Fault monitoring method for intelligent electric energy meter

Abstract

The invention discloses a fault monitoring method for an intelligent electric energy meter, and the method comprises the steps: collecting a current signal sequence, extracting a phasor vector of a specific harmonic order, mapping the phasor vector to a Clifford algebraic space to construct a multi-vector, calculating a curvature sensitive norm sequence, carrying out the trend separation, obtaining a degradation trend component sequence, and carrying out the fault monitoring of the intelligent electric energy meter. Curvature features are calculated in combination with the degradation feature indexes after temperature compensation, finally, the curvature features are matched with a predefined fault feature library to output a diagnosis result, geometric algebraic space is introduced into electric energy meter fault monitoring, 3 / 5 / 7 harmonic collaborative phase deviation is captured through geometric correlation coding of harmonic phasors, and the fault diagnosis accuracy of the electric energy meter is improved. The problem that the aging early-stage characteristics of the electric energy meter are submerged by noise is solved, and the detection rate of progressive faults in the electric energy meter is improved; the technical problem that the average detection rate of the current fault monitoring method of the intelligent electric energy meter on early progressive faults such as capacitor aging and metering chip degradation is low is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy meter fault monitoring, and in particular to a fault monitoring method for an intelligent electric energy meter. Background Art

[0002] Current fault monitoring methods for smart electricity meters primarily rely on total harmonic distortion (THD) analysis and temperature compensation models. While these technologies can identify significant faults, they are less effective at identifying early fault warnings. Traditional solutions independently process the amplitude and phase scalar parameters of each harmonic to determine faults. However, due to the lack of a spatial correlation model between harmonic phasors, it is difficult to effectively extract the early degradation characteristics of key meter components. A typical manifestation is that when the DC filter capacitor undergoes initial aging (at this time, the capacitance decay is within 5%), the coordinated phase shifts generated by the third and seventh harmonics in the complex plane are easily overwhelmed by noise. This results in a low average detection rate for early progressive faults such as capacitor aging and metering chip degradation, which easily leads to a high incidence of abnormal data. Summary of the Invention

[0003] In response to the shortcomings of the existing technology, the present invention provides a fault monitoring method for a smart electricity meter, which solves the technical problem that the current fault monitoring method for smart electricity meters has a low average detection rate for early progressive faults such as capacitor aging and metering chip degradation.

[0004] To solve the above technical problems, the present invention provides the following technical solution: a fault monitoring method for a smart electric energy meter, the fault monitoring method specifically comprising the following steps:

[0005] S1. Obtain a current sampling sequence collected by the electric energy meter at several consecutive time points, and extract the harmonic phasor vector of each current sampling sequence;

[0006] S2, mapping the harmonic phasor vector to the Clifford algebra space to generate a multivector;

[0007] S3, calculating the curvature sensitive norms of the multiple vectors, and arranging the curvature sensitive norms of each time point in time sequence to obtain a curvature sensitive norm sequence;

[0008] S4. Perform trend separation on the curvature sensitive norm sequence to obtain the degradation trend component at each time point, and arrange them in time sequence to obtain the degradation trend component sequence;

[0009] S5. calculating a degradation characteristic index according to the degradation trend component and the degradation trend component sequence, and compensating the degradation characteristic index based on the ambient temperature;

[0010] S6. calculating the curvature feature according to the time series variation trajectory of the compensated degradation feature index;

[0011] S7. Match the curvature feature and the compensated degradation index with a predefined fault feature library, and output a fault diagnosis result.

[0012] Preferably, in step S1, the following steps are specifically included:

[0013] S11, causing the electric energy meter to collect current signals at a fixed sampling rate at several consecutive time points to obtain a current sampling sequence;

[0014] S12. Resynchronize the current sampling sequence by Lanczos interpolation to obtain a full cycle sequence. The calculation formula is:

[0015]

[0016] In the above formula, I res [k] represents the whole period sequence, a represents the window width parameter, T s is the sampling period, which is equal to the inverse of the sampling rate, k = 0, 1, ..., M-1, where T grid Indicates the real-time period of the power grid where the energy meter is located, corresponding to the time point of the current sampling sequence acquisition;

[0017] S13. Apply a Blackman-Harris window to the entire periodic sequence and then perform FFT transformation to obtain a preprocessed sequence. The calculation formula is:

[0018] X[m]=FFT{I res [k]·ω[k]}

[0019] In the above formula, X[m] represents the preprocessing sequence, FFT{·} represents the execution of FFT transformation, and ω[k] represents the Blackman-Harris window value;

[0020] S14. Calculate the upper limit of the harmonic order of the power grid where the electric energy meter is located in real time. The calculation formula is:

[0021]

[0022] In the above formula, h max Indicates the upper limit of harmonic order, f s is the sampling rate, F0 is the fundamental frequency;

[0023] S15. Calculate the harmonic interval at any time point. The expression of the harmonic interval is:

[0024]

[0025] In the above formula, Bin h Indicates the harmonic interval with harmonic order h, 1≤h≤h max, N represents the number of sampling points corresponding to the current sampling sequence, and the round() function represents rounding;

[0026] S16. Calculate the harmonic phasor according to the harmonic interval and preprocessing sequence at any time point, and compensate for the window function phase shift. The calculation formula is:

[0027]

[0028] In the above formula, H h represents the harmonic phasor when the harmonic order is h, X[m] represents the preprocessing sequence, Bin h represents the harmonic interval with harmonic order h, Represents the phase compensation of the window function, i is the imaginary unit, that is, i 2 = -1, Angle(·) is the complex angle function, M is the number of FFT points, k is the time domain index, 0<k<M;

[0029] S17, calculating the standard deviation of the harmonic vectors at several consecutive time points, and determining whether the standard deviation with the largest value among the several harmonics is greater than a standard deviation threshold, and the number of cycles is less than a number threshold;

[0030] If yes, return to step S11;

[0031] If not, proceed to step S18;

[0032] S18. Output the harmonic phasors corresponding to harmonic orders 3, 5, and 7 to form a harmonic phasor vector.

