A multi-source electric parameter connection identification method for an electric energy metering box

Abstract

The present application relates to the technical field of electric energy metering and low-voltage power distribution wiring identification, and particularly relates to a kind of multi-source electric parameter wiring identification method of electric energy metering box, the method is aimed at the target electric energy metering box to be identified target phase, obtains standard wiring topology model, cross window dual constraint model and multi-source electric parameter data sequence, generates power flow direction related sequence and index, identifies opportunity type dynamic excitation and forms cross window dual identification result, combines topological consistency, double anti-offset risk and dynamic control parameter matching candidate state, and outputs wiring identification result, the present application generates power flow direction sequence and reverse credible index based on multi-source electric parameter data, utilizes opportunity type dynamic excitation to form cross window dual identification result, and combines topological consistency, double anti-offset risk and dynamic control parameter matching candidate state, identifies symbol offset type wiring anomaly, improves the accuracy, interpretability and review value of wiring identification under complex bidirectional power flow.

Description

Technical Field

[0001] This invention relates to the field of electricity metering and low-voltage power distribution wiring identification technology, and more specifically, to a method for identifying multi-source electrical parameter wiring in an electricity metering box. Background Technology

[0002] With the widespread integration of distributed photovoltaic (PV) systems, energy storage devices, and bidirectional charging / discharging equipment into low-voltage distribution and user-side systems, the flow of electricity within metering boxes has gradually evolved from the traditional unidirectional power reception to a multi-branch power interaction pattern involving forward power reception, reverse power transmission, charging / discharging switching, and power interaction across multiple branches. For target metering boxes connected to distributed PV branches, energy storage branches, bidirectional charging / discharging branches, and power consumption branches, the wiring relationships between the main energy meter, branch energy meters, and the current transformers corresponding to the target to be identified directly affect the accuracy of the phase power direction, the direction of power accumulation, and the total / partial power verification results.

[0003] In conventional power consumption scenarios, wiring identification can typically be performed by combining on-site wiring records, meter-side power direction, differences in total and branch meter charge levels, phase sequence verification, and current transformer polarity checks. These methods are particularly valuable when load flow direction is relatively stable, branch operating states are relatively uniform, and abnormal behavior is readily apparent. However, with distributed photovoltaic systems transmitting power in reverse at midday, energy storage branches charging or discharging at different times, and bidirectional charging / discharging branches experiencing power flow switching when vehicles are connected, the actual power flow direction of the target phase to be identified may change multiple times within a continuous time window. Meter-side direction measurements collected at a single moment or net charge differences within a single cycle are insufficient to fully reflect the correspondence between wiring status and actual power flow.

[0004] Especially when current transformer polarity reversal and actual power flow direction reversal coexist, the two types of directional sign changes may cancel each other out in the meter-side measurement results. In this case, the power direction quantity on the total energy meter side may appear approximately consistent with the standard wiring topology, and the net energy difference may remain within a small range due to the mutual offsetting of forward and reverse energy. However, the phase power flow direction, energy component changes, and directional duality relationships across time windows of the branch energy meters may still exhibit abnormal characteristics. If identification is based solely on static topology verification, single-point power direction judgment, or the net energy difference between the total and branch meters, it is easy to mistake the apparent consistency for normal wiring, affecting the accuracy and stability of wiring anomaly troubleshooting.

[0005] Therefore, when the target power metering box is in a multi-branch access and bidirectional power flow operation environment, it is necessary to utilize opportunistic dynamic excitations such as distributed photovoltaic output changes, energy storage charging and discharging switching, bidirectional charging and discharging branch power reversal, and power consumption branch load fluctuations generated during natural operation. Combined with the power flow reference direction sequence, power flow state transition relationship, cross-window dual constraint, power residual characteristics, and standard wiring topology relationship, more granular wiring identification of the target phase to be identified is required. This is to identify the sign cancellation type wiring anomaly formed by the actual power flow direction reversal and the current transformer polarity reversal coupling, and to improve the reliability of wiring identification results under complex operating conditions. Summary of the Invention

[0006] To address the shortcomings of existing technologies, the present invention aims to provide a method for identifying the wiring connections of multiple electrical parameters in an energy metering box.

[0007] To achieve the above objectives, the present invention provides the following technical solution: A method for identifying multi-source electrical parameter wiring in an energy metering box is applied to a target energy metering box. The target energy metering box includes a main energy meter, branch energy meters, and current transformers corresponding to the target to be identified, and is connected to a distributed photovoltaic branch, an energy storage branch, a bidirectional charge-discharge branch, and a power consumption branch. The method includes the following steps: Step 1: For the target phase to be identified, obtain the standard wiring topology model, the cross-window dual constraint model, and the multi-source electrical parameter data sequence; Step 2: Based on the multi-source electrical parameter data sequence, generate the power flow reference direction sequence, the power flow state transition sequence, and the power flow reversal confidence index; Step 3: Based on the power flow state transition sequence and the multi-source electrical parameter data sequence, identify the opportunistic dynamic excitation events corresponding to the target to be identified, and generate cross-window dual identification results based on the opportunistic dynamic excitation events; Step 4: Based on the power flow reference direction sequence, standard wiring topology model, power flow reversal reliability index, and cross-window dual identification results, generate topology consistency judgment results, double-anti-countermeasure risk index, and dynamic control parameter set; Step 5: Construct a candidate state set based on the dynamic control parameter set. According to the power flow reference direction sequence, cross-window dual identification results, double anti-offset risk index and dynamic control parameter set, perform state matching on each candidate state in the candidate state set, identify whether the target phase to be identified has a sign-off type wiring anomaly formed by the actual power flow direction reversal and the current transformer polarity reversal coupling, and output the wiring identification result.

