A power quality monitoring and fault tracing method, device, medium, and product

Abstract

This application relates to the field of power quality monitoring technology, and in particular to a method, device, medium, and product for power quality monitoring and fault tracing. The method includes acquiring fine-grained electrical parameter data of the monitored distribution area and identifying fine-grained features contained in the fine-grained electrical parameter data; determining whether there are any hidden danger fine-grained features exceeding a preset characteristic tolerance range; if so, determining that the monitored distribution area contains a hidden danger power quality event; acquiring the distribution area topology structure of the monitored distribution area and determining the tracing area based on the distribution area topology structure and hidden danger fine-grained features; acquiring the instantaneous waveform data of the tracing area within a preset time window before and after the occurrence of the hidden danger power quality event, and performing fault location based on the instantaneous waveform data. This application facilitates timely detection of potential hidden dangers within a distribution area and enables accurate hazard tracing.

Description

Technical Field

[0001] This application relates to the field of power quality monitoring technology, and in particular to a method, device, medium, and product for power quality monitoring and fault tracing. Background Technology

[0002] As the final link of the distribution network facing power users, the power quality of the distribution area is directly related to the normal operation of sensitive equipment on the user side and the reliability of power supply. At present, conventional power quality monitoring methods mainly rely on the basic metering function of electricity meters or the status assessment of integrated terminals. However, existing electricity meters mainly undertake the functions of electricity metering and settlement. Although they integrate voltage and current sampling modules, the sampling rate is usually low. They can only calculate basic electrical parameters such as effective value and power, and do not have fine-grained sensing capabilities such as high-order harmonic analysis and transient waveform capture.

[0003] When an abnormal power quality event occurs in a transformer substation, conventional technical means can only record the time of the abnormality and a simple event type marker. It is impossible to detect potential hidden dangers in the substation in a timely manner, let alone accurately trace the source of the hidden danger. Therefore, it may delay the fault handling window and, in severe cases, may trigger a chain of faults. Summary of the Invention

[0004] In order to promptly identify potential hazards within the transformer area and accurately trace their sources, this application provides a method, equipment, medium, and product for power quality monitoring and fault tracing.

[0005] Firstly, this application provides a method for power quality monitoring and fault tracing, employing the following technical solution: A method for power quality monitoring and fault tracing, comprising: The fine-grained electrical parameter data of the area to be monitored is acquired, and the fine-grained features contained in the fine-grained electrical parameter data are identified. The fine-grained electrical parameter data is collected by the monitoring energy meter installed in the area to be monitored. Determine whether there are any hidden danger fine-grained features that exceed the preset feature tolerance range. If so, determine that the monitored area contains a hidden power quality event. The topology of the monitored area is obtained, and the source area is determined from the monitored area based on the topology and the fine-grained features of the hidden danger. The instantaneous waveform data of the source area is obtained within a preset time window before and after the occurrence of the hidden power quality event, and the fault is located based on the instantaneous waveform data of the area.

[0006] By adopting the above technical solution, and relying on the monitoring energy meters within the distribution area to collect fine-grained electrical parameter data and extract corresponding fine-grained features, it is easy to accurately capture subtle power quality anomalies that cannot be identified by conventional monitoring methods within the distribution area. By comparing the fine-grained features with preset feature tolerance ranges, it is easy to promptly identify the fine-grained features of potential hazards that exceed the limits, quickly determine the existence of potential power quality events within the distribution area, and realize early perception and early warning of potential hazards in the distribution area. At the same time, by combining the topology of the distribution area to be monitored with the fine-grained features of the hazards, the source tracing area is delineated. Then, by extracting the instantaneous waveform data of the area within a preset time window before and after the occurrence of the hazard event, the fault is accurately located. This realizes a closed-loop process from hazard identification, area delineation to accurate fault tracing, which greatly improves the efficiency of power quality hazard investigation and fault handling in the distribution network, thereby facilitating the stable operation of electrical equipment and the overall power supply reliability of the distribution network.

[0007] In one possible implementation, determining the source area from the monitored transformer area based on the transformer area topology and the fine-grained characteristics of the potential hazard includes: Determine the main propagation link of the fault impact corresponding to the fine-grained characteristics of the hidden danger from the topology of the transformer area; Obtain the hazard quantification value of the hazard fine-grained feature, and determine the fluctuation deviation degree based on the hazard quantification value and the corresponding preset feature tolerance range; Based on the fluctuation deviation and the main propagation link of the fault, the source area is determined.

[0008] By adopting the above technical solution, the main propagation link of fault impact corresponding to the fine-grained characteristics of hidden dangers in the distribution network topology is accurately matched. Based on the topological correlation of the distribution network, the propagation path of fault energy and abnormal parameters is locked, avoiding the blind delineation of the source tracing range. By calculating the quantification value of the hidden danger fine-grained characteristics and combining it with the preset characteristic allowable range, the fluctuation deviation degree is obtained, realizing the numerical characterization of the severity of power quality hidden dangers. Finally, the severity level of hidden dangers characterized by the fluctuation deviation degree and the spatial propagation range of the main propagation link of fault impact are combined to define the source tracing area, so that the delineation of the source tracing area not only conforms to the physical distribution law of the power grid topology, but also matches the fluctuation severity of the hidden danger anomaly.

[0009] In one possible implementation, determining the main propagation link of the fault impact corresponding to the fine-grained feature of the hidden danger from the transformer area topology includes: The direction-sensitive factor is extracted from the fine-grained features of the hidden danger. The direction-sensitive factor includes at least one of the phase angle jump direction of voltage sag, spatial distribution tendency of harmonic distortion, and polarity propagation direction of transient pulse. Based on the direction-sensitive factor, the main propagation link of the fault impact is determined from the transformer area topology. The fault impact propagation link includes the upstream power supply link and the downstream radiation link of the monitored energy meter.