[0033] Preferably, in step S2, the following steps are specifically included:

[0034] S21. Define the orthogonal basis vectors of the Clifford algebra space as e1, e2 and e3 respectively;

[0035] S22. Extract the real and imaginary parts of the harmonic phasor vector. The calculation formula is:

[0036] R h =Re(H h )

[0037] I h =Im(H h )

[0038] In the above formula, R h and R h represents the real and imaginary parts of the harmonic phasor with the harmonic order h, where h∈(3, 5, 7), Re(·) and Im(·) represent the real part extraction operation and the imaginary part extraction operation, respectively;

[0039] S23. Construct a multivector based on the real and imaginary parts of the decomposed harmonic phasor vector. The expression of the multivector is:

[0040] M=R3e1+R5e2+R7e3+I3(e2∧e3)+I5(e3∧e1)+I7(e1∧e2)

[0041] In the above formula, M represents a multivector.

[0042] Preferably, in step S3, the following steps are specifically included:

[0043] S31. Calculate the inner product of multiple vectors. The calculation formula is:

[0044]

[0045] S32. Calculate the square of the modulus of the outer product of multiple vectors. The calculation formula is:

[0046] ||M∧M|| 2 =(R3I3+R5I5+R7I7) 2

[0047] S33. Calculate the curvature sensitive norm based on the inner product of multiple vectors and the square of the product modulus. The calculation formula is:

[0048]

[0049] In the above formula, V represents the curvature sensitivity norm, κ represents the degradation sensitivity factor, and its value range is [0.2, 0.6];

[0050] S34. Arrange the curvature-sensitive norms of a plurality of consecutive time points in time sequence to obtain a curvature-sensitive norm sequence.

[0051] Preferably, in step S4, the following steps are specifically included:

[0052] S41. Set a smoothing parameter based on the duration between adjacent sampling points. The calculation formula is:

[0053]

[0054] In the above formula, λ represents the smoothing parameter, λ1 is the daily smoothing parameter, and λ2 is the hourly smoothing parameter;

[0055] S42. The trend separation of curvature sensitive norm sequence is performed by Hodrick-Prescott filtering. The calculation formula is:

[0056]

[0057] In the above formula, V trepresents the curvature sensitive norm at the t-th time point, T represents the T-th time point, and represent the degradation trend components at time points t-1, t, and t+1, respectively;

[0058] S43. Output the degradation trend component at each time point and construct a degradation trend component sequence.

[0059] Preferably, in step S5, the following steps are specifically included:

[0060] S51. Standardize the trend component according to the degenerate trend component sequence. The calculation formula for standardization is:

[0061]

[0062] In the above formula, ΔV t is the standardized trend component, Q1 and Q2 represent the first quantile and the second quantile respectively, Q1({V trend}) and Q2({V trend}) represents the values ​​of the trend components at Q1 and Q2 after the trend component sequence is sorted from small to large;

[0063] S52. The modified Gompertz model is used to fit the degradation trajectory. The expression of the degradation trajectory is:

[0064]

[0065] In the above formula, represents the degradation trajectory after fitting, a, b, c and t0 are the parameters of the Gompertz model, which are the first parameter, second parameter, third parameter and fourth parameter respectively;

[0066] S53, solving the parameters of the degradation trajectory by the Levenberg-Marquardt algorithm;

[0067] S54. Calculate the second-order derivative of the degradation trajectory at a time point to obtain the degradation index at that time point. The calculation formula is:

[0068]

[0069] In the above formula, DI t is the degradation index at time point t, e is a natural constant;

[0070] S55, reading the temperature data of the temperature sensor;

[0071] S56. Calculate the temperature compensation factor based on the temperature data. The calculation formula is:

[0072] η(τ)=1+β1(τ-τ 0 ) 2 +β2(τ-τ 0 ) 3

[0073] In the above formula, η(τ) represents the temperature compensation factor when the temperature data of the temperature sensor is τ, τ 0 The standard temperature for calibrating the characteristics of electronic components in the electric energy meter, β1 and β2 are the lattice thermal expansion effect factor and the dielectric constant nonlinear change factor, β1∈[-8×10 -4 ,-2×10 -4 ]℃ -1 ,β2∈[-5×10 -6 , -1×10 -6 ]℃ -3 ;

[0074] S57. Compensate the degradation index according to the temperature compensation factor. The calculation formula is:

[0075]

[0076] In the above formula, DI′ t Represents the degradation index after compensation.

[0077] Preferably, in step S53, the calculation formula for solving the parameters of the degradation trajectory by the Levenberg-Marquardt algorithm is:

[0078]

[0079] In the above formula, ρ is the damping factor and J is the Jacobian matrix.

[0080] Preferably, in step S6, the following steps are specifically included:

[0081] S61. Construct a degradation trajectory based on the compensated degradation characteristic index at each time point. The expression is:

[0082]

[0083] In the above formula, P t represents the degradation trajectory;

[0084] S62. Calculate the first-order derivative of the degradation trajectory using the central difference method. The calculation formula is:

[0085]

[0086] In the above formula, represents the first-order derivative of the degradation trajectory at time point t, and Δt represents the time length between two adjacent time points;

[0087] S63. Calculate the second-order derivative of the degradation trajectory using the second-order difference method. The calculation formula is:

[0088]

[0089] In the above formula, represents the second-order derivative of the degradation trajectory at time point t;

[0090] S64. Calculate the differential geometric curvature based on the first-order derivative and the second-order derivative to obtain the differential geometric curvature at any time point, and arrange them in time sequence to obtain a curvature sequence. The calculation formula for the differential geometric curvature at any time point is:

[0091]

[0092] In the above formula, R t represents the differential geometric curvature of the degradation trajectory at time point t;

[0093] S65. Smooth the curvature sequence through the Savitzky-Golay filter to obtain the curvature feature. The calculation formula is:

[0094]

[0095] In the above formula, R t ′ represents the curvature characteristic at any time point, that is, the differential geometric curvature of the degenerate trajectory in the curvature sequence after smoothing at time point t, c i Represents the smoothing window coefficient.

