Three-phase electric phase prediction grid connection device based on linear regression
Through a three-phase electrical phase prediction device based on linear regression, the problem of inaccurate phase difference prediction when the generator and the power grid are connected to the grid is solved, and accurate grid-connection prediction is achieved under complex grid waveform conditions, reducing the risk of shock current and improving grid-connection safety.
Patent Information
- Application Number
- CN202510202368.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-24
- Publication Date
- 2025-04-25
AI Technical Summary
In the prior art, when the generator is connected to the power grid, it is difficult to accurately predict the phase difference, resulting in a large phase deviation at the time of the grid connection, resulting in an impact current, which poses a safety hazard.
A three-phase electrical phase prediction device based on linear regression is adopted. Through the signal acquisition module, phase detection module and phase prediction module, combined with the mechanical action delay characteristics of the switching switch, the grid connection time is predicted in advance to reduce the impact of harmonics on phase calculation.
It realizes accurate prediction of grid connection time under power grid conditions with poor waveform quality, avoid excessive phase difference, reduce shock current, and improve grid connection safety.
Smart Images
Figure CN119891362A_ABST
Abstract
Description
Technical Field
[0001] The invention belongs to the field of electrical equipment, and in particular relates to a three-phase electric phase prediction grid-connected device based on linear regression. Background Art
[0002] Grid connection refers to physically connecting two electrically unconnected systems or generators. The purpose of this device is to achieve grid connection between the generator and the power grid. However, before performing the grid connection operation, the frequency, voltage, phase, and phase sequence of the generator must be consistent with the frequency, voltage, phase, and phase sequence of the power grid in order to connect to the grid for power generation (the four conditions are exactly the same, which is called synchronization; there is a little deviation, but it is within the allowed range, which is called quasi-synchronization). The present invention is quasi-synchronous grid connection, which requires the phase sequence to be consistent, the frequency difference is less than 0.1hz, the voltage effective value is less than 5%, and the phase difference is less than 3°.
[0003] Therefore, there is a frequency difference on both sides, and therefore the phase difference on both sides is in a dynamic process of change. At this time, it is required to capture the opportunity when the phase difference is close to zero under the premise that the voltage, frequency and phase sequence meet the requirements to complete the grid-connected action. However, due to the certain mechanical delay of the switching action, after the action is started when the phase difference is calculated to be close to zero, the actual phase at the time of grid-connection will have a large deviation, resulting in a large impact current, which has certain safety hazards to electrical equipment. At the same time, existing phase prediction is often used for power switching between generators, photovoltaic inverters, etc. These power sources have good waveform quality and few harmonics, but are rarely connected to the power grid. It also does not take into account the complex harmonics in the actual waveform of the power grid, which affects the phase calculation and leads to errors in phase prediction. As a result, the grid cannot be connected at the ideal time, resulting in equipment failure. Summary of the invention
[0004] In view of the deficiencies in the prior art, the present invention provides a three-phase power phase prediction grid-connected system based on linear regression. The idea of linear regression is used to reduce the influence of harmonics on phase calculation. At the same time, according to the changes in the frequency and phase difference between the mains and the generator, combined with the mechanical action delay characteristics of the switching switch itself, the switching command triggering moment is predicted in advance, thereby realizing accurate prediction of the grid-connected moment in the mains and generator of the power grid with poor waveform quality.
[0005] The technical solution adopted by the present invention to solve the technical problem is as follows:
[0006] A three-phase electric phase prediction grid-connected device based on linear regression, characterized in that it includes a power supply module, a signal acquisition module, a phase sequence detection module, a frequency detection module, a voltage amplitude detection module, a phase detection module, a phase prediction module, a data fusion module, a communication module, a grid-connected action module, and other additional modules;
[0007] The power supply module is electrically connected to the signal acquisition module, the phase sequence detection module, the frequency detection module, the voltage amplitude detection module, the phase detection module, the phase prediction module, the data fusion module, the communication module, the grid-connected action module and other additional modules;
[0008] The signal acquisition module is electrically connected to the power supply module, the phase sequence detection module, the frequency detection module, the voltage amplitude detection module, and the phase detection module.