[0010] By adopting the above technical solution, directional sensitive factors are extracted from the fine-grained features of potential hazards, and the extracted directional sensitive factors are matched with the topology of the transformer area. This makes it easier to accurately identify the main propagation link of the fault impact formed by the upstream power supply link and the downstream radiation link of the monitored electricity meter. This avoids the drawback of blindly delineating the link based solely on the topology, thereby improving the accuracy of fault propagation link identification.

[0011] In one possible implementation, determining the source region based on the fluctuation deviation and the main propagation link of the fault impact includes: Based on the fluctuation deviation degree and the direction sensitivity factor, the upstream power supply link and the first tracing weight and the second tracing weight of the downstream radiation link in the main propagation link of the fault impact are determined, wherein the first tracing weight is greater than the second tracing weight; When the fluctuation deviation is greater than 1, the maximum upstream traceability level of the upstream power supply link and the maximum downstream traceability level of the downstream radiation link are identified. Based on the maximum number of upstream traceability levels, the first traceability weight, the fluctuation deviation degree, and the preset first traceability level calculation formula, the first traceability level corresponding to the upstream power supply link is determined. Based on the maximum number of downstream traceability levels, the second traceability weight, the fluctuation deviation degree, and the preset second traceability level calculation formula, the second traceability level corresponding to the downstream radiation link is determined. Starting from the monitored electricity meter, trace the first tracing level along the upstream power supply link and trace the second tracing level along the downstream radiation link. The physical area covered by all the monitored electricity meters traced is determined as the tracing area.

[0012] By adopting the above technical solution, and by combining the fluctuation deviation degree and the direction sensitivity factor to assign differentiated first and second traceability weights to the upstream power supply link and the downstream radiation link respectively, the priority allocation of traceability is made more in line with the operation mechanism of the distribution network. When the fluctuation deviation degree is greater than 1, by defining the maximum number of upstream traceability levels of the upstream power supply link and the maximum number of downstream traceability levels of the downstream radiation link respectively, it is easier to avoid the investigation scope being too large due to unlimited traceability. Then, by using the preset calculation formulas for the first and second traceability levels respectively, and combining the corresponding number of traceability levels and traceability weights, the actual traceability levels of the upstream and downstream are accurately solved, realizing the quantitative calculation of traceability levels rather than empirical delineation. Finally, by combining differentiated weight configuration, quantitative calculation of levels and precise traceability of topology levels, the traceability area is refined, quantitative and adaptively delineated.

[0013] In one possible implementation, determining the upstream power supply link and the first tracing weight and the second tracing weight of the downstream radiation link in the main propagation link of the fault impact based on the fluctuation deviation degree and the direction sensitivity factor includes: The directional consistency coefficient and directional confidence are extracted from the directional sensitivity factor, and the directional modulation value is determined based on the directional consistency coefficient and the directional confidence. Based on the fluctuation deviation, the upstream basic tracing weight of the upstream power supply link in the main propagation link of the fault impact is determined, and the upstream basic tracing weight is optimized based on the direction modulation value to obtain the first tracing weight. Based on the fluctuation deviation, the downstream basic tracing weight of the downstream radiation link in the main propagation link affected by the fault is determined, and the downstream basic tracing weight is optimized based on the direction modulation value to obtain the second tracing weight.

[0014] By adopting the above technical solution, the directional consistency coefficient and directional confidence are extracted from the directional sensitive factor and the directional modulation value is calculated. This facilitates the quantitative characterization of the regularity and credibility of the propagation direction of power quality anomalies. By introducing the directional modulation value, the upstream and downstream basic traceability weights are modified and optimized respectively. Finally, the first traceability weight and the second traceability weight that are accurately adapted to the scenario are obtained. This makes it easier for the allocation of traceability weights in the upstream and downstream links to take into account both the severity level of the hidden danger and the credibility of the disturbance propagation direction. This makes the subsequent traceability level calculation and traceability area delineation more in line with the actual propagation characteristics of power quality disturbances in the transformer area.

[0015] In one possible implementation, when the upstream power supply link includes preset key electrical equipment, optimizing the upstream basic traceability weight based on the directional modulation value to obtain the first traceability weight includes: Acquire historical hidden danger event records of the upstream power supply link within a preset historical time window. Each historical hidden danger event record includes the historical occurrence time of the historical hidden danger power quality event and the quantified value of the historical hidden danger severity. Identify the interval between the occurrence time of the historical hidden danger power quality event and the occurrence time of the hidden danger power quality event, and determine the upstream historical hidden danger parameters of the upstream power supply link based on the total number of historical hidden dangers of all historical hidden danger power quality events, the occurrence interval of each hidden danger, the quantified value of the historical hidden danger severity of each historical hidden danger power quality event, and the preset hidden danger parameter calculation formula; The upstream basic tracing weight is optimized based on the directional modulation value and the upstream historical hidden danger parameters to obtain the first tracing weight.

[0016] By adopting the above technical solution, in scenarios where the upstream power supply link includes preset key electrical equipment, the upstream historical hidden danger parameters are obtained by extracting the occurrence time and severity values ​​of historical hidden dangers, and combining them with the total number of historical hidden dangers and the interval between occurrences. This facilitates the numerical representation of the frequency, temporal correlation, and severity level of historical hidden dangers in the upstream power supply link. Finally, the upstream basic source tracing weight is jointly optimized by combining the directional modulation value and the upstream historical hidden danger parameters, realizing multi-dimensional and composite adaptive correction of the source tracing weight. This facilitates the improvement of the fit and accuracy of source tracing level calculation and source tracing area delineation.

[0017] Secondly, this application provides an electronic device that adopts the following technical solution: An electronic device comprising: At least one processor; Memory; At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, the at least one application being configured to: perform the above-described power quality monitoring and fault tracing method.

[0018] Thirdly, this application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium includes: a computer program stored thereon that can be loaded by a processor and execute the above-described power quality monitoring and fault tracing method.

[0019] Fourthly, this application provides a computer program product, which adopts the following technical solution: A computer program product includes a computer program that, when executed by a processor, implements the above-mentioned power quality monitoring and fault tracing method.

