Non-intrusive load identification method and system

Abstract

This application provides a non-intrusive load identification method and system. The method includes: generating a expected aliasing current based on a first current and a first voltage, and a single current of a target load in a preset database; determining the actual aliasing current based on a second current and a second voltage; generating an expected current waveform, an actual current waveform, an expected voltage-current trajectory, and an actual voltage-current trajectory based on each expected aliasing current, the aliasing current, a preset sampling start point, a sequence of sampling points, and a preset sampling voltage at each sampling point; generating expected current harmonic components and actual current harmonic components based on the expected current waveform and the actual current waveform; and determining whether the load is a target load based on the expected current waveform, the actual current waveform, the expected voltage-current trajectory, the actual voltage-current trajectory, the expected current harmonic components, and the actual current harmonic components. This method achieves fast and accurate identification of the target load, requires no steady-state condition, and has a small amount of cached data during the identification process.

Description

Technical Field

[0001] This application relates to the field of malicious load identification technology, and more specifically, to a non-invasive load identification method and system. Background Technology

[0002] With economic development, increased population density, and correspondingly larger electricity consumption, university student dormitories have also seen a rise in student numbers and electricity demands. However, this has also led to a more serious threat of electrical fires. Most fires in university student dormitories are caused by the improper use of high-power loads such as immersion heaters and induction cookers. Therefore, identifying and monitoring various types of loads is of great significance for ensuring electrical safety.

[0003] Existing technologies extract a single load waveform by subtracting waveforms from two time periods based on steady-state waveforms. Analysis of this single load waveform then determines whether it is a pre-defined target load. This method requires significant storage space, necessitating the storage of multiple cycles for subtraction. It is slow to identify loads with rapidly changing currents that cannot reach a steady state. Furthermore, long subtraction intervals result in severe waveform distortion, leading to a high false positive rate for load identification and impacting the accuracy of load monitoring. Summary of the Invention

[0004] The purpose of this application is to address the shortcomings of the prior art by providing a non-invasive load identification method and system to improve the accuracy of malicious load identification.

[0005] To achieve the above objectives, the technical solutions adopted in the embodiments of this application are as follows:

[0006] In a first aspect, embodiments of this application provide a non-invasive load identification method, the method comprising:

[0007] Obtain the first current and first voltage in the main circuit before the load to be identified is connected, and the second current and second voltage in the main circuit after the load to be identified is connected;

[0008] Based on the first current, the first voltage, and the single current of multiple target loads in the preset database, the expected aliasing current to be connected to each target load is generated.

[0009] Based on the second current and the second voltage, determine the actual aliasing current after connecting the load to be identified;

[0010] Based on the expected aliasing current, the actual aliasing current, the preset sampling start point, the sequence of each sampling point, and the preset sampling voltage at each sampling point, generate the expected current waveform, the actual current waveform, the expected voltage and current trajectory, and the actual voltage and current trajectory, and determine the expected current harmonic components and the actual current harmonic components.

[0011] Based on the expected current waveform, the actual current waveform, the expected voltage and current trajectory, the actual voltage and current trajectory, the expected current harmonic components, and the actual current harmonic components, determine whether the load to be identified is the target load.

[0012] Optionally, the process of generating a single current for the target load is as follows:

[0013] After a single target load is connected to the main circuit, the current and voltage of the main circuit are acquired in real time.

[0014] The current is sampled at a preset sampling start point within each preset period to obtain the sampled current corresponding to each period. The sampled current corresponding to each period is cached into a sampled current array, wherein the voltage at the preset sampling start point satisfies a preset condition.

[0015] The average value of each of the sampled current arrays is accumulated according to the sampling sequence, and then stored in a new array as a single current of the target load.

[0016] Optionally, generating the expected aliasing current for each target load based on the first current, the first voltage, and the single current of multiple target loads in a preset database includes:

[0017] The first current is sampled at a preset sampling start point within each preset period to obtain the sampled first current;

[0018] The sampled first current is superimposed with the single current of each target load to obtain the expected aliasing current to be connected to the target load.

[0019] Optionally, determining the actual aliasing current after connecting the load to be identified based on the second current and the second voltage includes:

[0020] The second current is sampled at a preset sampling start point within each preset period to obtain the actual aliasing current after the load to be identified is connected.

[0021] Optionally, the step of generating expected current waveforms, actual current waveforms, expected voltage-current trajectories, and actual voltage-current trajectories based on each expected aliasing current, the actual aliasing current, a preset sampling start point, a sequence of sampling points, and a preset sampling voltage at each sampling point, and determining each expected current harmonic component and the actual current harmonic component, includes:

[0022] The expected current waveform is generated based on the expected aliasing current, the preset sampling start point, and the sequence of each sampling point;

[0023] The actual current waveform is generated based on the actual aliasing current, the preset sampling start point, and the sequence of each sampling point;

[0024] Based on the expected aliasing current and the preset sampling voltage at each sampling point, the expected voltage and current trajectory is generated.

[0025] The actual voltage and current trajectory is generated based on the actual aliasing current and the preset sampling voltage at each sampling point.

[0026] Based on the expected aliasing current and the actual aliasing current, the expected current harmonic components and the actual current harmonic components are determined.

[0027] Optionally, generating the expected voltage-current trajectory based on the expected aliasing current and the preset sampling voltage at each sampling point includes:

[0028] The expected voltage and current trajectory is generated by using the preset sampling voltage at each sampling point as the horizontal axis and the expected aliasing current at each sampling point as the vertical axis.

[0029] Optionally, determining the expected current harmonic components and the actual current harmonic components based on the expected aliasing current and the actual aliasing current includes:

[0030] Perform a Fast Fourier Transform on the expected aliasing current to obtain the expected complex spectrum corresponding to the expected aliasing current, and extract the harmonic components of the expected current in the expected complex spectrum;

[0031] Perform a Fast Fourier Transform on the actual aliasing current to obtain the actual complex spectrum corresponding to the actual aliasing current, and extract the actual current harmonic components from the actual complex spectrum.

[0032] Optionally, determining whether the load to be identified is the target load based on the expected current waveform, the actual current waveform, the expected voltage and current trajectory, the actual voltage and current trajectory, the expected current harmonic components, and the actual current harmonic components includes:

[0033] Feature extraction is performed on the expected voltage and current trajectory to obtain the area and perimeter of the expected trajectory;

[0034] Feature extraction is performed on the actual voltage and current trajectory to obtain the actual trajectory area and actual trajectory perimeter;

[0035] The trajectory area similarity is determined based on the expected trajectory area and the actual trajectory area.

[0036] The trajectory perimeter similarity is determined based on the expected trajectory perimeter and the actual trajectory perimeter.

[0037] Determine the current waveform similarity based on the expected current waveform and the actual current waveform;

[0038] The similarity of current harmonic components is determined based on the expected current harmonic components and the actual current harmonic components.

[0039] Based on the trajectory area similarity, trajectory perimeter similarity, current waveform similarity, and current harmonic component similarity, it is determined whether the load to be identified is the target load.

[0040] Optionally, the preset database includes: preset target load harmonic similarity threshold, trajectory area phase velocity threshold, trajectory perimeter similarity threshold, and waveform similarity threshold.

[0041] Optionally, determining whether the load to be identified is the target load based on the trajectory area similarity, the trajectory perimeter similarity, the current waveform similarity, and the current harmonic component similarity includes:

[0042] If the similarity of the current harmonic components is greater than the harmonic threshold, and the similarity of the current waveform is greater than the preset waveform similarity threshold, and the similarity of the trajectory area is greater than the preset trajectory area similarity threshold, and the similarity of the trajectory perimeter is greater than the preset trajectory perimeter similarity threshold, then the load to be identified is determined to be the target load; otherwise, the load to be identified is determined not to be the target load.