[0009] The phase sequence detection module is electrically connected to the power supply module, the signal acquisition module, the data fusion module, and the communication module;
[0010] The frequency detection module is electrically connected to the power supply module, the signal acquisition module, the data fusion module, and the communication module;
[0011] The voltage amplitude detection module is electrically connected to the power supply module, the signal acquisition module, the data fusion module, and the communication module;
[0012] The phase detection module is electrically connected to the power supply module, the signal acquisition module, the phase prediction module, the data fusion module, and the communication module;
[0013] The phase prediction module is electrically connected to the power supply module, the signal acquisition module, the data fusion module, and the communication module;
[0014] The data fusion module is electrically connected to the power supply module, the signal acquisition module, the phase sequence detection module, the frequency detection module, the voltage amplitude detection module, the phase detection module, the phase prediction module, the communication module, and the grid-connected action module;
[0015] The communication module is electrically connected to the power module, the signal acquisition module, the phase sequence detection module, the frequency detection module, the voltage amplitude detection module, the phase detection module, the phase prediction module, the data fusion module, and the grid-connected action module;
[0016] The grid-connected action module is electrically connected to the power module, the data fusion module, and the communication module;
[0017] Preferably, the three-phase electric phase prediction grid-connected device based on linear regression is characterized in that the signal acquisition module includes a voltage transformer and an ADC sampler.
[0018] Preferably, the three-phase electric phase prediction grid-connected device based on linear regression is characterized in that the phase sequence detection module includes a core calculation module and a basic input and output module.
[0019] Preferably, the three-phase electric phase prediction grid-connected device based on linear regression is characterized in that the frequency detection module includes a core calculation module, a timer module, and a basic input and output module.
[0020] Preferably, the three-phase electric phase prediction grid-connected device based on linear regression is characterized in that the voltage amplitude detection module includes a core calculation module and a basic input and output module.
[0021] Preferably, the three-phase electric phase prediction grid-connected device based on linear regression is characterized in that the phase detection module includes a core calculation module and a basic input and output module.
[0022] Preferably, the three-phase electric phase prediction grid-connected device based on linear regression is characterized in that the phase prediction module includes a core calculation module and a basic input and output module.
[0023] Preferably, the three-phase power phase prediction grid-connected device based on linear regression is characterized in that the communication module includes an RS485 communication module and a Bluetooth communication module.
[0024] Preferably, the three-phase power phase prediction grid-connected device based on linear regression is characterized in that the additional module includes a screen display module and a human-computer interaction module.
[0025] The beneficial effects of the present invention are as follows: the design of the present invention uses Bluetooth and RS485 communications in human-computer interaction, and taking into account both wired and wireless communications can allow operators to interact easily, adapt to various complex situations, and improve the convenience and timeliness of operator maintenance, monitoring, and use.
[0026] The beneficial effect of the present invention is that the design of the present invention can consider the point where the action delay of the switching switch is predicted to be zero during the process of connecting the generator to the power grid and issue a grid connection instruction in advance, thereby avoiding excessive phase difference at the actual grid connection moment, excessive impact current, and safety hazards.
[0027] The beneficial effects of the present invention are as follows: the design of the present invention adopts the idea of linear regression for compensation when calculating the phase angle of the generator and the power grid, which can avoid the influence of harmonics in the power grid, resulting in errors in phase difference calculation, and then leading to phase prediction failure, resulting in excessive phase difference at the actual grid-connected moment, excessive impact current, and safety hazards. BRIEF DESCRIPTION OF THE DRAWINGS
[0028] 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 these drawings without paying creative work.
