of power fault. Among them, high-resistance grounding faults are easily confused with normal three-phase imbalance, electromagnetic interference, load fluctuations and other operating conditions due to their large fault resistance and weak fault current. Therefore, they are a key focus of distribution network fault diagnosis.

[0003] The fault criteria of relevant power distribution network fault diagnosis technologies mostly rely on zero-sequence voltage, zero-sequence current amplitude or simple phase relationship, and the fault type under the background of three-phase imbalance.

[0004] Therefore, how to accurately diagnose faults in the power distribution network is an urgent problem to be solved. Summary of the Invention

[0005] Therefore, it is necessary to provide a fault diagnosis method, device, computer equipment, storage medium, and program product that can accurately diagnose faults in power distribution networks, addressing the aforementioned technical problems.

[0006] Firstly, this application provides a fault diagnosis method, including:

[0007] In response to determining that an anomaly exists in the distribution network based on first electrical data corresponding to at least one first cycle, second electrical data corresponding to at least one second cycle of the distribution network is obtained; wherein, at least one first cycle includes the current cycle, and at least one second cycle is the cycle following the current cycle; the electrical data includes zero-sequence voltage data and zero-sequence current data;

[0008] The average zero-sequence impedance angle is determined based on the first electrical data corresponding to the current cycle and the second electrical data corresponding to at least one second cycle.

[0009] The fault diagnosis results of the distribution network are determined based on the average zero-sequence impedance angle.

[0010] In one embodiment, determining the average zero-sequence impedance angle based on first electrical data corresponding to the current cycle and second electrical data corresponding to at least one second cycle includes:

[0011] For each second cycle, the electrical data mutation amount corresponding to the second cycle is determined based on the difference between the first electrical data corresponding to the current cycle and the second electrical data corresponding to the second cycle.

[0012] The average zero-sequence impedance angle is determined based on the electrical data mutation amount corresponding to each second cycle.

[0013] In one embodiment, electrical data abrupt changes include zero-sequence voltage abrupt changes and zero-sequence current abrupt changes;

[0014] Based on the electrical data abrupt changes corresponding to each second cycle, the average zero-sequence impedance angle is determined, including:

[0015] For each second cycle, the active power change and reactive power change corresponding to the second cycle are determined based on the zero-sequence voltage change and zero-sequence current change corresponding to the second cycle.

[0016] By integrating the active power fluctuations corresponding to each second cycle in the time domain, the first cumulative fluctuation corresponding to the active power is obtained; and,

[0017] By integrating the reactive power mutations corresponding to each second cycle in the time domain, the second cumulative mutation of reactive power is obtained.

[0018] The average zero-sequence impedance angle is determined based on the first and second cumulative mutation values.

[0019] In one embodiment, the fault diagnosis method further includes:

[0020] If the first zero-sequence voltage data corresponding to the current cycle is greater than or equal to the fault diagnosis initiation threshold of the distribution network, an anomaly is determined to exist in the distribution network; or,

[0021] If the first zero-sequence voltage data corresponding to the current cycle is greater than or equal to the fault diagnosis initiation threshold of the distribution network, the trend of electrical data change is determined based on the first electrical data of other cycles besides the current cycle in each first cycle; and,

[0022] When the trend of electrical data changes approaches the fault diagnosis initiation threshold, it is determined that there is an anomaly in the distribution network; among them, other cycles include cycles before the current cycle.

[0023] In one embodiment, the fault diagnosis method further includes:

[0024] Acquire reference zero-sequence voltage data when the distribution network experiences the maximum three-phase voltage imbalance under normal operating conditions;

[0025] Based on the reference zero-sequence voltage data, the fault diagnosis initiation threshold of the distribution network is determined.

[0026] In one embodiment, the fault diagnosis result of the distribution network is determined based on the average zero-sequence impedance angle, including:

[0027] If the average zero-sequence impedance angle does not exceed the preset angle range, the fault diagnosis result of the distribution network is determined to be a single-phase high-resistance grounding fault in the occurrence area.

[0028] If the average zero-sequence impedance angle exceeds the preset angle range, the fault diagnosis result is determined to be the occurrence of the target fault; the target fault includes at least one of external disturbance, load switching or normal three-phase unbalanced operating conditions.

[0029] Secondly, this application also provides a fault diagnosis device, comprising:

[0030] The first acquisition module is configured to, in response to determining that an anomaly exists in the distribution network based on the first electrical data corresponding to at least one first cycle of the distribution network, acquire the second electrical data corresponding to at least one second cycle of the distribution network; wherein, at least one first cycle includes the current cycle, and at least one second cycle is a cycle following the current cycle; the electrical data includes zero-sequence voltage data and zero-sequence current data;

[0031] The first determining module is used to determine the average zero-sequence impedance angle based on the first electrical data corresponding to the current cycle and the second electrical data corresponding to at least one second cycle.

[0032] The second determining module is used to determine the fault diagnosis results of the distribution network based on the average zero-sequence impedance angle.

[0033] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0034] In response to determining that an anomaly exists in the distribution network based on first electrical data corresponding to at least one first cycle, second electrical data corresponding to at least one second cycle of the distribution network is obtained; wherein, at least one first cycle includes the current cycle, and at least one second cycle is the cycle following the current cycle; the electrical data includes zero-sequence voltage data and zero-sequence current data;

[0035] The average zero-sequence impedance angle is determined based on the first electrical data corresponding to the current cycle and the second electrical data corresponding to at least one second cycle.

