A method for online monitoring and automatic diagnosis of power quality at grid connection points

Abstract

The present invention relates to the field of power quality monitoring and diagnosis, and specifically, to a method for online monitoring and automatic diagnosis of power quality at a grid connection point. It includes the following steps: calculating the effective value of voltage and current, power parameters, extracting the amplitude and phase of each harmonic, and calculating the total harmonic distortion; using an electromagnetic field coupling effect model to solve the coupled electromagnetic field equation; based on a time-space evolution model combined with an electromagnetic field reflection coefficient, using an anomaly detection function to locate the time and location of the problem; constructing a multi-scale electromagnetic reverse analysis model, combining a dynamic environmental compensation model, introducing an electromagnetic field reflection coefficient and frequency offset, and constructing a fault diagnosis function. The method for online monitoring and automatic diagnosis of power quality at a grid connection point achieves precise control of the operating state of the power grid and efficient fault management by integrating a time-space evolution model, a multi-scale electromagnetic reverse analysis model, an anomaly detection function, and a dynamic environmental compensation model.

Description

Technical Field

[0001] The present invention relates to the field of power quality monitoring and diagnosis, and in particular to an online monitoring and automatic diagnosis method for power quality of a grid-connected point. Background Art

[0002] A method for online monitoring and automatic diagnosis of power quality at grid connection points aims to improve the detection accuracy of power quality problems and optimize the efficiency of grid fault location. Through the comprehensive application of time-space evolution model, multi-parameter fusion anomaly detection function, multi-scale electromagnetic inverse analysis model and dynamic environmental compensation model, the operating status of grid equipment and power quality parameters are controlled to achieve accurate diagnosis of power quality problems and timely warning of fault occurrence.

[0003] Existing online monitoring and automatic diagnosis methods for power quality usually find it difficult to fully capture the multi-frequency components and spatial distribution characteristics in a complex electromagnetic environment. In addition, due to environmental electromagnetic interference and load fluctuations, it can lead to inaccurate harmonic distortion detection, inaccurate fault location, and failure of voltage fluctuation analysis. In summary, a method for online monitoring and automatic diagnosis of power quality at a grid-connected point is provided. Summary of the invention

[0004] The purpose of the present invention is to provide a method for online monitoring and automatic diagnosis of power quality at a grid connection point, so as to solve the problems raised in the above background technology, namely, inaccurate harmonic distortion detection, inaccurate fault location and ineffective voltage fluctuation analysis due to environmental electromagnetic interference and load fluctuation.

[0005] To achieve the above object, the present invention provides a method for online monitoring and automatic diagnosis of power quality at a grid connection point, comprising the following steps:

[0006] S1. Real-time collection of voltage, current, frequency and spatial electromagnetic field distribution of power quality at the grid connection point to calculate voltage RMS, current RMS, active power, reactive power, apparent power and power factor, extract the amplitude and phase of each harmonic in the voltage and current waveforms, and calculate the voltage and current total harmonic distortion;

[0007] S2. Using the electromagnetic field coupling effect model, by solving the coupled electromagnetic field equations, analyze the dynamic changes of the electromagnetic field around the voltage, current and harmonics in the grid connection point, calculate the harmonic voltage at different frequencies using the amplitude and phase of each harmonic in the voltage and current waveforms, and update the power quality parameters;

[0008] S3. Based on harmonic voltage, the time-space evolution model is used in combination with the electromagnetic field reflection coefficient to analyze the dynamic changes of power quality, and finally the anomaly detection function is used to locate the time and location of power quality problems.

[0009] S4. Construct a multi-scale electromagnetic reverse analytical model, calculate the wavelet transform coefficients, use the multi-scale electromagnetic reverse analytical model in combination with the dynamic environment compensation model, and introduce the electromagnetic field reflection coefficient and frequency offset to construct a fault diagnosis function, and determine the time and location of the power quality fault based on the results of the fault diagnosis function.

[0010] As a further improvement of the technical solution, in S1, the voltage V(t), current I(t), frequency and spatial electromagnetic field distribution of the power quality at the grid connection point are collected to calculate the effective value of voltage, effective value of current, active power, reactive power, apparent power and power factor. The specific method is as follows:

[0011] Voltage RMS V rms :

[0012]

[0013] Current effective value I rms :

[0014]

[0015] Wherein, T is the period of the AC signal, specifically the reciprocal of the frequency f; v(t) is the instantaneous voltage, which represents the voltage value at time t; i(t) is the instantaneous current, which represents the current value at time t;

[0016] Active power P:

[0017] P=V rms I rms cos(φ);

[0018] Reactive power Q:

[0019] Q=V rms I rms sin(φ);

[0020] Apparent power S:

[0021] S=V rms I rms ;

[0022] Power Factor PF:

[0023]

[0024] Where φ is the phase difference, which represents the phase angle difference between voltage and current.

[0025] As a further improvement of the technical solution, in S1, the amplitude and phase of each harmonic in the voltage and current waveforms are extracted to calculate the total harmonic distortion of the voltage and current. The specific steps are as follows:

[0026] S1.1, using fast Fourier transform, convert the voltage and current data into frequency domain signals;

[0027]

[0028] Where N is the total number of sampling points; x[n] is the signal value of the nth sampling point; X[k] is the complex value of the frequency component k, including amplitude and phase;

[0029] S1.2. Extracting harmonic amplitude A n and phase φ n , convert the harmonic amplitude into the effective harmonic amplitude, and calculate the total harmonic distortion of the voltage;

[0030]

[0031] φ n =arg(X[n]);

[0032] Among them, |X[n]| is the modulus of the frequency component; arg(X[n]) is the phase angle of the frequency component;

[0033] The effective amplitude of the nth harmonic V n :

[0034]

[0035] Voltage total harmonic distortion THD V :

[0036]

[0037] Among them, V n is the effective amplitude of the nth harmonic.

