Distributed photovoltaic power quality monitoring method and system and frequency deviation calculation method

Abstract

The invention discloses a distributed photovoltaic power quality monitoring method. The method comprises the following steps: acquiring original waveform data of the Internet of Things in real time through a serial peripheral interface; calculating cyclic wave voltage amplitude, cyclic wave harmonic voltage and / or current amplitude, inter-harmonic voltage and / or current amplitude, harmonic active power, positive sequence, negative sequence and zero sequence components of voltage and / or current and unbalance degree according to the original waveform data; and calculating an instantaneous flicker value through a filter according to the original waveform data, and carrying out transient rise, transient drop and interruption event monitoring according to the original waveform data. According to the invention, the technical problems of how to monitor the electric energy quality quickly in real time and how to calculate the frequency deviation in real time are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power quality detection, and in particular to a distributed photovoltaic power quality monitoring method, system and frequency deviation calculation method. Background Art

[0002] The grid-connected operation of distributed power sources is an important development direction of distributed power generation. After the distributed power sources are connected to the grid, they will interact with the public grid. Taking photovoltaics as an example, the installed capacity of photovoltaic power stations continues to increase, and the advancement of control technology has gradually increased the penetration rate of photovoltaic power generation in the grid. Limited by the randomness, volatility, and intermittency of light resources, the impact on the power quality of the grid after access will be more prominent. In addition, photovoltaic power generation systems generally use converter technology to connect to the grid, and large photovoltaic power stations are usually equipped with power electronic equipment such as reactive power compensation that are prone to harmonic pollution. When these devices work abnormally, they will also cause serious power quality problems. With the rapid development of the photovoltaic industry, it is particularly important to ensure the normal operation of the power grid and power-consuming equipment after it operates together with the large power grid, and reduce unnecessary losses caused by power quality problems.

[0003] At present, although the devices used for power quality monitoring are fully functional, they are expensive. The detection of power quality parameters of photovoltaic systems is mostly regular or irregular, and power quality problems are rarely monitored online for a long time and continuously. In order to achieve power quality monitoring and control and digital coverage of the entire network, the power quality monitoring function is embedded in the smart meter. In order to reduce the cost of power quality monitoring, a separate power quality module is mainly used to obtain the original waveform calculation method by inserting it into the IoT meter. And because the original waveform data transmitted to the module by the IoT meter is always fixed frequency points, the frequency deviation cannot be calculated. For distributed photovoltaics, when the power grid is interrupted due to electrical failures or natural factors, the photovoltaic grid-connected power generation system still supplies power to the surrounding loads, thus forming a self-sufficient power supply island that the power company cannot control. Accurate and timely detection of the island effect is a key issue in the design of photovoltaic grid-connected power generation systems. Among them, the main parameters used to detect the island effect are frequency deviation and voltage deviation. The patent document with application number CN202311627750.7 discloses a power quality module and harmonic analysis method, which obtains the original sampling waveform, calculates the real and imaginary parts of the sampling sequence after reversing the original sampling waveform; calculates the effective value of the fundamental wave and the effective value of multiple harmonics through the calculated real and imaginary parts; obtains the harmonic distortion rate through the effective values ​​of the fundamental wave and harmonics, and determines that when the harmonic distortion rate exceeds the set threshold during the sampling period, an event is generated and reported to the power meter. In this way, since the original waveform data that the IoT meter has always transmitted to the power quality module is a fixed number of frequency points, even if the frequency changes, the IoT meter will perform a difference on the original waveform data, and the fixed 256 sampling points or 128 sampling points will be transmitted, and the frequency deviation cannot be calculated, that is, the frequency deviation cannot be monitored in real time, and thus it cannot support the anti-islanding effect detection needs in distributed photovoltaic power generation systems. Therefore, it is urgent to propose a distributed photovoltaic power quality monitoring method, system and frequency deviation calculation method to solve the technical problem of how to monitor power quality in real time and quickly and calculate frequency deviation in real time. Summary of the invention

[0004] The main purpose of the present invention is to propose a distributed photovoltaic power quality monitoring method, system and frequency deviation calculation method, aiming to solve the technical problem of how to monitor power quality in real time and quickly and calculate frequency deviation in real time.

