Power transformation aliasing scene-based energy consumption identification system

Abstract

The invention relates to the technical field of electric energy identification, in particular to an energy consumption identification system based on a power transformation aliasing scene, which comprises a disturbance boundary extraction module, a boundary stability judgment module, an aliasing state identification module, a periodic power calculation module and an energy consumption attribution judgment module. According to the method, a voltage sag record with a phase offset characteristic is formed through continuous tracking of the periodic voltage fluctuation starting and ending point indexes, a stable fluctuation judgment mechanism is constructed in combination with offset statistics, and accurate identification of aliasing sections is realized by means of disturbance amplitude and frequency extraction. A power sequence is constructed on the basis of voltage and current synchronous sampling values, period integration is completed, a high-precision calculation result of energy consumption is formed, fine division of cross interference period energy consumption under the condition of no physical isolation is achieved, dependence of external metering equipment is avoided, independent quantization of overlapping load energy consumption is achieved, and the calculation efficiency is improved. And a feasible means is provided for energy efficiency evaluation in a non-intrusive load identification and high-coupling scene.

Description

Technical Field

[0001] The present invention relates to the field of electric energy identification technology, and in particular to an energy consumption identification system based on a power conversion aliasing scenario. Background Art

[0002] The field of electric energy identification technology involves the precise identification and quantitative calculation of the operating status, electric energy consumption characteristics, and changing patterns of various energy-consuming devices in power systems. This includes the collection and analysis of basic electrical parameters such as current and voltage, the modeling and identification of electric energy usage behavior, and non-invasive methods for identifying electric energy usage in multi-load interference or complex environments. It is widely used in systemic scenarios such as smart grids, energy-saving monitoring, load management, and energy efficiency evaluation. Among them, the traditional energy consumption identification system based on substation aliasing scenarios refers to a system that independently identifies the electric energy consumption of each device in the presence of multiple substation devices operating simultaneously and interfering with each other. This is usually achieved by installing multiple independent electric energy meters to collect the electric parameters of each device in a physically isolated manner, or relying on existing classified electric energy data to estimate the energy consumption data of each channel in a channel-by-channel matching manner.

[0003] Traditional energy consumption identification in substation overlapping scenarios mainly relies on multiple independent electricity meters to collect electrical equipment parameters in a physically isolated manner, or to perform path matching and inference based on existing classified electricity data. In the context of multiple variable loads connected in parallel and harmonic disturbances, it lacks the ability to identify real-time disturbance characteristics and is difficult to accurately divide overlapping energy consumption areas. For example, the temporary fluctuations generated when multiple devices are started will be uniformly classified into a single channel, causing misjudgment and energy consumption attribution deviation. In practical applications where independent metering equipment cannot be installed or the data classification is unclear, both system identification performance and data resolution are limited. Summary of the Invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and propose an energy consumption identification system based on a substation aliasing scenario.

[0005] To achieve the above objectives, the present invention adopts the following technical solutions: an energy consumption identification system based on a substation aliasing scenario includes: The disturbance boundary extraction module obtains periodic voltage signal data under the parallel operation state of the substation load, collects the start and end point index positions of the voltage fluctuation area within each cycle, calculates the phase offset, calculates the absolute value of the offset and records it, and generates a voltage sag event record; The boundary stability determination module calculates the difference range between the maximum and minimum offset values ​​in any five consecutive cycles based on the voltage sag event record. If the difference range is less than the voltage fluctuation tolerance, it is determined to be a stability boundary and the cycle number is recorded to generate a disturbance stability cycle list; The aliasing state identification module extracts the disturbance peak amplitude and the number of disturbances in the corresponding period based on the disturbance stable period list, calculates the periodic disturbance intensity, and marks it as an aliasing period if it is greater than the total harmonic distortion rate threshold. It also locates the start and end index intervals of all overlapping segments and generates an aliasing period identification result. The cycle power calculation module extracts the point-by-point sampling values ​​of the voltage and current in the corresponding section according to the aliasing cycle identification result, performs multiplication of the corresponding time points to form a power sequence, and accumulates and integrates the sequence to obtain the total energy consumption in the unit cycle, and outputs the cycle energy consumption calculation data.

[0006] As a further solution of the present invention, the voltage sag event record includes an offset absolute value statistic, a phase offset index, and an event start and end mark; the disturbance stability period list includes a period number set, a tolerance comparison result, and a difference range evaluation index; the aliasing period identification result includes an aliasing period number, an overlapping section index, and a total harmonic limit exceedance mark; and the period energy consumption calculation data includes a power time series, a period cumulative energy value, and a voltage and current sampling pair.

[0007] As a further solution of the present invention, the disturbance boundary extraction module includes: The periodic voltage acquisition submodule collects the voltage signal data of each cycle when the substation load is in parallel operation, draws the voltage fluctuation curve within each cycle, and extracts the voltage extreme points, voltage fluctuation edge points and corresponding timestamp information in each cycle to generate the periodic voltage signal fluctuation value; The start and end index positioning submodule detects the voltage fluctuation region boundary within each cycle based on the periodic voltage signal fluctuation value, identifies the index positions of the voltage fluctuation start and end points using a set voltage fluctuation determination threshold, and determines whether the fluctuation region has stability characteristics based on the boundary changes of adjacent cycles, thereby obtaining a voltage fluctuation boundary index sequence; The phase shift statistics submodule performs difference statistics and absolute value operations on the start and end index positions in consecutive cycles according to the voltage fluctuation boundary index sequence and the voltage fluctuation measurement requirements, summarizes the offset degree of the boundary index in the cycle sequence, and obtains the voltage sag event records statistically.