[0036] The fault diagnosis results of the distribution network are determined based on the average zero-sequence impedance angle.

[0037] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0038] In response to determining that an anomaly exists in the distribution network based on first electrical data corresponding to at least one first cycle, second electrical data corresponding to at least one second cycle of the distribution network is obtained; wherein, at least one first cycle includes the current cycle, and at least one second cycle is the cycle following the current cycle; the electrical data includes zero-sequence voltage data and zero-sequence current data;

[0039] The average zero-sequence impedance angle is determined based on the first electrical data corresponding to the current cycle and the second electrical data corresponding to at least one second cycle.

[0040] The fault diagnosis results of the distribution network are determined based on the average zero-sequence impedance angle.

[0041] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0042] In response to determining that an anomaly exists in the distribution network based on first electrical data corresponding to at least one first cycle, second electrical data corresponding to at least one second cycle of the distribution network is obtained; wherein, at least one first cycle includes the current cycle, and at least one second cycle is the cycle following the current cycle; the electrical data includes zero-sequence voltage data and zero-sequence current data;

[0043] The average zero-sequence impedance angle is determined based on the first electrical data corresponding to the current cycle and the second electrical data corresponding to at least one second cycle.

[0044] The fault diagnosis results of the distribution network are determined based on the average zero-sequence impedance angle.

[0045] The aforementioned fault diagnosis method, apparatus, computer equipment, storage medium, and program product, in response to determining an anomaly in the distribution network based on first electrical data corresponding to at least one first cycle, acquires second electrical data corresponding to at least one second cycle of the distribution network; wherein, at least one first cycle includes the current cycle, and at least one second cycle is a cycle following the current cycle; the electrical data includes zero-sequence voltage data and zero-sequence current data; the average zero-sequence impedance angle is determined based on the first electrical data corresponding to the current cycle and the second electrical data corresponding to at least one second cycle; the fault diagnosis result of the distribution network is determined based on the average zero-sequence impedance angle; in the above process, the average zero-sequence impedance angle can effectively distinguish fault types such as high-resistance faults within the zone, external disturbances, and normal three-phase unbalanced operating conditions, and still has a high fault identification accuracy under three-phase unbalanced operating conditions, thereby enabling accurate fault diagnosis of the distribution network. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a diagram illustrating the application environment of a fault diagnosis method in one embodiment;

[0048] Figure 2 This is a flowchart illustrating a fault diagnosis method in one embodiment;

[0049] Figure 3 This is a flowchart illustrating a fault diagnosis method in another embodiment;

[0050] Figure 4 This is a flowchart illustrating a fault diagnosis method in yet another embodiment;

[0051] Figure 5 This is a flowchart illustrating the steps for obtaining high-precision electrical data in one embodiment;

[0052] Figure 6 This is a flowchart illustrating the steps for calculating the fault diagnosis initiation threshold in one embodiment;

[0053] Figure 7 This is a flowchart illustrating the steps for calculating the average zero-sequence impedance angle in one embodiment.

[0054] Figure 8 This is a structural block diagram of a fault diagnosis device in one embodiment;

[0055] Figure 9 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0056] 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.

[0057] The fault diagnosis method provided in this application embodiment can be applied to, for example, Figure 1In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or located on the cloud or other network servers. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, projection devices, etc. Portable wearable devices can include smartwatches, smart bracelets, head-mounted devices, etc. Head-mounted devices can be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc. Server 104 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0058] In one exemplary embodiment, such as Figure 2 As shown, a fault diagnosis method is provided, which can be applied to... Figure 1 Taking the server in the example, the following steps are included:

[0059] S210, in response to determining that there is an anomaly in the distribution network based on the first electrical data corresponding to at least one first cycle of the distribution network, the second electrical data corresponding to at least one second cycle of the distribution network is obtained; wherein, at least one first cycle includes the current cycle, and at least one second cycle is the cycle after the current cycle; the electrical data includes zero-sequence voltage data and zero-sequence current data.

[0060] Optionally, the first cycle may include the current cycle. Alternatively, the first cycle may include the current cycle and at least one cycle preceding the current cycle. In some embodiments, the industrial frequency of the cycle is 50 Hz, and the period of one cycle is 20 milliseconds.

[0061] The first cycle can be understood as the cycle before the fault diagnosis of the distribution network is started; the second cycle can be understood as the cycle after the fault diagnosis of the distribution network is started.

[0062] In some embodiments, the electrical data (first electrical data or second electrical data) can be preprocessed high-precision electrical data. The electrical data can be obtained according to steps A1-A6:

[0063] A1: Under the conditions of no-load, no-fault, and no-current operation of the distribution network line, obtain reference electrical data corresponding to multiple cycles in the distribution network.

[0064] A2, average the reference electrical data obtained after removing outliers to obtain zero-point offset data.

[0065] In some embodiments, the zero-point offset data can be obtained according to the following formula:

[0066]

[0067] in, This represents the zero-point offset data; Indicates the first The reference electrical data corresponding to each cycle; N represents the number of cycles.

[0068] A3, acquire the raw electrical data of the distribution network at at least one target cycle; the target cycle is the first cycle or the second cycle.

[0069] The raw electrical data can be analog-to-digital converter (ADC) data.