[0005] To achieve the above object, the present invention provides a distributed photovoltaic power quality monitoring method, wherein the distributed photovoltaic power quality monitoring method comprises the following steps:

[0006] Get the raw waveform data of IoT in real time through the serial peripheral device interface;

[0007] The cyclic voltage amplitude, cyclic harmonic voltage and / or current amplitude, interharmonic voltage and / or current amplitude, harmonic active power, positive sequence, negative sequence and zero sequence components of voltage and / or current and the degree of imbalance are calculated according to the original waveform data; and the instantaneous flicker value is calculated through a filter according to the original waveform data, and temporary rise, temporary sag and interruption events are monitored according to the original waveform data.

[0008] One of the preferred solutions, the calculation of the cyclic voltage amplitude based on the original waveform data is specifically as follows:

[0009] The single-cycle voltage amplitude is obtained through the original waveform data, and the voltage amplitude of M cycles is obtained by averaging the voltage amplitudes of M consecutive single cycles; the single-cycle voltage amplitude is:

[0010]

[0011] Among them, U RMS is the single-cycle voltage amplitude, N is the number of single-cycle sampling points, u(t n ) is t n The instantaneous voltage sampling value at time.

[0012] One of the preferred solutions is that the calculation of the frequency harmonic voltage and / or current amplitude, the interharmonic voltage and / or current amplitude and the harmonic active power according to the original waveform data is specifically as follows:

[0013] Based on the M-cycle analysis window, the frequency spectrum components are obtained by discrete Fourier transform;

[0014] Calculate the harmonic voltage and / or current amplitude and the interharmonic voltage and / or current amplitude of N cycles by using a subgroup algorithm;

[0015] The active power of each harmonic is calculated according to the amplitude of the harmonic voltage and the harmonic current.

[0016] In one of the preferred solutions, the sub-spectral components are:

[0017]

[0018] Wherein, X(k) is the kth spectral component, x(n) is the voltage sampling point data or the current sampling point data, and DFT[x(n)] is the spectral components obtained by performing discrete Fourier transform on the voltage sampling point data or the current sampling point data. is the rotation factor.

[0019] One of the preferred solutions is to calculate the instantaneous flicker value through a filter based on the original waveform data, specifically:

[0020] The voltage half-cycle effective value is obtained according to the original waveform data, and the voltage variation amplitude and voltage variation frequency are calculated; the instantaneous flicker is calculated according to the IEC standard, and the voltage sampling data in the original waveform data is subjected to square, low-pass, high-pass, weighted filtering and smoothing processing in turn to calculate the instantaneous flicker value of several time periods.

[0021] One of the preferred solutions, the calculation of the positive sequence, negative sequence and zero sequence components and the degree of imbalance of the voltage and / or current according to the original waveform data is specifically as follows:

[0022] Perform harmonic analysis on the original waveform data to obtain fundamental voltage and fundamental current, and decompose the fundamental voltage and fundamental current into symmetrical components; the symmetrical components are:

[0023]

[0024] in, are the positive sequence component, negative sequence component and zero sequence component of voltage or current respectively, is the voltage or current component measured by the three channels A / B / C of the IoT table, and a is a complex operator;

[0025] The positive sequence, negative sequence, zero sequence components and unbalance degree of the voltage and / or current are calculated according to the fundamental wave voltage and fundamental wave current.

[0026] In one of the preferred solutions, the degree of imbalance is:

[0027]

[0028] Among them, d A (A) is the unbalance degree of three-phase voltage or current, A + is the voltage or current positive sequence component, A - is the negative sequence component of voltage or current.

[0029] One of the preferred solutions, the monitoring of temporary rise, temporary drop and interruption events based on the original waveform data is specifically as follows:

[0030] The half-cycle effective value is calculated based on the original waveform data; the half-cycle effective value is:

[0031]

[0032] in, is the effective value of half a cycle, N is the number of sampling points of the cycle, u(t n ) is t n The instantaneous voltage sampling value at the moment;

[0033] The start or end of a temporary rise, temporary drop and interruption event is determined according to the half-cycle effective value.

[0034] A system including the distributed photovoltaic power quality monitoring method includes:

[0035] A serial peripheral device interface and a voltage calculation module, a harmonic calculation module, a flicker calculation module, a three-phase unbalance calculation module and a transient monitoring module connected to the serial peripheral device interface;

[0036] The serial peripheral device interface is used to receive the original waveform data of the IoT table in real time;

[0037] The voltage calculation module is used to calculate the amplitude of the cycle voltage according to the original waveform data;

[0038] The harmonic calculation module is used to calculate the amplitude of the frequency harmonic voltage and / or current, the amplitude of the interharmonic voltage and / or current and the harmonic active power according to the original waveform data;

[0039] The flicker calculation module is used to calculate the instantaneous flicker value through a filter according to the original waveform data;

[0040] The three-phase unbalance calculation module is used to calculate the positive sequence, negative sequence and zero sequence components and the degree of unbalance of the voltage and / or current according to the original waveform data;

[0041] The transient monitoring module is used to monitor temporary rise, temporary drop and interruption events according to the original waveform data.

