Malignant load identification method suitable for electric energy meter and power consumption terminal, electric energy meter and power consumption terminal

Abstract

The application discloses a malignant load identification method suitable for electric energy meters and use terminals, and an electric energy meter and a use terminal, and belongs to the technical field of load identification. The method comprises the following steps: establishing a target malignant load set and collecting current data of the target malignant load set, extracting frequency domain features, and then constructing a reference frequency domain combined feature vector of each load; acquiring target working condition current data in actual operation, and constructing a to-be-identified frequency domain combined feature vector in the same way; and judging whether a malignant load exists or not by calculating the cosine similarity between the two vectors. The application further discloses an electric energy meter and a use terminal based on the method. The application overcomes the limitation of the prior art which only depends on the harmonic amplitude by constructing a combined feature vector by comprehensively using the amplitude and phase information of harmonics and performing similarity matching, realizes more accurate and reliable identification of the malignant load, and effectively reduces the misjudgment rate.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of load identification, and particularly relates to a malignant load identification method suitable for electric energy meters and use collecting terminals, an electric energy meter and a use collecting terminal. BACKGROUND

[0002] In collective electricity consumption places such as dormitories, office buildings and factories, in order to prevent fire and circuit overload accidents caused by the use of illegal high-power electrical appliances or poor-quality electrical appliances (collectively referred to as "malignant load"), the method of limiting the maximum power consumption is usually adopted for management. Once the power exceeds the preset threshold, the system will take intervention measures such as power failure to prevent circuit overload or fire risk. This identification method based on power threshold is simple in principle and convenient to implement, and has become the mainstream means of malignant load prevention and control.

[0003] However, the traditional power limiting method cannot identify the malignant load used. For example, the "power socket" or "power saver" devices on the market use diodes and other components to perform half-wave rectification on alternating current, shape the current waveform, make the effective power of the load in the power grid significantly reduced, and thus avoid the monitoring of the power threshold. Such devices not only bypass the safety supervision, but also cause the continuous use of illegal electrical appliances, and inject a large amount of harmonics into the power grid due to the distortion of the current waveform, polluting the power grid and causing potential damage to other devices on the same line, which constitutes a new safety hazard.

[0004] To address the above problems, the existing technology further introduces an identification scheme based on harmonic analysis. According to the characteristic that the harmonic content of the current of the malignant load (especially the load after waveform shaping) significantly increases when it is working, the method monitors the amplitude of specific harmonics (such as 3rd and 5th harmonics) and sets corresponding thresholds to identify potential malignant loads. However, this method still has the following defects in actual application: on the one hand, many compliant nonlinear loads, such as computers, LED lighting devices and switching power supplies, will also produce rich harmonics when they are running normally, which can easily lead to false positives and interfere with normal power consumption; on the other hand, relying only on the amplitude information of a few harmonic components cannot fully capture the load characteristics, and the identification accuracy and system robustness are insufficient when facing loads of complex types or variable shaping strategies. SUMMARY

[0005] The present application provides a method for identifying malignant loads suitable for electric energy meters and use collecting terminals, electric energy meters and use collecting terminals, which overcomes the high false positive rate problem caused by relying only on harmonic component analysis in the prior art.

[0006] The technical scheme of the present application is as follows:

[0007] A malignant load identification method suitable for electric energy meter and terminal, comprising the following steps:

[0008] Step S1. Create a target malignant load set, and for each target malignant load in the set, establish a malignant load working condition and obtain current sampling data under the malignant load working condition;

[0009] Step S2. Traverse the target malignant load set, for each target malignant load, extract frequency domain feature information from the corresponding current sampling data, then determine the amplitude-related harmonic number sequence and the phase-related harmonic number sequence corresponding to the target malignant load based on the frequency domain feature information, and obtain the reference amplitude feature vector and the reference phase feature vector corresponding to the target malignant load based on the amplitude-related harmonic number sequence and the phase-related harmonic number sequence and the frequency domain feature information, and construct a reference frequency domain combined feature vector based on the reference amplitude feature vector and the reference phase feature vector;

[0010] Step S3. Obtain current sampling data under the target working condition, extract frequency domain feature information therefrom, then construct a to-be-identified amplitude feature vector and a to-be-identified phase feature vector based on the frequency domain feature information of the current sampling data obtained in this step and the amplitude-related harmonic number sequence and the phase-related harmonic number sequence obtained in step S2, and construct a to-be-identified frequency domain combined feature vector based on the to-be-identified amplitude feature vector and the to-be-identified phase feature vector;

[0011] Step S4. Calculate the similarity between the to-be-identified frequency domain combined feature vector and the reference frequency domain combined feature vector corresponding to each target malignant load, and determine whether there is a malignant load in the target working condition based on the similarity.