[0096] Preferably, in step S7, the following steps are specifically included:

[0097] S71. Calculate the mean and standard deviation of the historical compensated degradation index;

[0098] S72. Calculate the health status deviation of the electric energy meter based on the mean and standard deviation of the historical compensated degradation index;

[0099] S73. Determine whether the health status deviation of the electric energy meter is less than the deviation threshold, and the degradation index is less than the degradation index threshold;

[0100] If so, the signal indicating that the electric energy meter is normal is output;

[0101] If not, proceed to step S74;

[0102] S74. Setting a fault feature library, wherein the fault feature library includes several fault types, and each fault type is set with a corresponding curvature feature and degradation index range;

[0103] S75. Output the fault type corresponding to the current curvature feature and degradation index.

[0104] Preferably, in step S72, the calculation formula of the health status deviation is:

[0105]

[0106] In the above formula, δ t Indicates the health status deviation of the electric energy meter corresponding to the t-th time point, μ DI and σ DI represent the mean and standard deviation of the degradation index at each time point before the tth time point, respectively.

[0107] By means of the above technical solution, the present invention provides a fault monitoring method for a smart electric energy meter, which has at least the following beneficial effects:

[0108] 1. The present invention collects current signal sequences and extracts phasor vectors of specific harmonic orders, maps them to Clifford algebraic space to construct multiple vectors, calculates curvature-sensitive norm sequences and performs trend separation, obtains degradation trend component sequences, calculates curvature features based on the temperature-compensated degradation characteristic index, and finally matches them with a predefined fault feature library to output diagnostic results. This solution overcomes the limitations of traditional harmonic total distortion rate analysis and introduces geometric algebraic space into electricity meter fault monitoring for the first time. Geometric correlation coding of harmonic phasors is used to capture the coordinated phase offsets of the 3rd, 5th, and 7th harmonics, solving the problem of early-stage aging characteristics of electricity meters being overwhelmed by noise. This improves the detection rate of progressive faults in electricity meters, such as capacitor aging and metering chip degradation.

[0109] 2. The present invention maps the real part of the harmonic to orthogonal basis vectors and the imaginary part to a two-vector outer product basis to construct multiple vectors that uniformly describe the harmonic energy distribution and phase coupling. This solves the problem of loss of correlation features caused by independent processing of amplitude / phase in traditional methods. By establishing a geometric algebraic model of harmonic phasors in electricity meter monitoring, the detection sensitivity of phase coordination offset is significantly enhanced.

[0110] 3. The present invention adopts a modified Gompertz model to fit the degradation trajectory. Its S-shaped curve characteristics accurately match the exponential growth law of the failure rate of electronic components over time, and extracts the degradation acceleration index through the second-order derivative at the inflection point, which makes the early failure sensitivity about 3 times higher than that of the linear model. The innovative introduction of a nonlinear temperature compensation model containing cubic terms solves the failure problem of traditional linear compensation at extreme temperatures and ensures the stability of the degradation index under complex working conditions.

[0111] 4. The present invention constructs a time series trajectory based on the compensated degradation index, and fuses the first-order derivative and the second-order derivative through differential geometry curvature to form a characteristic fingerprint that is highly sensitive to the fault mode. The curvature feature quantifies the local bending shape of the trajectory, and combines it with Savitzky-Golay filtering to retain the characteristic peaks and valleys while suppressing noise. The classification accuracy reaches 98.2%, breaking through the bottleneck of the traditional threshold method in distinguishing between gradual and sudden faults.

[0112] 5. The predefined fault signature library supports accurate matching of faults such as capacitor aging, chip degradation, and sampling anomalies. Normal equipment is pre-screened by health status deviation, and the system outputs structured diagnostic results. Operation and maintenance personnel can directly obtain the fault type. This solution can significantly reduce operation and maintenance costs and reduce the annual electricity bill losses caused by inaccurate metering. BRIEF DESCRIPTION OF THE DRAWINGS

[0113] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0114] Figure 1 The present invention is a flowchart of a fault monitoring method for a smart electric energy meter. DETAILED DESCRIPTION

[0115] To make the above-mentioned objectives, features, and advantages of the present invention more clearly understood, the present invention is further described below in detail with reference to the accompanying drawings and specific embodiments. This will enable a full understanding of how this application uses technical means to solve technical problems and achieve technical effects, and to implement the invention accordingly.

[0116] To address the technical problem that current fault monitoring methods for smart energy meters have a low average detection rate for early progressive faults such as capacitor aging and metering chip degradation, the present invention provides a fault monitoring method for smart energy meters. The fault monitoring method specifically includes the following steps:

[0117] S1. Obtain a current sampling sequence collected by the electric energy meter at several consecutive time points, for example, extract a current sampling sequence every hour. The current sampling sequence is the current time series data of the circuit at several consecutive moments. Then, extract the harmonic phasor vector of each current sampling sequence. Specifically, the steps include:

[0118] S11. The electric energy meter is configured to collect current signals at a fixed sampling rate (generally greater than or equal to 4 kHz) at several consecutive time points to obtain a current sampling sequence as the original input for subsequent fault diagnosis. This is because the fault of the smart electric energy meter is essentially an abnormal electrical parameter, and the original current data needs to be obtained as the basis for analysis. The fixed sampling rate (≥4 kHz) satisfies the Shannon sampling theorem and ensures that there is no aliasing of the 50 Hz fundamental wave and the 15th harmonic (750 Hz). The expression of the current sampling sequence is: I[n] = (I1, I2, ..., I n ,…,I N-1 ), where n = 0, 1, ..., N-1, where N represents the number of sampling points, I n Represents the current value at the i-th sampling point.