[0042] A frequency deviation calculation method for a distributed photovoltaic power quality monitoring system comprises the following steps:

[0043] Performing interrupt configuration on a chip select pin of a serial peripheral device interface, and performing interrupt detection on the serial peripheral device interface in real time;

[0044] When the serial peripheral device interface receives the original waveform data of the IoT table, it determines whether the serial peripheral device interface triggers an interrupt; and calculates the interval time between two consecutive falling edge or rising edge interrupts of the serial peripheral device interface;

[0045] Continuously accumulate the interval time between multiple rising edge or falling edge interrupts to calculate the real-time frequency value;

[0046] A frequency deviation value is calculated according to the real-time frequency value.

[0047] In the above technical solution of the present invention, the distributed photovoltaic power quality monitoring method comprises: acquiring the original waveform data of the Internet of Things in real time through the serial peripheral device interface;

[0048] The cyclic voltage amplitude, cyclic harmonic voltage and / or current amplitude, interharmonic voltage and / or current amplitude, harmonic active power, positive sequence, negative sequence and zero sequence components of voltage and / or current and unbalance are calculated according to the original waveform data; and the instantaneous flicker value is calculated through a filter according to the original waveform data, and temporary rise, temporary drop and interruption event monitoring is performed according to the original waveform data. The present invention solves the technical problem of how to monitor the power quality in real time and quickly and calculate the frequency deviation in real time.

[0049] In the present invention, a separate distributed photovoltaic power quality monitoring system is plugged into the Internet of Things meter to obtain raw waveform data, thereby calculating voltage deviation, harmonics, unbalance and transient event power quality monitoring quantities, thereby realizing power quality monitoring of the photovoltaic grid-connected power generation system.

[0050] In the present invention, the original waveform data transmitted by the Internet of Things to the serial peripheral device interface are all fixed frequency points. When the frequency cannot be calculated by the number of sampling points, the frequency is calculated by obtaining the time difference of the falling edge or rising edge of the sampling interrupt pin, thereby realizing real-time calculation of the frequency deviation, ensuring that the frequency deviation can be monitored in real time, and supporting the detection requirements of anti-islanding effect in distributed photovoltaic power generation systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.

[0052] Figure 1 A schematic diagram of a distributed photovoltaic power quality monitoring method according to an embodiment of the present invention;

[0053] Figure 2 A schematic diagram of a distributed photovoltaic power quality monitoring system according to an embodiment of the present invention;

[0054] Figure 3 A schematic diagram of a frequency deviation calculation method for a distributed photovoltaic power quality monitoring system according to an embodiment of the present invention.

[0055] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with the implementation methods and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0056] 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.

[0057] Furthermore, the technical solutions between the various embodiments of the present invention may be combined with each other, but this must be based on the fact that they can be implemented by ordinary technicians in the field. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0058] See also Figure 1 According to one aspect of the present invention, the present invention provides a distributed photovoltaic power quality monitoring method, wherein the distributed photovoltaic power quality monitoring method comprises the following steps:

[0059] Get the raw waveform data of IoT in real time through the serial peripheral device interface;

[0060] The cyclic voltage amplitude, cyclic harmonic voltage and / or current amplitude, interharmonic voltage and / or current amplitude, harmonic active power, positive sequence, negative sequence and zero sequence components of voltage and / or current and the degree of imbalance are calculated according to the original waveform data; and the instantaneous flicker value is calculated through a filter according to the original waveform data, and temporary rise, temporary sag and interruption events are monitored according to the original waveform data.

[0061] Specifically, in this embodiment, the calculation of the cyclic voltage amplitude according to the original waveform data is specifically as follows:

[0062] The single-cycle voltage amplitude is obtained through the original waveform data, and the voltage amplitude of M cycles is obtained by averaging the voltage amplitudes of M consecutive single cycles; the single-cycle voltage amplitude is:

[0063]

[0064] Among them, U RMS is the single-cycle voltage amplitude, N is the number of single-cycle sampling points, u(t n ) is t n The instantaneous voltage sampling value at time.