[0070] A4. For each target cycle, determine the corrected electrical data corresponding to the target cycle based on the difference between the original electrical data and the zero-point offset data corresponding to the target cycle.

[0071] In some embodiments, the corrected electrical data can be obtained according to the following formula:

[0072]

[0073] in, This indicates the corrected electrical data corresponding to the current target cycle; This represents the original electrical data corresponding to the current target frequency. It is understandable that different target frequencies will have different corrected electrical data, and consequently, different original electrical data.

[0074] The above steps, by correcting the original electrical data, can eliminate the inherent zero-point drift error of sampling channels such as current transformers, operational amplifiers, and ADCs, ensuring the accuracy of weak current sampling references.

[0075] A5 performs a moving average filtering process on the corrected electrical data corresponding to each target cycle to obtain filtered electrical data.

[0076] In some instances, for each target cycle, the filter electrical data corresponding to the target cycle is determined based on the correction electrical data corresponding to the target cycle and a preset number of target cycles preceding the target cycle.

[0077] In some embodiments, the filtered electrical data can be obtained according to the following formula:

[0078]

[0079] in, This indicates the filtered electrical data corresponding to the current target cycle; Indicates the length of the sliding window; This represents the correction electrical data corresponding to the nk-th target cycle; n represents the position of the current target cycle in at least one target cycle; n is a positive integer greater than or equal to 1 and less than or equal to M.

[0080] The above steps, by performing moving average filtering on the corrected electrical data, can suppress random electromagnetic interference and white noise, smooth the waveform, and retain the transient characteristics of the fault. Moreover, the computational load is small, making it compatible with the computing power of embedded microcontrollers.

[0081] A6. For each target cycle, the high-precision electrical data after preprocessing is determined based on the difference between the filtered electrical data corresponding to the target cycle and the filtered electrical data corresponding to the previous target cycle.

[0082] In some embodiments, for each target cycle, if the difference in filtered electrical data is greater than the anomaly determination threshold, the filtered electrical data corresponding to the previous target cycle is used as the preprocessed high-precision electrical data corresponding to the target cycle; if the difference in filtered electrical data is not greater than the anomaly determination threshold, the filtered electrical data corresponding to the previous target cycle is used as the preprocessed high-precision electrical data corresponding to the target cycle.

[0083] In some embodiments, the filtered electrical data difference can be expressed as: ;like ,but ;like ,but ;in, This represents the filtered electrical data corresponding to the previous target cycle; Indicates the threshold for anomaly detection; This represents the preprocessed high-precision electrical data corresponding to the current target cycle.

[0084] By following the steps A1 to A6 above, we can avoid the situation where fault signal sampling is severely affected by phenomena such as weak fault currents in the distribution network being submerged by hardware zero drift, on-site electromagnetic interference, and spikes, thus providing reliable data support for subsequent fault diagnosis.

[0085] In some embodiments, an anomaly in the distribution network can be determined by the following steps: if the first zero-sequence voltage data corresponding to the current cycle is greater than or equal to the fault diagnosis initiation threshold of the distribution network, an anomaly in the distribution network can be determined.

[0086] In some embodiments, an anomaly in the distribution network can be determined according to the following steps: when the first zero-sequence voltage data corresponding to the current cycle is greater than or equal to the fault diagnosis initiation threshold of the distribution network, the electrical data change trend is determined based on the first electrical data of other cycles besides the current cycle in each first cycle; and when the electrical data change trend tends toward the fault diagnosis initiation threshold, an anomaly in the distribution network is determined; wherein, other cycles include cycles before the current cycle.

[0087] In some embodiments, other cycles may include a preset number of cycles preceding the current cycle, such as two cycles preceding the current cycle.

[0088] It is important to understand that before a fault occurs in the distribution network (such as a high-resistance ground fault), the electrical data for at least one cycle may be abnormal. For example, the trend of electrical data changes for at least one cycle may tend towards the fault diagnosis initiation threshold. Therefore, if the trend of electrical data changes determined based on the first electrical data of other cycles (excluding the current cycle) tends towards the fault diagnosis initiation threshold, and the first zero-sequence voltage data corresponding to the current cycle is greater than or equal to the fault diagnosis initiation threshold of the distribution network, it can be determined that there is an anomaly in the distribution network.

[0089] In some embodiments, the electrical data can be used as a reference electrical data, and for each other cycle, the difference between the first electrical data corresponding to the other cycle and the reference electrical data can be used as the electrical data mutation amount corresponding to the other cycle. Based on the electrical data mutation amount of each other cycle, the electrical data change trend can be determined.

[0090] In some embodiments, the aforementioned fault diagnosis initiation threshold may be preset.

[0091] In some embodiments, the above-mentioned fault diagnosis initiation threshold may also be determined according to the following steps: obtaining reference zero-sequence voltage data when the distribution network experiences the maximum three-phase voltage imbalance under normal operating conditions; and determining the fault diagnosis initiation threshold of the distribution network based on the reference zero-sequence voltage data.

[0092] In some embodiments, the reference zero-sequence voltage data can be understood as the zero-sequence voltage amplitude. The fault diagnosis initiation threshold can be obtained using the following formula:

[0093]

[0094] in, Indicates the fault diagnosis start threshold; This represents the reliability coefficient, which is generally taken as 1.2 to 1.5 and can be adjusted according to the actual situation.