[0020] In summary, this application includes at least one of the following beneficial technical effects: By collecting fine-grained electrical parameter data from monitoring electricity meters within the distribution area and extracting corresponding fine-grained features, it is easy to accurately capture subtle power quality anomalies that cannot be identified by conventional monitoring methods. By comparing the fine-grained features with preset feature tolerance ranges, it is easy to promptly identify out-of-limit fine-grained features of potential hazards, quickly determine the existence of potential power quality events within the distribution area, and achieve early detection and warning of potential hazards. At the same time, by combining the topology of the distribution area under monitoring with the fine-grained features of potential hazards, the source area is delineated. Then, by extracting the instantaneous waveform data of the area within a preset time window before and after the occurrence of the hazard event, the fault is accurately located. This realizes a closed-loop process from hazard identification and area delineation to accurate fault tracing, which greatly improves the efficiency of power quality hazard investigation and fault handling in the distribution network, thereby facilitating the stable operation of electrical equipment and the overall power supply reliability of the distribution network.

[0021] By combining fluctuation deviation and direction sensitivity factors to assign differentiated first and second traceability weights to upstream power supply links and downstream radial links, the priority allocation of traceability is made more in line with the operation mechanism of the distribution network. When the fluctuation deviation is greater than 1, the maximum number of upstream traceability levels for upstream power supply links and the maximum number of downstream traceability levels for downstream radial links are defined to avoid excessive investigation scope caused by unlimited traceability. Then, the preset calculation formulas for the first and second traceability levels are used to accurately solve the actual traceability levels of upstream and downstream respectively, combined with the corresponding number of traceability levels and traceability weights. This achieves quantitative calculation of traceability levels rather than empirical delineation. Finally, by combining differentiated weight configuration, quantitative calculation of levels and accurate traceability of topology levels, the traceability area is refined, quantitative and adaptively delineated. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating a power quality monitoring and fault tracing method according to an embodiment of this application; Figure 2 This is a schematic diagram of a process for determining a traceability area in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0023] The following is in conjunction with the appendix Figures 1 to 3 This application will be described in further detail.

[0024] After reading this specification, those skilled in the art may make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they fall within the scope of the claims of this application.

[0025] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] It should be noted that, in the optional embodiments of this application, the data related to object information, when applied to specific products or technologies, requires the permission or consent of the object. Furthermore, the collection, use, and processing of this data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. In other words, if the embodiments of this application involve data related to an object, it must be obtained with the object's authorization and consent, the authorization and consent of relevant departments, and in accordance with the relevant laws, regulations, and standards of the country and region. If the embodiments involve personal information, the acquisition of all personal information requires the individual's consent. If sensitive information is involved, the separate consent of the information subject is required. The embodiments also need to be implemented with the object's authorization and consent.

[0027] Specifically, this application provides a method for power quality monitoring and fault tracing, executed by an electronic device. This electronic device can be a server or a terminal device. The server 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. The terminal device can be a smartphone, tablet, laptop, desktop computer, etc., but is not limited to these. The terminal device and the server can be directly or indirectly connected via wired or wireless communication, and this application does not impose any limitations on this.

[0028] refer to Figure 1 , Figure 1 This is a flowchart illustrating a power quality monitoring and fault tracing method according to an embodiment of this application. The method includes steps S110-S140, wherein: Step S110: Obtain fine-grained electrical parameter data of the area to be monitored, and identify the fine-grained features contained in the fine-grained electrical parameter data. The fine-grained electrical parameter data is collected by the monitoring energy meter installed in the area to be monitored.

[0029] Specifically, the monitored area refers to the end-point power supply area that requires power quality monitoring and fault tracing, is powered by a distribution transformer, and covers several power users and branch lines. Energy meters installed in the monitored area collect fine-grained electrical parameter data, which can be directly uploaded to electronic equipment. This fine-grained electrical parameter data includes, but is not limited to, high-sampling-rate raw instantaneous waveform sampling data of three-phase voltage and current, and time-series sampling sequences with high-precision timestamps; instantaneous amplitude, real-time phase angle, and frequency time-series fluctuation data of three-phase phase voltage, line voltage, phase current, and line current; amplitude and phase of harmonic voltage and harmonic current, total harmonic distortion rate, and interharmonic distribution data, etc. Fine-grained features can be identified from the fine-grained electrical parameter data based on a preset feature recognition algorithm. These fine-grained features include, but are not limited to, voltage sags. Features such as phase angle jump characteristics, harmonic distortion spatial distribution characteristics, transient pulse polarity propagation characteristics; harmonic amplitude exceeding limits characteristics, total harmonic distortion rate exceeding limits characteristics, interharmonic frequency clustering characteristics; instantaneous amplitude fluctuation characteristics of voltage and current, grid frequency offset characteristics, power factor distortion drift characteristics; three-phase voltage imbalance characteristics, three-phase current load imbalance characteristics, voltage flicker fluctuation amplitude characteristics; transient disturbance start and end time characteristics, disturbance duration characteristics, load impact sudden change characteristics; waveform distortion rate characteristics, zero-crossing point offset characteristics, phase timing offset characteristics, etc., can be limited by relevant technical personnel according to actual monitoring needs. The preset feature recognition algorithm can be any one or more combinations of time-domain waveform sudden change detection algorithm, FFT fast Fourier transform harmonic decomposition algorithm, wavelet transform transient feature extraction algorithm, Hilbert-Huang transform time-frequency analysis algorithm, three-phase imbalance degree calculation algorithm, voltage flicker calculation algorithm, phase difference timing analysis algorithm, and threshold exceeding discrimination algorithm, and is not specifically limited in this application embodiment.

[0030] Step S120: Determine whether there are any hidden danger fine-grained features that exceed the preset feature tolerance range. If so, determine that the monitored area contains a hidden power quality event.