[0065] Specifically, in this embodiment, the calculation of the frequency harmonic voltage and / or current amplitude, the interharmonic voltage and / or current amplitude and the harmonic active power according to the original waveform data is specifically as follows:

[0066] Based on the M-cycle analysis window, the frequency spectrum components are obtained by discrete Fourier transform;

[0067] Calculate the harmonic voltage and / or current amplitude and the interharmonic voltage and / or current amplitude of N cycles by using a subgroup algorithm;

[0068] The active power of each harmonic is calculated according to the amplitude of the harmonic voltage and the harmonic current.

[0069] Specifically, in this embodiment, the sub-spectral components are:

[0070]

[0071] Wherein, X(k) is the kth spectral component, x(n) is the voltage sampling point data or the current sampling point data, and DFT[x(n)] is the spectral components obtained by performing discrete Fourier transform on the voltage sampling point data or the current sampling point data. is the rotation factor.

[0072] Specifically, in this embodiment, the instantaneous flicker value is calculated through a filter according to the original waveform data, specifically:

[0073] The voltage half-cycle effective value is obtained according to the original waveform data, and the voltage variation amplitude and voltage variation frequency are calculated; the instantaneous flicker is calculated according to the IEC standard, and the voltage sampling data in the original waveform data is subjected to square, low-pass, high-pass, weighted filtering and smoothing processing in turn to calculate the instantaneous flicker value of several time periods.

[0074] Specifically, in this embodiment, the positive sequence, negative sequence and zero sequence components of the voltage and / or current and the degree of imbalance are calculated according to the original waveform data, specifically:

[0075] Perform harmonic analysis on the original waveform data to obtain fundamental voltage and fundamental current, and decompose the fundamental voltage and fundamental current into symmetrical components; the symmetrical components are:

[0076]

[0077] in, are the positive sequence component, negative sequence component and zero sequence component of voltage or current respectively, is the voltage or current component measured by the three channels A / B / C of the IoT table, and a is a complex operator;

[0078] The positive sequence, negative sequence, zero sequence components and unbalance degree of the voltage and / or current are calculated according to the fundamental wave voltage and fundamental wave current.

[0079] Specifically, in this embodiment, the imbalance degree is:

[0080]

[0081] Among them, d A (A) is the unbalance degree of three-phase voltage or current, A + is the voltage or current positive sequence component, A - is the negative sequence component of voltage or current.

[0082] Specifically, in this embodiment, the monitoring of temporary rise, temporary drop and interruption events according to the original waveform data is specifically as follows:

[0083] The half-cycle effective value is calculated based on the original waveform data; the half-cycle effective value is:

[0084]

[0085] in, is the effective value of half a cycle, N is the number of sampling points of the cycle, u(t n ) is t n The instantaneous voltage sampling value at the moment;

[0086] The start or end of a temporary rise, temporary drop and interruption event is determined according to the half-cycle effective value.

[0087] See also Figure 2 According to one aspect of the present invention, the present invention provides a distributed photovoltaic power quality monitoring system, wherein the distributed photovoltaic power quality monitoring system comprises:

[0088] A serial peripheral device interface and a voltage calculation module, a harmonic calculation module, a flicker calculation module, a three-phase unbalance calculation module and a transient monitoring module connected to the serial peripheral device interface;

[0089] The serial peripheral device interface is used to receive the original waveform data of the IoT table in real time;

[0090] The voltage calculation module is used to calculate the amplitude of the cycle voltage according to the original waveform data;

[0091] The harmonic calculation module is used to calculate the amplitude of the frequency harmonic voltage and / or current, the amplitude of the interharmonic voltage and / or current and the harmonic active power according to the original waveform data;

[0092] The flicker calculation module is used to calculate the instantaneous flicker value through a filter according to the original waveform data;

[0093] The three-phase unbalance calculation module is used to calculate the positive sequence, negative sequence and zero sequence components and the degree of unbalance of the voltage and / or current according to the original waveform data;

[0094] The transient monitoring module is used to monitor temporary rise, temporary drop and interruption events according to the original waveform data.

[0095] Specifically, in this embodiment, after the distributed photovoltaic power quality monitoring system is plugged into the Internet of Things meter, the two communicate with each other for identity authentication. After the authentication is completed, the Internet of Things meter starts the serial peripheral device interface and begins to continuously transmit raw waveform data with a fixed frequency point to the distributed photovoltaic power quality monitoring system, with 128 or 256 sampling points fixed per cycle; after the serial peripheral device interface receives the raw waveform data, it deframes the raw waveform data and extracts the raw waveform data without frame header, frame tail and other information, wherein the raw waveform data includes current sampling data and voltage sampling data, which is not specifically limited in the present invention and can be set according to needs.