[0043] Secondly, embodiments of this application also provide a non-invasive load identification device, the device comprising:

[0044] The acquisition module acquires the first current and first voltage in the main circuit before the load to be identified is connected, and the second current and second voltage in the main circuit after the load to be identified is connected.

[0045] The generation module is used to generate the expected aliasing current to be connected to each target load based on the first current, the first voltage, and the single current of multiple target loads in the preset database.

[0046] The determination module is used to determine the actual aliasing current after the load to be identified is connected, based on the second current and the second voltage.

[0047] The determination module is used to generate expected current waveforms, actual current waveforms, expected voltage and current trajectories, and actual voltage and current trajectories based on the expected aliasing currents, the actual aliasing currents, the preset sampling start points, the sequence of each sampling point, and the preset sampling voltage at each sampling point, and to determine the expected current harmonic components and the actual current harmonic components.

[0048] The determination module is used to determine whether the load to be identified is the target load based on the expected current waveform, the actual current waveform, the expected voltage and current trajectory, the actual voltage and current trajectory, the expected current harmonic component, and the actual current harmonic component.

[0049] Optionally, the generation module is specifically used for:

[0050] After a single target load is connected to the main circuit, the current and voltage of the main circuit are obtained.

[0051] The current is sampled at a preset sampling start point within each preset period to obtain the sampled current corresponding to each period. The sampled current corresponding to each period is cached into a sampled current array, wherein the voltage at the preset sampling start point satisfies a preset condition.

[0052] The average value of each of the sampled current arrays is accumulated according to the sampling sequence, and then stored in a new array as a single current of the target load.

[0053] Optionally, the generation module is specifically used for:

[0054] The first current is sampled at a preset sampling start point within each preset period to obtain the sampled first current;

[0055] The sampled first current is superimposed with the single current of each target load to obtain the expected aliasing current to be connected to the target load.

[0056] Optionally, the determining module is specifically used for:

[0057] The second current is sampled at a preset sampling start point within each preset period to obtain the actual aliasing current after the load to be identified is connected.

[0058] Optionally, the determining module is specifically used for:

[0059] The expected current waveform is generated based on the expected aliasing current, the preset sampling start point, and the sequence of each sampling point;

[0060] The actual current waveform is generated based on the actual aliasing current, the preset sampling start point, and the sequence of each sampling point;

[0061] Based on the expected aliasing current and the preset sampling voltage at each sampling point, the expected voltage and current trajectory is generated.

[0062] The actual voltage and current trajectory is generated based on the actual aliasing current and the preset sampling voltage at each sampling point.

[0063] Based on the expected aliasing current and the actual aliasing current, the expected current harmonic components and the actual current harmonic components are determined.

[0064] Optionally, the determining module is specifically used for:

[0065] The expected voltage and current trajectory is generated by using the preset sampling voltage at each sampling point as the horizontal axis and the expected aliasing current at each sampling point as the vertical axis.

[0066] Optionally, the determining module is specifically used for:

[0067] Perform a Fast Fourier Transform on the expected aliasing current to obtain the expected complex spectrum corresponding to the expected aliasing current, and extract the harmonic components of the expected current in the expected complex spectrum;

[0068] Perform a Fast Fourier Transform on the actual aliasing current to obtain the actual complex spectrum corresponding to the actual aliasing current, and extract the actual current harmonic components from the actual complex spectrum.

[0069] Optionally, the determining module is specifically used for:

[0070] Feature extraction is performed on the expected voltage and current trajectory to obtain the area and perimeter of the expected trajectory;

[0071] Feature extraction is performed on the actual voltage and current trajectory to obtain the actual trajectory area and actual trajectory perimeter;

[0072] The trajectory area similarity is determined based on the expected trajectory area and the actual trajectory area.

[0073] The trajectory perimeter similarity is determined based on the expected trajectory perimeter and the actual trajectory perimeter.

[0074] Determine the current waveform similarity based on the expected current waveform and the actual current waveform;

[0075] The similarity of current harmonic components is determined based on the expected current harmonic components and the actual current harmonic components.

[0076] Based on the trajectory area similarity, trajectory perimeter similarity, current waveform similarity, and current harmonic component similarity, it is determined whether the load to be identified is the target load.

[0077] Optionally, the preset database includes: preset target load harmonic similarity threshold, trajectory area phase velocity threshold, trajectory perimeter similarity threshold, and waveform similarity threshold.

[0078] Optionally, the determining module is specifically used for:

[0079] If the similarity of the current harmonic components is greater than the harmonic threshold, and the similarity of the current waveform is greater than the preset waveform similarity threshold, and the similarity of the trajectory area is greater than the preset trajectory area similarity threshold, and the similarity of the trajectory perimeter is greater than the preset trajectory perimeter similarity threshold, then the load to be identified is determined to be the target load; otherwise, the load to be identified is determined not to be the target load.

[0080] Thirdly, embodiments of this application also provide a non-intrusive load identification system, including: an analog-to-digital conversion unit and a processing unit.

[0081] The analog-to-digital conversion unit is used to sample the current and voltage of the circuit.

[0082] The processing unit is used to perform the steps of the method described in the first aspect to identify whether a target load exists in the main circuit.

[0083] Fourthly, embodiments of this application also provide an electronic device, including: a processor, a storage medium, and a bus, wherein the storage medium stores program instructions executable by the processor, and when the application runs, the processor communicates with the storage medium via the bus, and the processor executes the program instructions to perform the steps of the non-intrusive load identification method described in the first aspect.

[0084] Fifthly, embodiments of this application also provide a computer-readable storage medium storing a computer program, which is read and executes the steps of the non-intrusive load identification method described in the first aspect.

[0085] The beneficial effects of this application are:

[0086] This application provides a non-intrusive load identification method and system. By using the first current and first voltage in the main circuit before the load to be identified is connected, and the single current and single voltage of the load to be identified in a preset database, the expected aliasing current of the load to be identified can be determined. Then, by using the second current and second voltage in the main circuit after the load to be identified is connected, the actual aliasing current can be determined. Finally, based on the expected aliasing current, the actual aliasing current, and the generated expected current waveform, actual current waveform, expected voltage and current trajectory, actual voltage and current trajectory, expected current harmonic component, and actual current harmonic component, it can be determined whether the load to be identified is the target load. When determining whether a load to be identified is the target load, the current and voltage in the main circuit can be directly collected, and the load can be identified based on the collected current and voltage. This eliminates the need for load identification chips to detect load access events, saving circuit board space and cost. By directly judging the expected aliasing current and voltage of the load to be identified against the actual aliasing current and voltage, the method can quickly identify the load and avoid the large storage space required for storing numerous frequency subtraction operations in existing technologies. It also addresses the issue of severely distorted waveforms extracted when performing subtraction operations with long intervals for loads with rapidly changing currents that cannot reach a steady state, leading to a high false positive rate and affecting the accuracy of load monitoring. Furthermore, this method is applicable to the identification of various loads with high accuracy. Attached Figure Description

[0087] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0088] Figure 1 A scenario diagram provided for an embodiment of this application;

[0089] Figure 2 A flowchart illustrating a non-invasive load identification method provided in this application embodiment;

[0090] Figure 3 This is a schematic diagram illustrating the current variation of the main circuit when different loads are connected, provided in an embodiment of this application.