[0012] As a further improvement of the malignant load identification method suitable for electric energy meter and terminal, the step of extracting frequency domain feature information from current sampling data is:

[0013] Step A1. Calculate the direct current component of the current sampling data;

[0014] Let the signal frequency be , the sampling frequency be , and the number of sampling points per cycle be :

[0015] ;

[0016] The current sampling data is accumulated by an integer multiple of cycles, and the direct current component is calculated according to the following formula:

[0017] ;

[0018] ;

[0019] wherein, I DC represents the direct current component of the current channel, I (n) represents the current sampling value of the n-th moment in the current sampling data, N represents the number of points required for calculating the direct current component, N0 represents the integer frequency of the accumulation, which is a preset value, INT represents the down rounding operation;

[0020] Step A2. Subtract the direct current component from the current sampling value of each sampling point in the current sampling data respectively to obtain the direct current removed sampling data;

[0021] I (n) represents the current value of the n-th moment in the direct current removed sampling data, I (n) represents the current sampling value of the n-th moment in the original current sampling data;

[0022] Step A3. Perform Fourier analysis on the direct current removed sampling data to obtain the amplitude percentage and phase of each harmonic; the specific steps are as follows:

[0023] Take N current sampling values from the direct current removed sampling data to perform harmonic analysis, and calculate the current waveform spectrum information of the taken N current sampling values according to the following formula, the first harmonic is represented as:

[0024] ,

[0025] wherein, j is the imaginary unit, ω is the rotation factor;

[0026] then the modulus of the first harmonic is:

[0027] ;

[0028] in the formula, Re (a) is the real part of the first harmonic a, Im (a) is the imaginary part of the first harmonic a;

[0029] the amplitude of the first harmonic is:

[0030] ; ​​​​​​​​​​​​​

[0031] The amplitude percentage of the subharmonic is:

[0032] ;

[0033] Phase of subharmonics for:

[0034] ;

[0035] In the formula: It is the arctangent function.

[0036] As a further improvement to the malignant load identification method applicable to electricity meters and user terminals, in step S2, the method for determining the amplitude-related harmonic number sequence and phase-related harmonic number sequence corresponding to the target malignant load based on frequency domain feature information is as follows: set amplitude-related harmonic number judgment conditions and phase-related harmonic number judgment conditions for the target malignant load, form an amplitude-related harmonic number sequence from the harmonic numbers in the frequency domain feature information of the target malignant load that meet the amplitude-related harmonic number judgment conditions, and form a phase-related harmonic number sequence from the harmonic numbers in the frequency domain feature information of the target malignant load that meet the phase-related harmonic number judgment conditions.

[0037] As a further improvement to the malicious load identification method applicable to electricity meters and user terminals, in step S2, a reference amplitude feature vector is constructed. The method is as follows:

[0038] ;

[0039] in, Represents the amplitude-dependent harmonic order sequence The first in There is one element, which represents the harmonic order. Indicates the length of the amplitude-dependent harmonic order sequence. Indicates the current target's malicious load. Percentage of the amplitude of the second harmonic;

[0040] Constructing reference phase eigenvectors The method is as follows:

[0041] ;

[0042] in, Represents the phase-correlated harmonic order sequence The first in There is one element, which represents the harmonic order. Indicates the length of the phase-correlated harmonic order sequence. Indicates the current target's malicious load. Phase of the subharmonic.

[0043] As a further improvement to the malicious load identification method applicable to electricity meters and user terminals, in step S2, the method for constructing a reference frequency domain combined feature vector based on the reference amplitude feature vector and the reference phase feature vector is as follows: the elements of the reference amplitude feature vector and the reference phase feature vector are concatenated in sequence to obtain the reference frequency domain combined feature vector.

[0044] ;

[0045] In the above formula, This is the reference frequency domain combined feature vector.