[0008] In one embodiment, the standard wiring topology model includes at least the standard power flow direction and standard mirror correspondence of the target phase to be identified; the cross-window dual constraint model constrains the power flow direction migration relationship, electrical component reciprocity relationship and residual shielding relationship of the target phase to be identified between the forward power receiving state and the reverse power supply state.

[0009] In one embodiment, the multi-source electrical parameter data sequence includes a total table-side electrical parameter quantum sequence and a branch-side electrical parameter quantum sequence; The total surface-side electrical parameter quantum sequence includes the surface-side orientation quantities of the target phase to be identified; the surface-side orientation quantities include power orientation quantities and charge orientation quantities; The branch-side electrical parameter quantum sequence includes branch characteristic quantities; the branch characteristic quantities include the phase power flow direction, phase power change and branch operating status of distributed photovoltaic branches, energy storage branches, bidirectional charge and discharge branches and power consumption branches on the target phase to be identified.

[0010] In one embodiment, the generation of the power flow reference direction sequence, the power flow state transition sequence, and the power flow reversal reliability index includes: dividing the multi-source electrical parameter data sequence into multiple continuous time windows according to time; calculating the phase power balance index of each continuous time window based on branch characteristic quantities; determining the power flow direction of the window by combining the surface direction quantity and the branch characteristic quantity; arranging the power flow directions of each window in chronological order to generate the power flow reference direction sequence; generating the power flow state transition sequence based on the changes in the power flow direction of adjacent continuous time windows; and generating the power flow reversal reliability index based on the reversal amplitude, reversal duration, and zero-crossing duration of the power flow direction of the window.

[0011] In one embodiment, the cross-window duality identification result includes window group information, directional duality features, energy residual features, and power flow state cross-window duality index; the window group information includes opportunistic dynamic excitation window group, opportunistic dynamic excitation credibility, and cross-power flow state duality window group.

[0012] In one embodiment, the opportunistic dynamic excitation window group is determined based on the continuous time window before and after the occurrence of the opportunistic dynamic excitation event; the cross-power flow state dual window group is determined based on the forward power receiving time window and the reverse power supply time window in the opportunistic dynamic excitation window group; the opportunistic dynamic excitation reliability is generated based on the power change amplitude, state duration, power flow direction change amplitude and zero crossing duration corresponding to the opportunistic dynamic excitation event.

[0013] In one embodiment, the directional duality features include the directional consistency and directional divergence relationships between the surface directional quantities and the power flow reference direction sequence within the cross-current state dual window group; the energy residual features include the energy component differences and net energy differences between the total energy meter and the branch energy meter; and the power flow state cross-window duality index is generated based on the degree of deviation between the directional duality features and the energy residual features and the cross-window duality constraint model.

[0014] In one embodiment, the generation of the anti-dumping and counter-offsetting risk index includes: generating a directional divergence strength index based on the directional duality features in the power flow reference direction sequence and the cross-window duality identification results; generating a topology consistency judgment result and a topology surface consistency strength based on the matching relationship between the directional duality features and the standard wiring topology model; generating a charge component shielding strength based on the charge residual features in the cross-window duality identification results; and generating the anti-dumping and counter-offsetting risk index based on the cross-window duality identification results, the power flow reversal confidence index, the directional divergence strength index, the topology surface consistency strength, and the charge component shielding strength.

[0015] In one embodiment, the set of dynamic control parameters includes at least the observation duration of the opportunistic dynamic excitation window group, the sampling frequency of the multi-source electrical parameter data sequence, the power direction discrimination weight, the power residual feature weight, the weight of the cross-window dual identification result, the prior weight of the candidate state, and the evidence attribute of the topological consistency judgment result.

[0016] In one embodiment, the candidate state set includes four candidate states: forward energization and normal polarity of the current transformer, forward energization and reverse polarity of the current transformer, reverse energization and normal polarity of the current transformer, and reverse energization and reverse polarity of the current transformer.