[0038] As a further improvement of the present technical solution, in S2, the electromagnetic field coupling effect model is used to analyze the dynamic changes of the electromagnetic field around the voltage, current and harmonics in the grid connection point by solving the coupled electromagnetic field equation, and the harmonic voltage at different frequencies is calculated by using the amplitude and phase of each harmonic in the voltage and current waveforms, the power quality parameters are updated, and the frequency fluctuation of the grid connection point is analyzed:

[0039] S2.1. Use the electromagnetic field coupling effect model to analyze the dynamic changes of the electromagnetic field around the grid connection point by solving the coupled electromagnetic field equations;

[0040] Coupled electromagnetic field equations:

[0041]

[0042] Where, E is the electric field strength; H is the magnetic field strength; is the curl of the electric field intensity; is the curl of magnetic field intensity; μ is magnetic permeability; ∈ is electrical conductivity; J is current density; is the partial derivative of magnetic field intensity H with respect to time t; is the partial derivative of the electric field intensity E with respect to time t;

[0043] S2.2. Calculate the harmonic voltage V at different frequencies using the amplitude and phase of each harmonic in the voltage and current waveforms h (t);

[0044]

[0045] Among them, A n is the harmonic amplitude A n ;ω0 is the fundamental frequency;φ n is the phase;

[0046] S2.3. Update the harmonic active power, harmonic reactive power, harmonic apparent power and power factor parameters considering the influence of harmonics;

[0047] Harmonic active power P h :

[0048]

[0049] Harmonic reactive power Q h :

[0050]

[0051] Harmonic apparent power S h :

[0052]

[0053] Power factor PF considering harmonics h :

[0054]

[0055] Among them, V n is the effective value of the nth harmonic voltage; I n is the effective value of the nth harmonic current; φ n is the phase difference between the nth harmonic voltage and current;

[0056] S2.4, calculating the frequency offset Δf and the frequency change rate ROCOF;

[0057] Δf=f measured -f nominal ;

[0058]

[0059] Where Δf is the frequency offset; f measured is the actual measurement frequency; f nominal is the rated frequency; ROCOF is the frequency change rate; Δt is the time interval.

[0060] As a further improvement of the technical solution, in S3, based on the harmonic voltage, the time-space evolution model is used in combination with the electromagnetic field reflection coefficient to analyze the dynamic change of the power quality, and finally the abnormal detection function is used to locate the occurrence time and location of the power quality problem. The specific steps are as follows:

[0061] S3.1. Based on the harmonic voltage, calculate the time-space evolution model and analyze the dynamic changes of harmonic voltage parameters at the grid connection point;

[0062] S3.2. Decompose the harmonic voltage into incident voltage and reflected voltage Calculate the electromagnetic field reflection coefficient R(t) at the grid connection point;

[0063] S3.3, set rated voltage V nominal and sampling time T s , calculate the voltage flicker P st , assess the impact of voltage fluctuations on power quality;

[0064] S3.4. Comprehensively consider the harmonic voltage, electromagnetic field reflection coefficient R(t) and voltage flicker P st , and introduce the influence of frequency offset Δf to calculate the anomaly detection function D anomaly (t), used to locate the time and location of power quality problems.

[0065] As a further improvement of the technical solution, in S3.1, based on the harmonic voltage, the time-space evolution model is calculated to analyze the dynamic changes of the harmonic voltage parameters at the grid connection point. The specific method is as follows:

[0066] Time-space evolution model:

[0067]

[0068] Among them, v is the propagation speed of electromagnetic waves; γ(r) is the attenuation coefficient of electromagnetic waves; V h (r, t) is the harmonic voltage at position r at time t; is the harmonic voltage V h The partial derivative with respect to time t; S(r,t) is the abnormal event source term, which represents the interference source at position r and time t;

[0069] Based on the calculated abnormal event source items, possible abnormal event sources are identified. The abnormal event source items represent electromagnetic interference and faults occurring at specific locations and times.

[0070] In S3.2, the harmonic voltage is decomposed into the incident voltage and reflected voltage Calculate the electromagnetic field reflection coefficient R(t) at the grid connection point. The specific method is as follows:

[0071]

[0072] in, is the incident voltage; is the reflected voltage; R(t) is the electromagnetic field reflection coefficient, which is used to evaluate the change in reflection intensity.

[0073] As a further improvement of the technical solution, the rated voltage V is set in S3.3 nominal and sampling time T s , calculate the voltage flicker P st , evaluate the impact of voltage fluctuations on power quality, the specific methods are as follows:

[0074]

[0075] Among them, P st is voltage flicker, indicating the intensity of voltage fluctuation; T s is the sampling time, i.e., the time interval for observing and measuring voltage fluctuations; V(t) is the voltage value at time t; V nominal is the rated voltage;

[0076] S3.4 comprehensively considers the harmonic voltage, electromagnetic field reflection coefficient R(t) and voltage flicker P st , and introduce the influence of frequency offset Δf(t) to calculate the anomaly detection function D anomaly (t) is used to locate the time and location of power quality problems. The specific method is as follows:

[0077]

[0078] Among them, D anomaly (t) is the anomaly detection function, which is used to quantify and locate the abnormal intensity and location of power quality problems; V is the integration area, which is the spatial area around the grid connection point and is used to calculate the abnormal signal strength of all points in space; k f is the frequency deviation coefficient; Δf(t) is the frequency deviation, the fluctuation of the power grid frequency at time t;

[0079] When calculating the anomaly detection function value D anomalyWhen (t) is high, it means that there is a significant abnormality in the power quality at that time and location;

[0080] When calculating the anomaly detection function value D anomaly When (t) is low, it means that the power quality is relatively stable at that time and location, with fewer abnormal changes.