[0046] As a further improvement to the malicious load identification method applicable to electricity meters and user terminals, in step S3, the method for constructing the amplitude feature vector to be identified is as follows:

[0047] ;

[0048] In the above formula, Represents the amplitude-dependent harmonic order sequence The first in There is one element, which represents the harmonic order. Indicates the length of the amplitude-dependent harmonic order sequence. Indicating the frequency domain feature information under the current target operating condition Percentage of the amplitude of the second harmonic;

[0049] The method for constructing the phase feature vector to be identified is as follows:

[0050] ;

[0051] in, Represents the phase-correlated harmonic order sequence The first in There is one element, which represents the harmonic order. Indicates the length of the phase-correlated harmonic order sequence. Indicates the current target's malicious load. The relative phase of the subharmonics;

[0052] for The relative phase of subharmonics The calculation method is as follows:

[0053] ;

[0054] In the formula: The first harmonic phase of the current target malignant load is the theoretical fundamental phase. represents the phase of the second harmonic in the current target working condition, represents the phase of the second harmonic in the current target working condition, represents the modulo operation.

[0055] As a further improvement of the malignant load identification method suitable for electric energy meters and power consumption terminals, in step S3, the manner of constructing the to-be-identified frequency domain combined feature vector based on the to-be-identified amplitude feature vector and the to-be-identified phase feature vector is that: the elements of the to-be-identified amplitude feature vector and the to-be-identified phase feature vector are spliced in order to obtain the to-be-identified frequency domain combined feature vector, that is:

[0056] ;

[0057] In the above formula, is the to-be-identified frequency domain combined feature vector.

[0058] As a further improvement of the malignant load identification method suitable for electric energy meters and power consumption terminals, in step S4, the similarity between the to-be-identified frequency domain combined feature vector and the reference frequency domain combined feature vector is expressed as the combined feature vector angle .

[0059] ;

[0060] In the formula, is the vector inner product operation, is the vector 2 norm operation.

[0061] The manner of judging whether the target working condition has a malignant load based on the similarity is that: if the combined feature vector angle between the to-be-identified frequency domain combined feature vector of the target working condition and the reference frequency domain combined feature vector corresponding to a certain target malignant load is less than a preset threshold, it is determined that the current target malignant load exists in the target working condition.

[0062] The application further discloses an electric energy meter, which comprises a processor and a memory.

[0063] The application further discloses a power consumption terminal, which comprises a processor and a memory.

[0064] Compared with the prior art, the application has the following beneficial effects:

[0065] 1.The method breaks through the limitation of traditional malignant load identification method which only relies on a few harmonic amplitude threshold values for judgment, introduces the multi-dimensional feature combination of amplitude and phase in the frequency domain, constructs the reference and to-be-identified frequency domain combination feature vector, and matches based on the similarity of the vector angle, thereby achieving more comprehensive and accurate identification of the load current waveform. The method can not only effectively distinguish between compliant nonlinear loads and malignant loads, but also significantly reduce the risk of misjudgment caused by similar harmonic content, thereby improving the identification accuracy and system robustness.

[0066] 2.The method constructs a combination feature vector containing amplitude and phase features, and uses the angle measurement method in the vector space to judge the similarity, so that the identification process has stronger capturing ability for the overall waveform features. Compared with the traditional method which only focuses on a single harmonic component, the method can extract more discriminative features from the amplitude percentage and relative phase of multiple harmonic orders, thereby maintaining stable identification performance when facing complex load types or waveform shaping strategy changes.

[0067] 3.In the frequency domain feature extraction stage, the method combines DC removal processing and Fourier analysis to accurately obtain the amplitude percentage and phase information of each harmonic, and further selects the key harmonic order sequence based on the preset conditions, effectively eliminates the non-representative harmonic components, and improves the information density and discrimination ability of the feature vector. The method ensures the calculation efficiency while enhancing the expression ability of the essential features of the malignant load waveform.

[0068] 4.The method also proposes a phase feature construction method based on relative phase, which correlates the measured harmonic phase with the theoretical fundamental phase, eliminates the phase offset caused by different sampling starting points, and makes the phase feature comparable and stable under different measurement conditions, thereby further improving the adaptability and reliability of the identification method. BRIEF DESCRIPTION OF DRAWINGS

[0069] Figure 1 is a time-domain waveform diagram of a diode load, the horizontal coordinate is the sampling point index value, and the vertical coordinate is the normalized sampling value. DETAILED DESCRIPTION

[0070] The technical solutions of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all embodiments.