[0095] The above process, by combining the reference zero-sequence voltage data and reliability coefficient under the maximum three-phase voltage imbalance state, dynamically designs the fault diagnosis start threshold. Compared with a single fixed fault diagnosis start threshold, it can avoid false starts caused by three-phase imbalance conditions and ensure reliable start-up of high-resistance faults.

[0096] In some embodiments, the effective value of the zero-sequence voltage corresponding to the current cycle can be determined based on the first zero-sequence voltage data corresponding to the current cycle, and then the effective value of the zero-sequence voltage can be compared with the fault diagnosis start threshold of the distribution network.

[0097] If an anomaly is determined in the distribution network, the distribution network fault diagnosis process can be initiated. First, the second electrical data corresponding to at least one second cycle of the distribution network can be obtained. Then, the following steps S220 can be executed. The process of obtaining the second electrical data can refer to the steps A1 to A6 mentioned above.

[0098] If it is determined that there are no abnormalities in the distribution network, the process can return to step S210.

[0099] S220, determine the average zero-sequence impedance angle based on the first electrical data corresponding to the current cycle and at least one second electrical data corresponding to the second cycle.

[0100] The average zero-sequence impedance angle can be understood as the average value of the phase difference between the zero-sequence voltage and the zero-sequence current.

[0101] In some embodiments, for each second cycle, the electrical data mutation amount corresponding to the second cycle can be determined based on the difference between the first electrical data corresponding to the current cycle and the second electrical data corresponding to the second cycle; and the average zero-sequence impedance angle can be determined based on the electrical data mutation amount corresponding to each second cycle.

[0102] In some embodiments, the first electrical data corresponding to the current cycle can be used as the reference electrical data. For each second cycle, the difference between the second electrical data corresponding to the second cycle and the reference electrical data can be used as the electrical data mutation amount corresponding to the second cycle.

[0103] In some embodiments, the reference electrical data includes a reference zero-sequence voltage and a reference zero-sequence current. Abrupt changes in the electrical data include abrupt changes in the zero-sequence voltage and abrupt changes in the zero-sequence current.

[0104] The zero-sequence voltage abrupt change can be obtained from the following formula:

[0105]

[0106] in, Indicates the zero-sequence voltage abrupt change; This represents the zero-sequence voltage data corresponding to the second cycle; This represents the reference zero-sequence voltage.

[0107] The zero-sequence current abrupt change can be obtained from the following formula:

[0108]

[0109] in, This represents the abrupt change in zero-sequence current; This represents the zero-sequence current data corresponding to the second cycle; This represents the reference zero-sequence current.

[0110] In some embodiments, the average zero-sequence impedance angle can be obtained according to the following steps: for each second cycle, based on the zero-sequence voltage change and the zero-sequence current change corresponding to the second cycle, determine the active power change and reactive power change corresponding to the second cycle; perform time-domain integration on the active power change corresponding to each second cycle to obtain the first cumulative change corresponding to the active power; and perform time-domain integration on the reactive power change corresponding to each second cycle to obtain the second cumulative change corresponding to the reactive power; determine the average zero-sequence impedance angle based on the first cumulative change and the second cumulative change.

[0111] In some embodiments, the change in active power for each second cycle can be obtained according to the following formula:

[0112]

[0113] The reactive power surge can be obtained using the following formula:

[0114]

[0115] in, This indicates the sudden change in active power. This indicates the sudden change in reactive power. This represents the phase difference between zero-sequence voltage and zero-sequence current.

[0116] In some embodiments, the first cumulative mutation amount can be obtained according to the following formula:

[0117]

[0118] The second cumulative mutation amount can be obtained using the following formula:

[0119]

[0120] in, This represents the first cumulative mutation amount corresponding to the active power; This represents the second cumulative mutation amount corresponding to reactive power; T represents the total duration corresponding to each second cycle.

[0121] In some embodiments, the arctangent of the ratio of the second cumulative mutation amount to the first cumulative mutation amount can be used as the average zero-sequence impedance angle.

[0122] In some embodiments, the average zero-sequence impedance angle can be obtained according to the following formula:

[0123]

[0124] in, This represents the average zero-sequence impedance angle.

[0125] S230 determines the fault diagnosis results of the distribution network based on the average zero-sequence impedance angle.

[0126] In some embodiments, the average zero-sequence impedance angle can be normalized to a standard range of 0° to 360° to eliminate quadrant offset and phase distortion. Then, the normalized average zero-sequence impedance angle is compared with a preset angle range to determine the fault diagnosis result of the distribution network.

[0127] In some embodiments, the preset angle range can be set to In practice, the boundary values ​​of the preset angle range can be adjusted according to the line topology, neutral grounding method, load characteristics, etc., with an adjustment range of 5° to adapt to complex field conditions.

[0128] In some embodiments, if the average zero-sequence impedance angle does not exceed the preset angle range, the fault diagnosis result of the distribution network is determined to be a single-phase high-resistance grounding fault in the occurrence zone; if the average zero-sequence impedance angle exceeds the preset angle range, the fault diagnosis result is determined to be a target fault; the target fault includes at least one of external disturbance, load switching, or normal three-phase unbalanced operating conditions.

[0129] Among them, the normal three-phase unbalanced operating condition can be understood as the three-phase unbalanced operating condition occurring under normal operating conditions.

[0130] In some embodiments, in the event of a single-phase high-resistance ground fault within the affected area, the protection device can be triggered to alarm and execute the corresponding action logic. In the event of a target fault, the protection device can be triggered to lock out and reset to the real-time monitoring state.