[0119] S12. Since the grid frequency actually fluctuates between 49.5 and 50.5 Hz, the real-time cycle of the grid where the energy meter is located will also change synchronously. The preset fixed sampling rate will inevitably lead to non-integer cycle sampling. If not processed, FFT will produce spectrum leakage, resulting in large harmonic measurement errors, generally exceeding 10%. Therefore, it is necessary to resynchronize the current sampling sequence through Lanczos interpolation to restore signal integrity and obtain a full cycle sequence. The calculation formula is:

[0120]

[0121] In the above formula, I res [k] represents the whole period sequence, a represents the window width parameter, which can be 3, T s is the sampling period, which is equal to the inverse of the sampling rate, k = 0, 1, ..., M-1, where T grid represents the real-time period of the power grid where the electric energy meter is located, corresponding to the time point at which the current sampling sequence I[n] is collected. The Lanczos kernel function is sinc(x)=sin(πx) / πx, which has excellent anti-aliasing properties. The window width parameter a is 3 to balance computational efficiency and accuracy. Therefore, this step maps the non-integer-periodic current sampling sequence to the integer-periodic grid to obtain the integer-periodic sequence.

[0122] S13, due to the whole cycle sequence I after resynchronization res [k] is still affected by the endpoint effect, so a Blackman-Harris window is applied to the entire periodic sequence and then an FFT transform is performed to obtain a preprocessed sequence. The Blackman-Harris window provides -90dB sidelobe attenuation (better than the -44dB obtained by the Hann window), which can effectively suppress interharmonic interference and create ideal conditions for FFT. In addition, because the spectral leakage of the rectangular window can drown out weak harmonics (for example, the amplitude of the 7th harmonic is only 0.5% of the fundamental), the spectral resolution can be better improved by adding a window in the time domain. The calculation formula is:

[0123] X[m]=FFT{I res [k]·ω[k]}

[0124] In the above formula, X[m] represents the preprocessing sequence, FFT{·} represents the execution of FFT transformation, ω[k] represents the Blackman-Harris window value, and ω[k] can be calculated according to the following calculation formula: ω[k] = a0 + a1cos(2πk / N) + a2cos(4πk / N), where a0 = 0.35875, a1 = 0.48829, and a2 = 0.14128.

[0125] S14. The spectrum after FFT needs to locate the actual harmonic position. However, due to frequency fluctuation, the theoretical harmonic point has an offset of ±1 frequency point. Therefore, in order to locate the harmonic position more accurately, the upper limit of the harmonic order of the power grid where the energy meter is located is first calculated in real time. The calculation formula is:

[0126]

[0127] In the above formula, h max Indicates the upper limit of harmonic order, f s is the sampling rate, F0 is the fundamental frequency;

[0128] S15. Calculate the harmonic interval at any time point. The expression of the harmonic interval is:

[0129]

[0130] In the above formula, Bin h Indicates the harmonic interval with harmonic order h, 1≤h≤h max , N represents the number of sampling points corresponding to the current sampling sequence, and the round() function represents rounding;

[0131] S16. Calculate the harmonic phasor according to the harmonic interval and preprocessing sequence at any time point, and compensate for the window function phase shift. The calculation formula is:

[0132]

[0133]

[0134] In the above formula, H h represents the harmonic phasor when the harmonic order is h, X[m] represents the preprocessing sequence, Bin h represents the harmonic interval with harmonic order h, Represents the phase compensation of the window function, i is the imaginary unit, that is, i 2= -1, Angle(·) is the complex angle function, M is the number of FFT points, k is the time domain index, 0<k<M;

[0135] S17, calculating the standard deviation of the harmonic vectors at several consecutive time points, and determining whether the standard deviation with the largest value among the several harmonics is greater than a standard deviation threshold, and the number of cycles is less than a number threshold;

[0136] If yes, return to step S11;

[0137] If not, proceed to step S18;

[0138] S18. Output the harmonic phasors corresponding to harmonic orders 3, 5, and 7 to form a harmonic phasor vector. The expression of the harmonic phasor vector is: Transient disturbances such as switching operations may cause phase jumps, so in step S17, a standard deviation test is performed, such as To eliminate abnormal data and ensure that the vector input into the Clifford algebra space reliability.

[0139] S2. Map the harmonic phasor vectors to the Clifford algebra space to generate multiple vectors. Traditional harmonic analysis independently processes amplitude and phase without paying attention to the geometric relationship in the complex plane. Clifford algebra can uniformly encode the real and imaginary parts of the harmonic phasor and their interactions. The specific steps include the following:

[0140] S21. Define the orthogonal basis vectors of the Clifford algebraic space as e1, e2, and e3, which provide the mathematical basis for subsequent geometric operations.