[0071] Embodiment one

[0072] A malignant load identification method suitable for electric energy meters and terminal devices is disclosed in this embodiment, which includes the following steps:

[0073] Step S1. Create a target malignant load set, and for each target malignant load, establish a malignant load condition and obtain current sampling data under the malignant load condition.

[0074] For the convenience of description, the malignant load with a diode as a rectifier is collectively referred to as a diode load, and the diode load is taken as an example of a target malignant load in this embodiment. The time-domain waveform of the diode load is as follows: Figure 1 For simplicity, the amplitudes in the schematic diagram are normalized. Obviously, the waveform in the schematic diagram only has a positive half cycle.

[0075] The malignant load condition can be built using real electrical appliances or by simulation.

[0076] The current signal is continuously sampled to obtain multiple sets of current signal data, which are saved to a cache. According to the Nyquist sampling theorem, the sampling frequency should be greater than twice the highest cutoff frequency of the signal. According to engineering experience, the sampling frequency is preferably more than 10 times the signal frequency. For a 50 Hz signal, the sampling frequency can be considered to be 6.4 kHz or 12.8 kHz. For simplicity, continuous sampling is generally started from the zero-crossing point and two cycles are taken.

[0077] Step S2. Traverse the target malignant load set, and for each target malignant load, extract frequency domain feature information from the corresponding current sampling data, then determine the amplitude-related harmonic number sequence and the phase-related harmonic number sequence corresponding to the target malignant load based on the frequency domain feature information, and then obtain the reference amplitude feature vector and the reference phase feature vector corresponding to the target malignant load based on the amplitude-related harmonic number sequence and the phase-related harmonic number sequence and the frequency domain feature information, and construct a reference frequency domain combined feature vector based on the reference amplitude feature vector and the reference phase feature vector.

[0078] Further, the step of extracting frequency domain feature information from the current sampling data is:

[0079] Step A1. Calculate the DC component of the current sampling data.

[0080] Assuming that the signal frequency is , the sampling frequency is , and the number of sampling points per cycle is , then the number of sampling points per cycle is:

[0081] .

[0082] The current sampling data is accumulated by an integer multiple of cycles, and the DC component is calculated according to the following formula:

[0083] ;

[0084] ;

[0085] in, Represents the DC component of the current path. Indicates the first current sampling data Current sampling value at each moment, This indicates the number of points that need to be accumulated to calculate the DC component. This indicates the cumulative integer number of cycles, which is a preset value (e.g., 2 cycles). This indicates the floor function.

[0086] Step A2. Subtract the DC component from the current sample value at each sampling point in the current sampling data to obtain the de-DC sampling data.

[0087] The first DC sampling data The current value at each moment is . The first in the original current sampling data The current sampling value at each moment.

[0088] Step A3. Perform Fourier analysis on the DC sampling data to obtain the amplitude percentage and phase of each harmonic.

[0089] Based on engineering experience, two cycles of current samples are taken from the DC sampling data for harmonic analysis. The length of the selected current samples is denoted as... Then we have: .

[0090] Calculate the amount according to the following formula. Current waveform spectrum information of each current sample value The subharmonic is represented as:

[0091] , ;

[0092] in: For imaginary units, is the rotation factor.

[0093] but The magnitude of the second harmonic is:

[0094] ;

[0095] In the formula, for Subharmonic The real part, for Subharmonic The imaginary part.

[0096] The amplitude of the sub-harmonic is:

[0097] ;

[0098] The percentage of the amplitude of the sub-harmonic is:

[0099] .

[0100] The phase of the sub-harmonic is:

[0101] ;

[0102] In the formula: is the arctangent function.

[0103] Further, the manner of determining the amplitude-related harmonic number sequence and the phase-related harmonic number sequence corresponding to the target malicious load based on the frequency domain feature information is: setting an amplitude-related harmonic number judgment condition and a phase-related harmonic number judgment condition for the target malicious load, grouping the harmonic numbers in the frequency domain feature information of the target malicious load that meet the amplitude-related harmonic number judgment condition into an amplitude-related harmonic number sequence, and grouping the harmonic numbers in the frequency domain feature information of the target malicious load that meet the phase-related harmonic number judgment condition into a phase-related harmonic number sequence.