[0091] Figure 4 A flowchart illustrating the second non-invasive load identification method provided in this application embodiment;

[0092] Figure 5A flowchart illustrating the third non-invasive load identification method provided in this application embodiment;

[0093] Figure 6 A schematic diagram of the current of a main circuit connected to a single electric bicycle, provided for an embodiment of this application;

[0094] Figure 7 A schematic diagram of the current of a single AUX mini solar panel connected to the main circuit is provided for an embodiment of this application;

[0095] Figure 8 A schematic diagram of the current connected to a single electric kettle in the main circuit is provided for an embodiment of this application;

[0096] Figure 9 A flowchart illustrating the fourth non-invasive load identification method provided in this application embodiment;

[0097] Figure 10 A schematic diagram of the VI trajectory of an electric kettle provided in an embodiment of this application;

[0098] Figure 11 A schematic diagram of the VI trajectory of an induction cooker kettle provided in an embodiment of this application;

[0099] Figure 12 This is a schematic diagram of the VI trajectory of a microwave oven provided in an embodiment of this application;

[0100] Figure 13 A schematic diagram of the VI trajectory of a laptop computer provided in an embodiment of this application;

[0101] Figure 14 A flowchart illustrating the fifth non-invasive load identification method provided in this application embodiment;

[0102] Figure 15 A flowchart illustrating the sixth non-invasive load identification method provided in this application embodiment;

[0103] Figure 16 A schematic diagram of an apparatus for a non-invasive load identification method provided in an embodiment of this application;

[0104] Figure 17 This is a structural block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0105] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the accompanying drawings in this application are for illustrative and descriptive purposes only and are not intended to limit the scope of protection of this application. Furthermore, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of this application. It should be understood that the operations in the flowcharts may not be implemented in sequence, and steps without logical contextual relationships may be reversed or implemented simultaneously. In addition, those skilled in the art, guided by the content of this application, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts.

[0106] Furthermore, the described embodiments are merely some, not all, of the embodiments of this application. The components of the embodiments of this application described and illustrated herein can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0107] It should be noted that the term "comprising" will be used in the embodiments of this application to indicate the presence of the features declared thereafter, but does not exclude the addition of other features.

[0108] Optionally, the non-intrusive load identification method provided in this application embodiment can be applied to an electronic device, such as a mobile phone, tablet computer, laptop computer, PDA, desktop computer, or other terminal device with computing power and display function, or it can be a server. Specifically, it can be applied to applications in the terminal device, such as mobile phone apps (APP) or computer application systems.

[0109] The following is a detailed explanation of the specific implementation process of the non-intrusive load identification provided in the embodiments of this application.

[0110] Figure 1 This is a schematic diagram of the architecture of a load identification system provided in an embodiment of this application, such as... Figure 1 As shown, a metering chip can be used in the main circuit to collect the real-time current and real-time voltage of the main circuit, and send the collected real-time current and real-time voltage to the MCU. The MCU uses the method provided in the embodiment of this application to identify whether the load is the target load.

[0111] Specifically, this load identification system can be applied to circuit breakers, as shown in the reference. Figure 1 The circuit breaker may also include a signal processing module. This module is used to connect to the power line to acquire voltage and current signals, such as the live wire (L) and the neutral wire (N). It performs amplification, reduction, and / or filtering on the voltage and current signals transmitted through the power line. The MCU can communicate bidirectionally with the metering chip. The MCU can control the metering chip to acquire the processed voltage and current signals in real time at a preset sampling frequency, obtaining sampled data and sending it to the MCU. The MCU then performs load identification based on the sampled data, obtains the load identification result, and sends the result to a host computer connected to the MCU.

[0112] Figure 2 This is a flowchart illustrating a non-intrusive load identification method provided in an embodiment of this application. The subject executing this method is the aforementioned electronic device. Figure 2 As shown, the method includes:

[0113] S101. Obtain the first current and first voltage in the main circuit before the load to be identified is connected, and the second current and second voltage in the main circuit after the load to be identified is connected.

[0114] The load to be identified can refer to any electrical device, such as a refrigerator, television, rice cooker, induction cooker, water dispenser, lamp, computer, etc.

[0115] The first current refers to the real-time current of the main circuit during a period of time before the load to be identified is connected; that is, the first current refers to multiple consecutive currents within a certain period. The first voltage refers to the real-time voltage of the main circuit during a period of time before the load to be identified is connected; that is, the first voltage refers to multiple consecutive voltages within a certain period. The second current refers to the real-time current of the main circuit during a period of time after the load to be identified is connected; that is, the second current refers to multiple consecutive currents within a certain period. The second voltage refers to the real-time voltage of the main circuit during a period of time after the load to be identified is connected; that is, the second voltage refers to multiple consecutive voltages within a certain period.

[0116] For example, if the main circuit is connected to the load to be identified at time t1, the first current refers to the real-time current within a preset time period before time t1, and the first voltage refers to the real-time voltage within the preset time period before time t1. The second current refers to the real-time current within a preset time period after time t1, and the second voltage refers to the real-time voltage within the preset time period after time t1.

[0117] Figure 3 This is a schematic diagram showing the current changes when the main circuit is connected to different loads, as provided in an embodiment of this application.

[0118] S102. Based on the first current, the first voltage, and the single current of multiple target loads in the preset database, generate the expected aliasing current to be connected to each target load.

[0119] The preset database can pre-store the single current and single voltage of multiple target loads. Multiple target loads can refer to various types of custom loads. The single current for a target load refers to the current obtained when only that target load is connected in the main circuit. This single current is sampled current array data, which represents the current at multiple sampling points within a sampling period. If there are N sampling points, the single current refers to the single current of the target load at each of the N sampling points. Here, N sampling points refers to the number of sampling points in a single period; current sampling can be performed for a single period or multiple periods.

[0120] Optionally, the determined expected aliasing current is also the current at multiple sampling points, i.e., the expected aliasing current at each sampling point out of N sampling points, and also the voltage at multiple sampling points, i.e., the voltage at each sampling point out of N sampling points. Based on the first current and first voltage in the main circuit before the load to be identified is connected, and the single current and single voltage in the main circuit when only each target load is present, which are pre-stored in the preset database, the expected aliasing current when each target load is connected can be obtained using a preset method.

[0121] For example, if the single current of three target loads is stored in a preset database, the expected aliasing current 1, the expected aliasing current 2, and the expected aliasing current 3 of the main circuit to be connected to target load 1, the expected aliasing current 2 to be connected to target load 2, and the expected aliasing current 3 to be connected to target load 3 can be obtained by using a preset method based on the first current and the single current of these three target loads.

[0122] S103. Determine the actual aliasing current after connecting the load to be identified based on the second current and the second voltage.

[0123] Optionally, the actual aliasing current can be obtained using a preset method based on the second current and second voltage obtained after connecting the load to be identified in the main circuit. The actual aliasing current refers to the current at multiple sampling points, that is, the actual aliasing current at each of the N sampling points.

[0124] S104. Based on each expected aliasing current, actual aliasing current, preset sampling start point, each sampling point sequence, and preset sampling voltage at each sampling point, generate each expected current waveform, actual current waveform, each expected voltage and current trajectory, and actual voltage and current trajectory, and determine each expected current harmonic component and actual current harmonic component.

[0125] The expected current waveform refers to the waveform of the expected aliasing current superimposed at multiple sampling points according to the sampling sequence. The actual current waveform refers to the waveform of the actual aliasing current at multiple sampling points. The expected voltage and current trajectory refers to the voltage and current trajectory generated with π as the abscissa and the expected aliasing current as the ordinate. The actual voltage and current trajectory refers to the voltage and current trajectory generated with π as the abscissa and the actual aliasing current as the ordinate. In other words, the expected voltage and current trajectory refers to the trajectory of the expected aliasing current changing with π, and the actual voltage and current trajectory refers to the trajectory of the actual aliasing current changing with π. The expected current harmonic component may include multiple expected sub-current harmonic components, and the actual current harmonic component may also include multiple actual sub-current harmonic components.

[0126] Optionally, the expected current waveform, the actual current waveform, the expected voltage and current trajectory, and the actual voltage and current trajectory are selected, and the expected current harmonic components and the actual current harmonic components are determined as expected features and actual features extracted using a preset method based on the expected aliasing current and the actual aliasing current.