[0008] As a further solution of the present invention, the boundary stability determination module includes: The offset difference extraction submodule extracts the offset values ​​corresponding to all cycles involved in each event based on the voltage sag event records, sorts the offset values ​​in each sequence into groups of five consecutive cycles, identifies the maximum and minimum values ​​in the sequence, uses the difference as the difference range of the current sequence, and obtains an offset difference range set; The voltage tolerance comparison submodule compares the difference range of each sequence with the voltage fluctuation tolerance threshold item by item based on the offset difference range set. If the difference range is less than the voltage fluctuation tolerance threshold, it is marked as a boundary stability period; otherwise, it is an unstable period, thereby obtaining a boundary stability label sequence; The stable period generation submodule extracts the numbers of all periods marked as stable states according to the boundary stability label sequence, establishes an index mapping according to the original order in the full period sequence, establishes a period sequence unique identification structure, and obtains a disturbance stable period list.

[0009] As a further solution of the present invention, the aliasing state identification module includes: The disturbance feature extraction submodule extracts the voltage disturbance data of the corresponding period based on the disturbance stability period list, counts the peak amplitude of the disturbance waveform and the total number of disturbance events in each period, and performs the above operations on all periods in sequence to obtain the input data for disturbance intensity calculation; The intensity threshold judgment submodule calculates the input data according to the disturbance intensity, calculates and obtains the disturbance intensity of each cycle, and judges it item by item with the total harmonic distortion rate threshold. If it is greater than the total harmonic distortion rate threshold, the cycle is marked as an aliasing cycle, and a corresponding structure between the cycle number and the judgment result is established to generate an aliasing cycle label sequence; The aliasing segment positioning submodule extracts the period numbers of all aliasing periods according to the aliasing period label sequence, performs segment positioning in the original voltage disturbance signal data, extracts the starting point index and ending point index of the disturbance curve in each aliasing period, records the start and end index values ​​of each disturbance interval and forms an aliasing segment index set to obtain the aliasing period identification result.

[0010] As a further solution of the present invention, the periodic power calculation module includes: The power sample extraction submodule locates the complete time interval of the period in the original signal sequence based on the sampling start and end point indexes of the corresponding period in the aliasing period identification result, extracts the voltage and current values ​​at each time point, and aligns them synchronously according to the time index. Each time point has a corresponding set of voltage-current data pairs, and the electrical parameter synchronization sequence is obtained. The instantaneous power accumulation submodule performs a product operation based on the voltage value and the current value at each time point in the electrical parameter synchronization sequence to form the instantaneous power data of each point, calculates and obtains the total energy consumption per unit cycle, and generates the cycle energy consumption integral data; The cycle energy consumption generation submodule reconstructs the energy consumption value sequence in all cycles according to the cycle energy consumption integral data in the order of cycle numbers, uses the cycle number as the index and the cycle energy consumption value as the content, integrates them into a two-dimensional cycle energy consumption array structure, and outputs the cycle energy consumption calculation data.

[0011] As a further embodiment of the present invention, the system further comprises: The energy consumption attribution determination module extracts three characteristic indicators, namely, boundary difference, voltage slope, and current amplitude, of all disturbance sources in the same period based on the periodic energy consumption calculation data, constructs a disturbance feature vector, and compares the similarity with the typical feature vector in the stable period. The module selects the load units with highly similar features and allocates the corresponding power to generate the energy consumption identification results of the substation aliasing scenario. The energy consumption identification result of the substation aliasing scenario includes a disturbance feature vector set, a similarity comparison matrix, and load unit energy consumption distribution information.

[0012] As a further solution of the present invention, the energy consumption attribution determination module includes: The feature acquisition submodule extracts a sampling sequence within the corresponding period based on the periodic energy consumption calculation data, sequentially extracts the voltage values ​​at the start and end points of the disturbance in the corresponding sampling point sequence, calculates the difference as the boundary difference, constructs a continuous difference sequence for all voltage sampling points in the disturbance segment, calculates the sequence mean as the voltage slope, and calculates the maximum amplitude of all current sampling points in the disturbance segment as the current amplitude, and integrates and obtains a disturbance feature vector set; The similarity vector comparison submodule compares each vector with the known typical load feature vectors in the stable period one by one according to the disturbance feature vector set, calculates the three-dimensional distance as the similarity index, sorts the distance values ​​from small to large, selects the target load number with the smallest distance as the matching load, and obtains the disturbance-load similarity mapping set; The energy consumption distribution generation submodule divides the energy consumption into equal parts according to the matching relationship between each disturbance source and the corresponding load number in the disturbance-load similarity mapping set, combined with the energy consumption value corresponding to the periodic energy consumption calculation data, and summarizes the allocated energy consumption of each disturbance source under the corresponding load number to obtain the energy consumption identification result of the substation aliasing scenario.

