A method for diagnosing and positioning abnormal power consumption based on a synchronous measurement array

Abstract

The present application relates to the technical fields of power system monitoring and anti-usage abnormality, and discloses a method for diagnosing and locating bypass usage abnormality based on a synchronous measurement array, comprising: deploying multiple PMUs (phasor measurement units) at key nodes of a distribution network to form a synchronous measurement array, and establishing a power grid topology model containing a key node admittance matrix and key node geographic coordinates; capturing voltage and current transient disturbance signals generated by bypass usage abnormality behavior using the synchronous measurement array, and extracting feature quantities perceived by each key node therefrom; calculating feature quantity differences between each pair of key nodes based on the feature quantities perceived by each key node to construct a fault feature node matrix representing spatial distribution characteristics of usage abnormality events; jointly processing the power grid topology model, the fault feature node matrix, transient features and steady-state features to determine the electrical location of the usage abnormality point; and mapping the electrical location to a geographic information system. The present application improves positioning accuracy and robustness.

Description

Technical Field

[0001] This invention relates to the field of power system monitoring and anti-electricity anomaly technology, specifically to a bypass-type electricity anomaly diagnosis and location method based on a synchronous measurement array. Background Technology

[0002] Bypassing power outages, especially illegal connections on the distribution network line side, is a long-standing problem plaguing power supply companies and causing significant economic losses. Unlike traditional methods of detecting power outages at the user's meter box, this type of anomaly occurs directly on the power line. By illegally connecting wires to the power line, the current completely bypasses the legally mandated electricity metering device, thus achieving the purpose of using electricity for free. Because this type of anomaly occurs in outdoor power corridors and is hidden from regular power inspections, it is difficult to detect and poses a significant challenge to the prevention of power outages.

[0003] Existing technologies for detecting abnormal electricity consumption mainly rely on statistical analysis of line loss rates in distribution areas or lines. They infer the presence of abnormal electricity consumption by monitoring the difference between electricity supply and sales in a specific area over a specific time period. However, this method is essentially a retrospective and macro-level statistical approach, which suffers from significant time lag. By the time an anomaly is detected through monthly or quarterly line loss analysis, the abnormal electricity consumption behavior may have already ceased, making on-site evidence collection impossible and hindering the recovery of lost electricity fees.

[0004] Furthermore, the location capabilities based on line loss analysis are extremely limited. In complex distribution networks, a single main line may connect multiple branches and a large number of users. Line loss data can only narrow down the anomaly area to a few transformer substations or a vast area of ​​several square kilometers or even tens of square kilometers. Within this area, maintenance personnel need to inspect thousands of poles and lines one by one, a massive and inefficient task, akin to finding a needle in a haystack. This not only results in a low success rate of detection but also consumes a significant amount of human resources. Although some advanced smart meters have certain anomaly detection functions, they are powerless to detect abnormal electricity consumption behavior occurring on the line side upstream of the meter.

[0005] Therefore, the power distribution network sector urgently needs a cutting-edge technology that can detect abnormal power consumption events in real time or near real time and accurately locate them, in order to bridge the huge technological gap between "detecting abnormalities" and "precisely striking" them. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and provide a bypass-type power consumption anomaly diagnosis and location method based on a synchronous measurement array that can achieve second-level alarm and tower-level precise positioning.

[0007] This invention is achieved through the following technical solution:

[0008] A bypass-type power consumption anomaly diagnosis and location method based on synchronous measurement array includes:

[0009] Multiple synchronous phasor measurement devices (PMUs) are deployed at key nodes of the distribution network to form a synchronous measurement array. Based on the systematic calibration of the clock deviation, equipment delay, and traveling wave propagation speed of each PMU, a power grid topology model including the admittance matrix of key nodes and the geographical coordinates of key nodes is established.

[0010] The synchronous measurement array is used to capture voltage and current transient disturbance signals generated by abnormal bypass power consumption behavior, and the characteristic quantities sensed by each key node are extracted from them. The characteristic quantities include the disturbance arrival time, voltage phase change, and voltage amplitude change.

[0011] Based on the feature quantities perceived by each of the key nodes, the feature quantity difference between each pair of key nodes is calculated, and the obtained feature quantity differences are weighted and fused to construct a fault feature node matrix that characterizes the spatial distribution characteristics of abnormal power consumption events.

[0012] The power grid topology model, the fault feature node matrix, and the transient and steady-state features are jointly processed to determine the electrical location of the abnormal power consumption point.

[0013] The electrical location is mapped to a geographic information system, and a traceable chain of evidence is generated for verifying and collecting evidence of abnormal electricity use.