[0017] Compared with the prior art, the present invention has the following beneficial effects: This invention takes the target phase to be identified in the target power metering box as the identification object, comprehensively acquires the standard wiring topology model, the cross-window dual constraint model and the multi-source electrical parameter data sequence, and generates the power flow reference direction sequence, power flow state transition sequence and power flow reversal reliability index based on the multi-source electrical parameter data sequence. This makes wiring identification no longer limited to the surface direction quantity or a single power difference at a single moment, but can judge the wiring status by combining the power flow direction changes within a continuous time window, thus improving the accuracy of wiring identification in a bidirectional power flow operation environment. Based on the power flow state transition sequence and multi-source electrical parameter data sequence, opportunistic dynamic excitation events are identified, and cross-window dual identification results are generated based on the opportunistic dynamic excitation events. It can utilize the power changes and power flow switching generated by distributed photovoltaic branches, energy storage branches, bidirectional charging and discharging branches and power consumption branches during natural operation to capture the directional dual relationship and power residual characteristics between the forward power receiving window and the reverse power supply window. This is helpful to reveal the abnormal performance of current transformer polarity reversal under the condition of actual power flow direction reversal, which is masked by the surface directional quantity. By combining the power flow reference direction sequence, standard wiring topology model, power flow reversal reliability index, and cross-window dual identification results, a topology consistency judgment result, a double-anti-cancellation risk index, and a dynamic control parameter set are generated. Based on this, a candidate state set is constructed for state matching. It can distinguish and identify between forward power supply and current transformer polarity normal, forward power supply and current transformer polarity reversed, reverse power supply and current transformer polarity normal, and reverse power supply and current transformer polarity reversed. In this way, it can identify symbol cancellation type wiring anomalies formed by the coupling of actual power flow direction reversal and current transformer polarity reversed, so that the output wiring identification results have strong evidence integrity, interpretability, and verification value. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the overall process of a multi-source electrical parameter wiring identification method for an electricity metering box according to the present invention; Figure 2 This is a schematic diagram illustrating the relationship between the standard wiring topology model and the cross-window dual constraint model of this invention; Figure 3 This is a schematic diagram illustrating the generation of cross-window duality recognition results using the opportunistic dynamic excitation event method of the present invention. Figure 4 This is a schematic diagram of performing state matching on the candidate state set of the present invention and outputting the wiring identification result. Detailed Implementation

[0019] Reference Figures 1 to 4 A method for identifying the wiring parameters of a multi-source electrical metering box is disclosed, applied to a target electrical metering box. The target electrical metering box includes a main energy meter, branch energy meters, and current transformers corresponding to the target to be identified, and is connected to a distributed photovoltaic branch, an energy storage branch, a bidirectional charging and discharging branch, and a power consumption branch. The method includes the following steps: Step 1: For the target phase to be identified, acquire the standard wiring topology model, the cross-window dual constraint model, and the multi-source electrical parameter data sequence. The standard wiring topology model records the standard correspondence and standard power flow direction relationships between the main energy meter, branch energy meters, current transformers, and each branch under the correct wiring condition of the target energy metering box. The cross-window dual constraint model records the directional migration, changes in electrical components, and residual masking relationships between the forward power receiving state and the reverse power supply state. The multi-source electrical parameter data sequence is collected from the main energy meter, branch energy meters, and connected branches, containing information on the power, electrical quantity, branch operating status, and power flow changes of the target phase in different time windows, providing a data foundation for wiring identification.

[0020] For the target phase to be identified in the target energy metering box, the installation files, sampling channel identifiers, phase identifiers, and terminal connection records of the total energy meter, branch energy meters, and current transformers corresponding to the target phase are read. Combined with the rated access positions of the distributed photovoltaic branch, energy storage branch, bidirectional charging and discharging branch, and power consumption branch connected to the target energy metering box, the standard power flow direction and standard mirror correspondence of the target phase under the correct wiring state are sorted out to form a standard wiring topology model. In the same process, based on the power direction, energy components, and phase energy change relationship between the total energy meter and the branch energy meter under the forward power receiving state, and in conjunction with the direction change relationship when the distributed photovoltaic branch sends power, the energy storage branch discharges power, or the bidirectional charging and discharging branch feeds back power under the reverse power sending state, a cross-window dual constraint model is established. This cross-window dual constraint model limits the power flow direction migration relationship, energy component reciprocity relationship, and residual shadowing relationship of the target phase under the forward power receiving state and the reverse power sending state. For example, when the output of the distributed photovoltaic branch is higher than the consumption of the power consumption branch and the energy storage branch stops charging at noon, the target phase to be identified changes from the forward power receiving state to the reverse power supply state. In the standard mirror correspondence, the total energy meter direction quantity and the power flow direction of each branch phase show a mirror change in opposite directions. For the same target phase to be identified, the total surface-side electrical parameter quantum sequence and the branch-side electrical parameter quantum sequence are collected under a unified time reference. The total surface-side electrical parameter quantum sequence records the surface-side directional quantities of the target phase to be identified, including power direction and charge direction. The branch-side electrical parameter quantum sequence records the branch characteristic quantities, including the phase power flow direction, phase charge change, and branch operating status of distributed photovoltaic branches, energy storage branches, bidirectional charge and discharge branches, and power consumption branches on the target phase to be identified. During the acquisition, data is saved in continuous time windows. Within the time window, the sampling time, phase, branch name, positive and negative power direction, charge increment, and branch operating status are retained. For example, when the power consumption branch continuously draws power at night, the distributed photovoltaic branch has no output, and the energy storage branch is in a charging state, the power direction quantity in the total meter side electrical parameter quantum sequence is positively charged, and the phase power flow direction of the power consumption branch and the energy storage branch in the branch side electrical parameter quantum sequence both point towards the load side; when the output of the distributed photovoltaic branch increases at noon and exceeds the demand of the power consumption branch, the power direction quantity in the total meter side electrical parameter quantum sequence shows a reverse power supply characteristic, and the phase power change of the distributed photovoltaic branch in the branch side electrical parameter quantum sequence forms a corresponding record, thereby obtaining a multi-source electrical parameter data sequence that matches the standard wiring topology model and the cross-window dual constraint model.

