Multi-source microgrid inertial support detection method, device and storage medium

Abstract

The present invention relates to a multi-source microgrid inertial support detection method, device, and storage medium. The method comprises: step S1: collecting discretized original voltage and current time series; step S2: extracting first voltage and current sequences corresponding to the measurement moments based on the original voltage and current time series; step S3: obtaining a measured frequency and a voltage-current phase difference based on the first voltage and current sequences, and obtaining active power based on the voltage-current phase difference; step S4: obtaining a reference frequency and a reference power based on the measured frequencies and active powers at multiple measurement moments; and step S5: obtaining equivalent inertia based on the reference frequency and reference power, combined with the measured frequency and active power at the measurement moment. Compared with the prior art, the present invention has advantages such as improved measurement accuracy of equivalent inertia in a multi-source microgrid environment.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-source microgrid perception and monitoring, and in particular to a multi-source microgrid inertial support detection method, device and storage medium. Background Art

[0002] With the rapid development of new energy technologies, grid-connected converters are increasingly being used in grid-connected applications, becoming an essential technology in the energy transition. Grid-connected converters offer frequency regulation and voltage control capabilities, enabling them to provide inertia support similar to synchronous generators. Providing inertial support for the larger grid has become a requirement for every renewable energy power plant and a key component of power generation quality. Therefore, measuring a microgrid's inertial support capability and improving its operational performance have become a hot topic of research.

[0003] Evaluating the grid's inertial support capacity is an emerging methodology that directly reflects the impact of renewable energy grids on grid inertia. This assessment can help renewable energy power plants better integrate into the grid, maintain the stability of the larger grid, and ensure that stored energy can quickly respond to grid energy gaps. However, challenges remain: 1. How to demonstrate the inertial support contribution of microgrids to the grid during grid integration; 2. How to ensure that multifunctional grid monitoring equipment generates timely alerts when damage occurs in the microgrid.

[0004] The current methods for evaluating the inertia support capability of microgrids mainly include evaluating the support capability based on the threshold of the system inertia ratio and the grid system inertia ratio, as well as the evaluation method based on the minimum inertia requirement.

[0005] For example, Chinese patent CN119471168A discloses a method, system, and device for detecting the inertia and damping coefficient of power electronic equipment. The method includes obtaining the AC voltage and AC current of the power electronic equipment to be tested in real time; determining the frequency and frequency change rate based on the AC voltage; determining the active power based on the AC voltage and AC current; generating a disturbance signal based on the frequency, active power, AC voltage, or AC current; when the disturbance signal is 0, no processing is performed; when the disturbance signal is 1, based on the frequency, frequency change rate, and active power, a frequency vector, a frequency change rate vector, and an active power vector during the disturbance period are obtained; and based on the frequency vector, the frequency change rate vector, and the active power vector, the inertia coefficient and damping coefficient of the power electronic equipment to be tested are determined. However, the above-mentioned prior art has the following defects:

[0006] 1. The measurement process relies on disturbance signals, resulting in insufficient steady-state assessment capabilities;

[0007] 2. The frequency is calculated by using a phase detector combined with a voltage-controlled oscillator to generate a feedback signal. However, it has weak adaptability to some non-stationary signals, such as renewable energy output containing harmonics. Therefore, it can only evaluate a single device. In the complex environment of a multi-source microgrid, the measurement errors of frequency and phase are too large, resulting in low accuracy and low flexibility. Summary of the Invention

[0008] The purpose of the present invention is to provide a multi-source microgrid inertial support detection method, device and storage medium in order to solve the defects of the above-mentioned prior art.