[0013] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by continuously tracking the start and end point indexes of periodic voltage fluctuations, a voltage sag record with phase offset characteristics is formed, and a stable fluctuation judgment mechanism is constructed in combination with offset statistics. The disturbance amplitude and frequency extraction are used to accurately identify the aliasing section, and a power sequence is constructed based on the synchronous sampling values ​​of voltage and current and the periodic integration is completed to form a high-precision calculation result of the energy consumption. This realizes the fine division of cross-interference periodic energy consumption without physical isolation, avoids dependence on external metering equipment, improves the accuracy of energy consumption attribution judgment in complex interference scenarios, and realizes independent quantification of overlapping load energy consumption, providing a feasible means for non-invasive load identification and energy efficiency evaluation in high-coupling scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 is a system flow chart of the present invention; Figure 2 This is a flow chart of the disturbance boundary extraction module of the present invention; Figure 3 This is a flow chart of the boundary stability determination module of the present invention; Figure 4 This is a flow chart of the aliasing state identification module of the present invention; Figure 5 This is a flow chart of the cycle power calculation module of the present invention; Figure 6 This is a flow chart of the energy consumption attribution determination module of the present invention. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0016] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0017] See also Figure 1 The energy consumption identification system based on the substation aliasing scenario includes: The disturbance boundary extraction module obtains periodic voltage signal data under the substation load parallel operation state, collects the start and end point index positions of the voltage fluctuation area within each cycle, calculates the phase offset of the start and end indexes in consecutive cycles (in accordance with the voltage fluctuation measurement method in the IEC61000-4-30 standard), calculates and records the absolute value of the offset, and generates a voltage sag event record (referring to the power quality monitoring standard); The boundary stability determination module calculates the difference between the maximum and minimum offset values ​​in any five consecutive cycles based on voltage sag event records. It compares this difference with the voltage fluctuation tolerance (implementing the ±10% standard of the "Power Quality Supply Voltage Deviation"). If the difference is less than the voltage fluctuation tolerance, it is determined to be a stability boundary and the cycle number is recorded, generating a list of disturbance stability cycles. The aliasing state identification module extracts the peak amplitude of the disturbance and the number of disturbances in the corresponding period based on the list of stable disturbance periods, calculates the periodic disturbance intensity, and compares it with the total harmonic distortion rate threshold (set as ≤8% according to the standard). If it is greater than the total harmonic distortion rate threshold, it is marked as an aliasing period, and the start and end index intervals of all overlapping segments are located to generate the aliasing period identification result; The cycle power calculation module extracts the point-by-point sampling values ​​of voltage and current in the corresponding section based on the aliasing cycle identification result, performs the product of the corresponding time points to form a power sequence, and accumulates and integrates the sequence to obtain the total energy consumption in the unit cycle, and outputs the cycle energy consumption calculation data; The energy consumption attribution determination module extracts three characteristic indicators, namely, boundary difference, voltage slope, and current amplitude, of all disturbance sources in the same period based on the periodic energy consumption calculation data, constructs a disturbance feature vector, and compares the similarity with the typical feature vector in the stable period. It selects the load units with highly similar features and allocates the corresponding power to generate the energy consumption identification results of the substation mixed scenario.

[0018] Voltage sag event records include offset absolute value statistics, phase offset index, and event start and end marks. The disturbance stability cycle list includes a cycle number set, tolerance comparison results, and difference range assessment indicators. The aliasing cycle identification results include the aliasing cycle number, overlapping section index, and total harmonic limit violation mark. The cycle energy consumption calculation data includes power time series, cycle cumulative energy value, and voltage and current sampling pairs. The substation aliasing scenario energy consumption identification results include a disturbance feature vector set, a similarity comparison matrix, and load unit energy consumption distribution information.

[0019] See also Figure 2 , the disturbance boundary extraction module includes: The periodic voltage acquisition submodule collects the voltage signal data of each cycle when the substation load is in parallel operation, draws the voltage fluctuation curve within each cycle, and extracts the voltage extreme points, voltage fluctuation edge points and corresponding timestamp information in each cycle to generate the periodic voltage signal fluctuation value; The voltage signal data of each cycle in the parallel operation state of the substation load is collected by setting 4 typical load outgoing line circuits in the actual urban power distribution system for sampling. The total load current is set between 20 and 50A. The sampling points are arranged at the load output end and a voltage sensor is configured to continuously record the voltage signal curve at a frequency of 5000Hz. The sampling time is 10 seconds, the single cycle is set to 20ms, and the corresponding number of sampling points is 100 points. The maximum and minimum values ​​of the waveform data are extracted in each cycle to identify the voltage extreme points. The sliding average correction method is used to filter out high-frequency interference. The extreme points are determined by judging the relationship between each point and the two points before and after it. Index position; in cycle 1, the maximum value is 235.6V (index 12) and the minimum value is 231.4V (index 48), indicating a fluctuation amplitude of 4.2V. Next, the waveform slope of each cycle is calculated, and the rate of change of each point is derived using the three-point central difference method. When the rate of change is continuously positively greater than 1.5V / ms, the starting point of the rising edge of the fluctuation is determined, and when the rate of change is continuously negatively less than −1.5V / ms, the ending point of the falling edge is determined. In cycle 2, the sliding detection starts at index 11 and ends at index 46, with a fluctuation amplitude of 4.8V and a cycle duration of 20ms. Finally, the fluctuation boundary characteristic data for different cycles is obtained, as shown in the table below.

[0020] Table 1 Periodic voltage fluctuation measurement data table: ; As shown in Table 1, the voltage fluctuation amplitudes from period 1 to period 4 are between 4.0 V and 5.1 V, and the corresponding start and end index intervals are mostly concentrated between 12 and 50, and the periodic voltage signal fluctuation values ​​are finally obtained.

[0021] The start-end index positioning submodule detects the voltage fluctuation region boundary within each cycle based on the periodic voltage signal fluctuation value. It identifies the index positions of the voltage fluctuation start and end points using the set voltage fluctuation determination threshold. It then determines whether the fluctuation region has stability characteristics based on the boundary changes of adjacent cycles and obtains the voltage fluctuation boundary index sequence. Based on the periodic voltage signal fluctuation value, the process of detecting the voltage fluctuation area boundary in each cycle is performed by identifying the fluctuation start and end indexes and comparing their range to see if it is greater than the set threshold. The fluctuation boundary identification threshold is set to 30 sampling points, corresponding to a time span of 6ms. The specific operation is to extract the start and end indexes of each cycle in Table 4, calculate the fluctuation area span and compare it with the threshold. In cycle 1, the index span is 48-12=36, and in cycle 2, it is 46-11=35, both of which exceed the threshold. Therefore, it is determined to be a valid fluctuation segment, and then the amplitude information in this segment is extracted for subsequent boundary stability analysis. Subsequently, the starting and ending points of the fluctuations in different cycles are compared. The point indexes are compared horizontally to determine whether the continuity meets the stability standard. The stable boundary judgment condition is set as the fluctuation of the boundary start and end indexes in three consecutive cycles does not exceed ±5 sampling points, then the boundary is considered stable. In cycles 1 to 3, the starting indexes are 12, 11, and 13, respectively, with a variation range of ±1 to 2, and the ending indexes are 48, 46, and 50, with a variation range of ±2~4, which meet the stability standard. The index points are recorded to form a boundary index sequence of {12, 11, 13}, {48, 46, 50}, and each segment is numbered for subsequent offset calculation. Finally, the voltage fluctuation boundary index sequence is obtained.