[0131] In the above fault diagnosis method, in response to determining that there is an anomaly in the distribution network based on the first electrical data corresponding to at least one first cycle, the second electrical data corresponding to at least one second cycle of the distribution network is obtained; wherein, at least one first cycle includes the current cycle, and at least one second cycle is the cycle after the current cycle; the electrical data includes zero-sequence voltage data and zero-sequence current data; the average zero-sequence impedance angle is determined based on the first electrical data corresponding to the current cycle and the second electrical data corresponding to at least one second cycle; the fault diagnosis result of the distribution network is determined based on the average zero-sequence impedance angle; in the above process, the average zero-sequence impedance angle can effectively distinguish fault types such as high-resistance faults within the zone, external disturbances, and normal three-phase unbalanced operating conditions, and still has a high fault identification accuracy under three-phase unbalanced operating conditions, thereby enabling accurate fault diagnosis of the distribution network.

[0132] Based on the technical solutions of the above embodiments, this application also provides another optional embodiment, in which the fault diagnosis method is described in detail.

[0133] See Figure 3 The fault diagnosis method shown includes:

[0134] S310: Obtain reference zero-sequence voltage data when the distribution network experiences the maximum three-phase voltage imbalance under normal operating conditions.

[0135] S320 determines the fault diagnosis initiation threshold of the distribution network based on the reference zero-sequence voltage data.

[0136] S330, when the first zero-sequence voltage data corresponding to the current cycle in the distribution network is greater than or equal to the fault diagnosis start threshold of the distribution network, the electrical data change trend is determined based on the first electrical data of other cycles besides the current cycle in each first cycle; other cycles include cycles before the current cycle.

[0137] S340: When the trend of electrical data changes approaches the fault diagnosis initiation threshold, it is determined that there is an anomaly in the distribution network.

[0138] S350, for each second cycle, determine the electrical data mutation amount corresponding to the second cycle based on the difference between the first electrical data corresponding to the current cycle and the second electrical data corresponding to the second cycle.

[0139] S360, based on the zero-sequence voltage change and zero-sequence current change corresponding to the second cycle, determine the active power change and reactive power change corresponding to the second cycle.

[0140] S370, perform time-domain integration on the active power fluctuations corresponding to each second cycle to obtain the first cumulative fluctuation on the active power; and perform time-domain integration on the reactive power fluctuations corresponding to each second cycle to obtain the second cumulative fluctuation on the reactive power.

[0141] S380, determine the average zero-sequence impedance angle based on the first cumulative mutation amount and the second cumulative mutation amount.

[0142] S390 determines the fault diagnosis results of the distribution network based on the average zero-sequence impedance angle.

[0143] Specifically, if the average zero-sequence impedance angle does not exceed the preset angle range, the fault diagnosis result of the distribution network is determined to be a single-phase high-resistance grounding fault within the occurrence zone. If the average zero-sequence impedance angle exceeds the preset angle range, the fault diagnosis result is determined to be a target fault; the target fault includes at least one of external disturbances, load switching, or normal three-phase unbalanced operating conditions.

[0144] This application also provides a fault diagnosis method, such as Figure 4 As shown, the process first acquires the raw electrical data corresponding to the current cycle of the distribution network. Then, the raw electrical data is preprocessed to obtain high-precision electrical data. Next, a fault diagnosis initiation threshold is calculated. Then, it is determined whether the high-precision electrical threshold corresponding to the current cycle is greater than the fault diagnosis initiation threshold. If it is greater, the average zero-sequence impedance angle is calculated. If it is less, the process returns to the step of collecting the raw electrical data corresponding to the current cycle of the distribution network. Then, it is determined whether the average zero-sequence impedance angle is within a preset angle range. If it is, a high-resistance grounding fault within the distribution network zone is identified. If not, a target fault is identified in the distribution network. The target fault includes at least one of the following: external disturbance, load switching, or normal three-phase unbalanced operating conditions. Figure 5 The process of obtaining high-precision electrical data is shown. Figure 6 The process of calculating the fault diagnosis initiation threshold is shown. Figure 7 The process of calculating the average zero-sequence impedance angle is shown.

[0145] 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.

[0146] Based on the same inventive concept, this application also provides a fault diagnosis device for implementing the fault diagnosis method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more fault diagnosis device embodiments provided below can be found in the limitations of the fault diagnosis method described above, and will not be repeated here.

[0147] In one exemplary embodiment, such as Figure 8 As shown, a fault diagnosis device is provided, including: a first acquisition module 810, a first determination module 820, and a second determination module 830, wherein:

[0148] The first acquisition module 810 is configured to, in response to determining that an anomaly exists in the distribution network based on the first electrical data corresponding to at least one first cycle of the distribution network, acquire the second electrical data corresponding to at least one second cycle of the distribution network; wherein, at least one first cycle includes the current cycle, and at least one second cycle is a cycle following the current cycle; the electrical data includes zero-sequence voltage data and zero-sequence current data;

[0149] The first determining module 820 is used to determine the average zero-sequence impedance angle based on the first electrical data corresponding to the current cycle and the second electrical data corresponding to at least one second cycle.

[0150] The second determining module 830 is used to determine the fault diagnosis results of the distribution network based on the average zero-sequence impedance angle.