[0081] As a further improvement of the technical solution, in S4, a multi-scale electromagnetic reverse analytical model is constructed, wavelet transform coefficients are calculated, the multi-scale electromagnetic reverse analytical model is combined with a dynamic environment compensation model, and the electromagnetic field reflection coefficient and frequency offset are introduced to construct a fault diagnosis function. The specific steps are as follows:

[0082] S4.1. Construct a multi-scale electromagnetic inverse analytical model and calculate the wavelet transform coefficients;

[0083] S4.2, construct a dynamic environmental compensation model;

[0084] S4.3. Based on the wavelet transform coefficient, the electromagnetic field reflection coefficient R(t) and frequency offset Δf(t) are introduced to construct and calculate the fault diagnosis function F diagonsis (t), according to the fault diagnosis function F diagonsis (t) to determine the time and location of the power quality fault.

[0085] As a further improvement of the technical solution, in S4.1, a multi-scale electromagnetic reverse analytical model is constructed to calculate the wavelet transform coefficients, as follows:

[0086] Multiscale electromagnetic inverse analytical model equations:

[0087]

[0088] Where J(r, t) is the current density, which represents the current distribution at position r and time t; H(r, t) is the magnetic field intensity, which represents the spatial magnetic field distribution at position r and time t; ∈ is the dielectric constant; E(r, t) is the electric field intensity, which represents the spatial electric field distribution at position r and time t;

[0089] Wavelet transform coefficients are defined as:

[0090]

[0091] Among them, W V (a, b) are wavelet transform coefficients, representing the transformation result of the voltage signal at scale a and time position b; V(t) is the voltage value at time t; ψ * is the wavelet function; a is the scale parameter; b is the time displacement parameter;

[0092] In said S4.2, a dynamic environment compensation model is constructed;

[0093] V comp (t) = V(t) - α·ΔE(t);

[0094] Among them, V comp (t) is the compensated voltage; α is the compensation coefficient; ΔE(t) is the environmental influence function, which represents the influence of environmental changes on the voltage signal.

[0095] As a further improvement of the technical solution, in S4.3, based on the wavelet transform coefficient, the electromagnetic field reflection coefficient R(t) and the frequency offset Δf(t) are introduced to construct and calculate the fault diagnosis function F diagonsis (t), according to the fault diagnosis function F diagonsis (t) to determine the time and location of the power quality fault. The specific method is as follows:

[0096] Fault diagnosis function F diagonsis (t):

[0097]

[0098] Among them, F diagonsis (t) is the fault diagnosis function, which is used to diagnose and locate power quality faults; N is the number of scales representing the wavelet transform; is the coefficient of the compensated voltage under wavelet transform, indicating that the voltage signal is at scale a k and time position b k The change characteristics on the surface; R(t) is the electromagnetic field reflection coefficient; γ is the voltage signal compensation coefficient;

[0099] Set a diagnostic threshold, when the fault diagnosis function value F diagonsis When (t) exceeds the threshold, it is considered that a potential fault event is detected. When a significant peak value and abnormal change occur, it indicates that there may be a power quality fault at that time point. The size of the function value indicates the fault intensity.

[0100] Compared with the prior art, the present invention has the following beneficial effects:

[0101] 1. In the online monitoring and automatic diagnosis method of power quality at the grid connection point, based on the combined application of the time-space evolution model and the multi-scale electromagnetic inverse analysis model, the dynamic changes of harmonic voltage, electromagnetic field reflection characteristics and frequency offset at the grid connection point can be accurately analyzed, and the time and location of power quality problems can be accurately located.

[0102] 2. In the online monitoring and automatic diagnosis method of power quality at the grid-connected point, by constructing an abnormality detection function and comprehensively considering harmonic voltage, electromagnetic field reflection coefficient, voltage flicker and frequency offset, and combining with a dynamic environmental compensation model, high-precision diagnosis and rapid response to power quality problems in a complex electromagnetic environment can be achieved, thereby improving the stability and reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0103] Figure 1 The figure is a flow chart of the overall method of the present invention. DETAILED DESCRIPTION

[0104] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0105] Example:

[0106] See also Figure 1 As shown, this embodiment provides a method for online monitoring and automatic diagnosis of power quality at a grid-connected point, comprising the following steps:

[0107] S1. Real-time collection of voltage, current, frequency and spatial electromagnetic field distribution of power quality at the grid connection point to calculate voltage RMS, current RMS, active power, reactive power, apparent power and power factor, extract the amplitude and phase of each harmonic in the voltage and current waveforms, and calculate the voltage and current total harmonic distortion;

[0108] In this embodiment S1, the voltage V(t), current I(t), frequency and spatial electromagnetic field distribution of the power quality at the grid connection point are collected to calculate the effective value of voltage, effective value of current, active power, reactive power, apparent power and power factor. The specific method is as follows:

[0109] Voltage RMS V rms :

[0110]

[0111] Current effective value I rms :

[0112]

[0113] Wherein, T is the period of the AC signal, specifically the reciprocal of the frequency f; v(t) is the instantaneous voltage, which represents the voltage value at time t; i(t) is the instantaneous current, which represents the current value at time t;

[0114] Active power P:

[0115] P=V rms I rms cos(φ);

[0116] Reactive power Q:

[0117] Q=V rms I rms sin(φ);

[0118] Apparent power S:

[0119] S=V rms I rms ;

[0120] Power Factor PF:

[0121]

[0122] Where φ is the phase difference, which represents the phase angle difference between voltage and current.