[0096] Specifically, in this embodiment, the voltage calculation module calculates the cyclic voltage amplitude according to the original waveform data, specifically:

[0097] The single-cycle voltage amplitude is obtained through the original waveform data, and the voltage amplitude of M cycles is obtained by averaging the voltage amplitudes of M consecutive single cycles; the single-cycle voltage amplitude is:

[0098]

[0099] Among them, U RMS is the single-cycle voltage amplitude, N is the number of single-cycle sampling points, u(t n ) is t n Instantaneous voltage sampling value at the moment; in the present invention, M is 10, that is, the voltage amplitude of 10 consecutive cycles is taken to average the voltage amplitude of 10 cycles, and finally the voltage deviation value can be calculated based on the difference between the 10-cycle voltage amplitude and the rated voltage.

[0100] Specifically, in this embodiment, the harmonic calculation module is used to calculate the frequency harmonic voltage and / or current amplitude, interharmonic voltage and / or current amplitude and harmonic active power according to the original waveform data, specifically:

[0101] Based on the M-cycle analysis window, each sub-spectral component is obtained by discrete Fourier transform; in the present invention, a 10-cycle analysis window is used, that is, a harmonic calculation is performed every 10 cycles, which is suitable for harmonic analysis with volatility; the sub-spectral components are:

[0102]

[0103] Wherein, X(k) is the kth spectral component, x(n) is the voltage sampling point data or the current sampling point data, and DFT[x(n)] is the spectral components obtained by performing discrete Fourier transform on the voltage sampling point data or the current sampling point data. is the rotation factor;

[0104] The harmonic voltage and / or current amplitude, interharmonic voltage and / or current amplitude of N cycles are calculated by subgroup algorithm; since amplitude fluctuation will affect discrete Fourier transform, the energy of harmonic components may leak to adjacent interharmonic frequencies. In order to improve the evaluation accuracy, the harmonics and interharmonics use subgroup algorithm to calculate the harmonic voltage and / or current amplitude, interharmonic voltage and / or current amplitude; the harmonic subgroup is:

[0105]

[0106] in, is the defined 10-cycle harmonic subgroup, X(k) is the kth spectral component;

[0107] The interharmonic subgroups are:

[0108]

[0109] in, is the defined 10-cycle interharmonic subgroup;

[0110] According to the harmonic voltage and harmonic current amplitude, the active power of each harmonic is calculated; the active power of each harmonic is:

[0111] P h =U k I k cosφ k

[0112] Among them, P h is the active power of each harmonic, U k is the harmonic voltage amplitude, I k is the harmonic current amplitude, φ k is the phase angle between the harmonic current and the harmonic voltage.

[0113] Specifically, in this embodiment, the flicker calculation module is used to calculate the instantaneous flicker value through a filter according to the original waveform data, specifically:

[0114] The voltage half-cycle effective value is obtained according to the original waveform data, and the voltage variation amplitude and voltage variation frequency are calculated; the instantaneous flicker is calculated according to the IEC standard, and the voltage sampling data in the original waveform data is subjected to square, low-pass, high-pass, weighted filtering and smoothing processing in turn to calculate the instantaneous flicker value of several time periods.

[0115] Specifically, in this embodiment, in the flicker calculation module, flicker measurement is performed with reference to the IEC standard, and the voltage fluctuation amplitude and the voltage fluctuation frequency are calculated by analyzing and judging the effective value of the voltage half-cycle. The present invention is not limited thereto, and a conventional method can be specifically used; and with reference to the IEC standard, instantaneous flicker is calculated, and based on the design principle of the IEC flicker meter, the voltage sampling data is sequentially squared, low-pass, high-pass, weighted filtered and smoothed, and the statistical values ​​of the instantaneous flicker for 1 minute, 10 minutes and 2 hours are calculated; wherein, the high-pass filter bandpass cutoff frequency is 0.05 Hz, and the corresponding angular frequency is Ω p =0.05×2π, and the S-domain transfer function is realized by digitization as follows:

[0116]

[0117] Among them, G(s) is the S-domain transfer function of the high-pass filter, and s is the input of the filter, that is, the input voltage sampling data;

[0118] The low-pass processing is performed by using a low-pass filter. The low-pass filter is a sixth-order Butterworth low-pass filter with a cut-off frequency of 35 Hz. Its S-domain transfer function is:

[0119]

[0120] Where G'(s) is the S-domain transfer function of the low-pass filter, b0=b6=1, b1=b5=3.864, b2=b4=7.464, b5=9.141, Ω p =2π×35;