[0029] Figure 1 A schematic diagram of the connection structure of a three-phase electric phase prediction grid-connected device based on linear regression provided in an embodiment of the present invention;
[0030] Figure 2 A schematic flow chart of a phase difference prediction algorithm for a three-phase power phase prediction grid-connected device based on linear regression is provided in an embodiment of the present invention. DETAILED DESCRIPTION
[0031] 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.
[0032] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two elements. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.
[0033] like Figure 1 As shown, a three-phase electric phase prediction grid-connected device based on linear regression includes a power supply module, a signal acquisition module, a phase sequence detection module, a frequency detection module, a voltage amplitude detection module, a phase detection module, a phase prediction module, a data fusion module, a communication module, a grid-connected action module, and other additional modules;
[0034] The function of the power module is to provide operating energy for the entire system. The power module is electrically connected to the signal acquisition module, the phase sequence detection module, the frequency detection module, the voltage amplitude detection module, the phase detection module, the phase prediction module, the data fusion module, the communication module, the grid-connected action module and other additional modules;
[0035] The function of the signal acquisition module is to perform hardware amplitude conversion on the monitored three-phase low-voltage electricity, which converts 380v into the 0-3.3v amplitude range that can be collected by the microcontroller ADC, and scales the relatively high-voltage signal to the range that the microcontroller can handle. The signal acquisition module is electrically connected to the power module, phase sequence detection module, frequency detection module, voltage amplitude detection module, and phase detection module;
[0036] The function of the phase sequence detection module is to detect whether the phase sequence of the three-phase electricity on the generator side and the three-phase electricity on the grid side matches. The α and β components are obtained by Clark transformation through the acquired voltage data. The angle is calculated according to the direction of the α and β directions. The phase sequence is determined according to the direction of the angle value change on both sides to determine whether the phase sequence matches and meets the grid connection requirements. The phase sequence detection module is electrically connected to the power module, signal acquisition module, data fusion module, and communication module;
[0037] The function of the frequency detection module is to detect whether the frequency of the three-phase electricity on the generator side matches that of the three-phase electricity on the grid side. The frequency is obtained by collecting voltage data for a certain period of time and performing FFT calculation through the single-chip DSP. The frequency detection module is electrically connected to the power module, signal acquisition module, data fusion module, and communication module.
[0038] The function of the voltage amplitude detection module is to detect whether the voltage amplitudes of the three-phase electricity on the generator side and the three-phase electricity on the grid side match. By collecting the current voltage data on both sides in real time for comparison, it is determined whether the difference in the voltage amplitudes on both sides is within 5% to determine whether it meets the grid connection requirements. The voltage amplitude detection module is electrically connected to the power module, signal acquisition module, data fusion module, and communication module;
[0039] The function of the phase detection module is to calculate whether the phases of the three-phase electricity on the generator side and the three-phase electricity on the grid side match. By collecting voltage data on both sides in real time, the real-time phase is calculated through Clark transformation, inverse triangle algorithm and anti-jump algorithm. Whether the difference between the two sides is less than 3° is used to determine whether it meets the grid connection requirements. The phase detection module is electrically connected to the power module, signal acquisition module, phase prediction module, data fusion module and communication module;
[0040] The function of the phase prediction module is to predict the phase difference between the three-phase electricity on the generator side and the three-phase electricity on the grid side in real time, using a linear regression algorithm and phase difference integral summation, and then by limiting the screening conditions, accurately predicting the time point when the phase difference just meets the grid connection requirements based on the switching action delay. The phase prediction module is electrically connected to the power module, signal acquisition module, data fusion module, and communication module;
[0041] The function of the data fusion module is to collect and integrate all data, and provide the data fusion to the phase sequence detection module, frequency detection module, voltage amplitude detection module, phase detection module, and phase prediction module. The data includes the real-time voltage information of the three-phase electricity on both sides, the components after Clark transformation, the transformation array of FFT, and the phase array, phase difference array, and integral change rate array required for phase prediction. The data fusion module is electrically connected to the power module, signal acquisition module, phase sequence detection module, frequency detection module, voltage amplitude detection module, phase detection module, phase prediction module, communication module, and grid-connected action module;
[0042] The function of the communication module is to transmit data and instructions between the human-computer interaction module and the single-chip microcomputer. The communication methods include RS485 communication and Bluetooth communication. The transmission content includes data such as voltage, current, phase, frequency, etc. of three-phase electricity, as well as the opening and closing control instructions of circuit breakers, relays and automatic switching switches. The communication module is electrically connected to the power module, signal acquisition module, phase sequence detection module, frequency detection module, voltage amplitude detection module, phase detection module, phase prediction module, data fusion module, and grid-connected action module;
[0043] The function of the grid-connected action module is to control the opening and closing of the circuit breaker, relay and automatic transfer switch. The grid-connected action module is electrically connected to the power module, data fusion module and communication module;
[0044] Furthermore, the signal acquisition module includes a voltage transformer and an ADC sampler.