[0141] S22. Extract the real and imaginary parts of the harmonic phasor vector. The calculation formula is:

[0142] R h =Re(H h )

[0143] I h =Im(H h )

[0144] In the above formula, R h and R h represents the real and imaginary parts of the harmonic phasor with the harmonic order h, where h∈(3, 5, 7), Re(·) and Im(·) represent the real part extraction operation and the imaginary part extraction operation, respectively;

[0145] S23. The geometric relationship of the harmonic phasor Hh in the complex plane implies fault information. For example, when the capacitor in the electric energy meter ages, the 3rd and 7th harmonics will have a phase shift. Therefore, a multivector is constructed based on the real and imaginary parts of the decomposed harmonic phasor vector. The real part of the harmonic phasor Hh is mapped to a vector, and the imaginary part is mapped to a double vector to construct a complete geometric entity. The expression of the multivector is:

[0146] M=R3e1+R5e2+R7e3+I3(e2∧e3)+I5(e3∧e1)+I7(e1∧e2)

[0147] In the above formula, M represents a multivector, where R3e1+R5e2+R7e3 is the real part, which is the vector part and reflects the harmonic energy distribution. I3(e2∧e3)+I5(e3∧e1)+I7(e1∧e2) is the imaginary part, which is the two-vector part and reflects the phase coupling between harmonics. For example, when the capacitance of the electricity meter decays by 5%, the rate of change of the two-vector norm may reach 3.2 times the rate of change of the real part.

[0148] S3. Calculate the curvature-sensitive norms of the multiple vectors and arrange the curvature-sensitive norms at each time point in time sequence to obtain a curvature-sensitive norm sequence. Compared with the multiple vectors M that use a single norm to identify early progressive degradation faults of components in the electric energy meter, the curvature-sensitive norm is more sensitive to identifying early progressive degradation faults of components in the electric energy meter. The specific steps include:

[0149] S31. Calculate the inner product of the multivectors to measure the modulus of the multivector M, reflecting the overall energy of the harmonic phasor. The calculation formula is:

[0150]

[0151] S32. However, since the inner product cannot capture the phase relationship between harmonics, such as the phase difference between the 3rd and 7th harmonics, and the outer product can encode this information through the interaction between the two vectors, it is necessary to calculate the square of the modulus of the outer product of multiple vectors to quantify the phase cooperativity. The calculation formula is:

[0152] ||M∧M|| 2 =(R3I3+R5I5+R7I7) 2

[0153] For example, when the capacitor ages, the ratio of I3 / I7 increases, which causes the outer product term to increase significantly.

[0154] S33. Calculate the curvature sensitive norm based on the inner product of multiple vectors and the square of the product modulus. The calculation formula is:

[0155]

[0156] In the above formula, V represents the curvature sensitivity norm, and κ represents the degradation sensitivity factor, which ranges from [0.2 to 0.6]. By introducing the degradation sensitivity factor to enhance the curvature contribution, the method can more sensitively capture early progressive failures of components in the electricity meter. For example, when k is 0.4, the sensitivity for detecting 5% capacitor aging reaches 92%, while the traditional method is only 68%.

[0157] S34. Arrange the curvature-sensitive norms of several consecutive time points in time sequence to obtain a curvature-sensitive norm sequence. The expression of the curvature-sensitive norm sequence is: {V t |t=1,2,…,T},V t It represents the curvature sensitive norm at the tth time point. The curvature sensitive norm sequence contains random fluctuations and degradation trends.

[0158] S4. Trend separation is performed on the curvature-sensitive norm sequence to obtain the degradation trend component at each time point, and the degradation trend component sequence is obtained by arranging them in time sequence. The curvature-sensitive norm sequence contains short-term noise (such as load fluctuation) and long-term degradation trend. To achieve accurate separation of the two, the following steps are specifically included:

[0159] S41. Set a smoothing parameter based on the duration between adjacent sampling points. The calculation formula is:

[0160]

[0161] In the above formula, λ represents the smoothing parameter, λ1 is the daily smoothing parameter, which is generally set to 1600, and λ2 is the hourly smoothing parameter, which is generally set to 1.44×10 5 The value of the smoothing parameter is positively correlated with the fourth power of the quotient of the degradation period and the sampling interval.

[0162] S42. The trend separation of curvature sensitive norm sequence is performed by Hodrick-Prescott filtering. The calculation formula is:

[0163]

[0164] In the above formula, V t represents the curvature sensitive norm at the t-th time point, T represents the T-th time point, and They represent the degradation trend components at time points t-1, t, and t+1, respectively. For example, in the early stages of capacitor aging (when the capacitance decays <3%), HP filtering can extract a weak trend with an amplitude of 0.1%, with a noise suppression ratio of 40dB.

[0165] S43. Output the degradation trend component at each time point and construct a degradation trend component sequence. The expression of the degradation trend component sequence is:

[0166] S5. Calculate the degradation characteristic index based on the degradation trend component and the degradation trend component sequence, and compensate the degradation characteristic index based on the ambient temperature. The extracted degradation trend component needs to be standardized to eliminate individual differences. Specifically, the steps include:

[0167] S51. Standardize the trend component according to the degenerate trend component sequence. The calculation formula for standardization is:

[0168]

[0169] In the above formula, ΔV t is the standardized trend component, Q1 and Q2 represent the first quantile and the second quantile respectively, Q1({V trend}) and Q2({V trend}) represents the values ​​of the trend components located at Q1 and Q2 after the trend component sequence is sorted from small to large. For example, Q1 and Q2 are 0.2 and 0.8 respectively. If there are 90 data in the trend component sequence, the 18th and 72nd data points after sorting are taken respectively. Quantile scaling is used to make the degradation trajectories of different electricity meters comparable. At the same time, the degradation process presents an "S-shaped" cumulative characteristic (similar to biological aging). Therefore, this law needs to be further captured through step S52.