[0121] The weighted filtering is processed by using a sensitivity weighted filter, and the S-domain transfer function of the sensitivity weighted filter is:

[0122]

[0123] Wherein, k, λ, ω1, ω2, ω3 and ω4 are parameters of the S-domain transfer function of the sensitivity weighted filter. In the present invention, k=1.74802, λ=2π×4.05981, ω1=2π×9.15494, ω2=2π×2027979, ω3=2π×1.22535, ω4=2π×21.9;

[0124] The system function of the first-order low-pass filter in the S domain is:

[0125]

[0126] After the voltage sampling data is processed by the above filter, the instantaneous flicker value of 10 minutes is calculated, that is, the statistical value of the instantaneous flicker of 10 minutes is:

[0127]

[0128] Among them, P st is the statistical value of instantaneous flicker for 10 minutes, P 1s It is the cumulative average value of the instantaneous flicker value with a cumulative probability exceeding 0.7%, the instantaneous flicker value with a cumulative probability exceeding 1%, and the instantaneous flicker value with a cumulative probability exceeding 1.5%. 3s , P 10s , P 50s Similarly;

[0129] P 50s =(P 30 +P 50 +P 80 ) / 3

[0130] P 10s =(P6+P8+P 10 +P 13 +P 17 ) / 5

[0131] P 3s =(P 2.2 +P3+P4) / 3

[0132] P 1s =(P 0.7 +P1+P 1.5 ) / 3

[0133] Among them, P k is the instantaneous flicker value with cumulative probability exceeding k%, such as k = 30, P 30 The instantaneous flicker value with a cumulative probability exceeding 30%, and the others are similar, and the present invention will not be described in detail;

[0134] The statistical value of the instantaneous flicker for 2 hours is:

[0135]

[0136] Among them, P lt is the statistical value of instantaneous flicker for 2 hours, where N is 12, that is, the instantaneous flicker value of 2 hours is divided into 12 10-minute instantaneous flicker values ​​for statistics, P sti is the instantaneous flicker value of the i-th 10 minutes.

[0137] Specifically, in this embodiment, the three-phase unbalance calculation module is used to calculate the positive sequence, negative sequence and zero sequence components of the voltage and / or current and the degree of unbalance according to the original waveform data, specifically:

[0138] Perform harmonic analysis on the original waveform data to obtain fundamental voltage and fundamental current, and decompose the fundamental voltage and fundamental current into symmetrical components; calculate the positive sequence, negative sequence, zero sequence components and imbalance of voltage and / or current according to the fundamental voltage and fundamental current; when the system voltage or current is asymmetrical, the asymmetrical components can be decomposed into symmetrical components (positive sequence and negative sequence) and zero sequence components of the same phase; the symmetrical components are:

[0139]

[0140] in, are the positive sequence component, negative sequence component and zero sequence component of voltage or current respectively, It is the voltage or current component measured by the three channels A / B / C of the IoT table, and a is a complex operator.

[0141] Specifically, in this embodiment, the imbalance degree is:

[0142]

[0143] Among them, d A (A) is the unbalance degree of three-phase voltage or current, A + is the voltage or current positive sequence component, A - is the negative sequence component of voltage or current.

[0144] Specifically, in this embodiment, the measured voltage and current components corresponding to the three channels of the electric meter A, B, and C are respectively represented by vectors, which are: The present invention is described by taking voltage sampling data as an example; the symmetrical components of the voltage sampling data are:

[0145]

[0146] Where a is a complex operator, a=e j120° , is the voltage positive sequence component, is the negative sequence voltage component, is the voltage zero sequence component; Among them, real represents the real part and imag represents the imaginary part; finally we can get:

[0147] Voltage positive sequence component

[0148] Negative sequence voltage component

[0149] Voltage zero sequence component

[0150] Three-phase voltage unbalance

[0151] Specifically, in this embodiment, the transient monitoring module is used to monitor temporary rise, temporary drop and interruption events according to the original waveform data, specifically:

[0152] The half-cycle effective value is calculated based on the original waveform data; the half-cycle effective value is:

[0153]

[0154] in, is the effective value of half a cycle, N is the number of sampling points of the cycle, u(t n ) is t n The instantaneous voltage sampling value at the moment;

[0155] Determine the start or end of a temporary rise, temporary drop and interruption event according to the half-cycle effective value;