[0045] Furthermore, the phase sequence detection module includes a core calculation module and a basic input and output module.
[0046] Furthermore, the frequency detection module includes a core calculation module, a timer module, and a basic input and output module.
[0047] Furthermore, the voltage amplitude detection module includes a core calculation module and a basic input and output module.
[0048] Furthermore, the phase detection module includes a core calculation module and a basic input and output module.
[0049] Furthermore, the phase prediction module includes a core calculation module and a basic input and output module.
[0050] Furthermore, the communication module includes an RS485 communication module and a Bluetooth communication module.
[0051] Furthermore, the additional modules include a screen display module and a human-computer interaction module.
[0052] The operation mechanism of the three-phase electric phase prediction grid-connected device based on linear regression includes the following steps:
[0053] Step 1: The three-phase electricity on the generator side and the three-phase electricity on the grid side are connected to the signal acquisition module. The 380V voltage signal is proportionally reduced through the voltage transformer, and then amplified into a sine wave with a range of 0-3.3V through the operational amplifier circuit and transmitted to the data fusion module.
[0054] Step 2: The data fusion module gives the collected voltage data to the phase sequence detection module, frequency detection module, voltage amplitude detection module, and phase detection module respectively to calculate the required corresponding data.
[0055] Step 3: The phase sequence detection module receives the voltage data integrated from the data fusion module, performs Clark transformation on the voltage data to obtain the α and β components, and uses the α and β components to calculate the rotation angle through inverse trigonometric functions. By judging the increase and decrease trend of the rotation angle, the current phase sequence can be known, and the phase sequence on the generator side and the grid side can be compared to determine whether they match.
[0056] Step 4: The frequency detection module receives the voltage data integrated from the data fusion module, stores the voltage data, performs fast Fourier transform after accumulating 2048 points, converts the time domain data into frequency domain data, and then screens out the maximum value in the frequency domain, which is the frequency. Compare and determine whether the frequencies on the generator side and the grid side match and whether the difference is within 0.1HZ.
[0057] Step 5: The voltage amplitude detection module receives the voltage data provided by the data fusion module, calculates the voltage amplitude in real time, and passes the historical maximum and local maximum values to the data fusion module to compare and determine whether the voltage amplitudes on the generator side and the grid side match and whether the difference is within 5%.
[0058] Step 6: The phase detection module receives the real-time voltage data and the historical maximum and local maximum values from the data fusion module, and uses the inverse trigonometric function to calculate the phase according to the current real-time voltage and maximum value. However, the current phase angle is only between zero and one hundred and eighty degrees. Then, a negative sign is added based on the judgment of the jump point to make the phase angle range reach plus or minus one hundred and eighty degrees. However, due to the existence of harmonics, the middle section of the phase angle waveform is tortuous at this time. The waveform is stored, and the front and rear sections of the waveform of the previous cycle are used to calculate the rate of change, which is compensated to the next cycle. The idea of linear regression is constructed to fit the tortuous phase angle waveform into a smooth phase angle waveform, which is output to the data fusion module for subsequent phase prediction.