[0021] Step 2: Based on the multi-source electrical parameter data sequence, generate a power flow reference direction sequence, a power flow state transition sequence, and a power flow reversal reliability index. Perform time windowing processing on the multi-source electrical parameter data sequence, and combine the changes in electrical parameters on the main meter side and branch side to determine the power flow direction of the target phase to be identified. The power flow reference direction sequence records the power flow direction judgment results of the target phase to be identified within each time window. The power flow state transition sequence records the changes in forward power receiving state, reverse power supply state, and zero-crossing state between adjacent time windows. The power flow reversal reliability index reflects the amplitude, duration, and stability of the power flow reversal process, avoiding misinterpreting short-term fluctuations, noise disturbances, or metering sampling errors as valid power flow reversals.

[0022] Based on the sampling timestamps of the multi-source electrical parameter data sequence, the total meter-side electrical parameter quantum sequence and the branch-side electrical parameter quantum sequence are divided into multiple continuous time windows. Each continuous time window is adjacent and does not overlap. Within a single continuous time window, the meter-side direction quantity of the target phase to be identified, the phase-by-phase power flow direction of the distributed photovoltaic branch, the phase-by-phase power flow direction of the energy storage branch, the phase-by-phase power flow direction of the bidirectional charging and discharging branch, and the phase-by-phase power flow direction of the power consumption branch are recorded. The phase-by-phase power balance index is calculated according to the same phase, the same time window, and the same power positive and negative convention. The phase-by-phase power balance index is calculated based on the phase-by-phase power flow direction and phase-by-phase power change of each branch in the branch characteristic quantity, reflecting the state of the target phase to be identified within the continuous time window, whether it is drawing power from the load side, sending power to the grid side, or exchanging power close to zero. The target to be identified is denoted as The continuous time window number is denoted as , No. A continuous time window is denoted as The window length is denoted as When it comes to duration calculations, Values ​​should be taken based on the actual time length; when calculating electrical components... Calculated on an hourly basis. Active power flow from the grid side to the user load side is considered positive power reception, with the direction value taken as... The active power flow fed to the grid from the user side, distributed photovoltaic branches, energy storage branches, or bidirectional charge-discharge branches is considered reverse power transmission, and the direction value is taken as... The near-zero power exchange state is set to 0. This is an indicator function that takes the value 1 when the condition is true and 0 when the condition is false. Internal target phase to be identified The branch-side electrical parameters are averaged to obtain the phase power balance index. Calculate using the following formula: For branches where the target energy metering box is not installed or not connected, the corresponding average power is counted as 0. Where, For the power branch circuit The average active power output within the area. For distributed photovoltaic branches in The average active power generated within the region. For energy storage branch lines Average active power of charging within the range, For energy storage branch lines The average active power of discharge within the range, For bidirectional charge and discharge branch in Average active power of charging within the range, For bidirectional charge and discharge branch in Average active power fed back within the system. Power direction discrimination threshold. Pick and The larger value in The standard deviation of power noise for samples near zero power; when there are no samples near zero power, Calculated based on the maximum power error corresponding to the accuracy class of the metering device; This is the capacity margin factor, and its value range is... to ; The rated capacity of the target phase to be identified. When At that time, the direction of the window current Pick ;when hour, Pick ;when hour, Set to 0. The power flow reference direction sequence is determined by... Arranged chronologically. The surface-side directional quantity is used as the object of verification in the cross-window dual comparison; during the identification of current transformer polarity anomalies, the power flow direction within the window is primarily based on the branch-side phase power balance index. Missing branch-side data, inconsistent timestamps, or an effective sampling ratio below [a certain threshold] may occur. A continuous time window is designated as an invalid window and is not included in the power flow state transition sequence, cross-power flow state dual window group, and candidate state matching.