[0151] In one embodiment, the first determining module 820 is specifically configured to: for each second cycle, determine the electrical data mutation amount corresponding to the second cycle based on the difference between the first electrical data corresponding to the current cycle and the second electrical data corresponding to the second cycle; and determine the average zero-sequence impedance angle based on the electrical data mutation amount corresponding to each second cycle.

[0152] In one embodiment, the electrical data abrupt changes include zero-sequence voltage abrupt changes and zero-sequence current abrupt changes; the first determining module 820 is specifically configured to: for each second cycle, determine the active power abrupt change and the reactive power abrupt change corresponding to the second cycle based on the zero-sequence voltage abrupt change and the zero-sequence current abrupt change corresponding to the second cycle; perform time-domain integration on the active power abrupt change corresponding to each second cycle to obtain a first cumulative abrupt change corresponding to the active power; and perform time-domain integration on the reactive power abrupt change corresponding to each second cycle to obtain a second cumulative abrupt change corresponding to the reactive power; and determine the average zero-sequence impedance angle based on the first cumulative abrupt change and the second cumulative abrupt change.

[0153] In one embodiment, the fault diagnosis device further includes: a third determining module, configured to determine that an anomaly exists in the distribution network when the first zero-sequence voltage data corresponding to the current cycle is greater than or equal to the fault diagnosis initiation threshold of the distribution network; or, when the first zero-sequence voltage data corresponding to the current cycle is greater than or equal to the fault diagnosis initiation threshold of the distribution network, to determine the electrical data change trend based on the first electrical data of other cycles besides the current cycle in each first cycle; and, when the electrical data change trend tends to the fault diagnosis initiation threshold, to determine that an anomaly exists in the distribution network; wherein, other cycles include cycles prior to the current cycle.

[0154] In one embodiment, the fault diagnosis device further includes: a second acquisition module, used to acquire reference zero-sequence voltage data when the distribution network experiences the maximum three-phase voltage imbalance under normal operating conditions; and a fourth determination module, used to determine the fault diagnosis initiation threshold of the distribution network based on the reference zero-sequence voltage data.

[0155] In one embodiment, the second determining module 830 is specifically used to: determine the fault diagnosis result of the distribution network as a single-phase high-resistance grounding fault in the occurrence zone when the average zero-sequence impedance angle does not exceed the preset angle range; and determine the fault diagnosis result as a target fault when the average zero-sequence impedance angle exceeds the preset angle range; the target fault includes at least one of external disturbance, load switching, or normal three-phase unbalanced operating conditions.

[0156] Each module in the aforementioned fault diagnosis device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0157] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 9As shown, this computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores relevant technologies such as first electrical data, second electrical data, average zero-sequence impedance angle, and fault diagnosis results. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a fault diagnosis method.

[0158] Those skilled in the art will understand that Figure 9 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.

[0159] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the fault diagnosis method provided in any of the above embodiments or to implement the following steps: in response to determining that an anomaly exists in the distribution network based on first electrical data corresponding to at least one first cycle, acquiring second electrical data corresponding to at least one second cycle of the distribution network; wherein, at least one first cycle includes the current cycle, and at least one second cycle is a cycle following the current cycle; the electrical data includes zero-sequence voltage data and zero-sequence current data; determining the average zero-sequence impedance angle based on the first electrical data corresponding to the current cycle and the second electrical data corresponding to at least one second cycle; and determining the fault diagnosis result of the distribution network based on the average zero-sequence impedance angle.

[0160] In one embodiment, when the processor executes the computer program, it further performs the following steps: for each second cycle, determining the electrical data mutation amount corresponding to the second cycle based on the difference between the first electrical data corresponding to the current cycle and the second electrical data corresponding to the second cycle; and determining the average zero-sequence impedance angle based on the electrical data mutation amount corresponding to each second cycle.

[0161] In one embodiment, the electrical data abrupt changes include zero-sequence voltage abrupt changes and zero-sequence current abrupt changes; when the processor executes the computer program, it further implements the following steps: for each second cycle, based on the zero-sequence voltage abrupt changes and zero-sequence current abrupt changes corresponding to the second cycle, determine the active power abrupt changes and reactive power abrupt changes corresponding to the second cycle; perform time-domain integration on the active power abrupt changes corresponding to each second cycle to obtain a first cumulative abrupt change corresponding to the active power; and perform time-domain integration on the reactive power abrupt changes corresponding to each second cycle to obtain a second cumulative abrupt change corresponding to the reactive power; and determine the average zero-sequence impedance angle based on the first cumulative abrupt change and the second cumulative abrupt change.

[0162] In one embodiment, when the processor executes the computer program, it further performs the following steps: determining that there is an anomaly in the distribution network when the first zero-sequence voltage data corresponding to the current cycle is greater than or equal to the fault diagnosis initiation threshold of the distribution network; or, when the first zero-sequence voltage data corresponding to the current cycle is greater than or equal to the fault diagnosis initiation threshold of the distribution network, determining the electrical data change trend based on the first electrical data of other cycles besides the current cycle in each first cycle; and determining that there is an anomaly in the distribution network when the electrical data change trend tends to the fault diagnosis initiation threshold; wherein, other cycles include cycles before the current cycle.

[0163] In one embodiment, when the processor executes the computer program, it further performs the following steps: acquiring reference zero-sequence voltage data when the distribution network experiences the maximum three-phase voltage imbalance under normal operating conditions; and determining the fault diagnosis initiation threshold of the distribution network based on the reference zero-sequence voltage data.