[0014] As an optimization, the specific process for systematically calibrating the clock deviation, equipment delay, and traveling wave propagation speed of each of the aforementioned synchronous phasor measurement devices (PMUs) is as follows:

[0015] Based on multiple historical disturbance events, the traveling wave propagation velocity v and the clock deviation of the i-th synchronous phasor measurement device (PMU) are jointly estimated by solving the following optimization problem. :

[0016] ;

[0017] Where K is the total number of historical events, and N is the number of PMUs. Let k be the time when the observed abnormal power consumption behavior of the kth bypass is observed at the critical node i. This represents the actual start time of the k-th bypass-type abnormal electricity consumption behavior. Let K be the spatial coordinates of the k-th event. Let i be the spatial coordinates of the key node. As a key node Clock deviation.

[0018] As an optimization, the power grid topology model includes a node admittance matrix Y assembled from distribution network line parameters and transformer parameters, and a set of coordinates in a spatial rectangular coordinate system obtained by transforming the geographic coordinates of each key node. The node admittance matrix Y is used to describe the linear relationship between the node injected current phasor I and the node voltage phasor V: I = YV.

[0019] As an optimization, the specific process for extracting the disturbance arrival time, voltage phase change, and voltage amplitude change of each key node is as follows:

[0020] After filtering and denoising the captured voltage and current transient disturbance signals, a matched filtering algorithm is used to determine the starting point of the transient waveform in order to obtain the precise arrival time of the disturbance. ;

[0021] Steady-state voltage waveforms were captured within preset time windows before and after the occurrence of the bypass-type abnormal power consumption behavior, and the average voltage phasor before the occurrence of the bypass-type abnormal power consumption behavior was calculated. and the average voltage phasor after the aforementioned bypass-type abnormal power consumption behavior ;

[0022] Based on the average voltage phasor, the voltage phase change and voltage amplitude change at each key node are calculated using the following formula: , ,

[0023] , These are the voltage phases before and after the bypass-type abnormal power consumption behavior detected by the key node i; , These are the voltage amplitudes before and after the bypass-type abnormal power consumption behavior detected by key node i; The voltage phase change at critical node i; The voltage phase amplitude change at critical node i; ; ; ; ; , The average voltage phasor before and after the bypass-type abnormal power consumption behavior detected by the critical node i.

[0024] As an optimization, the fault feature node matrix matrix elements Defined by the following formula:

[0025] ;

[0026] in, , , These represent key nodes. The time difference, phase difference, and amplitude difference of the arrival of bypass-type abnormal electricity consumption behavior; , , The weights are time difference, phase difference, and amplitude difference, and , , , , For the observation arrival times of key nodes i and j, , For the voltage phase change at critical nodes i and j; , The voltage phase amplitude change at critical nodes i and j; , , The uncertainty is the time difference, phase difference, and amplitude difference.

[0027] As an optimization, the joint processing to determine the electrical location of power consumption anomalies specifically involves parallel execution of a transient traveling wave positioning channel and a steady-state equivalent injection inversion channel. The transient traveling wave positioning channel is used to output continuous line coordinate estimates of the power consumption anomaly point. The steady-state equivalent injection inversion channel is used to output the index of the most suspicious node of the power consumption anomaly point. .

[0028] As an optimization, the transient traveling wave positioning channel obtains the estimated values ​​of the continuous line coordinates by solving for the minimum value of the following objective function. :

[0029] ;

[0030] in, The starting time of the bypass-type abnormal power consumption behavior is given by v, which is the traveling wave propagation velocity obtained through calibration, and r(x) is the spatial coordinate at arc length x. The variance of the arrival time, Let i be the observation arrival time of the key node. Let i be the three-dimensional coordinate vector of the key node i in the spatial rectangular coordinate system.

[0031] As an optimization, the steady-state equivalent injection inversion channel locates the most suspicious node index by solving the following optimization problem. :

[0032] ;

[0033] ;

[0034] in, The equivalent sparse current injection vector is defined on all N critical nodes, and its non-zero elements correspond to potential power consumption anomaly injection locations. and , , represent the increments of current and voltage phasors before and after the abnormal power consumption behavior, respectively; Y is the nodal admittance matrix. These are regularization coefficients used to control the sparsity of the solution vectors. The structured weight of the nth critical node is a value that incorporates prior information such as the length of the line connected to the node, impedance, or load level. To obtain the optimal sparse injection vector. for The nth component; The index of the most suspicious node is the node corresponding to the component with the largest amplitude in the injection vector.

[0035] As an optimization, the joint processing also includes fusing and discriminating the results of the transient traveling wave positioning channel and the steady-state equivalent injection inversion channel. The specific process is as follows:

[0036] Calculate a uniform generalized likelihood ratio test statistic. The generalized likelihood ratio test statistic It integrates the residual changes from both transient and steady-state channels;

[0037] When the generalized likelihood ratio test statistic Greater than the preset threshold When this occurs, an abnormal power consumption event is determined to have taken place;

[0038] Subsequently, the estimated coordinates of the continuous line were verified. With the index of the most suspicious node The consistency between the physical locations they represent, when the estimated coordinates of the continuous line... With the index of the most suspicious node The distance between the physical locations they represent is less than a preset threshold. At that time, the final electrical location of the power consumption anomaly point is output. , ), This represents the estimated continuous line coordinates of the abnormal power consumption point obtained through the transient traveling wave positioning channel. This represents the index of the most suspicious node of the power consumption anomaly point obtained through the steady-state equivalent injection inversion channel.