[0009] The purpose of the present invention can be achieved by the following technical solutions:

[0010] A multi-source microgrid inertial support detection method, comprising:

[0011] Step S1: collecting discretized original voltage time series and original current time series;

[0012] Step S2: extracting a first voltage sequence and a first current sequence corresponding to a measurement moment based on the original voltage time series sequence and the original current time series sequence, respectively, wherein the first voltage sequence and the first current sequence have the same length and the sampling moments of the same elements are the same;

[0013] Step S3: obtaining a measurement frequency and a voltage-current phase difference based on the first voltage sequence and the first current sequence, and obtaining active power based on the voltage-current phase difference;

[0014] Step S4: obtaining a reference frequency and a reference power based on the measured frequencies and active powers at multiple measurement moments;

[0015] Step S5: Based on the reference frequency and the reference power, the equivalent inertia is obtained in combination with the measured frequency and the active power at the measurement moment.

[0016] The step S3 comprises:

[0017] Detecting the number of zero-crossing points in the first voltage sequence, and obtaining a first reference frequency as a measurement frequency based on a time length corresponding to the first voltage sequence and the number of zero-crossing points;

[0018] Based on the obtained measurement frequency, discrete Fourier transform is performed on the first voltage sequence and the first current sequence respectively to obtain the voltage phase and the current phase at the same moment;

[0019] The difference between the voltage phase and the current phase obtained at the same moment is taken as the voltage-current phase difference;

[0020] Based on the measured voltage and current RMS values, the active power is obtained by combining the voltage and current phase difference:

[0021] P=U*I*sin△φ

[0022] Where: P is active power, U is the effective value of voltage, I is the effective value of current, and △φ is the phase difference between voltage and current.

[0023] The step S3 comprises:

[0024] Detecting the number of zero-crossing points in the first voltage sequence, and obtaining a first reference frequency based on a time length corresponding to the first voltage sequence and the number of zero-crossing points;

[0025] Detecting the number of zero-crossing points in the first current sequence, and obtaining a second reference frequency based on a time length corresponding to the first current sequence and the number of zero-crossing points;

[0026] obtaining a second reference frequency based on the first reference frequency and the second reference frequency;

[0027] Based on the obtained measurement frequency, discrete Fourier transform is performed on the first voltage sequence and the first current sequence respectively to obtain the voltage phase and the current phase at the same moment;

[0028] The difference between the voltage phase and the current phase obtained at the same moment is taken as the voltage-current phase difference;

[0029] Based on the measured voltage and current RMS values, the active power is obtained by combining the voltage and current phase difference:

[0030] P=U*I*sin△φ

[0031] Where: P is active power, U is the effective value of voltage, I is the effective value of current, and △φ is the phase difference between voltage and current.

[0032] Detecting the number of zero-crossing points in the first voltage sequence and obtaining the first reference frequency based on the time length corresponding to the first voltage sequence and the number of zero-crossing points includes:

[0033] Initialize the first statistical set to an empty set;

[0034] Selecting the first element in the first voltage sequence;

[0035] Product calculation steps: calculate the product of the currently selected element and the next element;

[0036] Determine whether the calculated product is greater than 0. If so, perform an iterative determination step. Otherwise, add a data pair consisting of the currently selected element and the next element in the first voltage sequence to the first statistical set as an element of the first statistical set, and perform an iterative determination step.

[0037] Iterative judgment step: judge whether the unselected elements in the first voltage sequence exceed 1. If so, calculate the first reference frequency according to the elements in the first statistical set. Otherwise, select the next element in the first voltage sequence and return to the product calculation step.

[0038] The calculating the first reference frequency according to the elements in the first statistical set includes:

[0039] Select the first and last elements in the first statistical set;

[0040] Calculate the mean of the sampling times of the two voltage values ​​in the first element as the start time, and the mean of the sampling times of the two voltage values ​​in the last element as the end time;

[0041] A first reference frequency is obtained based on the difference between the end time and the start time, and the number of elements in the first statistical set.

[0042] The first voltage sequence and the first current sequence are both normalized.

[0043] The reference frequency and reference power are obtained by maximum likelihood estimation.

[0044] The method further comprises:

[0045] Step S6: Determine whether the calculated equivalent inertia is within a pre-configured threshold range. If not, issue an alarm signal.

[0046] An electronic device includes a memory, a processor, and a program stored in the memory, wherein the processor implements the above method when executing the program.