[0123] Collect the distribution of electromagnetic fields in space, as follows:

[0124] Arrange electromagnetic field sensors at different locations of the grid connection point to measure the electric field strength E and magnetic field strength H;

[0125] In this embodiment S1, the amplitude and phase of each harmonic in the voltage and current waveforms are extracted to calculate the total harmonic distortion of the voltage and current. The specific steps are as follows:

[0126] S1.1, using fast Fourier transform, convert the voltage and current data into frequency domain signals;

[0127]

[0128] Where N is the total number of sampling points; x[n] is the signal value of the nth sampling point; X[k] is the complex value of the frequency component k, including amplitude and phase;

[0129] S1.2. Extracting harmonic amplitude A n and phase φ n , harmonic effective amplitude converts the harmonic amplitude into harmonic effective amplitude and calculates the voltage total harmonic distortion;

[0130]

[0131] φ n =arg(X[n]);

[0132] Among them, |X[n]| is the modulus of the frequency component; arg(X[n]) is the phase angle of the frequency component;

[0133] The effective amplitude of the nth harmonic V n :

[0134]

[0135] Voltage total harmonic distortion THD V :

[0136]

[0137] Among them, V n is the effective amplitude of the nth harmonic.

[0138] Fast Fourier transform is an efficient algorithm for calculating discrete Fourier transform and its inverse transform. In the context of power quality monitoring, the main function of fast Fourier transform is to convert time domain signals such as voltage and current into frequency domain signals. The voltage and current waveforms in the power system are usually not pure sine waves, but contain various harmonic components. The use of fast Fourier transform can quickly convert the time domain data of voltage and current into frequency domain data, and decompose the amplitude and phase of each harmonic.

[0139] S2. Using the electromagnetic field coupling effect model, by solving the coupled electromagnetic field equations, analyze the dynamic changes of the electromagnetic field around the voltage, current and harmonics in the grid connection point, calculate the harmonic voltage at different frequencies using the amplitude and phase of each harmonic in the voltage and current waveforms, and update the power quality parameters;

[0140] In this embodiment S2, the electromagnetic field coupling effect model is used to analyze the dynamic changes of the electromagnetic field around the voltage, current and harmonics in the grid connection point by solving the coupled electromagnetic field equation, and the harmonic voltage at different frequencies is calculated by using the amplitude and phase of each harmonic in the voltage and current waveforms, the power quality parameters are updated, and the frequency fluctuation of the grid connection point is analyzed:

[0141] S2.1. Use the electromagnetic field coupling effect model to analyze the dynamic changes of the electromagnetic field around the grid connection point by solving the coupled electromagnetic field equations;

[0142] Coupled electromagnetic field equations:

[0143]

[0144] Where, E is the electric field strength; H is the magnetic field strength; is the curl of the electric field intensity; is the curl of magnetic field intensity; μ is magnetic permeability; ∈ is electrical conductivity; J is current density; is the partial derivative of magnetic field intensity H with respect to time t; is the partial derivative of the electric field intensity E with respect to time t;

[0145] By solving the coupled electromagnetic field equations, the dynamic changes of the electric field E(r, t) and the magnetic field H(r, t) around the grid connection point can be obtained, which is used to analyze the propagation characteristics of voltage, current and harmonics.

[0146] S2.2. Calculate the harmonic voltage V at different frequencies using the amplitude and phase of each harmonic in the voltage and current waveforms h (t);

[0147]

[0148] Among them, A n is the harmonic amplitude A n ;ω0 is the fundamental frequency;φ n is the phase;

[0149] S2.3. Update the harmonic active power, harmonic reactive power, harmonic apparent power and power factor parameters considering the influence of harmonics;

[0150] Harmonic active power P h :

[0151]

[0152] Harmonic reactive power Q h :

[0153]

[0154] Harmonic apparent power S h :

[0155]

[0156] Power factor PF considering harmonics h :

[0157]

[0158] Among them, V n is the effective value of the nth harmonic voltage; I n is the effective value of the nth harmonic current; φ n is the phase difference between the nth harmonic voltage and current;

[0159] S2.4, calculating the frequency offset Δf and the frequency change rate ROCOF;

[0160] Δf=f measured -f nominal ;

[0161]

[0162] Where Δf is the frequency offset; f measured is the actual measurement frequency; f nominal is the rated frequency; ROCOF is the frequency change rate; Δt is the time interval.

[0163] S3. Based on harmonic voltage, the time-space evolution model is used in combination with the electromagnetic field reflection coefficient to analyze the dynamic changes of power quality, and finally the anomaly detection function is used to locate the time and location of power quality problems.

[0164] In S3, based on the harmonic voltage, the time-space evolution model is used in combination with the electromagnetic field reflection coefficient to analyze the dynamic change of the power quality, and finally the abnormal detection function is used to locate the time and location of the power quality problem. The specific steps are as follows:

[0165] S3.1. Based on the harmonic voltage, calculate the time-space evolution model and analyze the dynamic changes of harmonic voltage parameters at the grid connection point;

[0166] S3.2. Decompose the harmonic voltage into incident voltage and reflected voltage Calculate the electromagnetic field reflection coefficient R(t) at the grid connection point;

[0167] S3.3, set rated voltage V nominal and sampling time T s , calculate the voltage flicker P st , assess the impact of voltage fluctuations on power quality;

[0168] S3.4. Comprehensively consider the harmonic voltage, electromagnetic field reflection coefficient R(t) and voltage flicker P st , and introduce the influence of frequency offset Δf to calculate the anomaly detection function D anomaly (t), used to locate the time and location of power quality problems.