[0039] As an optimization, the generalized likelihood ratio test statistic... The calculation formula is:

[0040] ;

[0041] in, , These represent the optimal residuals of the steady-state branch under the assumptions of no events and with events, respectively. , ; , These are the optimal residuals of the transient branches under the assumptions of no events and with events, respectively. The optimal sparse injection vector obtained by solving; This represents the measured current phasor increments at all critical nodes; Y represents the voltage phasor increments measured at all critical nodes; Y is the node admittance matrix. The covariance matrix represents the increment of the current vector. Measurement uncertainty, for The inverse matrix.

[0042] As an optimization, the specific process of mapping the electrical location to a geographic information system and generating a traceable chain of evidence for verifying and collecting evidence of abnormal electricity use is as follows:

[0043] Based on the index of the most suspicious node in the electrical location With continuous line coordinate estimation Based on the aforementioned power grid topology model, the specific line segment where the abnormal power consumption point is located and its precise proportional location on the line segment are determined. Through spatial interpolation calculation, the electrical location is converted into geographic coordinates in a geographic information system. ;

[0044] Generate a structured chain of evidence package, which includes core location evidence, original and derived electrical features, timestamps, and device identifiers;

[0045] The evidence chain package is standardized and encapsulated, and alarm information and on-site evidence collection instructions are triggered simultaneously.

[0046] The core location evidence includes the geographic coordinates. The electrical position ( , and the generalized likelihood ratio test statistic. ;

[0047] The original and derived electrical characteristics include voltage and current vector increments. Disturbance arrival time and the fault feature node matrix ;

[0048] The timestamp and device identifier include the absolute time of the power outage event and the identification information of the synchronous phasor measurement device that generated the data.

[0049] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0050] 1. Synchronous measurement array + dual-channel joint positioning: The same position is estimated in parallel by two physical complementary channels: transient traveling wave positioning and steady-state equivalent injection sparse inversion. Then, the generalized likelihood ratio test statistic is fused to achieve second-level alarm and tower-level positioning.

[0051] 2. Adaptive calibration and structured inversion: By jointly estimating clock deviation and traveling wave velocity through multiple events and multiple baselines, system errors are eliminated; in the equivalent injection inversion, prior weights such as line length and impedance are introduced to match the inversion results with the illegal splicing patterns distributed along the line segment, thereby improving positioning accuracy and robustness.

[0052] 3. Unified Decision-Making and Closed-Loop Evidence Collection: A unified statistical framework is used to naturally fuse dual-channel evidence, and location consistency is used as the evidence collection trigger condition to reduce false alarms. Electrical evidence, spatial location, and on-site image evidence are packaged to form a traceable and verifiable complete evidence chain, realizing a closed loop from discovery and location to evidence collection. Attached Figure Description

[0053] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings:

[0054] Figure 1 This is a schematic diagram of the overall process of the method of the present invention.

[0055] Figure 2 A schematic diagram illustrating the deployment and spatiotemporal calibration effects of a synchronous measurement array.

[0056] Figure 3 This is a schematic diagram of the voltage transient disturbance signal waveforms captured at each measuring point when an abnormal power consumption behavior occurs. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0058] Example:

[0059] A power distribution line with a total length of 1000 meters is established. High-precision synchronous phasor measurement units (PMUs) are deployed at three key nodes along this line, located at 0 meters, 400 meters, and 800 meters, respectively. The power grid topology model contains 12 nodes, with the following line coordinates: [0, 90.90909091, 181.8181818, 272.7272727, 363.6363636, 454.5454545, 545.4545455, 36.3636364, 727.2727273, 818.1818182, 909.0909091, 1000 meters].

[0060] Assuming the nominal velocity of the traveling wave is The actual start time of a bypass-type abnormal power consumption behavior is 2.345 seconds, and the actual location is near node 7, at a point 550 meters along the line. A threshold is set for the generalized likelihood ratio test statistic. It is 200.

[0061] Step 1: Deployment and Calibration of Synchronous Measurement Array

[0062] After deploying the PMU at the key nodes, systematic calibration is performed. First, a unified spatiotemporal reference needs to be established. This includes the local timestamp of each PMU. There is a relationship with the unified time base t: ,in It is the fixed clock offset of the i-th PMU (which can also be understood as the measurement point) to be calibrated. It is random time noise.