[0023] For example, within the continuous time window from 10:00 to 10:05, the phase power flow of the distributed photovoltaic branch on the target phase to be identified is output to the grid side, the energy storage branch is in a stopped charging and discharging state, and the power consumption branch maintains a low power input, and the phase power balance index shows a reverse power supply characteristic; within the continuous time window from 18:00 to 18:05, the output of the distributed photovoltaic branch decreases, the power input of the power consumption branch increases, the energy storage branch is in a charging state, and the phase power balance index shows a positive power receiving characteristic; The power flow state transition sequence is formed by the power flow directions of adjacent effective continuous time windows in chronological order. A power flow reversal occurs when the direction shifts from forward power reception to reverse power transmission, or from reverse power transmission to forward power reception. When a reversal segment is established, let the set of stable windows before the reversal be denoted as . The set of stable windows after reversal is The set of zero-crossing windows is . It is a set of consecutive and oriented valid windows before the reversal. It is a set of consecutive and oriented valid windows after reversal. for and Between The effective window set. Inversion amplitude. Calculate using the following formula: Reverse duration according to The duration of zero crossing is obtained by summing the lengths of all windows within the window. according to The lengths of all windows within the window are summed. In the formula, For set Inside The mean, For set Inside The mean. A reliable indicator of trend reversal. Calculate using the following formula: In the formula, For effective reversal of the amplitude benchmark, ; To effectively reverse the duration benchmark, ; As a zero-crossing duration benchmark, ; The baseline length for continuous time windows is used, and for equal-length windows, the following is taken: For windows of unequal length, take the median of the window lengths. and The weights are non-negative and satisfy the following conditions: A set of implementation values . The closer it is to 1, the closer the reversal segment is to an effective trend reversal.

[0024] Step 3: Based on the power flow state transition sequence and multi-source electrical parameter data sequence, identify the opportunistic dynamic excitation events corresponding to the target to be identified, and generate cross-window dual identification results based on these events. The opportunistic dynamic excitation events are identified by utilizing the power changes and power flow switching phenomena generated during the operation of the target energy metering box itself. These events originate from changes in the operating states of distributed photovoltaic branches, energy storage branches, bidirectional charge-discharge branches, and power consumption branches, without relying on additional test loads or manual switching operations. Based on these events, the time windows before and after the events are analyzed to form cross-window dual identification results. These results reflect the dual relationship between surface-side directional quantities, residual power characteristics, and branch operating states under different power flow conditions, providing dynamic evidence for identifying symbol-cancelling type wiring anomalies.

[0025] Based on the migration records of forward power receiving state, reverse power supply state, and zero-crossing state in the power flow state transition sequence, event segments are extracted from the multi-source electrical parameter data sequence. Continuous time windows where the power direction quantity undergoes continuous change, the branch operating state changes synchronously, and the phase power balance index crosses the zero power interval are selected as the opportunistic dynamic excitation events corresponding to the target to be identified. These opportunistic dynamic excitation events are taken from load changes, distributed photovoltaic branch output changes, energy storage branch charging / discharging switching, or bidirectional charging / discharging branch power flow switching during the natural operation of the target energy metering box. No additional test load is connected, and the original branch operating state is not changed. Let the set of branches be ,in Indicates a distributed photovoltaic branch. Indicates the energy storage branch, Indicates a bidirectional charging and discharging branch. This indicates the branch circuit using electricity. For the first... A reversal segment, a branch. Power change between the stability window before and after the reversal Calculate using the following formula: The maximum value of the power change in each branch is denoted as In the formula, for Zhongren Branch Road, branch road exist Internal target phase to be identified The average active power calculated according to the unified power flow direction is positive for forward power reception and negative for reverse power transmission. When and At that time, determine the first Each inversion segment is an opportunistic dynamic stimulus event. The opportunistic dynamic stimulus window group corresponding to this opportunistic dynamic stimulus event consists of... and The corresponding continuous time windows are composed of. The reliable threshold for trend reversal is defined, and its value range is [value range missing]. to ; The threshold for event power change is taken as... and The larger value in; This is the event capacity margin coefficient, with a value range of [value range missing]. to ; and These represent the lower limits for the number of stable windows before and after reversal, respectively, both with a value not less than 3. (This refers to) the reliability of opportunistic dynamic incentives. Calculate using the following formula: In the formula, To establish a baseline for the number of stable windows, the value is set between 3 and 6. and The weights are non-negative and satisfy the following conditions: A set of implementation values .

[0026] The forward power receiving time window and the reverse power sending time window are extracted within the opportunistic dynamic excitation window group. The zero-crossing window is used in the calculation of the power flow reversal credibility index but is not included in the candidate state score. The branch with the largest value is designated as the primary triggering branch, and the remaining branches are designated as non-primary triggering branches. For any forward power receiving time window and any reverse power supply time window, the time interval between their center points is no greater than [value missing]. The power amplitude difference is not greater than Furthermore, the proportion of non-primary triggering branch operating in the same state is not less than At this time, the two form a dual window pair. The power amplitude difference is the absolute value of the difference between the absolute values ​​of the phase power balance indices of the two; the active power directions of the non-primary triggering branch are the same in the two time windows, and the absolute value of the difference in the average active power is not greater than When the non-master trigger branch is empty, it is recorded as having a consistent operating status. The proportion of non-master trigger branches with the same operating status is determined by the ratio of the number of non-master trigger branches with the same operating status to the total number of non-master trigger branches; when a non-master trigger branch is empty, the consistency ratio is calculated as follows: count. To allow for a pairing time interval, the value is [value to be filled in]. minutes to minute; Assuming the power amplitude is close to the threshold, take ; This is a power amplitude proximity coefficient, with a value range of [value range missing]. to ; The threshold for consistent branch operating status is defined, and its value range is [value range missing]. to When multiple window pairs satisfy the conditions, they are paired in ascending order of window center time interval, and time windows that have already been paired do not participate in pairing again; all paired dual window pairs within the same opportunistic dynamic excitation window group together constitute the cross-current state dual window group.