[0169] In this embodiment S3.1, the time-space evolution model provides a more comprehensive method to understand and monitor power quality problems in the power grid by describing the dynamic changes of harmonic voltage in time and space. The main purpose is to analyze and predict the dynamic behavior of power quality parameters in the power grid. By modeling and analyzing the time-space evolution of parameters such as harmonic voltage, the propagation path, attenuation characteristics and amplification effect of harmonics in the power grid are identified, which helps to understand the spatial distribution characteristics of harmonics; predict future changes in power quality parameters, especially in the case of load fluctuations or faults, and provide early warning information; by analyzing the time-space distribution of power quality parameters, the potential source of power quality problems can be identified;

[0170] In this embodiment S3.1, based on the harmonic voltage, the time-space evolution model is calculated to analyze the dynamic changes of the harmonic voltage parameters at the grid connection point. The specific method is as follows:

[0171] Time-space evolution model:

[0172]

[0173] Among them, v is the propagation speed of electromagnetic waves; γ(r) is the attenuation coefficient of electromagnetic waves; V h (r, t) is the harmonic voltage at position r at time t; is the harmonic voltage V h The partial derivative with respect to time t; S(r, t) is the abnormal event source term, which represents the interference source at position r and time t;

[0174] In this embodiment, the attenuation coefficient γ(r) of the electromagnetic wave represents the degree of energy loss of the electromagnetic wave when propagating in the medium, which depends on the physical properties of the medium;

[0175] Based on the calculated abnormal event source items, possible abnormal event sources are identified. The abnormal event source items represent electromagnetic interference and faults occurring at specific locations and times.

[0176] In the time-space evolution model, the abnormal event source term S(r, t) represents the interference source at position r and time t. The existence of this term indicates that there is abnormal activity at a certain location in the power grid at a certain time, such as harmonic injection, voltage sag, equipment failure, etc.

[0177] Time domain analysis of the abnormal event source term S(r, t): observe the change of S(r, t) over time and identify abnormal events that occur at a specific moment. Sudden or pulse signals may represent instantaneous faults or interference;

[0178] Spatial analysis of the abnormal event source term S(r,t): Analyze the spatial distribution of S(r,t) to identify the location of the abnormal event. If the source term at a certain location r is significantly higher than that at other locations, it may indicate that there is a device failure, nonlinear load or external interference source at that location.

[0179] In this embodiment S3.2, the harmonic voltage is decomposed into the incident voltage and reflected voltage Calculate the electromagnetic field reflection coefficient R(t) at the grid connection point. The specific method is as follows:

[0180]

[0181] in, is the incident voltage; is the reflected voltage; R(t) is the electromagnetic field reflection coefficient, which is used to evaluate the change in reflection intensity.

[0182] In this embodiment, the harmonic voltage is decomposed into the incident voltage and reflected voltage The specific steps are as follows:

[0183] Determine the propagation direction of electromagnetic waves based on the physical model of the electromagnetic field and the actual grid configuration; use boundary conditions to distinguish incident waves from reflected waves; use the physical properties of electromagnetic waves to decompose harmonic voltage into incident and reflected parts; the incident voltage is obtained by combining the forward propagation component of the harmonic voltage with the current; the reflected voltage is obtained by combining the reverse propagation component of the harmonic voltage with the current;

[0184] When the electromagnetic field reflection coefficient R(t) is large, it means that there is a significant difference between the incident voltage and the reflected voltage, which may reflect the strong reflection of electromagnetic waves at the boundary of the medium. A high reflection coefficient usually means that there is an abnormality in the power quality, such as harmonic distortion, voltage sag or swell, etc.

[0185] When the electromagnetic field reflection coefficient R(t) is small, it means that the difference between the incident voltage and the reflected voltage is small, indicating that the electromagnetic wave is weakly reflected at the medium boundary or no significant reflection occurs. This usually indicates that the power quality is stable at that time and location;

[0186] By analyzing the change of reflection coefficient over time, the occurrence time and intensity of abnormal power quality events can be identified. Fluctuations in reflection coefficient may correspond to abnormal phenomena in the power grid, such as load fluctuations or reflection changes caused by faults.

[0187] In this embodiment S3.3, the rated voltage V is set nominal and sampling time T s , calculate the voltage flicker P st , evaluate the impact of voltage fluctuations on power quality, the specific methods are as follows:

[0188]

[0189] Among them, P st is voltage flicker, indicating the intensity of voltage fluctuation; T s is the sampling time, i.e., the time interval for observing and measuring voltage fluctuations; V(t) is the voltage value at time t; V nominal is the rated voltage;

[0190] In this embodiment S3.3, the rated voltage V is set nominal For 220V and sampling time T s is ten seconds;

[0191] By calculating the voltage flicker P st Evaluate the impact of voltage fluctuations on power quality as follows:

[0192] Influence of high voltage flicker value: When P stWhen the value is high, it means that the voltage fluctuation is large and the power quality is poor. In this case, frequent voltage fluctuations may have an adverse effect on electrical equipment, resulting in reduced equipment performance, increased losses, and even equipment damage;

[0193] Influence of low voltage flicker value: When P st When the value is low, it means that the voltage fluctuation is small and the power quality is good. In this case, the voltage stability is high, the impact on power equipment is small, and the grid operation is more stable and efficient;

[0194] By calculating the voltage flicker P st The system can quantify the intensity and frequency of voltage fluctuations and evaluate the impact of voltage fluctuations on power quality. As an important indicator of power quality monitoring, voltage flicker helps to identify potential power quality problems and provide a scientific basis for grid management and power equipment protection.

[0195] In this embodiment S3.4, the harmonic voltage, electromagnetic field reflection coefficient R(t) and voltage flicker P are comprehensively considered. st , and introduce the influence of frequency offset Δf(t) to calculate the anomaly detection function D anomaly (t) is used to locate the time and location of power quality problems. The specific method is as follows:

[0196]

[0197] Among them, D anomaly (t) is the anomaly detection function, which is used to quantify and locate the abnormal intensity and location of power quality problems; V is the integration area, which is the spatial area around the grid connection point and is used to calculate the abnormal signal strength of all points in space; k f is the frequency deviation coefficient; Δf(t) is the frequency deviation, the fluctuation of the power grid frequency at time t;

[0198] When calculating the anomaly detection function value D anomaly When (t) is high, it means that there is a significant abnormality in the power quality at that time and location;

[0199] When calculating the anomaly detection function value D anomaly When (t) is low, it means that the power quality is relatively stable at that time and location, with fewer abnormal changes.