[0059] Step 7: The phase prediction module receives the phase angle array provided by the data fusion module, calculates the phase angle difference on both sides in real time, stores the waveform of the phase angle difference, calculates the adjacent phase angle difference, integrates and sums the phase angle difference, and counts the rate of change of the adjacent phase angle difference. According to the current phase angle difference and the rate of change of the phase angle difference, the phase angle difference after a specified time is predicted, and a local average predicted phase angle difference is counted at the same time. Finally, according to the current phase angle difference, the predicted phase angle difference, and the local average value of the predicted phase angle difference, the closing and grid-connected phase zeroing time point is locked at the same time, and the grid-connected instruction is issued in advance with the help of the communication module.
[0060] Step 8: Finally, the grid-connected action module receives the grid-connected instruction issued by the phase prediction module with the help of the communication module, closes the automatic transfer switch at the specified time point, and completes the grid-connected action.
[0061] The power module in this example uses a switching power supply solution to power the entire system. The input is 220v AC, which is converted into 15v by the power module and then further converted into 3.3v to power the entire system. The three-phase electricity collected by the signal acquisition module is 380v, so the corresponding voltage transformer parameter is 380:100. The analog circuit based on the AD623BRZ amplifier and the 12-bit AD sampler of the GD32F427Z single-chip microcomputer are further used. The phase sequence detection module, frequency detection module, voltage amplitude detection module, phase detection module, phase prediction module, data fusion module and the core calculation module, timer module, and general input and output module contained in these modules are all integrated with the GD32F427Z single-chip microcomputer. RS485 and Bluetooth communication are used to realize the interaction between the external ten-inch touch screen and the main control unit. The connection method of the system is that the signal acquisition module is connected to the three-phase electricity on the generator side and the grid side, the AD sampler of the single-chip microcomputer is connected to the output of the signal acquisition module, and the output of the AD sampler is connected to the central processing unit (COTEX-M core). The central processing unit outputs the data that requires human-computer interaction in the data fusion module to the external screen through the communication module for human-computer interaction.
[0062] Under normal operation, the three-phase electricity on the generator side and the grid side to be tested is first scaled by the signal acquisition module and the signal processing module to a sine wave of 380V AC into a sine wave with a range of 0-3.3V and then passed to the ADC module of the microcontroller for sampling. The sampled voltage data is handed over to the data fusion module. After the phase sequence detection module obtains the voltage data, it obtains the rotation angle through Clark transformation and inverse trigonometric function, and judges whether the phase sequence is consistent by the direction of the rotation angle. After the frequency detection module obtains the voltage data, it first stores it. After accumulating 2048 data, it converts the data in the time domain into the frequency domain through the fast Fourier transform, and selects the maximum value of the frequency domain to obtain the frequency on both sides. The frequency difference on both sides is determined by whether it is within 0.1HZ to determine whether it meets the grid connection conditions. The voltage amplitude detection module detects whether the voltage amplitude difference on both sides is within 5% in real time. The phase detection module calculates the phase of the current waveform through the inverse trigonometric function according to the components of the Clark transformation of the data fusion module and the stored maximum value, and then outputs a phase waveform ranging from -180° to +180° in real time according to the jump point of the waveform, such as the phase is positive when the waveform amplitude changes from large to small, and negative when it changes from small to large. However, due to the influence of grid harmonics, the middle section of the phase waveform is tortuous. We need to use the idea of linear regression to fit the phase waveform into a smooth one and then output it to the phase prediction module. The phase prediction module makes a difference between the phase waveforms on the generator side and the grid side to obtain the phase difference waveform, and then calculates the difference between adjacent phase differences, calculates the phase difference change rate according to time, and then predicts the phase difference value after the specified time according to the phase difference change rate and the known grid-connected switch action time, stores and statistically calculates the local phase difference average value, but because the grid-side frequency is not stable, the final grid-connected time point must be judged under the constraints of the current phase difference, predicted phase difference, and predicted phase difference average value, and then sends a signal to the grid-connected action module through the communication module. The final grid-connected action is to perform the grid-connected closing action at the specified time point.