[0027] Let the dual window group of the cross-current state be denoted as , This is the window group number. The total energy meter is located at... Internal target phase to be identified The average active power is denoted as The power direction quantity on the surface is denoted as ,in For symbolic functions, Time to take , Time to take , Time to take .when hour, Internally, a consistent directional relationship is formed; when and hour, An internal directional divergence relationship is formed. Directional divergence strength index. Calculate using the following formula: exist Internal, positive charge component on the main meter side Reverse charge component on the main meter side Branch reference positive electrical component and branch reference reverse electrical component Calculate using the following formula: In the formula, For the total electricity meter at time The active power of the target phase to be identified is collected and integrated using trapezoidal integration at each sampling point or by accumulating the time-division energy increments. In the calculation of the directional divergence intensity index and energy components, The integration time is converted to hours; the time units used for calculating the duration of power flow reversal and the duration of zero crossing are the same as those used for other purposes. The time unit remains consistent. Differences in power component values. Difference between net power consumption and net power consumption Calculate using the following formula: Normalized values ​​of electrical component differences Pick and The smaller value among them. Electrical component shielding strength. and trend state cross-window dual indicators Calculate using the following formula: In the formula, To prevent positive numbers with a denominator of zero from being calculated for the amount of electricity, the value should not exceed [a certain value]. of times; It is a dimensionless positive number, and its value is no greater than 100%. ; The threshold for net electricity consumption difference ranges from 0.03 to 0.08. The threshold for the difference in electrical component values ​​is [value range missing]. to ; and The weights are non-negative and satisfy the following conditions: A set of implementation values .

[0028] For example, in the forward power receiving time window before the output of the distributed photovoltaic branch increases, the power flow reference direction sequence shows that the target to be identified draws power from the load side, and the meter-side direction quantity also records forward power receiving. However, in the reverse power supply time window after the output of the distributed photovoltaic branch increases, the power flow reference direction sequence shows that the target to be identified supplies power to the grid side. If the meter-side direction quantity still shows a direction record similar to that of forward power receiving, and the net difference in electricity between the total electricity meter and the branch electricity meter is small, while the difference in electricity components is abnormally large, then a clear directional divergence relationship and residual shading characteristics are formed within the cross-power flow state dual window group, and the cross-window dual index of the power flow state increases accordingly.

[0029] Step 4: Based on the power flow reference direction sequence, standard wiring topology model, power flow reversal confidence index, and cross-window duality identification results, generate topology consistency judgment results, double-anti-reversal cancellation risk index, and a dynamic control parameter set. Perform joint analysis on the power flow direction judgment, standard wiring relationship, and cross-window duality evidence. The topology consistency judgment result reflects the degree of consistency between the currently measured directional relationship and the standard wiring topology model. The double-anti-reversal cancellation risk index reflects the risk of apparent normality caused by the mutual cancellation of surface directional quantities when actual power flow reversal and current transformer polarity reversal coexist. The dynamic control parameter set is formed based on the power flow reversal confidence index, cross-window duality identification results, and topology consistency judgment results. It includes parameters such as observation duration, sampling frequency, discrimination weight, and evidence attributes, providing a discrimination scale for candidate state matching.

[0030] Record the target phase to be identified in the standard wiring topology model The standard direction of power reception is , This indicates that the transverse dimension is positive when the device is energized in the forward direction under standard wiring conditions. Topological surface uniformity strength. Calculate using the following formula: In the formula, To prevent positive numbers with a zero denominator from being calculated, the value should not exceed [a certain value]. Anti-dumping and countervailing duty offsetting risk indicators Calculate using the following formula: In the formula, and They are respectively The credibility of opportunistic dynamic incentives and the credibility of trend reversal indices for opportunistic dynamic incentive events; and The weights are non-negative, and A set of implementation values The dynamic control parameters include the observation duration, sampling frequency, and power direction discrimination weight for the dual window group across the power flow state. Power residual feature weight Weights of cross-window dual recognition results Anti-dumping and countervailing duty offsetting risk weights Prior weights of candidate states Evidence attributes of topological consistency judgment results and risk thresholds for anti-dumping and countervailing duty offsetting. The observation duration and sampling frequency serve as data coverage constraints for the cross-current power flow state dual window group. Window groups that meet the effective sampling ratio requirements are included in the candidate state matching; these two factors do not separately form a scoring weighting term. The observation duration is obtained by summing the lengths of each continuous time window within the cross-current power flow state dual window group; the sampling frequency is taken as the actual sampling frequency of the multi-source electrical parameter data sequence. For candidate state prior weights, Let be any candidate state in the candidate state set. The evidentiary attributes of the topological consistency judgment result are determined by... and The comparison is more certain. This is denoted as topological surface consistency. This is recorded as topological surface inconsistency; This is the topological surface consistency threshold, and its value range is... Up to 0.80; The threshold for offsetting anti-dumping and countervailing duties is defined as follows, and its value range is: to . and All are non-negative values, satisfying When there are no historical verification records, and All When historical verification records exist, the results are obtained by normalization based on the correct identification ratio of each evidence type in the historical verification records. When no historical verification records exist, the four candidate states are... All values ​​are set to 0.25; if historical verification records exist, Obtained by normalization based on the proportion of occurrence of the corresponding candidate status in historical verification records.