[0063] The core of calibration is to accurately estimate the fixed clock offset of each PMU. And the actual traveling wave propagation speed v of the line. This is achieved by collecting K known locations (spatial coordinates). This is achieved using historical waveform data of disturbance events (such as planned switching). The joint estimation is accomplished by solving the following optimization problem:

[0064] ;

[0065] in, It is the time of arrival of the k-th event (abnormal power consumption behavior) observed at the i-th PMU. This is the true start time of the event. Solving this problem can yield a high-precision solution. And v. In this embodiment, the three measuring points obtained by solving are... The value is: [0;-0.000127226388960307;4.30503197221366e-06] seconds, and the calibrated traveling wave velocity v is 154000 m / s. The calibration results are as follows... Figure 2 As shown, this effectively eliminates system errors caused by device latency and path differences.

[0066] Simultaneously, a power grid topology model is established. This model comprises two core components:

[0067] 1. Node admittance matrix Y: Assembled from the impedance parameters of lines, transformer turns ratios, and impedance parameters in the distribution network, it describes the linear relationship I = YV between the node injected current phasor I and the node voltage phasor V. In this embodiment, the constructed 12th-order node admittance matrix Y is shown below. It is a symmetric sparse matrix, and the non-zero elements reflect the electrical connection relationships between nodes.

[0068] 0.5 -0.5 0 0 0 0 0 0 0 0 0 0 -0.5 1 -0.5 0 0 0 0 0 0 0 0 0 0 -0.5 1 -0.5 0 0 0 0 0 0 0 0 0 0 -0.5 1 -0.5 0 0 0 0 0 0 0 0 0 0 -0.5 1 -0.5 0 0 0 0 0 0 0 0 0 0 -0.5 1 -0.5 0 0 0 0 0 0 0 0 0 0 -0.5 1 -0.5 0 0 0 0 0 0 0 0 0 0 -0.5 1 -0.5 0 0 0 0 0 0 0 0 0 0 -0.5 1 -0.5 0 0 0 0 0 0 0 0 0 0 -0.5 1 -0.5 0 0 0 0 0 0 0 0 0 0 -0.5 1 -0.5 0 0 0 0 0 0 0 0 0 0 -0.5 0.5

[0069] 2. Spatial coordinate set Based on the geographical coordinates (latitude) of each PMU installation point ,longitude Elevation The spatial rectangular coordinates are obtained through precise coordinate transformation. First, the radius of curvature of the primordial and trochanteric spheres is calculated using an Earth ellipsoid model (such as WGS-84). Thus, the geocentric coordinates are obtained:

[0070] ;

[0071] ;

[0072] ;

[0073] Let be the Earth's equatorial radius, and e be the first eccentricity.

[0074] For ease of calculation, a reference site is selected. By using the rotation matrix R from the standard ECEF to ENU (East-North-Sky) coordinate system, the geocentric coordinates are converted to station-centered coordinates with the reference point as the origin:

[0075] ;

[0076] It is a standard ECEF→ENU rotation matrix (identical orthogonal matrix).

[0077] This yields the set of spatial coordinates of all measurement points used in subsequent calculations. The coordinate set calculated in this embodiment is shown below:

[0078] -236.695 -155.047 -73.3982 8.250384 89.86722 171.4466 253.0261 334.6055 416.1849 497.3267 578.4548 659.5829

[0079] Step 2: Fault Characteristic Perception

[0080] When a bypass-type power outage occurs on the line, the synchronous measurement array will capture the transient voltage and current disturbance signals caused by the sudden connection of the load. The waveform diagram is shown below. Figure 3 As shown. Each PMU records the disturbance signal and extracts three key feature quantities from it:

[0081] 1. Disturbance arrival time After filtering and denoising the captured transient signal, a matched filtering algorithm is used for estimation. The core of this approach is to find a match that minimizes the noise in the observed signal. The maximum time delay of the cross-correlation function with the matching template g(t) :

[0082] , For measuring points transient waveform, To match the template, To estimate the arrival time.

[0083] 2. Voltage phase change With amplitude change :

[0084] Steady-state voltage waveforms were captured within preset time windows before and after the occurrence of an abnormal power consumption event, and the average voltage phasor before the event was calculated. and the average voltage phasor after the event .

[0085] Calculate the change at each node:

[0086] ;

[0087] ;

[0088] in, To obtain the phasor angle, To obtain the phasor amplitude.

[0089] ;

[0090] ;

[0091] This represents the average voltage vector within the steady-state window after the event. The average voltage vector within the steady-state window before the event. The average current vector within the steady-state window after the event. This is the average current vector within the steady-state window before the event.

[0092] In this embodiment, the traveling wave velocity is 154000. Extracted and The values ​​shown below exhibit a regular change from the beginning to the end of the line, which is used for steady-state analysis.