[0200] In this embodiment, in power quality monitoring and diagnosis, the frequency offset is introduced into the abnormality detection function D anomaly The frequency offset Δf(t) in (t) can reflect the change of grid frequency due to load change, generator adjustment, equipment failure or other disturbances. The introduction of frequency offset Δf(t) enables the anomaly detection function to capture early signs of frequency offset.

[0201] Anomaly detection function D anomaly (t) combines parameters such as harmonic voltage, electromagnetic field reflection coefficient, and voltage flicker, and introduces frequency offset Δf(t) to further enhance the diagnostic capability of the function, enabling it to capture power quality problems caused by frequency changes. For example, a slight frequency offset may trigger the amplification effect of harmonics, or interact with other power quality problems, leading to more serious system instability.

[0202] S4. Construct a multi-scale electromagnetic reverse analytical model, calculate the wavelet transform coefficients, use the multi-scale electromagnetic reverse analytical model in combination with the dynamic environment compensation model, and introduce the electromagnetic field reflection coefficient and frequency offset to construct a fault diagnosis function, and determine the occurrence time and location of the power quality fault based on the results of the fault diagnosis function;

[0203] In S4, a multi-scale electromagnetic reverse analytical model is constructed, wavelet transform coefficients are calculated, and a fault diagnosis function is constructed by combining the multi-scale electromagnetic reverse analytical model with a dynamic environment compensation model, and introducing electromagnetic field reflection coefficient and frequency offset. The specific steps are as follows:

[0204] S4.1. Construct a multi-scale electromagnetic inverse analytical model and calculate the wavelet transform coefficients;

[0205] S4.2, construct a dynamic environmental compensation model;

[0206] S4.3. Based on the wavelet transform coefficient, the electromagnetic field reflection coefficient R(t) and frequency offset Δf(t) are introduced to construct and calculate the fault diagnosis function F diagonsis (t), according to the fault diagnosis function F diagonsis (t) to determine the time and location of the power quality fault.

[0207] In this embodiment, a multi-scale electromagnetic reverse analysis model is constructed to analyze and diagnose power quality problems. Through multi-scale analysis, different frequency components and spatial distribution characteristics of power quality problems can be identified, and the time and location of the problem can be accurately located. Wavelet transform can be used to analyze power quality signals in time and frequency, extract key features, and identify abnormal phenomena. Through the dynamic environmental compensation model, the model can adapt to the changing environmental conditions in the power grid and provide more accurate diagnostic results.

[0208] The purpose of constructing a multiscale electromagnetic inverse analysis model is to provide an efficient tool to identify, locate and diagnose power quality problems in complex electromagnetic environments by performing multiscale and multiresolution analysis on power quality signals and combining the effects of electromagnetic reflection coefficient and frequency offset.

[0209] In this embodiment S4.1, a multi-scale electromagnetic reverse analytical model is constructed and wavelet transform coefficients are calculated as follows:

[0210] Multiscale electromagnetic inverse analytical model equations:

[0211]

[0212] Where J(r, t) is the current density, which represents the current distribution at position r and time t; H(r, t) is the magnetic field intensity, which represents the spatial magnetic field distribution at position r and time t; ∈ is the dielectric constant; E(r, t) is the electric field intensity, which represents the spatial electric field distribution at position r and time t;

[0213] Wavelet transform coefficients are defined as:

[0214]

[0215] Among them, W V (a, b) are wavelet transform coefficients, representing the transformation result of the voltage signal at scale a and time position b; V(t) is the voltage value at time t; ψ * is the wavelet function; a is the scale parameter; b is the time displacement parameter;

[0216] In this embodiment S4.2, a dynamic environment compensation model is constructed;

[0217] V comp (t) = V(t) - α·ΔE(t);

[0218] Among them, V comp (t) is the compensated voltage; α is the compensation coefficient; ΔE(t) is the environmental influence function, which represents the influence of environmental changes on the voltage signal.

[0219] In this embodiment, the dynamic environmental compensation model is constructed to eliminate or reduce the influence of environmental factors on voltage signals in power quality analysis, thereby improving the accuracy of power quality monitoring and diagnosis. The specific method is as follows:

[0220] Identify influencing factors: It is necessary to identify which environmental factors will affect the power quality signal, including:

[0221] Temperature changes: affect the conductivity and resistance of the equipment, causing voltage and current fluctuations;

[0222] Humidity changes: affect insulation resistance and leakage current, especially in high humidity environments;

[0223] Load fluctuations: Rapid changes in load can cause fluctuations in voltage and current;

[0224] External electromagnetic interference: electromagnetic interference from other devices or external sources affects the purity of the power quality signal;

[0225] Data collection and preprocessing: Use environmental sensors to collect environmental change data in real time;

[0226] Establish compensation model: Establish dynamic environmental compensation model based on the collected data.

[0227] In this embodiment S4.3, based on the wavelet transform coefficient, the electromagnetic field reflection coefficient R(t) and the frequency offset Δf(t) are introduced to construct and calculate the fault diagnosis function F diagonsis (t), according to the fault diagnosis function F diagonsis (t) to determine the time and location of the power quality fault. The specific method is as follows:

[0228] Fault diagnosis function F diagonsis (t):

[0229]

[0230] Among them, F diagonsis (t) is the fault diagnosis function, which is used to diagnose and locate power quality faults; N is the number of scales representing the wavelet transform; is the coefficient of the compensated voltage under wavelet transform, indicating that the voltage signal is at scale a k and time position b k The change characteristics on the surface; R(t) is the electromagnetic field reflection coefficient; γ is the voltage signal compensation coefficient;

[0231] Set a diagnostic threshold, when the fault diagnosis function value F diagonsis When (t) exceeds the threshold, it is considered that a potential fault event is detected. When a significant peak value and abnormal change occur, it indicates that there may be a power quality fault at that time point. The size of the function value indicates the fault intensity.