[0031] Specifically, for each type of fine-grained feature, a pre-configured preset feature tolerance range is retrieved, establishing a one-to-one matching relationship between the feature and the tolerance range. Real-time values, time-series curves, and morphological parameters of each fine-grained feature are substituted into the corresponding preset feature tolerance range for comparison. Each fine-grained feature is checked to determine if it exceeds the limit, exhibits time-series fluctuations exceeding bandwidth, or has distortion patterns deviating from the normal range. All fine-grained features exceeding the limit are filtered out and marked as potential hazard features. If at least one potential hazard feature is found, a potential power quality event is determined to have occurred in the monitored area. If all fine-grained features fall within their corresponding preset feature tolerance ranges, no potential power quality event is determined to have occurred in the monitored area. Different fine-grained features correspond to different preset feature tolerance ranges, which can be obtained by statistical fitting of rated operating conditions, national standard power quality limits, and historical normal operating data within the monitored area. These ranges serve as the baseline boundary for determining whether a fine-grained feature is abnormal.

[0032] Step S130: Obtain the topology of the monitoring area and determine the source area from the monitoring area based on the topology and fine-grained characteristics of the hidden danger.

[0033] Specifically, the transformer substation topology represents the network topology model of the physical connections, hierarchical relationships, and upstream and downstream power supply links between distribution transformers, branch lines, monitoring energy meters, distribution nodes, and power users within the substation to be monitored. This model includes node numbers, line connection paths, hierarchical distribution, and equipment affiliation information, which can be uploaded to electronic equipment in advance by relevant personnel. The model analyzes the disturbance type, abnormal intensity, and propagation direction of the current hazard's fine-grained characteristics, identifying whether it is harmonic distortion, voltage sag, transient pulse, or three-phase imbalance disturbance, and extracting the spatial propagation patterns implicit in the features. The disturbance attributes of the hazard's fine-grained characteristics are matched with the line routing, node hierarchy, and upstream and downstream links of the substation topology to pinpoint the propagation path and affected branches of the disturbance energy. Combining the topological hierarchical relationships and the severity of the hazard's abnormal characteristics, the investigation boundary is narrowed, eliminating irrelevant areas of the substation without electrical connections or affected by disturbances, ultimately defining a unique and identifiable source area within the substation to be monitored. The source tracing area is the suspected fault source and the range of disturbance propagation within the entire monitored area. It is the limited investigation area for subsequent waveform data analysis and precise fault location.

[0034] Step S140: Obtain the instantaneous waveform data of the source area within a preset time window before and after the occurrence of the hidden power quality event, and locate the fault based on the instantaneous waveform data of the area.

[0035] Specifically, the occurrence time of the potential power quality event is read as the reference time. Centered on the reference time, the start and end times of the sampling are determined according to the preset time window rules to obtain the preset time window, which is used to completely capture the entire process of disturbance initiation, development, and attenuation. High-sampling-rate raw instantaneous waveform sampling data within the preset time window is retrieved. The fault transient waveform segment corresponding to the monitored energy meter is extracted from the regional instantaneous waveform data. The fault transient waveform segment is matched with multiple standard fault waveform templates in the preset fault waveform template library. Each standard fault waveform template corresponds to a fault type and a preset fault location interval. The waveform similarity between the fault transient waveform segment and each standard fault waveform template is calculated. The fault type corresponding to the standard fault waveform template with the highest waveform similarity is determined as the current fault type, and its corresponding fault location interval is determined as the fault location interval. The method for constructing the fault waveform template library may include: acquiring fault waveform data measured or simulated at different preset fault locations in the transformer area within a historical time period; extracting fault transient features from the fault waveform data at each preset fault location, including but not limited to fault phase, transient dominant frequency, decay time constant, and waveform envelope; and associating and storing the fault transient features at each preset fault location with their corresponding fault type and fault location interval to form the fault waveform template library.

[0036] In this embodiment, by collecting fine-grained electrical parameter data from monitoring energy meters within the distribution area and extracting corresponding fine-grained features, it is easy to accurately capture subtle power quality anomalies that cannot be identified by conventional monitoring methods within the distribution area. By comparing the fine-grained features with preset feature tolerance ranges, it is easy to promptly identify out-of-limit fine-grained features of potential hazards, quickly determine the existence of potential power quality events within the distribution area, and achieve early detection and warning of potential hazards in the distribution area. At the same time, by combining the topology of the distribution area to be monitored with the fine-grained features of potential hazards, the source area is delineated. Then, by extracting the instantaneous waveform data of the area within a preset time window before and after the occurrence of the hazard event, the fault is accurately located. This realizes a closed-loop process from hazard identification and area delineation to accurate fault tracing, which greatly improves the efficiency of power quality hazard investigation and fault handling in the distribution network, thereby facilitating the stable operation of electrical equipment and the overall power supply reliability of the distribution network.

[0037] Furthermore, to improve the accuracy of determining the source area, determining the source area from the monitored transformer area based on the transformer area topology and fine-grained characteristics of potential hazards can specifically include: The main propagation link of fault impact corresponding to the fine-grained features of hidden dangers is determined from the topology of the transformer area; the hazard quantification value of the fine-grained features of hidden dangers is obtained, and the fluctuation deviation degree is determined based on the hazard quantification value and the corresponding preset feature tolerance range; the source area is determined based on the fluctuation deviation degree and the main propagation link of fault impact.

[0038] Specifically, power quality disturbances in distribution networks do not spread randomly; they are conducted, attenuated, and distributed only along electrically connected topological lines. The physical topology determines the propagation path of the disturbance, providing a structural basis for link screening. Since different fine-grained characteristics of potential hazards have inherent propagation patterns—for example, voltage sags are often propagated from upstream to downstream, and nonlinear load harmonics are often fed back from downstream loads to upstream—it is necessary to extract the implicit propagation direction information from the fine-grained characteristics of potential hazards. Then, using the monitoring energy meter node exhibiting the hazard characteristics as the baseline node, the main propagation link of the fault impact can be identified from the distribution area topology. Furthermore, to improve the accuracy of determining the main propagation link of the fault impact, the determination of the main propagation link of the fault impact corresponding to the fine-grained characteristics of potential hazards from the distribution area topology can specifically include: Directional sensitive factors are extracted from the fine-grained characteristics of potential hazards. These factors include at least one of the following: the phase angle jump direction of voltage sag, the spatial distribution tendency of harmonic distortion, and the polarity propagation direction of transient pulses. Based on these directional sensitive factors, the main propagation links of fault impacts are determined from the transformer area topology. These links include the upstream power supply link and the downstream radiation link of the monitoring energy meter.