[0104] This embodiment takes a diode load as an example, and the corresponding frequency domain feature information is shown in Table 1.

[0105] Table 1: Amplitude percentage and phase of each harmonic of the diode load waveform.

[0106]

[0107] For the diode load, the amplitude-related harmonic number judgment condition refers to simultaneously meeting the following two conditions:

[0108] Condition 1: The amplitude percentage is less than 0.5% or greater than 2%;

[0109] Condition 2: The harmonic number is less than or equal to 10 times.

[0110] Therefore, the amplitude-related harmonic number sequence of the diode load is .

[0111] For the diode load, the phase-related harmonic number judgment condition refers to simultaneously meeting the following two conditions:

[0112] Condition 1: The amplitude percentage is greater than 2%;

[0113] Condition 2: The harmonic number is less than or equal to 10 times.

[0114] Therefore, the phase-related harmonic number sequence of the diode load is .

[0115] Further, the reference amplitude feature vector is constructed in the following manner:

[0116] ;

[0117] wherein, represents the i-th element in the amplitude-related harmonic number sequence , which represents the harmonic number, represents the length of the amplitude-related harmonic number sequence, represents the amplitude percentage of the i-th harmonic of the current target malignant load. Further, the reference phase feature vector is constructed in the following manner:

[0118]

[0119] ;

[0120] wherein, represents the i-th element in the phase-related harmonic number sequence , which represents the harmonic number, represents the length of the phase-related harmonic number sequence, represents the phase of the i-th harmonic of the current target malignant load. Further, the reference frequency domain combined feature vector is constructed based on the reference amplitude feature vector and the reference phase feature vector in the following manner: the elements of the reference amplitude feature vector and the reference phase feature vector are spliced in order to obtain the reference frequency domain combined feature vector. That is:

[0121]

[0122] ;

[0123] In the above formula, is the reference frequency domain combined feature vector.

[0124] Step S3. Obtain current sampling data under the target working condition, extract frequency domain feature information therefrom, and then construct a to-be-identified amplitude feature vector and a to-be-identified phase feature vector based on the frequency domain feature information of the current sampling data obtained in this step and the amplitude-related harmonic number sequence and the phase-related harmonic number sequence obtained in step S2, and construct a to-be-identified frequency domain combined feature vector based on the to-be-identified amplitude feature vector and the to-be-identified phase feature vector.

[0125] ​​​​​​Specifically, the method for extracting frequency domain feature information from the current sampling data under the target operating condition is the same as the method for extracting frequency domain feature information in step S2, and will not be elaborated here.

[0126] Furthermore, the method for constructing the feature vector of the amplitude to be identified is as follows:

[0127] ;

[0128] In the above formula, Represents the amplitude-dependent harmonic order sequence The first in There is one element, which represents the harmonic order. Indicates the length of the amplitude-dependent harmonic order sequence. This indicates the frequency domain feature information under the current target operating condition. The percentage of the amplitude of the subharmonic.

[0129] Furthermore, the method for constructing the phase feature vector to be identified is as follows:

[0130] ;

[0131] in, Represents the phase-correlated harmonic order sequence The first in There is one element, which represents the harmonic order. Indicates the length of the phase-correlated harmonic order sequence. Indicates the current target's malicious load. The relative phase of the subharmonics.

[0132] for The relative phase of subharmonics The calculation method is as follows:

[0133] ;

[0134] In the formula: The first harmonic phase of the current target malignant load is the theoretical fundamental phase. This indicates the frequency domain feature information under the current target operating condition. Phase of the subharmonic, This indicates the modulo operation.

[0135] Furthermore, the method for constructing the combined frequency domain feature vector based on the amplitude feature vector and the phase feature vector to be identified is as follows: the elements of the amplitude feature vector and the phase feature vector to be identified are concatenated in order to obtain the combined frequency domain feature vector to be identified. That is:

[0136] ;

[0137] In the above formula, is a to-be-identified frequency domain combined feature vector.

[0138] Step S4. Calculate the similarity between the to-be-identified frequency domain combined feature vector and the reference frequency domain combined feature vector corresponding to each target malicious load, respectively, and determine whether the target working condition has a malicious load based on the similarity.