[0232] In this embodiment, the fault diagnosis function F is constructed and calculated. diagonsis (t) It is necessary to comprehensively consider multiple parameters, including wavelet transform coefficients, electromagnetic field reflection coefficients, frequency offset and compensated voltage signals, in order to comprehensively analyze the sources and impacts of power quality problems and improve the accuracy and reliability of fault diagnosis;

[0233] The setting of the diagnostic threshold can be based on the statistical analysis of historical data; once the fault diagnosis function value F is detected diagonsis When (t) exceeds the threshold, the time point t is recorded as the occurrence time of the fault.

[0234] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and the above embodiments and descriptions are only preferred examples of the present invention, and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A method for online monitoring and automatic diagnosis of power quality at a grid connection point, characterized in that: The following steps are involved: S1. Real-time collection of voltage, current, frequency and spatial electromagnetic field distribution of power quality at the grid connection point to calculate voltage RMS, current RMS, active power, reactive power, apparent power and power factor, extract the amplitude and phase of each harmonic in the voltage and current waveforms, and calculate the voltage and current total harmonic distortion; S2. Using the electromagnetic field coupling effect model, by solving the coupled electromagnetic field equations, analyze the dynamic changes of the electromagnetic field around the voltage, current and harmonics in the grid connection point, calculate the harmonic voltage at different frequencies using the amplitude and phase of each harmonic in the voltage and current waveforms, and update the power quality parameters; S3. Based on harmonic voltage, the time-space evolution model is used in combination with the electromagnetic field reflection coefficient to analyze the dynamic changes of power quality, and finally the anomaly detection function is used to locate the time and location of power quality problems. Compute anomaly detection function , used to locate the time and location of power quality problems. The specific methods are as follows: ； in, is an anomaly detection function, used to quantify and locate the abnormal intensity and location of power quality problems; is the integration area, i.e., the spatial area around the grid connection point, which is used to calculate the abnormal signal strength of all points in the space; is the frequency shift coefficient; is the frequency offset at time The fluctuation of the grid frequency, is the propagation speed of electromagnetic waves; is the attenuation coefficient of electromagnetic waves; For the moment In Location Harmonic voltage at is the harmonic voltage About Time The partial derivative of When calculating the anomaly detection function value When it is high, it means that there is a significant abnormality in the power quality at that time and location; When calculating the anomaly detection function value When it is lower, it means that the power quality is relatively stable at that time and location, with fewer abnormal changes; S4. Construct a multi-scale electromagnetic reverse analytical model, calculate the wavelet transform coefficients, use the multi-scale electromagnetic reverse analytical model in combination with the dynamic environment compensation model, and introduce the electromagnetic field reflection coefficient and frequency offset to construct a fault diagnosis function, and determine the occurrence time and location of the power quality fault based on the results of the fault diagnosis function; Fault diagnosis function : ; in, It is a fault diagnosis function used to diagnose and locate power quality faults; is the number of scales representing the wavelet transform; is the coefficient of the compensated voltage under wavelet transform, which indicates the voltage signal on the scale and time location The changing characteristics of is the electromagnetic field reflection coefficient; is the voltage signal compensation coefficient, is the voltage after compensation; Set a diagnostic threshold. When the fault diagnosis function value When the threshold is exceeded, it is considered that a potential fault event is detected. When significant peaks and abnormal changes occur at the same time, it indicates that there may be a power quality fault at that time point. The size of the function value indicates the fault intensity.

2. The method for online monitoring and automatic diagnosis of power quality at a grid connection point according to claim 1, characterized in that: In S1, the voltage of the power quality at the grid connection point is collected. , Current , frequency and spatial electromagnetic field distribution are used to calculate the effective value of voltage, effective value of current, active power, reactive power, apparent power and power factor. The specific method is as follows: Voltage RMS : ； Current RMS : ； in, is the period of the AC signal, specifically the frequency The reciprocal of is the instantaneous voltage, indicating the Voltage value; is the instantaneous current, indicating the The current value; Active Power : ； Reactive power : ； Apparent power : ； Power Factor : ； in, is the phase difference, which represents the phase angle difference between voltage and current.

3. The method for online monitoring and automatic diagnosis of power quality at a grid connection point according to claim 1, characterized in that: In S1, the amplitude and phase of each harmonic in the voltage and current waveforms are extracted to calculate the total harmonic distortion of the voltage and current. The specific steps are as follows: S1.1, using fast Fourier transform, convert the voltage and current data into frequency domain signals; ； in, is the total number of sampling points; For the The signal value of each sampling point; is the frequency component The complex value of , including amplitude and phase; S1.

2. Extracting harmonic amplitude and Phase , convert the harmonic amplitude into the effective harmonic amplitude, and calculate the total harmonic distortion of the voltage; ； ； in, is the modulus of the frequency component; is the phase angle of the frequency component; No. Subharmonic effective amplitude : ； Voltage total harmonic distortion : ； in, For the The effective amplitude of subharmonics.

4. The method for online monitoring and automatic diagnosis of power quality at a grid connection point according to claim 1, characterized in that: In S2, the electromagnetic field coupling effect model is used to analyze the dynamic changes of the electromagnetic field around the voltage, current and harmonics in the grid connection point by solving the coupled electromagnetic field equations, and the harmonic voltages at different frequencies are calculated using the amplitude and phase of each harmonic in the voltage and current waveforms, the power quality parameters are updated, and the frequency fluctuation of the grid connection point is analyzed: S2.