[0022] The phase offset statistics submodule performs difference statistics and absolute value calculations on the start and end index positions in consecutive cycles based on the voltage fluctuation boundary index sequence and the voltage fluctuation measurement requirements. It summarizes the degree of offset of the boundary index in the cycle sequence and obtains the voltage sag event records statistically. Based on the voltage fluctuation boundary index sequence, inter-cycle boundary offsets are statistically processed. The starting offset between cycle 2 and cycle 1 is 11-12 = -1, and the ending offset is 46-48 = -2. The starting offset between cycle 3 and cycle 2 is 13-11 = +2, and the ending offset is 50-46 = +4. The absolute value of each offset is taken as 1, 2, 2, and 4, respectively. The total offset of these four items is recorded as 9 sampling points. This is then compared with the event record baseline value, which is set to 5 sampling points. If the cumulative value of consecutive offsets exceeds 5, a sag event is recorded. Cycles 1 to 3 have reached 9, meeting the condition. Further determination is made as to whether the offset is persistent. For example, the starting offset of cycle 4 is 14-13 = +1, and the ending offset is 49-50 = -1. The absolute offset value is 2, and the cumulative total offset is 9 + 2 = 11. If the offset continues to exceed the limit, it is confirmed as a continuous sag segment. The start and end times and amplitudes are recorded in the sag event table, and the voltage sag event record is finally obtained.

[0023] See also Figure 3 , the boundary stability determination module includes: The offset difference extraction submodule extracts the offset values ​​corresponding to all cycles involved in each voltage sag event record. It sorts the offset values ​​in each sequence into groups of five consecutive cycles, identifies the maximum and minimum values ​​in the sequence, and uses the difference as the difference range of the current sequence to obtain the offset difference range set. Based on the voltage sag event records, all cycle data involved in each event are extracted and constructed into an offset value sequence in chronological order. The offset value is the fluctuation range of each cycle boundary index. Every five consecutive cycles are set as a sequence unit to form a sliding window structure. The first window is composed of cycles 1 to 5, the second window is composed of cycles 2 to 6, and so on. Within each five-cycle window, the corresponding five offset values ​​are extracted and sorted by numerical value. The maximum and minimum values ​​are recorded respectively, and the offset difference range corresponding to the window is obtained by the difference between the two. For example, in a certain event, the offset value sequence is {4, 7, 5, 3, 6, 9, 8}. The offset value of window 1 is {4, 7, 5, 3, 6}, with a maximum of 7, a minimum of 3, and a difference of 4. The offset value of window 2 is {7, 5, 3, 6, 9}, with a maximum of 9, a minimum of 3, and a difference of 6. Similarly, the offset difference range is calculated for all windows to form a difference range data sequence corresponding to the time window. Then, a number record table is established, each sequence is assigned an ID and bound to the starting cycle to form a traceable structure, as shown in the following table: Table 2 Offset difference measurement data table: ; As shown in Table 2, the difference range values ​​correspond to the window period numbers one by one, and are mapped to subsequent determination operations through number indexes, ultimately obtaining an offset difference range set.

[0024] The voltage tolerance comparison submodule compares the difference range of each sequence with the voltage fluctuation tolerance threshold item by item based on the offset difference range set. If the difference range is less than the voltage fluctuation tolerance threshold, it is marked as a boundary stability period; otherwise, it is an unstable period, and a boundary stability label sequence is obtained; According to the offset difference range set, each group of difference values ​​is compared with the set voltage fluctuation tolerance threshold. According to the ±10% regulation in the power industry standard "Power Quality Supply Voltage Deviation", the voltage reference value is set to 230V, and the corresponding tolerance bandwidth is 23V. However, since the offset value unit is the sampling point index, it needs to be converted into a sampling point change amplitude equal to the tolerance. After conversion, the voltage offset tolerance threshold is set to 3 sampling points. If a certain difference range is less than or equal to 3, it is marked as stable, otherwise it is unstable. For example, in Table 5, the difference of window 1 is 4 and that of window 2 is 6, both exceeding 3, so they are both marked as unstable. If the difference of a window is 2, it can be marked as a stable period. During the entire comparison process, each difference value needs to be binary judged and the corresponding label value 1 or 0 is output. At the same time, a label sequence table is established and bound to the window number of the difference source, as shown below: Table 3 Boundary stability label table: ; As shown in Table 3, the stable labels can be used as a basis for screening stable cycles, and finally a boundary stability label sequence is obtained.

[0025] The stable period generation submodule extracts all periods marked as stable states according to the boundary stability label sequence, establishes an index mapping according to the original order in the full period sequence, and establishes a unique identification structure for the period sequence to obtain a list of disturbance stable periods. According to the boundary stability label sequence, all window numbers with label values ​​of 1 are extracted, and the corresponding period number information is split and filtered. For example, window No. 3 in the label sequence is stable, and its corresponding period range is period 3 to 7. Then, the period numbers in this interval, namely periods 3, 4, 5, 6, and 7, are extracted respectively as the stable period candidate set. Then, the stable periods in all windows are merged, the repeated period numbers are removed, and they are arranged in the original sampling order to form a disturbance stable period sequence. In this process, each period number needs to be assigned a unique index identifier and recorded in the stable period mapping table for subsequent event aggregation and stable segment positioning operations. Finally, all marked period numbers are summarized and output as a stable period result set to form a complete period stable segment set, which is also output as a disturbance stable period list.