[0031] For example, under a five-minute sampling condition, if the midday reverse power supply state is maintained for six consecutive time windows, the zero-crossing duration is short, the directional divergence intensity index continues to rise, the net power difference does not significantly increase but the power component difference significantly increases, then the weight of the cross-window dual identification result and the power residual feature weight are increased, and the evidence attribute of the topological consistency judgment result is recorded as surface consistency evidence, and the double anti-counterfeiting risk index enters the high-risk range.

[0032] Step 5: Construct a candidate state set based on the dynamic control parameter set. According to the power flow reference direction sequence, cross-window dual identification results, double-anti-offset risk index, and dynamic control parameter set, perform state matching on each candidate state in the candidate state set. Identify whether the target phase to be identified has a sign-cancelled wiring anomaly caused by the actual power flow direction reversal and the current transformer polarity reversal coupling, and output the wiring identification result. This step establishes the candidate state set under the constraints of the dynamic control parameter set and matches the actual observation results of the target phase to be identified with each candidate state in the candidate state set. When performing state matching, the changes in power flow direction reflected by the power flow reference direction sequence, the differences between forward and reverse states reflected by the cross-window dual identification results, the shielding risk reflected by the double-anti-offset risk index, and the discrimination weights and evidentiary attributes given by the dynamic control parameter set are comprehensively considered. When the matching result indicates a coupling relationship between the actual power flow direction reversal and the current transformer polarity reversal, a sign-cancellation type wiring anomaly is identified in the target phase to be identified. The wiring identification result can include the target energy metering box, the target phase to be identified, the wiring anomaly type, the relevant time window, and the judgment criteria, which facilitates on-site verification and metering anomaly handling.

[0033] Candidate states are denoted as ,in , Indicates positive power reception. Indicates reverse power supply; , This indicates that the polarity of the current transformer is normal. This indicates that the polarity of the current transformer is reversed. Candidate state. exist The corresponding expected lateral direction quantity is The residual power matching factor is denoted as... ,when hour, Pick ;when hour, Pick The window-crossing dual matching factor is denoted as... ,when hour, Pick ;when hour, Pick Candidate state exist Local matching score within Calculate using the following formula: Within the same cross-current flow state dual window group, the polarity assumption of the current transformer Maintaining consistency, trend status According to each consecutive time window The value is determined. Score for reverse polarity connection of current transformer. Score for normal polarity of current transformer Calculate using the following formula: In the formula, This indicates that the candidate states in the local matching score are taken as... The rating at that time This indicates that the candidate states in the local matching score are taken as... The rating at that time. ,and Memory exists When the reverse power supply time window is reached, it is determined that the target phase to be identified has a sign-cancelled wiring anomaly caused by the actual power flow direction reversal and the current transformer polarity reversal coupling. The threshold for distinguishing candidate states is set, with a value ranging from 0.05 to 0.15.

[0034] The wiring identification results record the target energy metering box number, the target phase to be identified, the candidate status of the hit, the conclusion of the symbol cancellation type wiring anomaly, the opportunistic dynamic excitation window group, the cross-current flow state dual window group, the double anti-offset risk index, and the main evidence content. For example, if the directional quantity on the side of the total energy meter still shows positive power reception during the photovoltaic reverse power transmission, while the phase power change of the branch energy meter has shown power transmission characteristics, and the net power difference is small but the difference of power components continues to expand, then the wiring identification results are recorded as the target phase to be identified is suspected to have a symbol cancellation type wiring anomaly formed by the superposition of the current transformer polarity reversal and the actual power flow direction reversal.