[0139] Further, the similarity between the to-be-identified frequency domain combined feature vector and the reference frequency domain combined feature vector is the combined feature vector angle , which is expressed as:

[0140] ;

[0141] In the above formula, is a vector inner product operation, is a vector 2 norm operation.

[0142] Further, the way of determining whether the target working condition has a malicious load based on the similarity is: if the combined feature vector angle between the to-be-identified frequency domain combined feature vector of the target working condition and the reference frequency domain combined feature vector corresponding to a target malicious load is less than a preset threshold (3° in this embodiment), it is determined that the target working condition has the current target malicious load.

[0143] Taking a diode load as an example, if the combined feature vector angle between the diode load waveform combined feature vector and the detection waveform combined feature vector is less than 3°, it is determined that the current time domain waveform matches the time domain waveform of the diode load, and it is confirmed that the diode load currently exists.

[0144] Embodiment Two

[0145] This embodiment discloses an electric energy meter, which comprises a processor and a memory. A program stored in the memory is configured to be executed by the processor, and when executed, realizes the malicious load identification method in Embodiment One.

[0146] Embodiment Three

[0147] This embodiment discloses a terminal, which comprises a processor and a memory. A program stored in the memory is configured to be executed by the processor, and when executed, realizes the malicious load identification method in Embodiment One.

[0148] It should be noted that for those skilled in the art, it is obvious that the present application is not limited to the details of the above exemplary embodiments, and the present application can be realized in other specific forms without departing from the spirit or essential characteristics of the present application. The scope of the present application is defined by the claims rather than the above description.

Claims

1. A malignant load identification method suitable for electric energy meter and terminal, characterized in that: The method comprises the following steps: Step S1. A target malignant load set is created, and for each target malignant load in the target malignant load set, a malignant load condition is established and current sampling data under the malignant load condition is obtained; Step S2. The target malignant load set is traversed, and for each target malignant load, frequency domain feature information is extracted from the corresponding current sampling data, then amplitude-related harmonic order sequences and phase-related harmonic order sequences corresponding to the target malignant load are determined based on the frequency domain feature information, reference amplitude feature vectors and reference phase feature vectors corresponding to the target malignant load are obtained based on the amplitude-related harmonic order sequences and the phase-related harmonic order sequences and the frequency domain feature information, and a reference frequency domain combined feature vector is constructed based on the reference amplitude feature vectors and the reference phase feature vectors; The way of determining the amplitude-related harmonic order sequences and the phase-related harmonic order sequences corresponding to the target malignant load based on the frequency domain feature information is that amplitude-related harmonic order judgment conditions and phase-related harmonic order judgment conditions are set for the target malignant load, harmonic orders in the frequency domain feature information of the target malignant load that meet the amplitude-related harmonic order judgment conditions are combined to form the amplitude-related harmonic order sequences, and harmonic orders in the frequency domain feature information of the target malignant load that meet the phase-related harmonic order judgment conditions are combined to form the phase-related harmonic order sequences; Step S3. Current sampling data under a target condition is obtained, frequency domain feature information is extracted therefrom, then a to-be-identified amplitude feature vector and a to-be-identified phase feature vector are constructed based on the frequency domain feature information of the current sampling data obtained in this step and the amplitude-related harmonic order sequences and the phase-related harmonic order sequences obtained in step S2, and a to-be-identified frequency domain combined feature vector is constructed based on the to-be-identified amplitude feature vector and the to-be-identified phase feature vector; The way of constructing the to-be-identified amplitude feature vector is that: ； In the above formula, Represents the amplitude-dependent harmonic order sequence The first in There is one element, which represents the harmonic order. Indicates the length of the amplitude-dependent harmonic order sequence. Indicating the frequency domain feature information under the current target operating condition Percentage of the amplitude of the second harmonic; The way of constructing the to-be-identified phase feature vector is that: ； wherein, denotes the phase dependent harmonic number sequence the element at position denotes the harmonic number, denotes the length of the phase dependent harmonic number sequence, denotes the relative phase of the harmonic of the current target malignant load; For the relative phase of the sub-harmonic which is calculated as: ； In the formula: is the phase of the first harmonic of the current target malignant load, i.e., the fundamental phase of the theoretical waveform, represents the phase of the second harmonic in the frequency domain characteristic information under the current target working condition, is the phase of the second harmonic, represents the modulo operation; Step S4. Similarities between the to-be-identified frequency domain combined feature vector and reference frequency domain combined feature vectors corresponding to each target malignant load are calculated respectively, and whether a malignant load exists in the target condition is judged based on the similarities.