[0026] See also Figure 4 , the aliasing state recognition module includes: The disturbance feature extraction submodule extracts the voltage disturbance data of the corresponding period based on the disturbance stability period list, counts the peak amplitude of the disturbance waveform and the total number of disturbance events in each period, and performs the above operations on all periods in sequence to obtain the input data for disturbance intensity calculation; Based on the list of disturbance stable cycles, the voltage disturbance curve corresponding to each stable cycle is extracted, the number of disturbances occurring in each cycle is counted, and the key disturbance amplitude characteristics, the number of disturbance segment duration points and the disturbance time value are extracted in each disturbance segment. The number of disturbances is obtained by detecting the number of changes in the first-order differential slope of the waveform. The disturbance amplitude is the maximum value of the voltage change, the number of disturbance segment points is the number of sampling points between the start and end indexes, and the disturbance duration is calculated by multiplying the length of the segment by the single-point time interval (sampling frequency is 5000Hz, corresponding to 0.2ms / point). The number of disturbances with cycle number 1 is 3, and the disturbance amplitudes are 6.2V, 5.0V, and 1.0V, respectively. .8V, 7.0V, the number of disturbance points is 12, 10, 15 points, and the duration is 4.8ms, 4.0ms, 5.2ms; the number of disturbances in cycle number 2 is 4 times, and the disturbance amplitudes are 7.5V, 6.9V, 5.3V, and 6.1V respectively, the corresponding points are 10, 8, 9, 11, and the duration is 4.0ms, 3.2ms, 3.6ms, and 4.4ms; cycle 3 contains 2 disturbances with amplitudes of 8.0V and 7.2V, the duration points are 9 and 10 points, and the duration is 3.6ms and 4.0ms. The data extracted from each cycle are summarized in Table 4 to obtain the input data for disturbance intensity calculation.

[0027] Table 4 Input data for disturbance intensity calculation: ; The intensity threshold judgment submodule calculates the input data according to the disturbance intensity using the formula: ; The operation obtains the disturbance intensity of each cycle and makes a judgment item by item with the total harmonic distortion rate threshold. If it is greater than the total harmonic distortion rate threshold, the cycle is marked as an aliasing cycle with a label value of 1, otherwise it is 0. A corresponding structure between the cycle number and the judgment result is established to generate an aliasing cycle label sequence, where: Indicates the The perturbation intensity of each cycle, in V , Indicates the The first The peak amplitude of the disturbance (unit V), For the The number of sampling points of the disturbance, The single-point sampling time interval is in ms / point, and the set value is 0.2ms. is the duration of the corresponding disturbance (unit: ms), For the Number of disturbances in a cycle, threshold Derived from the statistical upper limit of 95% non-aliased periodic disturbance intensity in a typical 10kV distribution network sample; Based on the input data of disturbance intensity calculation, the number of disturbances in the cycle, the peak amplitude of each disturbance, the number of disturbance segments and the duration are substituted into the disturbance intensity formula, where ms is the sampling interval. Now we calculate the specific disturbance intensity for period 1: The first disturbance: ; Second perturbation: ; The third disturbance: ; The total perturbation intensity of period 1 is ; Calculation of period 2 disturbance intensity: ; ; The perturbation intensity of period 3 is: ; The disturbance intensity value is compared with the total harmonic distortion threshold For comparison, if It is marked as an aliasing period with a label value of 1, otherwise it is 0. The results are as follows: Table 5 Aliasing cycle label table: ; Finally, an aliasing period label sequence is obtained.

[0028] Periodic disturbance intensity is a composite indicator used to measure the combined impact of all disturbance events within a voltage cycle. Its core significance lies in simultaneously reflecting the amplitude, duration, and frequency of the disturbance. It not only takes into account the maximum change in each voltage fluctuation, but also introduces the time dimension of the fluctuation and the frequency of occurrence, thereby constructing a numerical dimension that can describe the "intensity" of the disturbance within the cycle. The larger the indicator, the more severe, concentrated, or frequent disturbance events there are within the cycle. It is the key basis for judging whether the cycle has reached an abnormal level of power quality (such as aliasing, voltage distortion, etc.), and can serve as an important reference for subsequent disturbance classification and identification and abnormal area determination.

[0029] The formula first calculates the perturbation event In the cycle The contribution of the event is calculated separately, and then the impact of all events is accumulated through summation to form a complete periodic disturbance intensity value. , the absolute value symbol is used in the formula This is to uniformly handle the impact of positive and negative disturbances on the amplitude, emphasizing that the intensity of disturbance only considers its absolute size, and then the number of disturbance duration points. Multiplication reflects the length expansion of the disturbance in the time dimension, multiplied by Convert to time units and normalize to represent the actual disturbance duration. The denominator is the duration of the corresponding disturbance segment. The square root is used to adjust the nonlinear effect of the disturbance response rhythm on the intensity. In physical terms, the shorter the disturbance time (i.e., fast and high-frequency disturbance), the stronger the impact on the system. Therefore, The reciprocal structure of is used to introduce a time compression correction term to form an amplitude-duration synergistic function of the disturbance intensity. Finally, by summing up all disturbance segments, the total amount of the cumulative effect of the disturbance energy within the period is obtained, which characterizes the overall intensity of the disturbance in the period.

[0030] The aliasing segment positioning submodule extracts the period numbers of all aliasing periods according to the aliasing period label sequence, performs segment positioning in the original voltage disturbance signal data, extracts the starting point index and ending point index of the disturbance curve within each aliasing period, records the start and end index values ​​of each disturbance interval and forms an aliasing segment index set to obtain the aliasing period identification result; According to the aliasing cycle label sequence, cycle 2 is selected as the aliasing cycle with a label value of 1. The start and end point indexes of its disturbance segment are located from the original disturbance index data. The four disturbance segments correspond to sampling points 20–30, 45–53, 60–69, and 76–87, respectively. The voltage start and end point differences are calculated for these four disturbance segments, and the disturbance amplitude filtering threshold is set to 3V. If the voltage change value of a segment is greater than the threshold, it is determined to be a valid disturbance segment. After detection, the first three segments all meet the conditions, while the fourth segment with a change amplitude of 2.4V does not meet the filtering requirements and is eliminated. Finally, there are three valid segments in aliasing cycle 2, namely: 20–30, 45–53, and 60–69, and the output is the aliasing cycle identification result.