[0156] For sag events: in a single-phase system, when the half-cycle RMS value is less than the sag threshold, the event starts, and when the half-cycle RMS value is greater than or equal to the sag threshold plus the hysteresis voltage, the event ends; in a multi-phase system, when the half-cycle RMS value of one or more channels is less than the sag threshold, the event starts, and when the half-cycle RMS values ​​of all channels are greater than or equal to the sag threshold plus the hysteresis voltage, the event ends;

[0157] For interruption events: in a single-phase system, when the half-cycle effective value is less than the interruption threshold, the event starts, and when the half-cycle effective value is greater than or equal to the interruption threshold plus the hysteresis voltage, the event ends; in a multi-phase system, when the half-cycle effective value of all channels is less than the interruption threshold, the event starts, and when the half-cycle effective value of any channel is greater than or equal to the interruption threshold plus the hysteresis voltage, the event ends;

[0158] For swell events: in a single-phase system, when the effective value of the half-cycle is greater than the swell threshold, the event starts; when the effective value of the half-cycle is less than or equal to the swell threshold plus the hysteresis voltage, the event ends; in a multi-phase system, when the effective value of the half-cycle of one or more channels is greater than the swell threshold, the event starts; when the effective value of the half-cycle of all channels is less than or equal to the swell threshold plus the hysteresis voltage, the event ends.

[0159] See also Figure 3 According to another aspect of the present invention, the present invention provides a frequency deviation calculation method for a distributed photovoltaic power quality monitoring system, comprising the following steps:

[0160] The chip select pin of the serial peripheral device interface is configured for interruption, and the serial peripheral device interface is detected for interruption in real time; the chip select pin of the serial peripheral device interface of the distributed photovoltaic power quality monitoring system is configured for interruption detection, and the priority of the interruption is configured as the highest priority, the serial peripheral device interface is a slave, and the IoT table is a host;

[0161] When the serial peripheral device interface receives the original waveform data of the IoT meter, it determines whether the serial peripheral device interface triggers an interrupt; and calculates the interval time between two consecutive falling or rising edge interrupts of the serial peripheral device interface; when the IoT meter performs real-time original sampling data transmission, the frequency is 50Hz, and the cycle data transmission is performed every 20ms. When the frequency changes, the cycle transmission interval time will also change accordingly. Between each transmission, the IoT meter will first pull down the chip select pin potential, and after the transmission is completed, the chip select pin potential will be pulled up. Therefore, when the distributed photovoltaic power quality monitoring system receives the original waveform sampling point cycle data transmitted in real time from the IoT meter, it first detects whether the IoT meter pulls down the chip select pin potential, that is, whether an interrupt is triggered;

[0162] Continuously accumulate the interval time between multiple rising edge or falling edge interrupts to calculate the real-time frequency value; according to the requirements of the real-time frequency value, it can be a frequency value of 1 second or shorter, and the corresponding frequency value of 1 second or shorter is calculated by accumulating the interval time between 50 falling edge or rising edge interrupts of the chip select pin or less interval time;

[0163] The frequency deviation value is calculated according to the real-time frequency value; the frequency deviation value is:

[0164] Δf=f x -f s

[0165] Where Δf is the frequency deviation value, f x is the real-time frequency value, f s is the standard frequency value.

[0166] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. All equivalent structural changes made using the contents of the present invention's specification and drawings, or directly / indirectly applied in other related technical fields, are included in the patent protection scope of the present invention.

Claims

1. A distributed photovoltaic power quality monitoring method, characterized in that: The following steps are involved: Get the raw waveform data of IoT in real time through the serial peripheral device interface; The cyclic voltage amplitude, cyclic harmonic voltage and / or current amplitude, interharmonic voltage and / or current amplitude, harmonic active power, positive sequence, negative sequence and zero sequence components of voltage and / or current and the degree of imbalance are calculated according to the original waveform data; and the instantaneous flicker value is calculated through a filter according to the original waveform data, and temporary rise, temporary sag and interruption events are monitored according to the original waveform data.

2. A distributed photovoltaic power quality monitoring method according to claim 1, characterized in that: The calculation of the cycle voltage amplitude according to the original waveform data is specifically as follows: The single-cycle voltage amplitude is obtained through the original waveform data, and the voltage amplitude of M cycles is obtained by averaging the voltage amplitudes of M consecutive single cycles; the single-cycle voltage amplitude is: Among them, U RMS is the single-cycle voltage amplitude, N is the number of single-cycle sampling points, u(t n ) is t n The instantaneous voltage sampling value at time.