[0127] S105. Based on the expected current waveform, the actual current waveform, the expected voltage and current trajectory, the actual voltage and current trajectory, the expected current harmonic components, and the actual current harmonic components, determine whether the load to be identified is the target load.

[0128] Optionally, a preset method can be used to determine whether the load to be identified is the target load based on the expected current waveform, the actual current waveform, the expected voltage and current trajectory, the actual voltage and current trajectory, the expected current harmonic components, and the actual current harmonic components. If the load to be identified is determined to be the target load, a prompt message can be output, where the target load refers to a custom malicious load.

[0129] In this embodiment, the expected aliasing current of each target load can be determined by the first current and first voltage in the main circuit before the load to be identified is connected, and the single current of each target load in the preset database. The actual aliasing current is determined by the second current and second voltage in the main circuit after the load to be identified is connected. The expected current waveform, actual current waveform, expected voltage and current trajectory, actual voltage and current trajectory, expected current harmonic component, and actual current harmonic component are generated based on the expected aliasing current, actual aliasing current, preset sampling point, and sampling voltage at each sampling point to determine whether the load to be identified is a target load. When determining whether a load to be identified is the target load, the current and voltage in the main circuit can be directly collected, and the load can be identified based on the collected current and voltage, eliminating the need for load detection through a load identification chip, thus saving circuit board space and cost. By directly judging the characteristics of the expected aliasing current and voltage and the actual aliasing current and voltage of the load to be identified, it is possible to determine whether the load to be identified is the target load, which can achieve rapid identification and avoid the problem of large storage space caused by storing a large number of cycles for differential calculation in the existing technology. It also solves the problem that for some loads with rapid current changes that cannot enter a steady state, the waveform extracted when differential calculation is performed at long intervals is severely distorted, resulting in a high misjudgment rate of load identification and affecting the accuracy of load monitoring.

[0130] Furthermore, this method is applicable to the identification of various different loads and has a high accuracy rate.

[0131] Figure 4 A flowchart illustrating the second non-invasive load identification method provided in this application embodiment is shown below. Figure 4 As shown, in S102 above, generating the expected aliasing current for each target load based on the first current, the first voltage, and the single current of multiple target loads in a preset database may include:

[0132] S201. Sample the first current at the preset sampling start point within each preset period to obtain the sampled first current.

[0133] If the preset sampling frequency is SampleFreq, then the sampling time interval dt = 1 / SampleFreq. For example, if the number of sampling points in one sampling period is N and the sampling period is T, then the sampling frequency SampleFreq = N / T. N can be, for example, 128 sampling points, 256 sampling points, etc.

[0134] Optionally, the preset sampling start point in this embodiment can be the voltage crossing zero point, the voltage crossing zero point, or other voltage points. It can be set according to the actual situation. The preset sampling start point means that the sampling start point is the same in each sampling cycle. For example, each sampling cycle starts sampling at the voltage zero crossing point. The first current after sampling can be represented by I1[n], where I1[n] is the current array data, and n refers to the sampling point number. The value of n is 0 to N. I1[0] is the first current at the first sampling point, I1[1] is the first current at the second sampling point, I1[2] is the first current at the third sampling point, and so on.

[0135] S202. The sampled first current is superimposed with the single current of each target load to obtain each expected aliasing current.

[0136] Optionally, since the single current is also a single current at each sampling point among multiple sampling points, and the sampling method of the single current is the same as that of the first current, which is sampling at the zero-crossing voltage point, the sampling point of the single current can be aligned with the sampling point of the first current after sampling.

[0137] Optionally, the single current of a target load can be represented by, for example, I[n]. Then, the sampled first current is superimposed on the single current. Specifically, the first current at each sampling point is superimposed on the single current at each sampling point to obtain the expected aliasing current at each sampling point.

[0138] For example, I1[0] can be added to I[0] to obtain the expected aliasing current at sampling point 1; I1[1] can be added to I[1] to obtain the expected aliasing current at sampling point 2; I1[2] can be added to I[2] to obtain the expected aliasing current at sampling point 3, and so on, to obtain the expected aliasing current I3[n] at each sampling point.

[0139] In this embodiment, aligning the sampling points by the voltage zero-crossing point facilitates subsequent superposition processing and makes it easier to obtain the expected aliasing current.

[0140] Optionally, determining the actual aliasing current based on the second current and the second voltage in S103 above may include:

[0141] Specifically, the sampling unit samples the second current in the second voltage at a preset sampling start point according to a preset sampling frequency, so as to obtain the actual aliasing current after the load to be identified is connected.

[0142] Figure 5 A flowchart illustrating the third non-invasive load identification method provided in this application embodiment is shown below. Figure 5 As shown, the process of generating a single current and a single voltage for the target load is as follows:

[0143] S301. After a single target load is connected to the main circuit, the voltage and current of the main circuit are obtained in real time.

[0144] Optionally, only a single target load can be connected to the main circuit. After the single target load is connected to the main circuit, the real-time voltage and real-time current of the main circuit can be obtained over a period of time. Figure 6 This is a schematic diagram of the current supplied to a single electric bicycle via the main circuit. Figure 7 This is a schematic diagram of the current connected to a single AUX mini solar water heater in the main circuit. Figure 8 This is a schematic diagram of the current when a single electric kettle is connected in the main circuit.

[0145] S302. Sample the current at the preset sampling start point within each preset period to obtain the sampling current corresponding to each period, and cache the sampling current corresponding to each period into a sampling current array.

[0146] The preset sampling start point can be the zero-crossing point of the voltage.

[0147] For example, at the first zero-crossing point of the acquired real-time voltage, the acquired real-time current can be sampled at a preset sampling frequency to obtain the sampled current corresponding to the first cycle, and cached as the first sampled current array. At the second zero-crossing point of the real-time voltage, the acquired real-time current is sampled at the preset sampling frequency to obtain the sampled current corresponding to the second cycle, and cached as the second sampled current array. At the third zero-crossing point of the real-time voltage, the acquired real-time current is sampled at the preset sampling frequency to obtain the sampled current corresponding to the third cycle, and cached as the third sampled current array. This process can be repeated to obtain multiple sets of sampled currents.

[0148] S303. Accumulate and average the sampling current arrays according to the sampling sequence, and store them as a new array as the single current of the target load according to the sampling sequence.

[0149] For example, the average of the current at sampling point 1 in the first sampling current array, the current at sampling point 1 in the second sampling current array, and the current at sampling point 1 in the third sampling current array is taken as the single current at sampling point 1.

[0150] In this embodiment, the current and voltage of the target load connected to the main circuit are collected in advance, and the single current of the load is determined based on the multiple sets of sampled currents obtained, so that the determined single current is more accurate. The determined single current of the load is stored in a preset database in advance, so that it can be directly obtained from the preset database to facilitate the subsequent determination of the aliasing current.

[0151] Figure 9 A flowchart illustrating the fourth non-invasive load identification method provided in this application embodiment is shown below. Figure 9 As shown, in S104 above, based on each expected aliasing current, the actual aliasing current, the preset sampling start point, the sequence of each sampling point, and the preset sampling voltage at each sampling point, each expected current waveform, the actual current waveform, each expected voltage and current trajectory, and the actual voltage and current trajectory are generated, and each expected current harmonic component and the actual current harmonic component are determined, which may include:

[0152] S401. Generate the expected current waveform based on the expected aliasing current, the preset sampling start point, and the sequence of each sampling point.

[0153] The preset sampling start point refers to the sampling points in each sampling period according to the preset sampling start point. The sequence of each sampling point refers to the sequence of each sampling point within a sampling period.