[0164] In one embodiment, when the processor executes the computer program, it further performs the following steps: if the average zero-sequence impedance angle does not exceed the preset angle range, it determines that the fault diagnosis result of the distribution network is a single-phase high-resistance grounding fault in the occurrence zone; if the average zero-sequence impedance angle exceeds the preset angle range, it determines that the fault diagnosis result is a target fault; the target fault includes at least one of external disturbance, load switching, or normal three-phase unbalanced operating conditions.

[0165] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When executed by a processor, the computer program implements the steps of the fault diagnosis method provided in any of the above embodiments or implements the following steps:

[0166] In response to determining that an anomaly exists in the distribution network based on first electrical data corresponding to at least one first cycle, second electrical data corresponding to at least one second cycle of the distribution network is acquired; wherein, at least one first cycle includes the current cycle, and at least one second cycle is the cycle following the current cycle; the electrical data includes zero-sequence voltage data and zero-sequence current data; the average zero-sequence impedance angle is determined based on the first electrical data corresponding to the current cycle and the second electrical data corresponding to at least one second cycle; and the fault diagnosis result of the distribution network is determined based on the average zero-sequence impedance angle.

[0167] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: for each second cycle, determining the electrical data mutation amount corresponding to the second cycle based on the difference between the first electrical data corresponding to the current cycle and the second electrical data corresponding to the second cycle; and determining the average zero-sequence impedance angle based on the electrical data mutation amount corresponding to each second cycle.

[0168] In one embodiment, the electrical data abrupt changes include zero-sequence voltage abrupt changes and zero-sequence current abrupt changes; when the computer program is executed by the processor, it further implements the following steps: for each second cycle, based on the zero-sequence voltage abrupt changes and zero-sequence current abrupt changes corresponding to the second cycle, determine the active power abrupt changes and reactive power abrupt changes corresponding to the second cycle; perform time-domain integration on the active power abrupt changes corresponding to each second cycle to obtain a first cumulative abrupt change corresponding to the active power; and perform time-domain integration on the reactive power abrupt changes corresponding to each second cycle to obtain a second cumulative abrupt change corresponding to the reactive power; and determine the average zero-sequence impedance angle based on the first cumulative abrupt change and the second cumulative abrupt change.

[0169] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: determining that there is an anomaly in the distribution network when the first zero-sequence voltage data corresponding to the current cycle is greater than or equal to the fault diagnosis initiation threshold of the distribution network; or, when the first zero-sequence voltage data corresponding to the current cycle is greater than or equal to the fault diagnosis initiation threshold of the distribution network, determining the electrical data change trend based on the first electrical data of other cycles besides the current cycle in each first cycle; and determining that there is an anomaly in the distribution network when the electrical data change trend tends to the fault diagnosis initiation threshold; wherein, other cycles include cycles before the current cycle.

[0170] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: acquiring reference zero-sequence voltage data when the distribution network experiences the maximum three-phase voltage imbalance under normal operating conditions; and determining the fault diagnosis initiation threshold of the distribution network based on the reference zero-sequence voltage data.

[0171] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: if the average zero-sequence impedance angle does not exceed the preset angle range, it determines that the fault diagnosis result of the distribution network is a single-phase high-resistance grounding fault in the occurrence zone; if the average zero-sequence impedance angle exceeds the preset angle range, it determines that the fault diagnosis result is a target fault; the target fault includes at least one of external disturbance, load switching, or normal three-phase unbalanced operating conditions.

[0172] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the fault diagnosis method provided in any of the above embodiments or implements the following steps: in response to determining that an anomaly exists in the distribution network based on first electrical data corresponding to at least one first cycle, acquiring second electrical data corresponding to at least one second cycle of the distribution network; wherein, at least one first cycle includes the current cycle, and at least one second cycle is a cycle following the current cycle; the electrical data includes zero-sequence voltage data and zero-sequence current data; determining the average zero-sequence impedance angle based on the first electrical data corresponding to the current cycle and the second electrical data corresponding to at least one second cycle; and determining the fault diagnosis result of the distribution network based on the average zero-sequence impedance angle.

[0173] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: for each second cycle, determining the electrical data mutation amount corresponding to the second cycle based on the difference between the first electrical data corresponding to the current cycle and the second electrical data corresponding to the second cycle; and determining the average zero-sequence impedance angle based on the electrical data mutation amount corresponding to each second cycle.

[0174] In one embodiment, the electrical data abrupt changes include zero-sequence voltage abrupt changes and zero-sequence current abrupt changes; when the computer program is executed by the processor, it further implements the following steps: for each second cycle, based on the zero-sequence voltage abrupt changes and zero-sequence current abrupt changes corresponding to the second cycle, determine the active power abrupt changes and reactive power abrupt changes corresponding to the second cycle; perform time-domain integration on the active power abrupt changes corresponding to each second cycle to obtain a first cumulative abrupt change corresponding to the active power; and perform time-domain integration on the reactive power abrupt changes corresponding to each second cycle to obtain a second cumulative abrupt change corresponding to the reactive power; and determine the average zero-sequence impedance angle based on the first cumulative abrupt change and the second cumulative abrupt change.