[0093]

[0094] Step 3: Feature Matrix Construction

[0095] The single-point features obtained in the previous step are transformed into point-to-point difference relationships to construct a matrix that can characterize the spatial distribution features of events.

[0096] Based on the disturbance signal characteristics sensed at each measurement point, a "fault feature node matrix" is formed by the time difference / phase difference / amplitude difference between pairs of measurement points.

[0097] Calculate the characteristic differences between all pairwise PMUs:

[0098] Time difference of arrival: ;

[0099] Phase change difference: ;

[0100] Difference in amplitude variation: ;

[0101] These differences are weighted and fused to construct a fault feature node matrix. Its matrix elements are defined as follows: ;

[0102] in, , , These represent key nodes. The time difference, phase difference, and amplitude difference of the arrival of bypass-type abnormal electricity consumption behavior; , , The weights are time difference, phase difference, and amplitude difference, and , , , , For the observation arrival times of key nodes i and j, , For the voltage phase change at critical nodes i and j; , The voltage phase amplitude change at critical nodes i and j; , , This represents the uncertainty corresponding to the time difference, phase difference, and amplitude difference. This matrix comprehensively reflects the spatial orientation information of the power outage event relative to the measurement array. The matrix constructed in this embodiment... The matrix is ​​shown below. Compared with the node matrix obtained in the first step Pairing is used for joint solutions.

[0103] 0 -0.06031 -0.23689 1.86578 1.173458 0.674496 0.250749 -0.00846 0.676413 1.293889 1.698504 1.837602 0.060308 0 -0.17658 1.926088 1.233766 0.734805 0.311057 0.051845 0.736721 1.354198 1.758812 1.89791 0.236892 0.176583 0 2.102672 1.41035 0.911388 0.487641 0.228428 0.913305 1.530781 1.935396 2.074494 -1.86578 -1.92609 -2.10267 0 -0.69232 -1.19128 -1.61503 -1.87424 -1.18937 -0.57189 -0.16728 -0.02818 -1.17346 -1.23377 -1.41035 0.692322 0 -0.49896 -0.92271 -1.18192 -0.49704 0.120431 0.525046 0.664144 -0.6745 -0.7348 -0.91139 1.191284 0.498962 0 -0.42375 -0.68296 0.001917 0.619393 1.024008 1.163106 -0.25075 -0.31106 -0.48764 1.615031 0.922709 0.423747 0 -0.25921 0.425664 1.04314 1.447755 1.586853 0.008464 -0.05184 -0.22843 1.874244 1.181922 0.68296 0.259213 0 0.684877 1.302353 1.706968 1.846066 -0.67641 -0.73672 -0.9133 1.189367 0.497045 -0.00192 -0.42566 -0.68488 0 0.617476 1.022091 1.161189 -1.29389 -1.3542 -1.53078 0.571891 -0.12043 -0.61939 -1.04314 -1.30235 -0.61748 0 0.404615 0.543713 -1.6985 -1.75881 -1.9354 0.167276 -0.52505 -1.02401 -1.44775 -1.70697 -1.02209 -0.40461 0 0.139098 -1.8376 -1.89791 -2.07449 0.028178 -0.66414 -1.16311 -1.58685 -1.84607 -1.16119 -0.54371 -0.1391 0

[0104] Step 4: Joint solution for location

[0105] This step is the core of the invention, employing parallel processing of transient and steady-state dual channels and performing fusion discrimination.

[0106] 1. Transient traveling wave positioning channel:

[0107] This channel treats power anomalies as a source of emitting traveling waves and performs geometric positioning based on the time difference of the traveling waves arriving at different PMUs.

[0108] Let the event follow the geometric curve of the line. The arc length coordinates are (m), traveling wave velocity (m / s), starting time (s), then we have:

[0109] ;

[0110] in arc length spatial coordinates, It is meaningless for calculating intermediate quantities.

[0111] The transient branch is obtained as follows:

[0112] ;

[0113] in Let Variance be the Time Difference of Arrival (TDOA). Calculate the continuous position estimate of the transient branch. and covariance r(x) is the spatial coordinate at arc length x. In this embodiment, it is calculated as follows: 4070.7953 0.1437, estimated location rice.

[0114] 2. Steady-state equivalent injection inversion pathway:

[0115] This channel treats power anomalies as additional current injection points on grid nodes and utilizes their "sparse" nature for localization. Its physical model is as follows:

[0116] ,in This represents the increment of the current phasor. This represents the voltage phasor increment. For noise, Equivalent Injected Current Vector Then, the weighted minimum absolute shrinkage and selection algorithm is used:

[0117] ;

[0118] in The equivalent sparse current injection vector is defined on all N critical nodes, and its non-zero elements correspond to potential power consumption anomaly injection locations. for The nth component; Regularity coefficient (dimensionless). It incorporates structured weights that combine prior information such as line length, impedance, and load level, making the inversion more consistent with the actual abnormal power consumption pattern of illegal connections distributed along the line segment. It also provides an index for the primary suspicious locations of steady-state channels. In this embodiment, the calculation is as follows: , Most suspicious node index .