[0154] Specifically, if the expected aliasing current is the expected aliasing current at multiple sampling points, then the preset sampling start point can be used as the starting point of the abscissa, the sequence of each sampling point can be used as the abscissa, and the expected aliasing current at each sampling point can be used as the ordinate to obtain the expected current waveform. For example, sampling point n can be used as the abscissa, and the expected aliasing current I3[n] at sampling point n can be used as the ordinate.

[0155] Optionally, for each expected aliasing current, a corresponding expected current waveform can be generated. For example, for expected aliasing current 1, expected current waveform 1 can be generated based on expected aliasing current 1, a preset sampling start point, and a sequence of sampling points; for expected aliasing current 2, expected current waveform 2 can be generated based on expected aliasing current 1, a preset sampling start point, and a sequence of sampling points; and for expected aliasing current 3, expected current waveform 3 can be generated based on expected aliasing current 1, a preset sampling start point, and a sequence of sampling points.

[0156] S402. Generate the actual current waveform based on the actual aliasing current, the preset sampling start point, and the sequence of each sampling point.

[0157] Specifically, the actual aliasing current is the actual aliasing current at multiple sampling points. Therefore, the preset sampling start point can be used as the starting point of the abscissa, the sequence of each sampling point can be used as the abscissa, and the actual aliasing current at each sampling point can be used as the ordinate to obtain the actual current waveform. For example, sampling point n can be used as the abscissa, and U3[n] at sampling point n can be used as the ordinate.

[0158] S403. Generate the expected voltage and current trajectory based on the expected aliasing current and the preset sampling voltage at each sampling point.

[0159] Optionally, the expected aliasing current can be used as the horizontal axis, and the preset sampling voltage at each sampling point can be used as the vertical axis to generate the expected voltage and current trajectory using a preset method. Each time the current is sampled, the sampling voltage at each sampling point is approximately equal, that is, for the obtained actual aliasing current, expected aliasing current, and single current, the sampling voltage at each sampling point is approximately equal, which is the preset sampling voltage.

[0160] Optionally, for each expected aliasing current, a corresponding expected voltage and current trajectory can be generated. For example, for expected aliasing current 1, expected voltage and current trajectory 1 can be generated based on expected aliasing current 1 and the preset sampling voltage at each sampling point; for expected aliasing current 2, expected voltage and current trajectory 2 can be generated based on expected aliasing current 2 and the preset sampling voltage at each sampling point; for expected aliasing current 3, expected voltage and current trajectory 3 can be generated based on expected aliasing current 3 and the preset sampling voltage at each sampling point.

[0161] S404. Generate the actual voltage and current trajectory based on the actual aliasing current and the preset sampling voltage at each sampling point.

[0162] Optionally, the actual aliasing current can be used as the horizontal axis, and the preset sampling voltage at each sampling point can be used as the vertical axis to generate the actual voltage trajectory using a preset method. Figure 10 This is a schematic diagram of the VI trajectory of an electric kettle provided in an embodiment of this application. Figure 11 This is a schematic diagram of the VI trajectory of an induction cooker kettle provided in an embodiment of this application. Figure 12 This is a schematic diagram of the VI trajectory of a microwave oven provided in an embodiment of this application. Figure 13 This is a schematic diagram of the VI trajectory of a laptop computer provided in an embodiment of this application.

[0163] S405. Determine the expected current harmonic components and the actual current harmonic components based on the expected aliasing current and the actual aliasing current.

[0164] Specifically, the expected current harmonic components can be determined using a preset method based on the expected aliasing current, and the actual current harmonic components can be determined using a preset method based on the actual aliasing current.

[0165] Optionally, for each expected aliasing current, the expected current harmonic component corresponding to that expected aliasing current can be determined. For example, for expected aliasing current 1, the expected current harmonic component 1 corresponding to that expected aliasing current 1 can be determined; for expected aliasing current 2, the expected current harmonic component 2 corresponding to that expected aliasing current 2 can be determined; and for expected aliasing current 3, the expected current harmonic component 3 corresponding to that expected aliasing current 3 can be determined.

[0166] In this embodiment, the expected aliasing current and the extracted expected aliasing features are compared with the actual aliasing current and the extracted actual aliasing features, so as to facilitate the subsequent comparison of the expected aliasing features and the actual aliasing features.

[0167] Optionally, the generation of the expected voltage and current trajectory in S403 above, based on the expected aliasing current and the preset sampling voltage at each sampling point, may include:

[0168] Specifically, the expected voltage and current trajectories can be generated by plotting each sampling point on the x-axis and the expected aliasing current at each sampling point on the y-axis. Voltage and current trajectories are a method for describing the relationship between voltage and current in a circuit, particularly suitable for nonlinear components or systems. They are commonly used to analyze the behavior of nonlinear components such as diodes, transistors, and the magnetization curves of ferromagnetic materials. On a VI trajectory plot, voltage is typically plotted on the x-axis, while current is plotted on the y-axis. The shape of the trajectory can provide rich information about the component's characteristics.

[0169] First, the sampling voltage at each sampling point can be normalized to a preset sampling voltage within the ±1 range. Then, the normalized sampling voltage at each sampling point is used as the horizontal axis, and the expected aliasing current at each sampling point is also normalized. The normalized expected aliasing current is used as the vertical axis to generate the expected voltage and current trajectory.

[0170] When normalizing at each sampling point, the maximum MaxVoltage and minimum MinVoltage can be determined using the formula.

[0171] NormalizedValue = (U[n] - MinVoltage) * 2 / (MaxVoltage - MinVoltage) – 1 normalizes the sampled voltage at each sampling point. Where U[n] is the sampled voltage at each sampling point.

[0172] Optionally, in this embodiment, since the sampling start points are aligned each time the current and voltage are sampled, and the sampling is performed according to a preset sampling frequency, the sampling points are aligned each time. Therefore, the sampled voltages at each sampling point obtained by sampling the first voltage of the main circuit before the load to be identified is connected, the sampled voltages at each sampling point obtained by sampling the second voltage of the main circuit after the load to be identified is connected, and the sampled voltages at each sampling point obtained by sampling the voltage of each individual target load when it is connected to the main circuit are all approximately equal.

[0173] Optionally, since the load to be identified corresponds to multiple expected aliasing currents, each expected voltage and current trajectory can be generated based on each expected aliasing current and the preset sampling voltage at each sampling point. For example, for expected aliasing current 1, expected voltage and current trajectory 1 can be generated; for expected aliasing current 2, expected voltage and current trajectory 2 can be generated; and for expected aliasing current 3, expected voltage and current trajectory 3 can be generated.

[0174] Optionally, the generation of the actual voltage and current trajectory in S404 above, based on the actual aliasing current and the preset sampling voltage at each sampling point, may include:

[0175] Specifically, the preset sampling voltage at each sampling point can be normalized to the ±1 range. Then, the normalized sampling voltage at each sampling point is used as the horizontal axis. At the same time, the actual aliasing current at each sampling point is also normalized, and the normalized expected aliasing current is used as the vertical axis to generate the expected voltage and current trajectory.

[0176] When normalizing the preset sampling voltage at each sampling point, the normalization method is the same as the process of normalizing the preset sampling voltage at each sampling point in S403 mentioned above, and will not be repeated here.

[0177] Figure 14 A flowchart illustrating the fifth non-invasive load identification method provided in this application embodiment is shown below. Figure 14 As shown, determining the expected current harmonic components and the actual current harmonic components based on the expected aliasing current and the actual aliasing current in S405 above may include:

[0178] S501. Perform a fast Fourier transform on the expected aliasing current to obtain the expected complex spectrum corresponding to the expected aliasing current, and extract the expected current harmonic components in the expected complex spectrum.

[0179] Specifically, the expected aliasing current can be subjected to a fast Fourier transform using the following formula (a).