3. A distributed photovoltaic power quality monitoring method according to any one of claims 1-2, characterized in that: The calculation of the frequency harmonic voltage and / or current amplitude, interharmonic voltage and / or current amplitude and harmonic active power according to the original waveform data is specifically as follows: Based on the M-cycle analysis window, the frequency spectrum components are obtained by discrete Fourier transform; Calculate the harmonic voltage and / or current amplitude and the interharmonic voltage and / or current amplitude of N cycles by using a subgroup algorithm; The active power of each harmonic is calculated according to the amplitude of the harmonic voltage and the harmonic current.

4. A distributed photovoltaic power quality monitoring method according to claim 3, characterized in that: The sub-spectral components are: Wherein, X(k) is the kth spectral component, x(n) is the voltage sampling point data or the current sampling point data, and DFT[x(n)] is the spectral components obtained by performing discrete Fourier transform on the voltage sampling point data or the current sampling point data. is the rotation factor.

5. A distributed photovoltaic power quality monitoring method according to any one of claims 1-2, characterized in that: The instantaneous flicker value is calculated through a filter according to the original waveform data, specifically: The voltage half-cycle effective value is obtained according to the original waveform data, and the voltage variation amplitude and voltage variation frequency are calculated; the instantaneous flicker is calculated according to the IEC standard, and the voltage sampling data in the original waveform data is subjected to square, low-pass, high-pass, weighted filtering and smoothing processing in turn to calculate the instantaneous flicker value of several time periods.

6. A distributed photovoltaic power quality monitoring method according to any one of claims 1-2, characterized in that: The calculation of the positive sequence, negative sequence and zero sequence components of the voltage and / or current and the degree of imbalance according to the original waveform data is specifically as follows: Perform harmonic analysis on the original waveform data to obtain fundamental voltage and fundamental current, and decompose the fundamental voltage and fundamental current into symmetrical components; the symmetrical components are: in, are the positive sequence component, negative sequence component and zero sequence component of voltage or current respectively, is the voltage or current component measured by the three channels A / B / C of the IoT table, and a is a complex operator; The positive sequence, negative sequence, zero sequence components and unbalance degree of the voltage and / or current are calculated according to the fundamental wave voltage and fundamental wave current.

7. A distributed photovoltaic power quality monitoring method according to claim 6, characterized in that: The degree of imbalance is: Among them, d A (A) is the unbalance degree of three-phase voltage or current, A + is the voltage or current positive sequence component, A - is the negative sequence component of voltage or current.

8. A distributed photovoltaic power quality monitoring method according to any one of claims 1-2, characterized in that: The monitoring of temporary rise, temporary drop and interruption events according to the original waveform data is specifically as follows: The half-cycle effective value is calculated based on the original waveform data; the half-cycle effective value is: in, is the effective value of half a cycle, N is the number of sampling points of the cycle, u(t n ) is t n The instantaneous voltage sampling value at the moment; The start or end of a temporary rise, temporary drop and interruption event is determined according to the half-cycle effective value.

9. A system comprising a distributed photovoltaic power quality monitoring method according to any one of claims 1 to 8, characterized in that: include: A serial peripheral device interface and a voltage calculation module, a harmonic calculation module, a flicker calculation module, a three-phase unbalance calculation module and a transient monitoring module connected to the serial peripheral device interface; The serial peripheral device interface is used to receive the original waveform data of the IoT table in real time; The voltage calculation module is used to calculate the amplitude of the cycle voltage according to the original waveform data; The harmonic calculation module is used to calculate the amplitude of the frequency harmonic voltage and / or current, the amplitude of the interharmonic voltage and / or current and the harmonic active power according to the original waveform data; The flicker calculation module is used to calculate the instantaneous flicker value through a filter according to the original waveform data; The three-phase unbalance calculation module is used to calculate the positive sequence, negative sequence and zero sequence components and the degree of unbalance of the voltage and / or current according to the original waveform data; The transient monitoring module is used to monitor temporary rise, temporary drop and interruption events according to the original waveform data.

10. A frequency deviation calculation method for a distributed photovoltaic power quality monitoring system, characterized in that: The following steps are involved: Performing interrupt configuration on a chip select pin of a serial peripheral device interface, and performing interrupt detection on the serial peripheral device interface in real time; When the serial peripheral device interface receives the original waveform data of the IoT table, it is determined whether the serial peripheral device interface triggers an interrupt; And calculate the interval time between two consecutive falling edge or rising edge interrupts of the serial peripheral device interface; Continuously accumulate the interval time between multiple rising edge or falling edge interrupts to calculate the real-time frequency value; A frequency deviation value is calculated according to the real-time frequency value.

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