[0119] 3. Fusion and discrimination:

[0120] Calculate the uniform generalized likelihood ratio test statistic. This is to comprehensively determine whether there is an abnormal power supply and whether the dual-channel positioning results are consistent:

[0121] ;

[0122] in: , These represent the optimal residuals of the steady-state branch under the assumptions of no events and with events, respectively. , ; , These are the optimal residuals of the transient branches under the assumptions of no events and with events, respectively. The optimal sparse injection vector obtained by solving; This represents the measured current phasor increments at all critical nodes; Y represents the voltage phasor increments measured at all critical nodes; Y is the node admittance matrix. The covariance matrix represents the increment of the current vector. Measurement uncertainty, for The inverse matrix.

[0123] Perform a position consistency check again: ,in This is a consistency threshold. If it passes, the electrical position is output. , ).

[0124] In this embodiment, An abnormal power supply was detected. A location consistency check was then performed. Rice and The distance to the physical location represented (between node 6 and node 7) is within the allowed threshold. Internal verification passed. The final output shows the electrical location of the power anomaly point. , ).

[0125] Step 5: Linking Geographic Mapping with Evidence Collection

[0126] Map the aforementioned electrical locations to a geographic information system. If the location results indicate that the abnormal power consumption point is located on the line segment between node i and node j, let its electrical equivalent distance ratio on the line segment be... Its spatial coordinates can be calculated using the following formula: ;

[0127] in, and These are the geographic coordinates (i.e., GIS coordinates (m)) of nodes i and j, respectively. can be The arc length ratio of this line segment is determined or is determined by Adjacency weights are calculated.

[0128] Automatically generate a structured, traceable chain of evidence package, including:

[0129] Key locational evidence: geographic coordinates Electrical location (552.5,7), generalized likelihood ratio test statistic 4461.15.

[0130] Primary and derived electrical characteristics: voltage and current phasor increments Disturbance arrival time Fault feature node matrix TDOA residual curve, sparse inversion spectrum .

[0131] Timestamp and Device Identifier: Absolute time of the event (2.345 seconds), and the device ID of the relevant PMU.

[0132] On-site evidence: Linked on-site snapshots or video data.

[0133] This evidence chain is packaged in a standardized manner and simultaneously triggers alarms and on-site evidence collection instructions, providing complete, traceable, and verifiable evidence for law enforcement and electricity recovery.

[0134] Compared with the actual location (550 meters, node 7), the final positioning result of this embodiment has a line identification error of only 2.5 meters, and the node identification is completely correct, which fully verifies the accuracy and effectiveness of the present invention.

[0135] In summary, the present invention has the following effects:

[0136] 1. Synchronous measurement array + dual-channel joint positioning: The same position is estimated in parallel by two physical complementary channels, TDOA traveling wave positioning and admittance-equivalent injection sparse inversion. Then, GLRT statistics are fused to achieve second-level alarm + tower-level positioning.

[0137] 2. Structured regularization of equivalent injection sparse inversion, in The solution incorporates prior weights for line length, impedance, and load, along with group sparsity, to accommodate the "illegal connections distributed along line segments" scenario. Furthermore, it utilizes multi-point geometry and historical disturbances for automatic estimation. This improves the accuracy of TDOA and avoids systematic deviations caused by fixed empirical values, thus completing the adaptive calibration of traveling wave velocity.

[0138] 3. Unify GLRT and consistency thresholds, in order to A unified statistical framework is used to naturally fuse dual-channel evidence and uses "location consistency" as the evidence collection trigger condition to reduce false alarms. GIS-linked evidence collection and evidence package standards have been completed, packaging "electrical evidence (phasor / residual / spectral) + image evidence (capture / video) + equipment signature + timestamp" to form a traceable and verifiable evidence chain.

[0139] 4. Second-level alarm and pole / segment-level positioning:

[0140] The traditional "daily-weekly" post-event comparison for line loss / meter reading has been upgraded to "millisecond-second" online alarms; this is achieved through dual-source consistency (transient) With steady state The positioning granularity is converged from the substation level to the specific tower / segment.

[0141] 5. Adaptive calibration of traveling wave velocity and time deviation improves accuracy and robustness:

[0142] Through multi-event, multi-baseline joint estimation This eliminates fixed equipment delays and path differences, avoids systematic deviations caused by fixed empirical wave speeds, significantly reduces TDOA residuals, and improves positioning stability.