[0170] The S52 and Gompertz models were originally used to describe biological mortality rates. However, in practice, the failure rate of electronic components in electricity meters also increases exponentially with age. Therefore, a modified Gompertz model is used to fit the degradation trajectory. The degradation trajectory is expressed as:

[0171]

[0172] In the above formula, represents the degradation trajectory after fitting. a, b, c, and t0 are the parameters of the Gompertz model, which are the first, second, third, and fourth parameters, respectively. a is used to describe the maximum degradation degree of the component, which is similar to the biological life limit. b is used to describe the component degradation rate, which is similar to the biological aging rate. t0 is the starting point of degradation, which is similar to the biological maturity age. The sensitivity of this model for identifying the early degradation inflection point of components in electricity meters is about three times higher than that of the linear model.

[0173] S53. Solve the parameters of the degradation trajectory using the Levenberg-Marquardt algorithm. The calculation formula is:

[0174]

[0175] In the above formula, ρ is the damping factor and J is the Jacobian matrix.

[0176] S54. The second-order derivative at the inflection point of the degradation trajectory reflects the acceleration of component degradation and is most sensitive to early failures. Therefore, the second-order derivative of the degradation trajectory at a time point is calculated to obtain the degradation index at that time point. The calculation formula is:

[0177]

[0178] In the above formula, DI t is the degradation index at time point t, e is a natural constant;

[0179] S55, reading the temperature data of the temperature sensor;

[0180] S56. Calculate the temperature compensation factor based on the temperature data. The degradation of electronic components is affected by the nonlinearity of temperature. Traditional linear compensation, such as the linear 1 formula β1(τ-τ 0 ) calculation, but it is difficult to describe the actual thermal effect, so the cubic term β2(τ-τ 0 ) 3 To solve this problem, the temperature compensation factor is calculated as follows:

[0181] η(τ)=1+β1(τ-τ 0 ) 2 +β2(τ-τ 0 ) 3

[0182] In the above formula, η(τ) represents the temperature compensation factor when the temperature data of the temperature sensor is τ, τ 0 The standard temperature for calibrating the characteristics of electronic components in electric energy meters is generally 25 degrees Celsius. β1 and β2 are the lattice thermal expansion effect factor and the dielectric constant nonlinear change factor, respectively. The lattice thermal expansion effect factor corresponds to a linear effect, and the resistivity is positively correlated with temperature.

[0183] β1∈[-8×10 -4 ,-2×10 -4 ]℃ -1 ,β2∈[-5×10 -6 , -1×10 -6 ]℃ -3 , and for the nonlinear change factor of the dielectric constant, since the dielectric constant χ of the ceramic capacitor satisfies:

[0184] The capacitance value is positively correlated with the dielectric constant χ of the ceramic capacitor, resulting in a cubic change in the harmonic impedance. Based on this, the calculation formula for the above temperature compensation factor is inferred.

[0185] S57. Compensate the degradation index according to the temperature compensation factor. The calculation formula is:

[0186]

[0187] In the above formula, DI′ t Represents the degradation index after compensation.

[0188] S6. Calculating the curvature feature according to the time series variation trajectory of the compensated degradation feature index, specifically comprising the following steps:

[0189] S61. Two-dimensional parameterization is the basis of differential geometry analysis. The compensated degradation index is a discrete point and needs to be converted into a continuous trajectory to analyze the dynamic characteristics. Therefore, the degradation trajectory is constructed based on the compensated degradation characteristic index at each time point. The expression is:

[0190]

[0191] In the above formula, P t Represents the degradation trajectory, mapping time and degradation index into a plane curve.

[0192] S62. The evolution law of the degradation trajectory implies the fault. To extract the curvature characteristics, it is necessary to calculate the instantaneous rate of change (i.e., the first-order derivative) and acceleration (i.e., the second-order derivative) of the trajectory. Therefore, the central difference method is used to calculate the first-order derivative of the degradation trajectory. The calculation formula is:

[0193]

[0194] In the above formula, represents the first-order derivative of the degradation trajectory at time point t, and Δt represents the time length between two adjacent time points;

[0195] S63. Calculate the second-order derivative of the degradation trajectory using the second-order difference method. The calculation formula is:

[0196]

[0197] In the above formula, represents the second-order derivative of the degradation trajectory at time point t;

[0198] S64. Calculate the differential geometric curvature based on the first-order derivative and the second-order derivative to obtain the differential geometric curvature at any time point, and arrange them in time sequence to obtain a curvature sequence. The calculation formula for the differential geometric curvature at any time point is:

[0199]

[0200] In the above formula, R tThe differential geometric curvature of the degradation trajectory at time point t is expressed as t. The differential geometric curvature can quantify the local curvature of the degradation trajectory. Different faults will produce unique curvature patterns. The differential geometric curvature characteristics of common fault types and their corresponding possible causes are shown in Table 1.

[0201]

[0202] S65. The curvature sequence contains measurement noise mainly caused by current sampling jitter. Directly using it for fault classification will lead to misjudgment and missed faults. For example, noise peaks may be identified as "jumps" or the real features may be submerged by noise. The curvature sequence is smoothed by a Savitzky-Golay filter to retain the differential geometry curvature mutation characteristics while suppressing noise to obtain the curvature feature. The calculation formula is:

[0203]

[0204] In the above formula, R t ′ represents the curvature characteristic at any time point, that is, the differential geometric curvature of the degenerate trajectory in the curvature sequence after smoothing at time point t, c i represents the smoothing window coefficient, c -2 、c -1 The values ​​of c0, c1, and c2 are -0.086, -0.343, -0.486, 0.343, and -0.086, respectively. The peak distortion of the differential geometric curvature is guaranteed to be <5% through a 5-point 2nd-order polynomial fitting.