[0031] See also Figure 5 , the cycle power calculation module includes: The power sample extraction submodule locates the complete time interval of the cycle in the original signal sequence based on the sampling start and end point indexes of the corresponding cycle in the aliasing cycle identification result, extracts the voltage and current values ​​at each time point, and synchronizes them according to the time index. Each time point has a corresponding set of voltage-current data pairs, and the electrical parameter synchronization sequence is obtained. Based on the start and end index positions of the cycle number 1 in the aliasing cycle identification result, all sampling points in the corresponding time period in the voltage and current sampling data are located, and the voltage and current values ​​of each sampling point are extracted. Combined with the sampling frequency of 5000Hz, the sampling interval of each point is confirmed to be 0.2ms. The sampling data are aligned one by one according to the time point sequence to form a one-to-one corresponding voltage-current sampling pair. The adjacent difference operation is further performed on the voltage sampling point sequence to obtain the disturbance amplitude of each point. , such as the voltage difference between point 2 and point 1 is V is the disturbance amplitude value of the second point. The same method is used to obtain the disturbance amplitude of the third point: V; then trace back 10 points to construct the current disturbance frequency count of the current point For example, there are 2 current disturbance mutation events in the window near point 2 and 1 at point 3. Finally, the cumulative time of each point relative to the starting point of the cycle is counted. For example, point 1 is 0.2ms, point 2 is 0.4ms, and point 3 is 0.6ms. The above sampling point parameters are combined into a periodic sample table as shown below: Table 6 Periodic sampling parameters: ; As shown in Table 6, a sampling point-parameter alignment structure has been constructed with cycle number 1 to obtain the electrical parameter synchronization sequence.

[0032] The instantaneous power accumulation submodule performs a product operation based on the voltage and current values ​​at each time point in the electrical parameter synchronization sequence to form the instantaneous power data at each point using the formula: ; The total energy consumption per unit cycle is obtained by calculation, and the cycle energy consumption integral data is generated, where: Indicates the The total energy consumption per cycle, For cycle The number of internal sampling points, and Respectively represent The voltage and current values ​​of the sampling points are The absolute value of the difference between the current point voltage and the previous sampling point voltage, in V, is dimensionless after normalization; It represents the current disturbance count within a fixed window (such as 10 points) before the current sampling point, and is dimensionless after normalization; The cumulative time from the current sampling point to the start point of the cycle, in ms; The single-point sampling time interval, in ms; Based on the parameters of each sampling point in the electrical parameter synchronization sequence shown in Table 6, the weighted power of the disturbance is accumulated and integrated point by point according to the formula, and specific calculations are carried out for sampling points 2 and 3 respectively: Sampling point 2: ; ; ; ; ; Sampling point 3: ; ; ; ; ; Ignore the first point without disturbance, and only accumulate points 2 and 3 to obtain the total energy consumption integral of cycle number 1: ; The calculation result is the numerical result of energy consumption in cycle number 1, and the cycle energy consumption integral data is generated.

[0033] The total cycle energy consumption refers to the energy quantification result obtained by accumulating and integrating the instantaneous power values ​​of all sampling points on the time axis within a complete voltage cycle. It reflects the effective energy output generated by the interaction between voltage and current within the cycle. If there is disturbance, current shock or voltage distortion in the cycle, these factors will be reflected in the total energy consumption through instantaneous power changes. Therefore, this value not only represents the actual power transmission capacity within the cycle, but also introduces dynamic weight adjustment of disturbance amplitude and frequency into the structure, so that it has the ability to reveal the relationship between the disturbance characteristics of electrical parameters and energy response. The larger the total cycle energy consumption value, the denser the power fluctuation amplitude and interference frequency within the cycle. It is an important basic quantity for analyzing the energy behavior of the aliasing cycle, locating abnormal cycles and performing power quality classification and judgment.

[0034] The formula is first passed Get the The instantaneous power value of each sampling point represents the power intensity formed by the combined action of voltage and current at that moment. and disturbance frequency Add together to reflect the combined influence of the amplitude disturbance and frequency disturbance on the sampling point, taking into account the weakening trend of the system response strength caused by the disturbance propagating over time, and taking the cumulative time of the sampling point since the start of the cycle as the Construct the denominator term and construct the nonlinear time decay factor by square root operation , used to suppress the amplification effect of the disturbance in the latter part of the cycle on the overall power evaluation, and then multiplied by the sampling interval The result is mapped into a time domain integration unit, and finally the total interference power weight of all sampling points in the period is accumulated in the form of summation to form the energy disturbance assessment result. The overall structure integrates the instantaneous power of electrical parameters, disturbance characteristic intensity and time domain correction factor, and has the ability to analyze the dynamic response process.

[0035] The cycle energy consumption generation submodule reconstructs the energy consumption value sequence of all cycles according to the cycle energy consumption integral data in the order of cycle numbers, uses the cycle number as the index and the cycle energy consumption value as the content, integrates them into a two-dimensional cycle energy consumption array structure, and outputs the cycle energy consumption calculation data; Based on the cycle energy consumption integral data, record the integral value of cycle number 1, which is 2276.25. Then process the sample areas of cycle numbers 2 and 3, and obtain their energy consumption integral values ​​of 2211.30 and 2137.88 respectively. Combine the three cycle numbers and energy consumption values ​​to construct a cycle-power integral table, which is summarized as follows: Table 7 Cycle energy consumption calculation data table: ; As shown in Table 7, the energy consumption integral values ​​corresponding to periods 1 to 3 have been calculated and sorted, and the output is the period energy consumption calculation data.