[0039] Specifically, the direction-sensitive factor is a quantitative characteristic parameter extracted from the fine-grained characteristics of potential hazards, uniquely characterizing the propagation direction and spatial distribution trend of power quality disturbances. It includes at least three core components: the direction of voltage sag phase angle jump, the spatial distribution tendency of harmonic distortion, and the direction of transient pulse polarity propagation. It is used to determine whether the disturbance propagates from top to bottom or bottom to top. Among them, the direction of voltage sag phase angle jump is the offset direction of the abrupt change in the zero-crossing point and phase angle of the three-phase voltage waveform when a voltage sag event occurs. It can quantitatively characterize the disturbance propagating from the upstream power grid towards the downstream. The load side still shows the phase change trend of transmission from downstream load to upstream power grid; the spatial distribution tendency of harmonic distortion is the difference and clustering trend of node harmonic distortion rate, amplitude of each harmonic in the upstream and downstream of the topology, and between different branch lines, used to determine whether the harmonic source is concentrated on the upstream power supply side or the downstream load side; the polarity propagation direction of transient pulses is the transmission direction of the positive and negative polarities of the pulses along the topology line and the in-direction attenuation when transient pulses such as lightning impacts and load switching surges occur in the power lines of the monitored area, which can directly determine the location of the disturbance source. The three types of parameters can be analyzed and separated one by one from the fine-grained characteristics of the hidden danger, the phase angle offset under voltage sag events can be extracted, and the phase angle jump direction can be calculated; the strength distribution of harmonic distortion indicators of each monitoring node in the monitored area can be statistically analyzed to summarize the spatial distribution tendency of harmonic distortion; the positive and negative polarities and time sequence transmission law of transient pulse waveforms can be identified to obtain the polarity propagation direction of transient pulses, and a set of direction-sensitive factors can be formed.

[0040] Using the monitored electricity meter that detected fine-grained characteristics of potential hazards as the baseline node, an electrical hierarchy traversal and tracing is performed within the topology of the monitored area. First, tracing upwards from the baseline node to the distribution transformer, multiple initial upstream power supply links are formed. Simultaneously, extending downwards from the baseline node along each branch line to the end load and lower-level monitoring nodes, multiple initial downstream radial links are formed. Then, a direction-sensitive factor is used to verify each of the multiple initial upstream power supply links and initial downstream radial links. Based on the voltage sag phase angle jump trend, the phase timing changes of each initial link are compared to determine the direction of disturbance propagation; based on the spatial distribution trend of harmonic distortion, the harmonic distortion rate and harmonic amplitude differences of each link monitoring node are compared to lock the link with high harmonic concentration; based on the polarity of transient pulses and the timing and attenuation law of arrival at each node, the source and propagation path of transient disturbances are determined.

[0041] By combining the verification results of the three types of sensitive factors, the dominant propagation direction of the disturbance is determined. Candidate effective links with high matching degree and strong correlation of disturbance signals are selected from the initial upstream and downstream links. Then, based on the propagation direction from upstream to downstream or from downstream to upstream, the core upstream effective link and the core downstream effective link are selected respectively. After integration, deduplication, and elimination of irrelevant branches and redundant nodes, the final upstream power supply link and downstream radiation link are obtained respectively. The two together constitute the main propagation link of the fault impact.

[0042] Identify the median and radius of the preset characteristic tolerance interval corresponding to the quantified value of the hazard. For example, the preset characteristic tolerance interval is [a, b], where the median is (a+b) / 2 and the radius is half the width of the preset characteristic tolerance interval (ba) / 2. Substitute the quantified value of the hazard, the median and radius of the corresponding preset characteristic tolerance interval into the preset fluctuation deviation calculation formula to obtain the fluctuation deviation corresponding to the fine-grained characteristic of the hazard. The preset fluctuation deviation calculation formula is: ; Among them, V f V is the quantification value for potential hazards. m R is the median value of the preset feature tolerance interval, and R is the radius of the preset feature tolerance interval. The purpose of introducing the fluctuation deviation degree is to quantify the severity of the hidden danger features into a continuous value, so that it can drive the dynamic adaptive adjustment of the range of the traceability area. That is, minor anomalies are traced only within a small range, while severe anomalies are traced in depth, thereby achieving the best balance between the accuracy of fault location and the computational cost of the system.

[0043] Based on the fluctuation deviation, different hierarchical topologies can be applied to the upstream power supply link and downstream radiation link in the main propagation link of the fault impact to obtain the final source region. Furthermore, to improve the accuracy of determining the source region, the method provided in this application, when determining the source region based on the fluctuation deviation and the main propagation link of the fault impact, may specifically include steps S210-S250, such as... Figure 2 As shown, where: Step S210: Based on the fluctuation deviation degree and the direction sensitivity factor, determine the upstream power supply link and the first tracing weight and the second tracing weight of the downstream radiation link in the main propagation link of the fault impact, wherein the first tracing weight is greater than the second tracing weight.