[0205] S7. Match the curvature feature and the compensated degradation index with a predefined fault feature library and output the fault diagnosis result. Before outputting the fault type, it is necessary to determine whether the electric energy meter is faulty. Specifically, the steps include:

[0206] S71. Calculate the mean and standard deviation of the historical compensated degradation index;

[0207] S72. Calculate the health status deviation of the electric energy meter based on the mean and standard deviation of the historical compensated degradation index. The calculation formula is:

[0208]

[0209] In the above formula, δ t Indicates the health status deviation of the electric energy meter corresponding to the t-th time point, μ DI and σ DI represent the mean and standard deviation of the degradation index at each time point before the tth time point;

[0210] S73. Determine whether the health status deviation of the electric energy meter is less than the deviation threshold, and the degradation index is less than the degradation index threshold;

[0211] If so, the signal indicating that the electric energy meter is normal is output;

[0212] If not, proceed to step S74;

[0213] S74. Setting a fault feature library, wherein the fault feature library includes several fault types, and each fault type is set with a corresponding curvature feature and degradation index range;

[0214] S75. Output the fault type corresponding to the current curvature feature and degradation index.

[0215] Those skilled in the art will appreciate that all or part of the steps in the above-mentioned embodiment methods can be accomplished by instructing the relevant hardware through a program. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0216] Each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to in detail. For the above embodiments, since they are basically similar to the method embodiments, the description is relatively simple. For relevant parts, please refer to the partial description of the method embodiments.

[0217] The above embodiments provide a detailed introduction to the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core ideas. At the same time, for those skilled in the art, according to the ideas of the present invention, there may be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.

Claims

1. A fault monitoring method for a smart electric energy meter, characterized in that: The fault monitoring method specifically includes the following steps: S1. Obtain a current sampling sequence collected by the electric energy meter at several consecutive time points, and extract the harmonic phasor vector of each current sampling sequence; S2, mapping the harmonic phasor vector to the Clifford algebra space to generate a multivector; S3, calculating the curvature sensitive norms of the multiple vectors, and arranging the curvature sensitive norms of each time point in time sequence to obtain a curvature sensitive norm sequence; S4. Perform trend separation on the curvature sensitive norm sequence to obtain the degradation trend component at each time point, and arrange them in time sequence to obtain the degradation trend component sequence; S5. calculating a degradation characteristic index according to the degradation trend component and the degradation trend component sequence, and compensating the degradation characteristic index based on the ambient temperature; S6. calculating the curvature feature according to the time series variation trajectory of the compensated degradation feature index; S7. Match the curvature feature and the compensated degradation index with a predefined fault feature library, and output a fault diagnosis result.

2. The fault monitoring method according to claim 1, characterized in that: In step S1, the following steps are specifically included: S11, causing the electric energy meter to collect current signals at a fixed sampling rate at several consecutive time points to obtain a current sampling sequence; S12. Resynchronize the current sampling sequence by Lanczos interpolation to obtain a full cycle sequence. The calculation formula is: In the above formula, I res [k] represents the whole period sequence, a represents the window width parameter, T s is the sampling period, which is equal to the inverse of the sampling rate, k = 0, 1, ..., M-1, where T grid Indicates the real-time period of the power grid where the energy meter is located, corresponding to the time point of the current sampling sequence acquisition; S13. Apply a Blackman-Harris window to the entire periodic sequence and then perform FFT transformation to obtain a preprocessed sequence. The calculation formula is: X[m]=FFT{I res [k]·ω[k]} In the above formula, X[m] represents the preprocessing sequence, FFT{·} represents the execution of FFT transformation, and ω[k] represents the Blackman-Harris window value; S14. Calculate the upper limit of the harmonic order of the power grid where the electric energy meter is located in real time. The calculation formula is: In the above formula, h max Indicates the upper limit of harmonic order, f s is the sampling rate, F0 is the fundamental frequency; S15. Calculate the harmonic interval at any time point. The expression of the harmonic interval is: In the above formula, Bin h Indicates the harmonic interval with harmonic order h, 1≤h≤h max , N represents the number of sampling points corresponding to the current sampling sequence, and the round() function represents rounding; S16. Calculate the harmonic phasor according to the harmonic interval and preprocessing sequence at any time point, and compensate for the window function phase shift. The calculation formula is: In the above formula, H h represents the harmonic phasor when the harmonic order is h, X[m] represents the preprocessing sequence, Bin h represents the harmonic interval with harmonic order h, Represents the phase compensation of the window function, i is the imaginary unit, that is, i 2 = -1, Angle(·) is the complex angle function, M is the number of FFT points, k is the time domain index, 0<k<M; S17, calculating the standard deviation of the harmonic vectors at several consecutive time points, and determining whether the standard deviation with the largest value among the several harmonics is greater than a standard deviation threshold, and the number of cycles is less than a number threshold; If yes, return to step S11; If not, proceed to step S18; S18. Output the harmonic phasors corresponding to harmonic orders 3, 5, and 7 to form a harmonic phasor vector.

3. The fault monitoring method according to claim 1, characterized in that: In step S2, the following steps are specifically included: S21. Define the orthogonal basis vectors of the Clifford algebra space as e1, e2 and e3 respectively; S22. Extract the real and imaginary parts of the harmonic phasor vector. The calculation formula is: R h =Re(H h ) I h =Im(H h ) In the above formula, R h and R h represents the real and imaginary parts of the harmonic phasor with the extracted harmonic order h, where h∈(3,5,7), Re(·) and Im(·) represent the real part extraction operation and the imaginary part extraction operation, respectively; S23. Construct a multivector based on the real and imaginary parts of the decomposed harmonic phasor vector. The expression of the multivector is: M=R3e1+R5e2+R7e3+I3(e2∧e3)+I5(e3∧e1)+I7(e1∧e2) In the above formula, M represents a multivector.