[0036] See also Figure 6 , the energy consumption attribution determination module includes: The feature acquisition submodule extracts the sampling sequence within the corresponding period based on the periodic energy consumption calculation data. The voltage values ​​at the start and end of the disturbance are sequentially extracted from the corresponding sampling point sequence, and the difference is calculated as the boundary difference. A continuous difference sequence is constructed for all voltage sampling points in the disturbance segment, and the sequence mean is calculated as the voltage slope. The maximum amplitude of all current sampling points in the disturbance segment is statistically calculated as the current amplitude, and the disturbance feature vector set is obtained through integration. Based on the periodic energy consumption calculation data, the starting position and ending position of each disturbance source in the periodic sampling interval are extracted in turn, and the voltage values ​​of the starting sampling point and the ending sampling point are recorded for each disturbance source segment. For example, the starting voltage of disturbance source 1 is 222.4V, the ending voltage is 225.1V, and the boundary difference is 2.7V. The starting voltage of disturbance source 2 is 219.7V, the ending voltage is 221.5V, and the boundary difference is 1.8V. Then, the first-order difference is performed on all voltage sampling points in each disturbance interval. The voltages of two adjacent points are calculated separately, and the average value is taken after the difference is made. The average difference value of disturbance source 1 is 0.86V / ms, and that of disturbance source 2 is 0.60V / ms, which is used as its voltage slope. At the same time, the maximum value is extracted from the current sequence of each disturbance segment. The maximum current value of disturbance source 1 is 5.3A, and that of disturbance source 2 is 4.9A. The boundary difference, voltage slope and current amplitude of the above three characteristic parameters are combined to form a three-dimensional parameter characteristic vector of the corresponding disturbance source, as shown in Table 8, and the disturbance characteristic vector set is obtained.

[0037] Table 8 Disturbance source characteristic parameters: ; As shown in Table 8, the voltage, current and dynamic change characteristic parameter sets corresponding to the disturbance source have been constructed as the three-dimensional disturbance feature input set.

[0038] The similarity vector comparison submodule compares each vector with the known typical load feature vectors in the stable period one by one based on the disturbance feature vector set, calculates the three-dimensional distance as the similarity index, sorts the vectors from small to large in distance value, selects the target load number with the smallest distance as the matching load, and obtains the disturbance-load similarity mapping set; According to the three-dimensional parameter feature vectors of disturbance source 1 and disturbance source 2 in Table 8, vector difference comparison is performed with the registered load vectors in the typical stable load feature sample library. The feature vector of disturbance source 1 (2.7, 0.86, 5.3) is aligned with the stable sample numbers A (2.6, 0.85, 5.4) and B (1.9, 0.72, 4.8) in sequence. The coordinate difference is calculated according to the three-dimensional features. The difference is squared, accumulated, and the square root is taken to obtain the three-dimensional distance between disturbance source 1 and samples A and B. The same is true for disturbance source 2. The distances between it and samples A and B are obtained as follows: sample A: 0.173, sample B: 0.153; the corresponding distances of disturbance source 2 are sample A: 0.852, sample B: 0.078. After sorting, disturbance source 1 is closest to sample B, and disturbance source 2 is closest to sample B. Therefore, disturbance sources 1 and 2 are matched to load B respectively, and a disturbance-load similarity mapping set is obtained.

[0039] Table 9 Similarity matching results table: ; As shown in Table 9, both disturbance source 1 and disturbance source 2 are closest to the typical eigenvector of load B, and a mapping relationship is established.

[0040] The energy consumption allocation generation submodule divides the energy consumption into equal parts according to the matching relationship between each disturbance source and the corresponding load number in the disturbance-load similarity mapping set, combined with the energy consumption value corresponding to the periodic energy consumption calculation data, and aggregates the allocated energy consumption of each disturbance source under the corresponding load number to obtain the energy consumption identification result of the substation aliasing scenario; Combined with the information that disturbance sources 1 and 2 correspond to load B in the disturbance-load similarity mapping set, the periodic energy consumption value within the period is distributed. Assuming the periodic energy consumption value is 2300J, since there are two disturbance sources within the period, the energy consumption is divided into 1150J to two disturbance items in equal proportion. Then, the energy consumption values ​​of disturbance sources 1 and 2 (1150J) are respectively distributed to their corresponding load B. Load B accumulates an energy consumption of 2300J, as shown in Table 10, and the energy consumption identification results of the substation aliasing scenario are obtained.

[0041] Table 10 Energy consumption distribution results: ; As shown in Table 10, load B is closest to the eigenvectors of all disturbance sources, so the total energy consumption value of the current cycle is accumulated.

[0042] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. The energy consumption identification system based on the substation aliasing scenario is characterized by: The system comprises: The disturbance boundary extraction module obtains periodic voltage signal data under the parallel operation state of the substation load, collects the start and end point index positions of the voltage fluctuation area within each cycle, calculates the phase offset, calculates the absolute value of the offset and records it, and generates a voltage sag event record; The boundary stability determination module calculates the difference range between the maximum and minimum offset values ​​in any five consecutive cycles based on the voltage sag event record. If the difference range is less than the voltage fluctuation tolerance, it is determined to be a stability boundary and the cycle number is recorded to generate a disturbance stability cycle list; The aliasing state identification module extracts the disturbance peak amplitude and the number of disturbances in the corresponding period based on the disturbance stable period list, calculates the periodic disturbance intensity, and marks it as an aliasing period if it is greater than the total harmonic distortion rate threshold. It also locates the start and end index intervals of all overlapping segments and generates an aliasing period identification result. The cycle power calculation module extracts the point-by-point sampling values ​​of the voltage and current in the corresponding section according to the aliasing cycle identification result, performs multiplication of the corresponding time points to form a power sequence, and accumulates and integrates the sequence to obtain the total energy consumption in the unit cycle, and outputs the cycle energy consumption calculation data.

2. The energy consumption identification system based on the power conversion aliasing scenario according to claim 1 is characterized in that: The voltage sag event record includes the offset absolute value statistics, the phase offset index, and the event start and end marks; the disturbance stability period list includes the period number set, the tolerance comparison result, and the difference range evaluation index; the aliasing period identification result includes the aliasing period number, the overlapping section index, and the total harmonic limit exceedance mark; the period energy consumption calculation data includes the power time series, the period cumulative energy value, and the voltage and current sampling pairs.