[0180]

[0181] Where I3[n] is the expected aliasing current at each sampling point, X[k] is the expected complex spectrum, k takes the value from 0 to N-1, and N is a number greater than or equal to twice 51, which can completely cover the 1st to 51st harmonics.

[0182] Optionally, for the k-th harmonic, the real and imaginary parts of the expected complex spectrum can be extracted, where the real part Re_k = Re_(X[k]) and the imaginary part Im_k = Im_(X[k]). The k-th harmonic is... In this embodiment, the expected current harmonic components are odd-order current harmonic components, that is, the 1st expected current harmonic component, the 3rd expected current harmonic component, the 5th expected current harmonic component, the 7th expected current harmonic component, etc. are extracted.

[0183] Optionally, the process for extracting the expected current harmonic components for each expected aliasing current is the same and will not be described in detail here. For example, for expected aliasing current 1, the expected current harmonic component 1 of expected aliasing current 1 can be obtained; for expected aliasing current 2, the expected current harmonic component 2 of expected aliasing current 2 can be obtained; and for expected aliasing current 3, the expected current harmonic component 3 of expected aliasing current 3 can be obtained.

[0184] S502. Perform a fast Fourier transform on the actual aliasing current to obtain the actual complex spectrum corresponding to the actual aliasing current, and extract the actual current harmonic components in the actual complex spectrum.

[0185] Optionally, the process of obtaining the actual complex spectrum corresponding to the actual aliasing current is the same as the process of obtaining the expected complex spectrum in S501, and the process of extracting the actual current harmonic components in the actual complex spectrum is the same as the process of extracting the expected current harmonic components in S501, which will not be elaborated here.

[0186] Among them, the extracted actual current harmonic components are also odd-order current harmonic components, that is, the 1st actual current harmonic component, the 3rd actual current harmonic component, the 5th actual current harmonic component, the 7th actual current harmonic component, etc. are extracted.

[0187] Figure 15 A flowchart illustrating the sixth non-invasive load identification method provided in this application embodiment is shown below. Figure 15 As shown, in step S105 above, determining whether the load to be identified is the target load based on the expected current waveform, the actual current waveform, the expected voltage and current trajectory, the actual voltage and current trajectory, the expected current harmonic components, and the actual current harmonic components may include:

[0188] S601. Extract features from the expected voltage and current trajectory to obtain the area and perimeter of the expected trajectory.

[0189] The calculation process for the expected trajectory area is as follows: Step 1: Calculate the instantaneous power P3(n) = U3[n] * I3[n] at each sampling point. Step 2: Apply the trapezoidal rule. If the sampling frequency is SampleFreq, then the time interval dt = 1 / SampleFreq, and the approximate value of the power integral within each sampling interval is P(n) = (P[n] + P[n+1]) * dt / 2. Step 3: The sum of the approximate values ​​of the power integral within all sampling intervals is taken as the expected trajectory area, then the expected trajectory area TotalArea = SUM(P(1):P(n)).

[0190] The calculation process for the expected trajectory perimeter is as follows: If there are N sampling points, with the x-coordinate of sampling point n being I3[n] and the y-coordinate being U3[n], then the length of the line segment from sampling point n to sampling point n+1 needs to be calculated. Then, the lengths of all line segments are summed to obtain the expected trajectory perimeter. Specifically, the Pythagorean theorem is first used to calculate the length of each line segment L(n-1) = SQRT((In-I(n-1))^2+(Un-U(n-1))^2), and the sum of the lengths of all line segments is taken as the expected trajectory perimeter C = SUM(L1:L(n)). If the expected trajectory perimeter is not closed, the length between the last segment and the first segment also needs to be calculated, ensuring that the calculated line segments cover all points that need to be connected.

[0191] For example, for voltage and current trajectory 1, the expected trajectory area 1 and the expected trajectory perimeter 1 can be obtained; for voltage and current trajectory 2, the expected trajectory area 2 and the expected trajectory perimeter 2 can be obtained; for voltage and current trajectory 3, the expected trajectory area 3 and the expected trajectory perimeter 3 can be obtained.

[0192] Optionally, a larger trace perimeter generally indicates a more complex relationship between voltage and current. This may suggest the presence of multiple operating modes or significant nonlinear behavior in the circuit. Energy conversion efficiency: In some cases, the size of the trace perimeter can also reflect the energy conversion efficiency of the circuit. For example, in switching power supply design, a larger trace perimeter may mean higher energy loss, as this can represent the additional energy loss caused by frequent switching operations.

[0193] Alternatively, the area enclosed by the trace is directly related to the average power consumption of the circuit over a complete cycle. For AC circuits, the trace area can be used to estimate the energy absorbed or released by the circuit over a cycle. For energy-consuming components (such as resistors), the area represents the energy consumed by the component over a cycle; for energy-storing components (such as capacitors and inductors), it represents the difference between the energy stored and released over a cycle.

[0194] Alternatively, when analyzing non-sinusoidal signals containing harmonics, changes in the trace area can also reflect changes in the harmonic content. Generally, signals containing more higher harmonics result in more irregular traces, thus increasing the trace area. The trace area can also serve as a way to assess the degree of nonlinearity in a circuit or component. For nonlinear components, the trace forms a closed shape, and its area can be used to quantitatively assess the severity of the nonlinearity.

[0195] Therefore, the perimeter and area of ​​the voltage and current traces provide important information about the circuit characteristics, which helps to understand the circuit's working principle and performance.

[0196] S602. Extract features from the actual voltage and current trajectory to obtain the actual trajectory area and actual trajectory perimeter.

[0197] Optionally, the process of obtaining the actual trajectory area is similar to the process of obtaining the expected trajectory area, and the process of obtaining the actual trajectory perimeter is similar to the process of obtaining the expected trajectory perimeter, which will not be elaborated here.

[0198] S603. Determine the trajectory area similarity based on the expected trajectory area and the actual trajectory area.

[0199] Specifically, the similarity between the expected trajectory area and the actual trajectory area can be determined using Euclidean distance, thus obtaining the trajectory area similarity. A smaller Euclidean distance indicates a higher similarity.

[0200] S604. Determine the similarity of the trajectory circumferences based on the expected trajectory circumference and the actual trajectory circumference.

[0201] Specifically, Euclidean distance can be used to determine the similarity between the expected trajectory perimeter and the actual trajectory perimeter, thus obtaining the trajectory perimeter similarity. Since the load to be identified corresponds to multiple expected trajectory perimeters, the actual trajectory perimeter needs to be compared with each expected trajectory perimeter to obtain the similarity of each trajectory perimeter.

[0202] For example, the trajectory perimeter similarity 1 can be determined based on the expected trajectory perimeter 1 and the actual trajectory perimeter; the trajectory perimeter similarity 2 can be determined based on the expected trajectory perimeter 2 and the actual trajectory perimeter; and the trajectory perimeter similarity 3 can be determined based on the expected trajectory perimeter 3 and the actual trajectory perimeter.

[0203] S605. Determine the similarity of the current waveforms based on the expected current waveform and the actual current waveform.

[0204] Alternatively, cosine similarity can be used to determine the similarity of the current waveforms. Cosine similarity determines how closely the actual current waveform resembles the expected current waveform of the load to be identified.

[0205] For example, the current waveform similarity 1 can be determined based on the expected current waveform 1 and the actual current waveform; the current waveform similarity 2 can be determined based on the expected current waveform 2 and the actual current waveform; and the current waveform similarity 3 can be determined based on the expected current waveform 3 and the actual current waveform.

[0206] Cosine similarity measures the degree of proximity between two non-zero vectors in a given direction, with a value ranging from -1 to 1. When two vectors completely overlap, the cosine similarity is 1; when they are perpendicular, it is 0; and when they are in opposite directions, it is -1. Specifically, it can be calculated using the following formula (II).