[0035] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for identifying the wiring of multiple electrical parameters in an electricity metering box, characterized in that, The method is applied to a target energy metering box; the target energy metering box includes a total energy meter, branch energy meters, and current transformers corresponding to the target to be identified, and is connected to a distributed photovoltaic branch, an energy storage branch, a bidirectional charge and discharge branch, and a power consumption branch; the method includes the following steps: Step 1: For the target phase to be identified, obtain the standard wiring topology model, the cross-window dual constraint model, and the multi-source electrical parameter data sequence; the cross-window dual constraint model constrains the power flow direction migration relationship, electrical component reciprocity relationship, and residual masking relationship of the target phase to be identified between the forward power receiving state and the reverse power supply state. Step 2: Based on the multi-source electrical parameter data sequence, generate the power flow reference direction sequence, the power flow state transition sequence, and the power flow reversal confidence index; The generation of the power flow reference direction sequence, power flow state transition sequence, and power flow reversal reliability index includes: dividing the multi-source electrical parameter data sequence into multiple continuous time windows according to time; calculating the phase power balance index of each continuous time window based on branch characteristic quantities; determining the power flow direction of the window by combining the surface direction quantity and branch characteristic quantities; arranging the power flow directions of each window in time order to generate the power flow reference direction sequence; generating the power flow state transition sequence based on the changes in the power flow direction of adjacent continuous time windows; and generating the power flow reversal reliability index based on the reversal amplitude, reversal duration, and zero-crossing duration of the power flow direction of the window. Step 3: Based on the power flow state transition sequence and the multi-source electrical parameter data sequence, identify the opportunistic dynamic excitation events corresponding to the target to be identified, and generate cross-window dual identification results based on the opportunistic dynamic excitation events; Based on the migration records of forward power receiving state, reverse power supply state and zero crossing state in the power flow state transition sequence, event segments are extracted from the multi-source power parameter data sequence. The continuous time window in which the power direction quantity changes continuously, the branch operating state changes synchronously and the phase power balance index crosses the zero power interval is selected as the opportunistic dynamic excitation event corresponding to the target to be identified. The results of cross-window duality identification include window group information, directional duality features, energy residual features, and cross-window duality index of power flow state; window group information includes opportunistic dynamic excitation window group, opportunistic dynamic excitation credibility, and cross-power flow state duality window group. Directional duality features include the directional consistency and directional divergence relationships between the surface directional quantities and the power flow reference direction sequence within the cross-current state dual window group; energy residual features include the differences in energy components and net energy difference between the total energy meter and the branch energy meter; the cross-window duality index of power flow state is generated based on the degree of deviation between the directional duality features and the energy residual features and the cross-window duality constraint model. Step 4: Based on the power flow reference direction sequence, standard wiring topology model, power flow reversal reliability index, and cross-window dual identification results, generate topology consistency judgment results, double-anti-countermeasure risk index, and dynamic control parameter set; The generation of the anti-dumping and counter-offsetting risk index includes: generating a directional deviation strength index based on the directional duality features in the power flow reference direction sequence and the cross-window duality identification results; generating a topology consistency judgment result and a topology surface consistency strength based on the matching relationship between the directional duality features and the standard wiring topology model; generating a charge component shielding strength based on the charge residual features in the cross-window duality identification results; and generating the anti-dumping and counter-offsetting risk index based on the cross-window duality identification results, the power flow reversal confidence index, the directional deviation strength index, the topology surface consistency strength, and the charge component shielding strength. Step 5: Construct a candidate state set based on the dynamic control parameter set. According to the power flow reference direction sequence, cross-window dual identification results, double anti-offset risk index and dynamic control parameter set, perform state matching on each candidate state in the candidate state set, identify whether the target phase to be identified has a sign-off type wiring anomaly formed by the actual power flow direction reversal and the current transformer polarity reversal coupling, and output the wiring identification result.

2. The method for identifying multi-source electrical parameter wiring in an energy metering box according to claim 1, characterized in that, The standard wiring topology model includes at least the standard power flow direction and standard mirror correspondence of the target phase to be identified.

3. The method for identifying multi-source electrical parameter wiring in an energy metering box according to claim 2, characterized in that, The multi-source electrical parameter data sequence includes the total table-side electrical parameter quantum sequence and the branch-side electrical parameter quantum sequence; The total surface-side electrical parameter quantum sequence includes the surface-side orientation quantities of the target phase to be identified; the surface-side orientation quantities include power orientation quantities and charge orientation quantities; The branch-side electrical parameter quantum sequence includes branch characteristic quantities; Branch characteristic quantities include the phase power flow direction, phase power change, and branch operating status of distributed photovoltaic branches, energy storage branches, bidirectional charge and discharge branches, and power consumption branches on the target phase to be identified.

4. The method for identifying multi-source electrical parameter wiring in an energy metering box according to claim 3, characterized in that, The opportunistic dynamic excitation window group is determined based on the continuous time window before and after the occurrence of the opportunistic dynamic excitation event; the cross-power flow state dual window group is determined based on the forward power receiving time window and the reverse power supply time window in the opportunistic dynamic excitation window group; the reliability of the opportunistic dynamic excitation is generated based on the power change amplitude, state duration, power flow direction change amplitude, and zero crossing duration corresponding to the opportunistic dynamic excitation event.

5. The method for identifying multi-source electrical parameter wiring in an energy metering box according to claim 4, characterized in that, The set of dynamic control parameters includes at least the observation duration of the opportunistic dynamic excitation window group, the sampling frequency of the multi-source electrical parameter data sequence, the power direction discrimination weight, the power residual feature weight, the weight of the cross-window dual identification result, the prior weight of the candidate state, and the evidence attributes of the topological consistency judgment result.

6. The method for identifying multi-source electrical parameter wiring in an energy metering box according to claim 5, characterized in that, The candidate state set includes four candidate states: forward energization and normal current transformer polarity, forward energization and reverse current transformer polarity, reverse energization and normal current transformer polarity, and reverse energization and reverse current transformer polarity.

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