[0044] Specifically, since voltage sags, harmonic inrushes, and transient surges mostly intrude into the distribution area from the upstream power grid, they have a greater impact on overall power quality and a higher probability of being the cause. Downstream radiation links are merely terminal load branches, mostly affected and passively bearing disturbances; the probability of spontaneously generating systemic power quality hazards is much lower than upstream. Therefore, the first source tracing weight is greater than the second source tracing weight. When determining the first and second source tracing weights based on fluctuation deviation and direction sensitivity factors, the direction sensitivity factor can first be normalized and quantized to obtain a direction sensitivity value. Then, the first and second source tracing weights are determined based on a preset source tracing weight mapping relationship. The preset source tracing weight mapping relationship is the correspondence between the parameter combination of direction sensitivity value and fluctuation deviation and the first and second source tracing weights. Specific details are not specifically limited in this application embodiment. Furthermore, to improve the accuracy of determining the first and second source tracing weights, the specific process may include: The directional consistency coefficient and directional confidence are extracted from the directional sensitivity factor, and the directional modulation value is determined based on the directional consistency coefficient and directional confidence. The upstream basic tracing weight of the upstream power supply link in the main propagation link affected by the fault is determined based on the fluctuation deviation, and the upstream basic tracing weight is optimized based on the directional modulation value to obtain the first tracing weight. The downstream basic tracing weight of the downstream radiation link in the main propagation link affected by the fault is determined based on the fluctuation deviation, and the downstream basic tracing weight is optimized based on the directional modulation value to obtain the second tracing weight.

[0045] Specifically, the directional consistency coefficient can be determined based on the degree of similarity among the phase angle jump direction of the voltage sag corresponding to the directional sensitivity factor, the spatial distribution tendency of harmonic distortion, and the polarity propagation direction of the transient pulse. The directional confidence level can be determined based on the ratio of the number of factors pointing to the main propagation link to the total number of factors in the directional sensitivity factor. The obtained directional consistency coefficient and directional confidence level are then imported into a preset modulation calculation formula to obtain the corresponding directional modulation value, where the preset modulation calculation formula is Φ=1+θ×ρ, Φ is the directional modulation value, θ is the directional consistency coefficient, and ρ is the directional confidence level.

[0046] The upstream basic traceability weight of the upstream power supply link is determined based on the first preset basic traceability weight mapping relationship and the fluctuation deviation degree. The first preset basic traceability weight mapping relationship is the correspondence between the fluctuation deviation degree and the upstream basic traceability weight. Then, the upstream basic traceability weight is optimized based on the directional modulation value to obtain the first traceability weight, i.e., first traceability weight = upstream basic traceability weight × directional modulation value. Similarly, the downstream basic traceability weight of the downstream radiation link can be determined based on the second preset basic traceability weight mapping relationship and the fluctuation deviation degree. The second preset basic traceability weight mapping relationship is the correspondence between the fluctuation deviation degree and the downstream basic traceability weight. Then, the downstream basic traceability weight is optimized based on the directional modulation value to obtain the second traceability weight, i.e., second traceability weight = downstream basic traceability weight × (2 - directional modulation value). The specific content of the first and second preset basic traceability weight mapping relationships is not specifically limited in this embodiment.

[0047] By introducing directional modulation values ​​to correct and optimize the basic traceability weights of upstream and downstream links, the first and second traceability weights that are accurately adapted to the scenario are finally obtained, which makes it easier to allocate traceability weights of upstream and downstream links while taking into account the severity level of hidden dangers and the credibility of the direction of disturbance propagation.

[0048] Step S220: When the fluctuation deviation is greater than 1, identify the maximum upstream traceability level of the upstream power supply link and the maximum downstream traceability level of the downstream radiation link.

[0049] Specifically, when the fluctuation deviation is greater than 1, it indicates that the disturbance energy of the potential power quality event in the monitored area is large and the distortion is strong. It may spread upstream and downstream along multiple levels of lines and branch nodes. In this case, it is necessary to identify the maximum number of upstream and downstream traceability levels and delineate the boundaries layer by layer according to the topology to accurately cover the actual impact range of the disturbance and ensure that the traceability area is complete and no fault branches are missed. When the fluctuation deviation is not greater than 1, it indicates that the deviation of the potential power quality event in the monitored area does not exceed the reasonable fluctuation bandwidth of the interval. It is only a local small fluctuation with weak disturbance energy and fast attenuation along the topology line. It does not have the ability to spread and propagate across levels over a large area. At this time, it is not necessary to trace upstream and downstream multiple levels of the topology. It is only necessary to limit the traceability area directly to the smallest local area around the monitored energy meter, simplifying the calculation and avoiding invalid full-area investigation.

[0050] The upstream power supply link is a finite link with a fixed topological boundary after screening and correction based on the fine-grained characteristics of hidden dangers and the direction sensitivity factor. Therefore, all electrical levels contained in the upstream power supply link itself are directly determined as the maximum number of upstream traceability levels. Similarly, all electrical levels contained in the downstream radial link itself are determined as the maximum number of downstream traceability levels.

[0051] Step S230: Determine the first traceability level corresponding to the upstream power supply link based on the maximum number of upstream traceability levels, the first traceability weight, the fluctuation deviation degree, and the preset calculation formula for the first traceability level.

[0052] Specifically, the maximum number of upstream traceability levels, the first traceability weight, and the fluctuation deviation are substituted into the preset first traceability level calculation formula to obtain the first traceability level corresponding to the upstream power supply link. The preset first traceability level calculation formula is: L1=min(L max1 ,⌊w1×(D f -1)×δ1⌋+1); where L1 is the first tracing level, L max1 Where w1 is the maximum number of upstream traceability levels, and D is the first traceability weight. f δ1 represents the fluctuation deviation, δ1 represents the preset first-level scaling factor, and ⌊⋅⌋ represents the floor function.

[0053] Step S240: Determine the second traceability level corresponding to the downstream radiation link based on the maximum number of downstream traceability levels, the second traceability weight, the fluctuation deviation degree, and the preset calculation formula for the second traceability level.

[0054] Specifically, the maximum number of downstream traceability levels, the second traceability weight, and the fluctuation deviation are substituted into the preset second traceability level calculation formula to obtain the second traceability level corresponding to the downstream radiation link. The preset second traceability level calculation formula is: L2=min( Lmax2,⌊w2×(D f −1)×δ2⌋), where L2 is the second tracing level, L max2 w2 is the maximum number of downstream traceability levels, and D is the second traceability weight. f δ2 is the fluctuation deviation degree, δ2 is the preset second-level scaling factor, and ⌊⋅⌋ is the floor function.