[0207]

[0208] Where A refers to the expected current waveform and B refers to the actual current waveform, A*B==SUMPRODUCT(A1:A(n),B1:B(n)), the magnitude of vector A||A||=SQRT(SUMSQ(A(1):A(n))), the magnitude of vector B||B||=SQRT(SUMSQ(B(1):B(n))), where A(n) refers to the expected aliasing current at each sampling point in the expected current waveform and B(n) refers to the actual aliasing current at each sampling point in the actual current waveform.

[0209] S606. Determine the similarity of current harmonic components based on the expected current harmonic components and the actual current harmonic components.

[0210] Alternatively, the similarity between the expected current harmonic components and the actual current harmonic components can be determined using Euclidean distance, thereby obtaining the current harmonic component similarity.

[0211] For example, the similarity of current harmonic components can be determined based on the expected current harmonic component 1 and the actual current harmonic component; the similarity of current harmonic components can be determined based on the expected current harmonic component 2 and the actual current harmonic component; and the similarity of current harmonic components can be determined based on the expected current harmonic component 3 and the actual current harmonic component.

[0212] S607. Based on the similarity of the trajectory area, the similarity of the trajectory perimeter, the similarity of the current waveform, and the similarity of the current harmonic components, determine whether the load to be identified is the target load.

[0213] Specifically, if the similarity of the current harmonic components is greater than the harmonic threshold, the similarity of the current waveform is greater than the preset waveform similarity threshold, the similarity of the trajectory area is greater than the preset trajectory area similarity threshold, and the similarity of the trajectory perimeter is greater than the preset trajectory perimeter similarity threshold, then the load to be identified is determined to be the target load; otherwise, the load to be identified is determined to be a non-target load.

[0214] Optionally, for each expected aliasing current, the trajectory area similarity, trajectory perimeter similarity, current waveform similarity, and current harmonic component similarity can be obtained. For example, for expected aliasing current 1, trajectory area similarity 1, trajectory perimeter similarity 1, current waveform similarity 1, and current harmonic component similarity 1 can be obtained. For expected aliasing current 2, trajectory area similarity 2, trajectory perimeter similarity 2, current waveform similarity 2, and current harmonic component similarity 2 can be obtained. For expected aliasing current 3, trajectory area similarity 3, trajectory perimeter similarity 3, current waveform similarity 3, and current harmonic component similarity 3 can be obtained.

[0215] For each expected aliasing current, there are multiple similarities. If the similarities corresponding to the expected aliasing current satisfy the following conditions, then the load to be identified is the target load. The following conditions are: the similarity of the current harmonic components is greater than the harmonic threshold, the similarity of the current waveform is greater than the waveform threshold, the similarity of the trajectory area is greater than the trajectory area threshold, and the similarity of the trajectory perimeter is greater than the trajectory perimeter threshold. In this case, the load to be identified is determined to be the target load.

[0216] In this embodiment, Euclidean distance is used to determine the similarity of trajectory area, trajectory perimeter, and current harmonic components, while cosine similarity is used to determine the similarity of current waveforms. Different similarity algorithms are sampled to determine the similarity between different features, allowing for targeted similarity calculations and making the obtained similarity scores more accurate. Furthermore, different similarity thresholds correspond to different features. When the similarity of each feature is greater than its corresponding threshold, the load to be identified is determined to be the target load, improving the accuracy of load identification.

[0217] Figure 16 This is a schematic diagram of an apparatus for a non-invasive load identification method provided in an embodiment of this application, as shown below. Figure 16 As shown, the device includes:

[0218] The acquisition module 701 acquires the first current and first voltage in the main circuit before the load to be identified is connected, and the second current and second voltage in the main circuit after the load to be identified is connected.

[0219] The generation module 702 is used to generate the expected aliasing current to be connected to each target load based on the first current, the first voltage, and the single current of multiple target loads in the preset database.

[0220] The determining module 703 is used to determine the actual aliasing current after the load to be identified is connected, based on the second current and the second voltage.

[0221] The determination module 703 is used to generate each expected current waveform, the actual current waveform, each expected voltage and current trajectory, and the actual voltage and current trajectory based on each expected aliasing current, the actual aliasing current, the preset sampling start point, each sampling point sequence, and the preset sampling voltage at each sampling point, and to determine each expected current harmonic component and the actual current harmonic component.

[0222] The determination module 703 is used to determine whether the load to be identified is the target load based on the expected current waveform, the actual current waveform, the expected voltage and current trajectory, the actual voltage and current trajectory, the expected current harmonic component, and the actual current harmonic component.

[0223] Optionally, the generation module 702 is specifically used for:

[0224] After a single target load is connected to the main circuit, the current and voltage of the main circuit are acquired in real time.

[0225] The current is sampled at a preset sampling start point within each preset period to obtain the sampled current corresponding to each period. The sampled current corresponding to each period is cached into a sampled current array, wherein the voltage at the preset sampling start point satisfies a preset condition.

[0226] The average value of each of the sampled current arrays is accumulated according to the sampling sequence, and then stored in a new array as a single current of the target load.

[0227] Optionally, the generation module 702 is specifically used for:

[0228] The first current is sampled at a preset sampling start point within each preset period to obtain the sampled first current;

[0229] The sampled first current is superimposed with the single current of each target load to obtain the expected aliasing current to be connected to the target load.

[0230] Optionally, the determining module 703 is specifically used for:

[0231] The second current is sampled at a preset sampling start point within each preset period to obtain the actual aliasing current after the load to be identified is connected.

[0232] Optionally, the determining module 703 is specifically used for:

[0233] The expected current waveform is generated based on the expected aliasing current, the preset sampling start point, and the sequence of each sampling point;

[0234] The actual current waveform is generated based on the actual aliasing current, the preset sampling start point, and the sequence of each sampling point;

[0235] Based on the expected aliasing current and the preset sampling voltage at each sampling point, the expected voltage and current trajectory is generated.

[0236] The actual voltage and current trajectory is generated based on the actual aliasing current and the preset sampling voltage at each sampling point.

[0237] Based on the expected aliasing current and the actual aliasing current, the expected current harmonic components and the actual current harmonic components are determined.

[0238] Optionally, the determining module 703 is specifically used for:

[0239] The expected voltage and current trajectory is generated by using the preset sampling voltage at each sampling point as the horizontal axis and the expected aliasing current at each sampling point as the vertical axis.

[0240] Optionally, the determining module 703 is specifically used for:

[0241] Perform a Fast Fourier Transform on the expected aliasing current to obtain the expected complex spectrum corresponding to the expected aliasing current, and extract the harmonic components of the expected current in the expected complex spectrum;

[0242] Perform a Fast Fourier Transform on the actual aliasing current to obtain the actual complex spectrum corresponding to the actual aliasing current, and extract the actual current harmonic components from the actual complex spectrum.

[0243] Optionally, the determining module 703 is specifically used for:

[0244] Feature extraction is performed on the expected voltage and current trajectory to obtain the area and perimeter of the expected trajectory;

[0245] Feature extraction is performed on the actual voltage and current trajectory to obtain the actual trajectory area and actual trajectory perimeter;

[0246] The trajectory area similarity is determined based on the expected trajectory area and the actual trajectory area.

[0247] The trajectory perimeter similarity is determined based on the expected trajectory perimeter and the actual trajectory perimeter.

[0248] Determine the current waveform similarity based on the expected current waveform and the actual current waveform;

[0249] The similarity of current harmonic components is determined based on the expected current harmonic components and the actual current harmonic components.

[0250] Based on the trajectory area similarity, trajectory perimeter similarity, current waveform similarity, and current harmonic component similarity, it is determined whether the load to be identified is the target load.

[0251] Optionally, the preset database includes: preset target load harmonic similarity threshold, trajectory area phase velocity threshold, trajectory perimeter similarity threshold, and waveform similarity threshold.