[0143] 6. Structured inversion and anti-avoidance design for connections along the line segment:

[0144] exist Prior weights for line length, impedance, and load are introduced in the solution. The sparse grouping allows the inversion to match the illegal connection patterns distributed along a line segment; the superimposed short-window detection strategies such as CUSUM significantly improve the detection capability for intermittent, low-duty-cycle bypasses.

[0145] 7. Project implementation is friendly and certification is completed in a closed loop:

[0146] Only the PMU needs to be added at the critical node and a one-time calibration needs to be completed. Once activated, it can be put into operation; alarms are triggered simultaneously to link GIS and generate evidence packages (phasor / residual / spectral map + on-site capture / video + timestamp and device signature), which facilitates law enforcement and power retrieval, and can be seamlessly integrated with existing GIS systems.

[0147] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A bypass-type power consumption anomaly diagnosis and location method based on synchronous measurement array, characterized in that, include: Multiple synchronous phasor measurement devices (PMUs) are deployed at key nodes of the distribution network to form a synchronous measurement array. Based on the systematic calibration of the clock deviation, equipment delay, and traveling wave propagation speed of each PMU, a power grid topology model including the admittance matrix of key nodes and the geographical coordinates of key nodes is established. The synchronous measurement array is used to capture voltage and current transient disturbance signals generated by abnormal bypass power consumption behavior, and the characteristic quantities sensed by each key node are extracted from them. The characteristic quantities include the disturbance arrival time, voltage phase change, and voltage amplitude change. Based on the feature quantities perceived by each of the key nodes, the feature quantity difference between each pair of key nodes is calculated, and the obtained feature quantity differences are weighted and fused to construct a fault feature node matrix that characterizes the spatial distribution characteristics of abnormal power consumption events. The power grid topology model, the fault feature node matrix, and the transient and steady-state features are jointly processed to determine the electrical location of the abnormal power consumption point. The electrical location is mapped to a geographic information system, and a traceable chain of evidence is generated for verifying and collecting evidence of abnormal electricity use.

2. The bypass-type power consumption anomaly diagnosis and location method based on synchronous measurement array according to claim 1, characterized in that, The specific process for systematically calibrating the clock deviation, equipment delay, and traveling wave propagation speed of each of the aforementioned synchronous phasor measurement devices (PMUs) is as follows: Based on multiple historical disturbance events, the traveling wave propagation velocity v and the clock deviation of the i-th synchronous phasor measurement device (PMU) are jointly estimated by solving the following optimization problem. : ； Where K is the total number of historical events, and N is the number of PMUs. Let k be the time when the observed abnormal power consumption behavior of the kth bypass is observed at the critical node i. This represents the actual start time of the k-th bypass-type abnormal electricity consumption behavior. Let K be the spatial coordinates of the k-th event. Let i be the spatial coordinates of the key node. As a key node Clock deviation.

3. The bypass-type power consumption anomaly diagnosis and location method based on synchronous measurement array according to claim 1, characterized in that, The power grid topology model includes a node admittance matrix Y assembled from distribution network line parameters and transformer parameters, and a set of coordinates in a spatial rectangular coordinate system obtained by transforming the geographic coordinates of each key node. The node admittance matrix Y is used to describe the linear relationship between the node injected current phasor I and the node voltage phasor V: I = YV.

4. The bypass-type power consumption anomaly diagnosis and location method based on synchronous measurement array according to claim 1, characterized in that, The specific process for extracting the arrival time of disturbances, voltage phase change, and voltage amplitude change of each key node is as follows: After filtering and denoising the captured voltage and current transient disturbance signals, a matched filtering algorithm is used to determine the starting point of the transient waveform in order to obtain the precise arrival time of the disturbance. ; Steady-state voltage waveforms were captured within preset time windows before and after the occurrence of the bypass-type abnormal power consumption behavior, and the average voltage phasor before the occurrence of the bypass-type abnormal power consumption behavior was calculated. and the average voltage phasor after the aforementioned bypass-type abnormal power consumption behavior ; Based on the average voltage phasor, the voltage phase change and voltage amplitude change at each key node are calculated using the following formula: , , , These are the voltage phases before and after the bypass-type abnormal power consumption behavior detected by the key node i; , These are the voltage amplitudes before and after the bypass-type abnormal power consumption behavior detected by key node i; The voltage phase change at critical node i; The voltage phase amplitude change at critical node i; ; ; ; ; , The average voltage phasor before and after the bypass-type abnormal power consumption behavior detected by the critical node i.

5. The bypass-type power consumption anomaly diagnosis and location method based on synchronous measurement array according to claim 1, characterized in that, The fault feature node matrix matrix elements Defined by the following formula: ； in, , , These represent key nodes. The time difference, phase difference, and amplitude difference of the arrival of bypass-type abnormal electricity consumption behavior; , , The weights are time difference, phase difference, and amplitude difference, and , , , , For the observation arrival times of key nodes i and j, , For the voltage phase change at critical nodes i and j; , The voltage phase amplitude change at critical nodes i and j; , , The uncertainty is the time difference, phase difference, and amplitude difference.