[0055] Step S250: Starting from the monitored electricity meter, trace the first traceability level along the upstream power supply link and trace the second traceability level along the downstream radiation link, and determine the physical range covered by all the monitored electricity meters that are traced as the traceability area.

[0056] Specifically, starting from the benchmark energy meter, the process proceeds along the established upstream power supply link, traversing the topology parent node, distribution intermediate nodes, and all monitored energy meters along the way, from bottom to top according to the first tracing level. A complete record of all nodes and meter locations traversed during the upward tracing is maintained, forming the first tracing level coverage set. Again starting from the benchmark energy meter, the process proceeds along the established downstream radial link, traversing the lower-level child nodes, branch lines, and all monitored energy meters along the way, from top to bottom according to the second tracing level. A complete record of all nodes and meter locations traversed during the downward tracing is maintained, forming the second tracing level coverage set.

[0057] The system summarizes all the monitored energy meters traced through the first and second traceability levels, as well as the corresponding line segments, user loads, and physical power supply ranges of each meter. It merges, removes duplicates, eliminates overlapping coverage areas, and delineates the physical power supply range of the transformer area jointly covered by all the monitored energy meters traced through the traceability path as a whole, forming a clearly defined, node-specific, and controllable fault investigation and waveform extraction limited area.

[0058] In this embodiment of the application, by combining the fluctuation deviation degree and the direction sensitivity factor to assign differentiated first and second traceability weights to the upstream power supply link and the downstream radiation link respectively, it is easier to make the traceability priority allocation more in line with the operation mechanism of the distribution network. When the fluctuation deviation degree is greater than 1, by defining the maximum number of upstream traceability levels of the upstream power supply link and the maximum number of downstream traceability levels of the downstream radiation link respectively, it is easier to avoid the investigation scope being too large due to unlimited traceability. Then, by using the preset first and second traceability level calculation formulas respectively, and combining the corresponding traceability level number and traceability weight, the actual traceability level of the upstream and downstream is accurately solved, realizing the quantitative calculation of the traceability level rather than empirical delineation. Finally, by combining the differentiated configuration of weights, the quantitative calculation of levels and the precise traceability of topology levels, the refined, quantitative and adaptive delineation of the traceability area is achieved.

[0059] Furthermore, when the upstream power supply link includes preset key electrical equipment, the upstream basic traceability weight is optimized based on the directional modulation value to obtain the first traceability weight, which may specifically include: The system acquires historical hazard event records of the upstream power supply link within a preset historical time window. Each historical hazard event record includes the historical occurrence time and the quantified value of the historical hazard severity of the historical hazard power quality event. The system identifies the hazard occurrence interval between the historical occurrence time of each historical hazard power quality event and the hazard occurrence time of the hazard power quality event. Based on the total number of historical hazards of all historical hazard power quality events, the occurrence interval of each hazard, the quantified value of the historical hazard severity of each historical hazard power quality event, and the preset hazard parameter calculation formula, the upstream historical hazard parameters of the upstream power supply link are determined. The upstream basic tracing weight is optimized based on the directional modulation value and the upstream historical hazard parameters to obtain the first tracing weight.

[0060] Specifically, key load equipment, industrial equipment, and high-power nonlinear electrical equipment in the upstream power supply link that are pre-marked, sensitive to the power quality of the distribution area, and prone to or susceptible to disturbances are the trigger conditions for upstream link weight optimization. When the upstream power supply link contains preset key electrical equipment, the upstream traceability weight optimization process is initiated; otherwise, the upstream basic traceability weight is directly used as the first traceability weight. A preset historical time window can be set based on actual needs, and all historical hidden danger event records corresponding to the upstream power supply link within the preset historical time window are extracted from the background database. Each record contains the historical hidden danger power quality event corresponding to the historical hidden danger occurrence time and the historical hidden danger severity quantification value. The historical hidden danger severity quantification value can be determined based on the deviation of the hidden danger fine-grained characteristics corresponding to the historical hidden danger power quality event from the preset characteristic allowable range.

[0061] Obtain the occurrence time of the current hidden power quality event. Calculate the time difference between the occurrence time of each historical hidden power quality event and the occurrence time of the current event to obtain the occurrence interval for each historical hidden power quality event. Count the total number of historical hidden power quality events within the preset historical time window. Combine this with the occurrence intervals of all hidden power quality events and the quantified severity values ​​of each historical hidden power quality event. Substitute these values ​​into the preset hidden power quality parameter calculation formula, and comprehensively consider the frequency, time proximity, and severity of the hidden power quality events to obtain the upstream historical hidden power quality parameters. The preset hidden power quality parameter calculation formula is as follows: ; Where H represents the upstream historical hidden danger parameter; M represents the total number of historical hidden dangers; S m Let m be the quantified value of the historical severity of the historical hidden power quality event. The interval between the occurrence time of the historical hidden power quality event (m-th) and the occurrence time of the hidden power quality event is the interval between the occurrence time of the historical hidden power quality event.

[0062] Finally, the directional modulation value and the calculated upstream historical hidden danger parameters are used as dual correction factors to perform weighting, correction and scaling operations on the upstream basic source tracing weight. The disturbance propagation direction tendency and the frequency characteristics of historical hidden dangers are introduced for collaborative parameter tuning to obtain the final first source tracing weight. This facilitates the multi-dimensional and composite adaptive correction of the source tracing weight, thereby improving the fit and accuracy of source tracing level calculation and source tracing area delineation.

[0063] This application provides an electronic device, such as... Figure 3 As shown, Figure 3 The illustrated electronic device 300 includes a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, for example, via a bus 302. Optionally, the electronic device 300 may also include a transceiver 304. It should be noted that in practical applications, the transceiver 304 is not limited to one type, and the structure of this electronic device 300 does not constitute a limitation on the embodiments of this application.

[0064] Processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 301 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0065] Bus 302 may include a pathway for transmitting information between the aforementioned components. Bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 302 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The symbol is represented by only one line, but this does not mean that there is only one bus or one type of bus.