[0252] Optionally, the determining module 703 is specifically used for:

[0253] If the similarity of the current harmonic components is greater than the harmonic threshold, and the similarity of the current waveform is greater than the preset waveform similarity threshold, and the similarity of the trajectory area is greater than the preset trajectory area similarity threshold, and the similarity of the trajectory perimeter is greater than the preset trajectory perimeter similarity threshold, then the load to be identified is determined to be the target load; otherwise, the load to be identified is determined not to be the target load.

[0254] Optionally, embodiments of this application also provide a non-intrusive load identification system, including: an analog-to-digital conversion unit and a processing unit.

[0255] The analog-to-digital conversion unit is used to sample the current and voltage of the circuit.

[0256] The processing unit is used to execute the method steps described in the above specific embodiments to identify whether there is a target load in the main circuit.

[0257] Figure 17 This is a structural block diagram of an electronic device 400 provided in an embodiment of this application. (See diagram below.) Figure 17 As shown, the electronic device may include: a processor 401 and a memory 402.

[0258] Optionally, a bus 403 may also be included, wherein the memory 402 is used to store machine-readable instructions executable by the processor 401. When the electronic device 400 is running, the processor 401 and the memory 402 communicate via the bus 403. When the machine-readable instructions are executed by the processor 401, the method steps in the above method embodiments are performed.

[0259] This application also provides a computer-readable storage medium storing a computer program, which, when run by a processor, executes the method steps described in the above-described non-intrusive load identification method embodiments.

[0260] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and devices described above can be referred to the corresponding processes in the method embodiments, and will not be repeated here. In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection can be through some communication interfaces; the indirect coupling or communication connection of devices or modules can be electrical, mechanical, or other forms.

[0261] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0262] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any changes 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.

Claims

1. A non-invasive load identification method, characterized in that, The method includes: Obtain the first current and first voltage in the main circuit before the load to be identified is connected, and the second current and second voltage in the main circuit after the load to be identified is connected; Based on the first current, the first voltage, and the single current of multiple target loads in the preset database, the expected aliasing current to be connected to each target load is generated. Based on the second current and the second voltage, determine the actual aliasing current after connecting the load to be identified; Based on the expected aliasing current, the actual aliasing current, the preset sampling start point, the sequence of each sampling point, and the preset sampling voltage at each sampling point, generate the expected current waveform, the actual current waveform, the expected voltage and current trajectory, and the actual voltage and current trajectory, and determine the expected current harmonic components and the actual current harmonic components. Based on the expected current waveform, the actual current waveform, the expected voltage and current trajectory, the actual voltage and current trajectory, the expected current harmonic components, and the actual current harmonic components, determine whether the load to be identified is the target load.

2. The non-invasive load identification method according to claim 1, characterized in that, The process of generating a single current for the target load is as follows: After a single target load is connected to the main circuit, the current and voltage of the main circuit are acquired in real time. The current is sampled at a preset sampling start point within each preset period to obtain the sampled current corresponding to each period. The sampled current corresponding to each period is cached into a sampled current array, wherein the voltage at the preset sampling start point satisfies a preset condition. The average value of each of the sampled current arrays is accumulated according to the sampling sequence, and then stored in a new array as a single current of the target load.

3. The non-invasive load identification method according to claim 1, characterized in that, The step of generating the expected aliasing current for each target load based on the first current, the first voltage, and single current data of multiple target loads in a preset database includes: The first current is sampled at a preset sampling start point within each preset period to obtain the sampled first current; The sampled first current is superimposed with the single current of each target load to obtain the expected aliasing current to be connected to the target load.

4. The non-invasive load identification method according to claim 1, characterized in that, Determining the actual aliasing current after connecting the load to be identified based on the second current and the second voltage includes: The second current is sampled at a preset sampling start point within each preset period to obtain the actual aliasing current after the load to be identified is connected.

5. The non-invasive load identification method according to claim 1, characterized in that, The process of generating expected current waveforms, actual current waveforms, expected voltage-current trajectories, and actual voltage-current trajectories based on the expected aliasing currents, the actual aliasing currents, the preset sampling start points, the sequence of sampling points, and the preset sampling voltages at each sampling point, and determining the expected current harmonic components and the actual current harmonic components, includes: The expected current waveform is generated based on the expected aliasing current, the preset sampling start point, and the sequence of each sampling point; The actual current waveform is generated based on the actual aliasing current, the preset sampling start point, and the sequence of each sampling point; Based on the expected aliasing current and the preset sampling voltage at each sampling point, the expected voltage and current trajectory is generated. The actual voltage and current trajectory is generated based on the actual aliasing current and the preset sampling voltage at each sampling point. Based on the expected aliasing current and the actual aliasing current, the expected current harmonic components and the actual current harmonic components are determined.

6. The non-invasive load identification method according to claim 5, characterized in that, The step of generating the expected voltage-current trajectory based on the expected aliasing current and the preset sampling voltage at each sampling point includes: The expected voltage and current trajectory is generated by using the preset sampling voltage at each sampling point as the horizontal axis and the expected aliasing current at each sampling point as the vertical axis.

7. The non-invasive load identification method according to claim 5, characterized in that, The step of determining the expected current harmonic components and the actual current harmonic components based on the expected aliasing current and the actual aliasing current includes: Perform a Fast Fourier Transform on the expected aliasing current to obtain the expected complex spectrum corresponding to the expected aliasing current, and extract the harmonic components of the expected current in the expected complex spectrum; Perform a Fast Fourier Transform on the actual aliasing current to obtain the actual complex spectrum corresponding to the actual aliasing current, and extract the actual current harmonic components from the actual complex spectrum.

8. The non-invasive load identification method according to claim 1, characterized in that, The step of determining whether the load to be identified is the target load based on the expected current waveform, the actual current waveform, the expected voltage and current trajectory, the actual voltage and current trajectory, the expected current harmonic components, and the actual current harmonic components includes: Feature extraction is performed on the expected voltage and current trajectory to obtain the area and perimeter of the expected trajectory; Feature extraction is performed on the actual voltage and current trajectory to obtain the actual trajectory area and actual trajectory perimeter; The trajectory area similarity is determined based on the expected trajectory area and the actual trajectory area. The trajectory perimeter similarity is determined based on the expected trajectory perimeter and the actual trajectory perimeter. Determine the current waveform similarity based on the expected current waveform and the actual current waveform; The similarity of current harmonic components is determined based on the expected current harmonic components and the actual current harmonic components. Based on the trajectory area similarity, trajectory perimeter similarity, current waveform similarity, and current harmonic component similarity, it is determined whether the load to be identified is the target load.

9. The method according to claim 1, characterized in that, The preset database includes: preset target load harmonic similarity threshold, trajectory area phase velocity threshold, trajectory perimeter similarity threshold, and waveform similarity threshold.

10. The non-invasive load identification method according to claim 7, characterized in that, The step of determining whether the load to be identified is the target load based on the trajectory area similarity, the trajectory perimeter similarity, the current waveform similarity, and the current harmonic component similarity includes: If the similarity of the current harmonic components is greater than the harmonic threshold, and the similarity of the current waveform is greater than the preset waveform similarity threshold, and the similarity of the trajectory area is greater than the preset trajectory area similarity threshold, and the similarity of the trajectory perimeter is greater than the preset trajectory perimeter similarity threshold, then the load to be identified is determined to be the target load; otherwise, the load to be identified is determined not to be the target load.

11. A non-invasive load identification system, characterized in that, include: Analog-to-digital conversion unit and processing unit; The analog-to-digital conversion unit is used to sample the current and voltage of the circuit. The processing unit is used to execute the steps of the non-intrusive load identification method according to any one of claims 1-10, so as to identify whether there is a target load in the main circuit.

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