6. The bypass-type power consumption anomaly diagnosis and location method based on synchronous measurement array according to claim 1, characterized in that, The joint processing to determine the electrical location of power consumption anomalies specifically involves the parallel execution of a transient traveling wave positioning channel and a steady-state equivalent injection inversion channel. The transient traveling wave positioning channel is used to output continuous line coordinate estimates of the power consumption anomaly point. The steady-state equivalent injection inversion channel is used to output the index of the most suspicious node of the power consumption anomaly point. .

7. The bypass-type power consumption anomaly diagnosis and location method based on synchronous measurement array according to claim 6, characterized in that, The transient traveling wave positioning channel obtains the estimated values ​​of the continuous line coordinates by solving for the minimum value of the following objective function. : ； in, The starting time of the bypass-type abnormal power consumption behavior is given by v, which is the traveling wave propagation velocity obtained through calibration, and r(x) is the spatial coordinate at arc length x. The variance of the arrival time, Let i be the observation arrival time of the key node. Let i be the three-dimensional coordinate vector of the key node i in the spatial rectangular coordinate system; The steady-state equivalent injection inversion channel locates the most suspicious node index by solving the following optimization problem. : ； ； in, The equivalent sparse current injection vector is defined on all N critical nodes, and its non-zero elements correspond to potential power consumption anomaly injection locations. and , , represent the increments of current and voltage phasors before and after the abnormal power consumption behavior, respectively; Y is the nodal admittance matrix. These are regularization coefficients used to control the sparsity of the solution vectors. The structured weight of the nth critical node is a value that incorporates prior information such as the length of the line connected to the node, impedance, or load level. To obtain the optimal sparse injection vector. for The nth component; The index of the most suspicious node is the node corresponding to the component with the largest amplitude in the injection vector.

8. The bypass-type power consumption anomaly diagnosis and location method based on synchronous measurement array according to claim 6, characterized in that, The joint processing also includes fusing and discriminating the results of the transient traveling wave positioning channel and the steady-state equivalent injection inversion channel. The specific process is as follows: Calculate a uniform generalized likelihood ratio test statistic. The generalized likelihood ratio test statistic It integrates the residual changes from both transient and steady-state channels; When the generalized likelihood ratio test statistic Greater than the preset threshold When this occurs, an abnormal power consumption event is determined to have taken place; Subsequently, the estimated coordinates of the continuous line were verified. With the index of the most suspicious node The consistency between the physical locations they represent, when the estimated coordinates of the continuous line... With the index of the most suspicious node The distance between the physical locations they represent is less than a preset threshold. At that time, the final electrical location of the power consumption anomaly point is output. , ), This represents the estimated continuous line coordinates of the abnormal power consumption point obtained through the transient traveling wave positioning channel. This represents the index of the most suspicious node of the power consumption anomaly point obtained through the steady-state equivalent injection inversion channel.

9. The bypass-type power consumption anomaly diagnosis and location method based on synchronous measurement array according to claim 8, characterized in that, The generalized likelihood ratio test statistic The calculation formula is: ； in, , These represent the optimal residuals of the steady-state branch under the assumptions of no events and with events, respectively. , ; , These are the optimal residuals of the transient branches under the assumptions of no events and with events, respectively. The optimal sparse injection vector obtained by solving; This represents the measured current phasor increments at all critical nodes; Y represents the voltage phasor increments measured at all critical nodes; Y is the node admittance matrix. The covariance matrix represents the increment of the current vector. Measurement uncertainty, for The inverse matrix.

10. A bypass-type power consumption anomaly diagnosis and location method based on a synchronous measurement array according to claim 9, characterized in that, The specific process of mapping the electrical location to a geographic information system and generating a traceable chain of evidence for verifying and collecting evidence of abnormal electricity use is as follows: Based on the index of the most suspicious node in the electrical location With continuous line coordinate estimation Based on the aforementioned power grid topology model, the specific line segment where the abnormal power consumption point is located and its precise proportional location on the line segment are determined. Through spatial interpolation calculation, the electrical location is converted into geographic coordinates in a geographic information system. ; Generate a structured chain of evidence package, which includes core location evidence, original and derived electrical features, timestamps, and device identifiers; The evidence chain package is standardized and encapsulated, and alarm information and on-site evidence collection instructions are triggered simultaneously. The core location evidence includes the geographic coordinates. The electrical position ( , and the generalized likelihood ratio test statistic. ; The original and derived electrical characteristics include voltage and current vector increments. Disturbance arrival time and the fault feature node matrix ; The timestamp and device identifier include the absolute time of the power outage event and the identification information of the synchronous phasor measurement device that generated the data.

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