1. Use the electromagnetic field coupling effect model to analyze the dynamic changes of the electromagnetic field around the grid connection point by solving the coupled electromagnetic field equations; Coupled electromagnetic field equations: ； ； in, is the electric field strength; is the magnetic field strength; is the curl of the electric field intensity; is the curl of the magnetic field intensity; is the magnetic permeability; is the conductivity; is the current density; is the magnetic field strength About Time The partial derivative of is the electric field strength About Time The partial derivative of S2.

2. Calculate the harmonic voltage at different frequencies using the amplitude and phase of each harmonic in the voltage and current waveforms ; ； in, is the harmonic amplitude ; is the fundamental frequency; is the phase; S2.

3. Update the harmonic active power, harmonic reactive power, harmonic apparent power and power factor parameters considering the influence of harmonics; Harmonic active power : ； Harmonic reactive power : ； Harmonic apparent power : ； Power factor considering harmonics : ； in, For the The effective value of subharmonic voltage; For the The effective value of subharmonic current; For the Phase difference between subharmonic voltage and current; S2.

4. Calculate frequency offset and frequency change rate ; ； ； in, is the frequency offset; is the actual measurement frequency; is the rated frequency; is the frequency change rate; is the time interval.

5. The method for online monitoring and automatic diagnosis of power quality at a grid connection point according to claim 1, characterized in that: In S3, based on the harmonic voltage, the time-space evolution model is used in combination with the electromagnetic field reflection coefficient to analyze the dynamic change of the power quality, and finally the abnormal detection function is used to locate the time and location of the power quality problem. The specific steps are as follows: S3.

1. Based on the harmonic voltage, calculate the time-space evolution model and analyze the dynamic changes of harmonic voltage parameters at the grid connection point; S3.

2. Decompose the harmonic voltage into incident voltage and reflected voltage , calculate the electromagnetic field reflection coefficient at the grid connection point ; S3.3, set rated voltage and sampling time , calculate voltage flicker , assess the impact of voltage fluctuations on power quality; S3.

4. Comprehensive consideration of harmonic voltage and electromagnetic field reflection coefficient And voltage flicker , and introduces a frequency offset The impact of , calculating the anomaly detection function , used to locate the time and location of power quality problems.

6. The method for online monitoring and automatic diagnosis of power quality at a grid connection point according to claim 5, characterized in that: In S3.1, based on the harmonic voltage, the time-space evolution model is calculated to analyze the dynamic changes of the harmonic voltage parameters at the grid connection point. The specific method is as follows: Time-space evolution model: ； in, is the propagation speed of electromagnetic waves; is the attenuation coefficient of electromagnetic waves; For the moment In Location Harmonic voltage at is the harmonic voltage About Time The partial derivative of is the abnormal event source item, indicating that and time The interference source at Based on the calculated abnormal event source items, possible abnormal event sources are identified. The abnormal event source items represent electromagnetic interference and faults occurring at specific locations and times. In S3.2, the harmonic voltage is decomposed into the incident voltage and reflected voltage , calculate the electromagnetic field reflection coefficient at the grid connection point , the specific method is as follows: ； in, is the incident voltage; is the reflected voltage; is the electromagnetic field reflection coefficient, which is used to evaluate the change in reflection intensity.

7. The method for online monitoring and automatic diagnosis of power quality at a grid connection point according to claim 5, characterized in that: The rated voltage is set in S3.3 and sampling time , calculate voltage flicker , evaluate the impact of voltage fluctuations on power quality, the specific methods are as follows: ； in, It is voltage flicker, which indicates the intensity of voltage fluctuation; is the sampling time, i.e., the time interval between observing and measuring voltage fluctuations; For at the moment Voltage value; is the rated voltage; S3.4 comprehensively considers harmonic voltage and electromagnetic field reflection coefficient And voltage flicker , and introduces a frequency offset The impact of Compute anomaly detection function , used to locate the time and location of power quality problems.

8. The method for online monitoring and automatic diagnosis of power quality at a grid connection point according to claim 1, characterized in that: In S4, a multi-scale electromagnetic reverse analytical model is constructed, wavelet transform coefficients are calculated, and a fault diagnosis function is constructed by combining the multi-scale electromagnetic reverse analytical model with a dynamic environment compensation model, and introducing electromagnetic field reflection coefficient and frequency offset. The specific steps are as follows: S4.

1. Construct a multi-scale electromagnetic inverse analytical model and calculate the wavelet transform coefficients; S4.2, construct a dynamic environmental compensation model; S4.

3. Introducing electromagnetic field reflection coefficient based on wavelet transform coefficient and frequency offset , construct and calculate the fault diagnosis function , according to the fault diagnosis function The results of the power quality fault analysis can be used to determine the time and location of the power quality fault.

9. The method for online monitoring and automatic diagnosis of power quality at a grid connection point according to claim 8, characterized in that: In S4.1, a multi-scale electromagnetic reverse analytical model is constructed and wavelet transform coefficients are calculated, as follows: Multiscale electromagnetic inverse analytical model equations: ； in, is the current density, expressed at position and time The current distribution at is the magnetic field strength, indicating that at position and time The spatial magnetic field distribution at is the dielectric constant; is the electric field strength, indicating that at position and time The spatial electric field distribution at ; Wavelet transform coefficients are defined as: ； in, is the wavelet transform coefficient, which indicates the voltage signal at scale and time location The transformation result on ; For at the moment Voltage value; is the wavelet function; is the scale parameter; is the time displacement parameter; In said S4.2, a dynamic environment compensation model is constructed; ； in, is the voltage after compensation; is the compensation coefficient; is the environmental impact function, which represents the impact of environmental changes on the voltage signal.

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