[0063] The design of the present invention can realize the phase difference prediction between the three-phase electricity of the generator and the three-phase electricity of the power grid, and can avoid the excessive phase difference at the actual grid-connected time due to the inherent action delay of the automatic transfer switch during the grid-connected process, thereby avoiding faults such as current shock caused by the current shock.
[0064] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.
Claims
1. A three-phase power phase prediction grid-connected device based on linear regression, characterized in that: Including power supply module, signal acquisition module, phase sequence detection module, frequency detection module, voltage amplitude detection module, phase detection module, phase prediction module, data fusion module, communication module, grid-connected action module, and other additional modules; The power supply module is electrically connected to the signal acquisition module, the phase sequence detection module, the frequency detection module, the voltage amplitude detection module, the phase detection module, the phase prediction module, the data fusion module, the communication module, the grid-connected action module and other additional modules; The signal acquisition module is electrically connected to the power supply module, the phase sequence detection module, the frequency detection module, the voltage amplitude detection module, and the phase detection module. The phase sequence detection module is electrically connected to the power supply module, the signal acquisition module, the data fusion module, and the communication module; The frequency detection module is electrically connected to the power supply module, the signal acquisition module, the data fusion module, and the communication module; The voltage amplitude detection module is electrically connected to the power supply module, the signal acquisition module, the data fusion module, and the communication module; The phase detection module is electrically connected to the power supply module, the signal acquisition module, the phase prediction module, the data fusion module, and the communication module; The phase prediction module is electrically connected to the power supply module, the signal acquisition module, the data fusion module, and the communication module; The data fusion module is electrically connected to the power supply module, the signal acquisition module, the phase sequence detection module, the frequency detection module, the voltage amplitude detection module, the phase detection module, the phase prediction module, the communication module, and the grid-connected action module; The communication module is electrically connected to the power supply module, the signal acquisition module, the phase sequence detection module, the frequency detection module, the voltage amplitude detection module, the phase detection module, the phase prediction module, the data fusion module, and the grid-connected action module; The grid-connected action module is electrically connected to the power supply module, the data fusion module and the communication module.
2. A three-phase power phase prediction grid-connected device based on linear regression as claimed in claim 1, characterized in that: The signal acquisition module includes a voltage transformer and an ADC sampler.
3. A three-phase power phase prediction grid-connected device based on linear regression as claimed in claim 1, characterized in that: The phase sequence detection module includes a core calculation module and a basic input and output module.
4. A three-phase electric phase prediction grid-connected device based on linear regression as claimed in claim 1, characterized in that: The frequency detection module includes a core calculation module, a timer module, and a basic input and output module.
5. The three-phase power phase prediction grid-connected device based on linear regression according to claim 1, characterized in that: The voltage amplitude detection module includes a core calculation module and a basic input and output module.
6. A three-phase electric phase prediction grid-connected device based on linear regression as claimed in claim 1, characterized in that: The phase detection module includes a core calculation module and a basic input and output module.
7. A three-phase power phase prediction grid-connected device based on linear regression as claimed in claim 1, characterized in that: The phase prediction module includes a core calculation module and a basic input and output module.
8. The three-phase power phase prediction grid-connected device based on linear regression according to claim 1, characterized in that: The communication module includes an RS485 communication module and a Bluetooth communication module.
9. The three-phase power phase prediction grid-connected device based on linear regression according to claim 1, characterized in that: The additional modules include a screen display module and a human-computer interaction module.