[0175] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: determining that there is an anomaly in the distribution network when the first zero-sequence voltage data corresponding to the current cycle is greater than or equal to the fault diagnosis initiation threshold of the distribution network; or, when the first zero-sequence voltage data corresponding to the current cycle is greater than or equal to the fault diagnosis initiation threshold of the distribution network, determining the electrical data change trend based on the first electrical data of other cycles besides the current cycle in each first cycle; and determining that there is an anomaly in the distribution network when the electrical data change trend tends to the fault diagnosis initiation threshold; wherein, other cycles include cycles before the current cycle.

[0176] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: acquiring reference zero-sequence voltage data when the distribution network experiences the maximum three-phase voltage imbalance under normal operating conditions; and determining the fault diagnosis initiation threshold of the distribution network based on the reference zero-sequence voltage data.

[0177] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: if the average zero-sequence impedance angle does not exceed the preset angle range, it determines that the fault diagnosis result of the distribution network is a single-phase high-resistance grounding fault in the occurrence zone; if the average zero-sequence impedance angle exceeds the preset angle range, it determines that the fault diagnosis result is a target fault; the target fault includes at least one of external disturbance, load switching, or normal three-phase unbalanced operating conditions.

[0178] Those skilled in the art will understand that all or part of the processes in the methods of 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, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory 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, artificial intelligence (AI) processors, etc., and are not limited to these.

[0179] 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 application.

[0180] 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 failure diagnosis method characterized by comprising: The method includes: In response to determining that an anomaly exists in the distribution network based on first electrical data corresponding to at least one first cycle, second electrical data corresponding to at least one second cycle of the distribution network is obtained; wherein, the at least one first cycle includes the current cycle, and the at least one second cycle is the cycle following the current cycle; the electrical data includes zero-sequence voltage data and zero-sequence current data; The average zero-sequence impedance angle is determined based on the first electrical data corresponding to the current cycle and the second electrical data corresponding to at least one second cycle. The fault diagnosis result of the distribution network is determined based on the average zero-sequence impedance angle.

2. The method of claim 1, wherein, The step of determining the average zero-sequence impedance angle based on the first electrical data corresponding to the current cycle and at least one second electrical data corresponding to a second cycle includes: For each second cycle, the electrical data mutation amount corresponding to the second cycle is determined based on the difference between the first electrical data corresponding to the current cycle and the second electrical data corresponding to the second cycle. The average zero-sequence impedance angle is determined based on the electrical data mutation amount corresponding to each second cycle.

3. The method of claim 2, wherein, The electrical data abrupt changes include zero-sequence voltage abrupt changes and zero-sequence current abrupt changes; The determination of the average zero-sequence impedance angle based on the electrical data abrupt changes corresponding to each second cycle includes: For each second cycle, the active power change and reactive power change corresponding to the second cycle are determined based on the zero-sequence voltage change and zero-sequence current change corresponding to the second cycle. By integrating the active power fluctuations corresponding to each second cycle in the time domain, the first cumulative fluctuation corresponding to the active power is obtained; and, By integrating the reactive power mutations corresponding to each second cycle in the time domain, the second cumulative mutation of reactive power is obtained. The average zero-sequence impedance angle is determined based on the first cumulative mutation amount and the second cumulative mutation amount.

4. The method of claim 1, wherein, The method further includes: If the first zero-sequence voltage data corresponding to the current cycle is greater than or equal to the fault diagnosis initiation threshold of the distribution network, it is determined that there is an anomaly in the distribution network; or, If the first zero-sequence voltage data corresponding to the current cycle is greater than or equal to the fault diagnosis initiation threshold of the distribution network, the electrical data change trend is determined based on the first electrical data of other cycles besides the current cycle in each first cycle; and, When the trend of the electrical data changes approaches the fault diagnosis initiation threshold, it is determined that there is an anomaly in the power distribution network; wherein, the other cycles include cycles preceding the current cycle.

5. The method of claim 4, wherein, The method further includes: Obtain reference zero-sequence voltage data when the distribution network experiences the maximum three-phase voltage imbalance under normal operating conditions; Based on the reference zero-sequence voltage data, the fault diagnosis initiation threshold of the distribution network is determined.

6. The method of claim 1, wherein, The step of determining the fault diagnosis result of the distribution network based on the average zero-sequence impedance angle includes: If the average zero-sequence impedance angle does not exceed the preset angle range, the fault diagnosis result of the distribution network is determined to be a single-phase high-resistance grounding fault in the occurrence area. If the average zero-sequence impedance angle exceeds the preset angle range, the fault diagnosis result is determined to be a target fault; the target fault includes at least one of external disturbance, load switching, or normal three-phase unbalanced operating conditions.

7. A failure diagnosing apparatus characterized by comprising: The device includes: The first acquisition module is configured to, in response to determining that an anomaly exists in the distribution network based on first electrical data corresponding to at least one first cycle, acquire second electrical data corresponding to at least one second cycle of the distribution network; wherein, the at least one first cycle includes the current cycle, and the at least one second cycle is a cycle following the current cycle; the electrical data includes zero-sequence voltage data and zero-sequence current data; The first determining module is used to determine the average zero-sequence impedance angle based on the first electrical data corresponding to the current cycle and the second electrical data corresponding to at least one second cycle. The second determining module is used to determine the fault diagnosis result of the distribution network based on the average zero-sequence impedance angle.

8. A computer device comprising a memory and a processor, the memory storing 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 stored thereon 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.

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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