3. The energy consumption identification system based on the power conversion aliasing scenario according to claim 1 is characterized in that: The disturbance boundary extraction module includes: The periodic voltage acquisition submodule collects the voltage signal data of each cycle when the substation load is in parallel operation, draws the voltage fluctuation curve within each cycle, and extracts the voltage extreme points, voltage fluctuation edge points and corresponding timestamp information in each cycle to generate the periodic voltage signal fluctuation value; The start and end index positioning submodule detects the voltage fluctuation region boundary within each cycle based on the periodic voltage signal fluctuation value, identifies the index positions of the voltage fluctuation start and end points using a set voltage fluctuation determination threshold, and determines whether the fluctuation region has stability characteristics based on the boundary changes of adjacent cycles, thereby obtaining a voltage fluctuation boundary index sequence; The phase shift statistics submodule performs difference statistics and absolute value operations on the start and end index positions in consecutive cycles according to the voltage fluctuation boundary index sequence and the voltage fluctuation measurement requirements, summarizes the offset degree of the boundary index in the cycle sequence, and obtains the voltage sag event records statistically.

4. The energy consumption identification system based on the power conversion aliasing scenario according to claim 1 is characterized in that: The boundary stability determination module includes: The offset difference extraction submodule extracts the offset values ​​corresponding to all cycles involved in each event based on the voltage sag event records, sorts the offset values ​​in each sequence into groups of five consecutive cycles, identifies the maximum and minimum values ​​in the sequence, uses the difference as the difference range of the current sequence, and obtains an offset difference range set; The voltage tolerance comparison submodule compares the difference range of each sequence with the voltage fluctuation tolerance threshold item by item based on the offset difference range set. If the difference range is less than the voltage fluctuation tolerance threshold, it is marked as a boundary stability period; otherwise, it is an unstable period, thereby obtaining a boundary stability label sequence; The stable period generation submodule extracts the numbers of all periods marked as stable states according to the boundary stability label sequence, establishes an index mapping according to the original order in the full period sequence, establishes a period sequence unique identification structure, and obtains a disturbance stable period list.

5. The energy consumption identification system based on the power conversion aliasing scenario according to claim 1 is characterized in that: The aliasing state identification module includes: The disturbance feature extraction submodule extracts the voltage disturbance data of the corresponding period based on the disturbance stability period list, counts the peak amplitude of the disturbance waveform and the total number of disturbance events in each period, and performs the above operations on all periods in sequence to obtain the input data for disturbance intensity calculation; The intensity threshold judgment submodule calculates the input data according to the disturbance intensity, calculates and obtains the disturbance intensity of each cycle, and judges it item by item with the total harmonic distortion rate threshold. If it is greater than the total harmonic distortion rate threshold, the cycle is marked as an aliasing cycle, and a corresponding structure between the cycle number and the judgment result is established to generate an aliasing cycle label sequence; The aliasing segment positioning submodule extracts the period numbers of all aliasing periods according to the aliasing period label sequence, performs segment positioning in the original voltage disturbance signal data, extracts the starting point index and ending point index of the disturbance curve in each aliasing period, records the start and end index values ​​of each disturbance interval and forms an aliasing segment index set to obtain the aliasing period identification result.

6. The energy consumption identification system based on the power conversion aliasing scenario according to claim 1 is characterized in that: The cycle power calculation module includes: The power sample extraction submodule locates the complete time interval of the period in the original signal sequence based on the sampling start and end point indexes of the corresponding period in the aliasing period identification result, extracts the voltage and current values ​​at each time point, and aligns them synchronously according to the time index. Each time point has a corresponding set of voltage-current data pairs, and the electrical parameter synchronization sequence is obtained. The instantaneous power accumulation submodule performs a product operation based on the voltage value and the current value at each time point in the electrical parameter synchronization sequence to form the instantaneous power data of each point, calculates and obtains the total energy consumption per unit cycle, and generates the cycle energy consumption integral data; The cycle energy consumption generation submodule reconstructs the energy consumption value sequence in all cycles according to the cycle energy consumption integral data in the order of cycle numbers, uses the cycle number as the index and the cycle energy consumption value as the content, integrates them into a two-dimensional cycle energy consumption array structure, and outputs the cycle energy consumption calculation data.

7. The energy consumption identification system based on the power conversion aliasing scenario according to claim 1 is characterized in that: The system further comprises: The energy consumption attribution determination module extracts three characteristic indicators, namely, boundary difference, voltage slope, and current amplitude, of all disturbance sources in the same period based on the periodic energy consumption calculation data, constructs a disturbance feature vector, and compares the similarity with the typical feature vector in the stable period. The module selects the load units with highly similar features and allocates the corresponding power to generate the energy consumption identification results of the substation aliasing scenario. The energy consumption identification result of the substation aliasing scenario includes a disturbance feature vector set, a similarity comparison matrix, and load unit energy consumption distribution information.

8. The energy consumption identification system based on the power conversion aliasing scenario according to claim 7 is characterized in that: The energy consumption attribution determination module includes: The feature acquisition submodule extracts a sampling sequence within the corresponding period based on the periodic energy consumption calculation data, sequentially extracts the voltage values ​​at the start and end points of the disturbance in the corresponding sampling point sequence, calculates the difference as the boundary difference, constructs a continuous difference sequence for all voltage sampling points in the disturbance segment, calculates the sequence mean as the voltage slope, and calculates the maximum amplitude of all current sampling points in the disturbance segment as the current amplitude, and integrates and obtains a disturbance feature vector set; The similarity vector comparison submodule compares each vector with the known typical load feature vectors in the stable period one by one according to the disturbance feature vector set, calculates the three-dimensional distance as the similarity index, sorts the distance values ​​from small to large, selects the target load number with the smallest distance as the matching load, and obtains the disturbance-load similarity mapping set; The energy consumption distribution generation submodule divides the energy consumption into equal parts according to the matching relationship between each disturbance source and the corresponding load number in the disturbance-load similarity mapping set, combined with the energy consumption value corresponding to the periodic energy consumption calculation data, and summarizes the allocated energy consumption of each disturbance source under the corresponding load number to obtain the energy consumption identification result of the substation aliasing scenario.

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