[0066] The memory 303 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0067] The memory 303 is used to store application code that executes the solution of this application, and its execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the content shown in the foregoing method embodiments.

[0068] Electronic devices include, but are not limited to: mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (such as in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Servers can also be included. Figure 3 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0069] This application provides a computer-readable storage medium storing a computer program that, when run on a computer, enables the computer to execute the corresponding content in the aforementioned method embodiments.

[0070] This application provides a computer program product including a computer program that, when executed by a processor, implements the methods described in any of the above embodiments.

[0071] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by 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 accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0072] The above description is only a partial embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for power quality monitoring and fault tracing, characterized in that, include: The fine-grained electrical parameter data of the area to be monitored is acquired, and the fine-grained features contained in the fine-grained electrical parameter data are identified. The fine-grained electrical parameter data is collected by the monitoring energy meter installed in the area to be monitored. Determine whether there are any hidden danger fine-grained features that exceed the preset feature tolerance range. If so, determine that the monitored area contains a hidden power quality event. The topology of the monitored area is obtained, and the source area is determined from the monitored area based on the topology and the fine-grained features of the hidden danger. The instantaneous waveform data of the source area is obtained within a preset time window before and after the occurrence of the hidden power quality event, and the fault is located based on the instantaneous waveform data of the area.

2. The power quality monitoring and fault tracing method according to claim 1, characterized in that, The process of determining the source area from the monitored transformer area based on the transformer area topology and the fine-grained characteristics of the hidden danger includes: Determine the main propagation link of the fault impact corresponding to the fine-grained characteristics of the hidden danger from the topology of the transformer area; Obtain the hazard quantification value of the hazard fine-grained feature, and determine the fluctuation deviation degree based on the hazard quantification value and the corresponding preset feature tolerance range; Based on the fluctuation deviation and the main propagation link of the fault, the source area is determined.

3. The power quality monitoring and fault tracing method according to claim 2, characterized in that, The step of determining the main propagation link of the fault impact corresponding to the fine-grained features of the hidden danger from the transformer area topology includes: The direction-sensitive factor is extracted from the fine-grained features of the hidden danger. The direction-sensitive factor includes at least one of the phase angle jump direction of voltage sag, spatial distribution tendency of harmonic distortion, and polarity propagation direction of transient pulse. Based on the direction-sensitive factor, the main propagation link of the fault impact is determined from the transformer area topology. The fault impact propagation link includes the upstream power supply link and the downstream radiation link of the monitored energy meter.

4. The power quality monitoring and fault tracing method according to claim 3, characterized in that, The determination of the source region based on the fluctuation deviation and the main propagation link of the fault impact includes: Based on the fluctuation deviation degree and the direction sensitivity factor, the upstream power supply link and the first tracing weight and the second tracing weight of the downstream radiation link in the main propagation link of the fault impact are determined, wherein the first tracing weight is greater than the second tracing weight; When the fluctuation deviation is greater than 1, the maximum upstream traceability level of the upstream power supply link and the maximum downstream traceability level of the downstream radiation link are identified. Based on the maximum number of upstream traceability levels, the first traceability weight, the fluctuation deviation degree, and the preset first traceability level calculation formula, the first traceability level corresponding to the upstream power supply link is determined. Based on the maximum number of downstream traceability levels, the second traceability weight, the fluctuation deviation degree, and the preset second traceability level calculation formula, the second traceability level corresponding to the downstream radiation link is determined. Starting from the monitored electricity meter, trace the first tracing level along the upstream power supply link and trace the second tracing level along the downstream radiation link. The physical area covered by all the monitored electricity meters traced is determined as the tracing area.

5. The power quality monitoring and fault tracing method according to claim 4, characterized in that, The determination of the upstream power supply link and the first tracing weight and the second tracing weight of the downstream radiation link in the main propagation link of the fault impact based on the fluctuation deviation degree and the direction sensitivity factor includes: The directional consistency coefficient and directional confidence are extracted from the directional sensitivity factor, and the directional modulation value is determined based on the directional consistency coefficient and the directional confidence. Based on the fluctuation deviation, the upstream basic tracing weight of the upstream power supply link in the main propagation link of the fault impact is determined, and the upstream basic tracing weight is optimized based on the direction modulation value to obtain the first tracing weight. Based on the fluctuation deviation, the downstream basic tracing weight of the downstream radiation link in the main propagation link affected by the fault is determined, and the downstream basic tracing weight is optimized based on the direction modulation value to obtain the second tracing weight.

6. The power quality monitoring and fault tracing method according to claim 5, characterized in that, When the upstream power supply link contains preset key electrical equipment, the optimization of the upstream basic traceability weight based on the direction modulation value to obtain the first traceability weight includes: Acquire historical hidden danger event records of the upstream power supply link within a preset historical time window. Each historical hidden danger event record includes the historical occurrence time of the historical hidden danger power quality event and the quantified value of the historical hidden danger severity. Identify the interval between the occurrence time of the historical hidden danger power quality event and the occurrence time of the hidden danger power quality event, and determine the upstream historical hidden danger parameters of the upstream power supply link based on the total number of historical hidden dangers of all historical hidden danger power quality events, the occurrence interval of each hidden danger, the quantified value of the historical hidden danger severity of each historical hidden danger power quality event, and the preset hidden danger parameter calculation formula; The upstream basic tracing weight is optimized based on the directional modulation value and the upstream historical hidden danger parameters to obtain the first tracing weight.

7. An electronic device, characterized in that, The electronic device includes: At least one processor; Memory; At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, the at least one application being configured to: perform a power quality monitoring and fault tracing method according to any one of claims 1-6.

8. A computer-readable storage medium, characterized in that, include: The computer program is stored and can be loaded by a processor and executed as described in any one of claims 1-6.

9. A computer program product, characterized in that, It includes a computer program, which, when executed by a processor, implements the steps of the power quality monitoring and fault tracing method according to any one of claims 1-6.

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