2. The malignant load identification method suitable for electric energy meter and terminal according to claim 1, characterized in that: The step of extracting the frequency domain feature information from the current sampling data is: Step A1. The direct current component of the current sampling data is calculated; Let the signal frequency be , the sampling frequency be , and the number of sampling points per cycle be . ； The direct current component is calculated according to the following formula by accumulating the current sampling data by an integer multiple of a cycle: ； ； wherein, represents a direct current component of the current channel, represents a current sampling value at the th time point in the current sampling data, represents a number of points required for calculating the direct current component, represents an integer number of cycles to be accumulated, which is a preset value, represents a floor operation; Step A2. The current sampling value of each sampling point in the current sampling data is subtracted by the direct current component respectively to obtain de-DC sampling data; The first DC sampling data The current value at each moment is ; The first in the original current sampling data Current sample value at each moment; Step A3. Fourier analysis is performed on the de-DC sampling data to obtain the amplitude percentage and the phase of each harmonic; the specific steps are as follows: A current sample value is taken from the de-DC sampled data for harmonic analysis, and the current waveform spectrum information of the taken current sample value is calculated according to the following formula, The second harmonic is expressed as: ， ； wherein: is the imaginary unit, is the rotation factor; Then The modulus of the sub-harmonic is: ； wherein is the real part of the second harmonic , is the imaginary part of the second harmonic . Amplitude of the sub-harmonic is: ； The amplitude percentage of the subharmonic is: ； the phase of the sub-harmonic is: ； In the formula: is the inverse tangent function.

3. The malignant load identification method suitable for electric energy meter and terminal according to claim 1, wherein, In step S2, the reference amplitude feature vector is constructed in the following way: ​ ； wherein, represents the amplitude-dependent harmonic number sequence at the th element of the sequence, which represents the harmonic number, represents the length of the amplitude-dependent harmonic number sequence, represents the amplitude percentage of the th harmonic of the current target malignant load; Constructing a reference phase feature vector in the following manner: ； wherein, denotes the phase related harmonic number sequence denotes the th element of the phase related harmonic number sequence, which element denotes the harmonic number, denotes the length of the phase related harmonic number sequence, denotes the phase of the th harmonic of the current target malignant load.

4. The malignant load identification method suitable for electric energy meter and terminal according to claim 3, wherein, In step S2, the way of constructing the reference frequency domain combined feature vector based on the reference amplitude feature vector and the reference phase feature vector is that the elements of the reference amplitude feature vector and the reference phase feature vector are sequentially spliced to obtain the reference frequency domain combined feature vector: ​ ； In the above formula, is a reference frequency-domain combined feature vector.

5. The malignant load identification method suitable for electric energy meter and terminal according to claim 1, wherein, In step S3, the way of constructing the to-be-identified frequency domain combined feature vector based on the to-be-identified amplitude feature vector and the to-be-identified phase feature vector is that the elements of the to-be-identified amplitude feature vector and the to-be-identified phase feature vector are sequentially spliced to obtain the to-be-identified frequency domain combined feature vector, that is: ​ ； In the above formula, is the frequency domain combination feature vector to be identified.

6. The malignant load identification method suitable for electric energy meter and usage terminal according to claim 1, wherein, In step S4, the similarity between the to-be-identified frequency domain combined feature vector and the reference frequency domain combined feature vector is represented as the combined feature vector angle denotes: ​ ； wherein is a vector inner product operation, is a vector 2-norm operation; The method for determining whether a target operating condition has a malignant load based on similarity is as follows: if the angle between the combined feature vectors of the target operating condition and the reference frequency domain combined feature vector corresponding to a certain malignant load is... If the load is less than the preset threshold, it is determined that there is a malignant load on the target in the target operating condition.

7. An electric energy meter, characterized by: A computer readable medium having stored thereon a program configured to cause a processor to implement the malignant load identification method according to any one of claims 1 to 6 when executed.

8. A terminal for data acquisition, characterized in that: A computer readable medium having stored thereon a program configured to cause a processor to implement the malignant load identification method according to any one of claims 1 to 6 when executed.

Images