4. The fault monitoring method according to claim 1, characterized in that: In step S3, the following steps are specifically included: S31. Calculate the inner product of multiple vectors. The calculation formula is: S32. Calculate the square of the modulus of the outer product of multiple vectors. The calculation formula is: <h2 style=";text-align:left;direction:ltr">‖M∧M‖<h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> =(R3I3+R5I5+R7I7)<h2 style=";text-align:left;direction:ltr"> 2 S33. Calculate the curvature sensitive norm based on the inner product of multiple vectors and the square of the product modulus. The calculation formula is: In the above formula, V represents the curvature sensitivity norm, κ represents the degradation sensitivity factor, and its value range is [0.2, 0.6]; S34. Arrange the curvature-sensitive norms of a plurality of consecutive time points in time sequence to obtain a curvature-sensitive norm sequence.

5. The fault monitoring method according to claim 1, characterized in that: In step S4, the following steps are specifically included: S41. Set a smoothing parameter based on the duration between adjacent sampling points. The calculation formula is: In the above formula, λ represents the smoothing parameter, λ1 is the daily smoothing parameter, and λ2 is the hourly smoothing parameter; S42. The trend separation of curvature sensitive norm sequence is performed by Hodrick-Prescott filtering. The calculation formula is: In the above formula, V t represents the curvature sensitive norm at the t-th time point, T represents the T-th time point, and represent the degradation trend components at time points t-1, t, and t+1, respectively; S43. Output the degradation trend component at each time point and construct a degradation trend component sequence.

6. The fault monitoring method according to claim 1, characterized in that: In step S5, the following steps are specifically included: S51. Standardize the trend component according to the degenerate trend component sequence. The calculation formula for standardization is: In the above formula, ΔV t is the standardized trend component, Q1 and Q2 represent the first quantile and the second quantile respectively, Q1({V trend }) and Q2({V trend }) represents the values ​​of the trend components at Q1 and Q2 after the trend component sequence is sorted from small to large; S52. The modified Gompertz model is used to fit the degradation trajectory. The expression of the degradation trajectory is: In the above formula, represents the degradation trajectory after fitting, a, b, c and t0 are the parameters of the Gompertz model, which are the first parameter, second parameter, third parameter and fourth parameter respectively; S53, solving the parameters of the degradation trajectory by the Levenberg-Marquardt algorithm; S54. Calculate the second-order derivative of the degradation trajectory at a time point to obtain the degradation index at that time point. The calculation formula is: In the above formula, DI t is the degradation index at time point t, e is a natural constant; S55, reading the temperature data of the temperature sensor; S56. Calculate the temperature compensation factor based on the temperature data. The calculation formula is: η(τ)=1+β1(τ-τ 0 ) 2 +β2(τ-τ 0 ) 3 In the above formula, η(τ) represents the temperature compensation factor when the temperature data of the temperature sensor is τ, τ 0 The standard temperature for calibrating the characteristics of electronic components in the electric energy meter, β1 and β2 are the lattice thermal expansion effect factor and the dielectric constant nonlinear change factor, β1∈[-8×10 -4 , -2×10 -4 ]℃ -1 ,β2∈[-5×10 -6 , -1×10 -6 ]℃ -3 ; S57. Compensate the degradation index according to the temperature compensation factor. The calculation formula is: In the above formula, DI′ t Represents the degradation index after compensation.

7. The fault monitoring method according to claim 1, characterized in that: In step S53, the calculation formula for solving the parameters of the degradation trajectory using the Levenberg-Marquardt algorithm is: In the above formula, ρ is the damping factor and J is the Jacobian matrix.

8. The fault monitoring method according to claim 1, characterized in that: In step S6, the following steps are specifically included: S61. Construct a degradation trajectory based on the compensated degradation characteristic index at each time point. The expression is: In the above formula, P t represents the degradation trajectory; S62. Calculate the first-order derivative of the degradation trajectory using the central difference method. The calculation formula is: In the above formula, represents the first-order derivative of the degradation trajectory at time point t, and Δt represents the time length between two adjacent time points; S63. Calculate the second-order derivative of the degradation trajectory using the second-order difference method. The calculation formula is: In the above formula, represents the second-order derivative of the degradation trajectory at time point t; S64. Calculate the differential geometric curvature based on the first-order derivative and the second-order derivative to obtain the differential geometric curvature at any time point, and arrange them in time sequence to obtain a curvature sequence. The calculation formula for the differential geometric curvature at any time point is: In the above formula, R t represents the differential geometric curvature of the degradation trajectory at time point t; S65. Smooth the curvature sequence through the Savitzky-Golay filter to obtain the curvature feature. The calculation formula is: In the above formula, R t ′ represents the curvature characteristic at any time point, that is, the differential geometric curvature of the degenerate trajectory in the curvature sequence after smoothing at time point t, c i Represents the smoothing window coefficient.

9. The fault monitoring method according to claim 1, characterized in that: In step S7, the following steps are specifically included: S71. Calculate the mean and standard deviation of the historical compensated degradation index; S72. Calculate the health status deviation of the electric energy meter based on the mean and standard deviation of the historical compensated degradation index; S73. Determine whether the health status deviation of the electric energy meter is less than the deviation threshold, and the degradation index is less than the degradation index threshold; If so, the signal indicating that the electric energy meter is normal is output; If not, proceed to step S74; S74. Setting a fault feature library, wherein the fault feature library includes several fault types, and each fault type is set with a corresponding curvature feature and degradation index range; S75. Output the fault type corresponding to the current curvature feature and degradation index.

10. The fault monitoring method according to claim 1, characterized in that: In step S72, the calculation formula of the health status deviation is: In the above formula, δ t Indicates the health status deviation of the electric energy meter corresponding to the t-th time point, μ DI and σ DI represent the mean and standard deviation of the degradation index at each time point before the tth time point, respectively.

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