[0047] A storage medium stores a program, which implements the above method when executed.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] 1. By extracting the first discrete voltage sequence and the first current sequence of equal length and corresponding time, the measurement frequency is obtained by zero-crossing detection, and the voltage and current phase difference is further obtained by discrete Fourier transform. This method can be applied to the complex environment of multi-source microgrids. On the one hand, it can solve the problem of large frequency and phase measurement errors caused by new energy sources, and improve the detection accuracy of the equivalent inertia of multi-source microgrids.

[0050] 2. The frequency is measured using the first voltage sequence, eliminating the need for a secondary calculation of the current.

[0051] 3. The first voltage sequence and the first current sequence are used to calculate the first reference frequency and the second reference frequency respectively. Whether the zero crossing point exists is determined by combining the method of whether the product is less than 0. In this way, the zero crossing point can be calculated with less computing power on the basis of low-frequency sampling, and the problem of excessive measurement frequency error caused by inaccurate actual time of the estimated zero crossing point can be minimized.

[0052] 4. The average of the two voltage values ​​contained in a single element in the first statistical set at the sampling moment is used as the calculation basis to reduce the sampling frequency requirement and improve the calculation accuracy of the measurement frequency on the basis of low-frequency sampling. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 Schematic diagram of the detection hardware system for a multi-source microgrid;

[0054] Figure 2 Schematic diagram of the main steps of the method of the present invention;

[0055] Figure 3 Schematic diagram of the detection network topology;

[0056] Among them: 1. Master station monitoring system, 2. Power grid, 3. Photovoltaic power generation device, 4. Wind power generation device, 5. Battery, 6. Other energy storage devices, 7. Slave station collection terminal, 8. Load. DETAILED DESCRIPTION

[0057] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0058] like Figure 1 As shown, the multi-source microgrid includes a photovoltaic power generation device 3, a wind power generation device 4, a battery 5 and other energy storage devices 6, and is connected to the power grid 2 to complete the networking. The power grid 2 is connected to a load, where Figure 3 As shown, the data of each part is collected by configuring the slave station collection terminal 7, and then transmitted to the master station monitoring system 1. Specifically, in this embodiment, the multi-source microgrid inertial support multifunctional wireless perception monitoring and management system is based on ZigBee to achieve communication.

[0059] A multi-source microgrid inertial support detection method, such as Figure 2 As shown, including:

[0060] Step S1: collecting discretized original voltage time series and original current time series;

[0061] The above data is collected by the slave acquisition terminal 7, which is implemented based on CC2530. The original current timing sequence is collected by the current acquisition module. The current acquisition module uses the ACS712 current detection module and uses CC2530 to configure the current data and serve as its main control. The inflowing current will be detected by the internal Hall sensor, and then output in the form of analog voltage through the VOUT pin after sampling and filtering processing circuits. The relationship between the output voltage and the input current is U=2.5V+185mV / I, where the input current is directional and is filtered by connecting to the filter capacitor.

[0062] The acquisition method of the discretized original voltage time series is similar, so it will not be described in detail.

[0063] Step S2: extracting a first voltage sequence and a first current sequence corresponding to the measurement moment based on the original voltage time series sequence and the original current time series sequence, respectively, wherein the first voltage sequence and the first current sequence have the same length and the sampling moments of the same elements are the same;

[0064] Specifically, in this embodiment, the detection moment is the sampling moment of the data at the middle position in the first voltage sequence and the first current sequence. Of course, in other embodiments, other methods can also be used. For example, the first voltage sequence and the first current sequence can be obtained in a sliding window form.

[0065] The first voltage sequence and the first current sequence are both normalized. In this embodiment, the normalization method is Yeo-Johnson transformation:

[0066]

[0067] in: is the collected signal, is the transformation coefficient, The data are normalized.

[0068] Step S3: obtaining a measurement frequency and a voltage-current phase difference based on the first voltage sequence and the first current sequence, and obtaining active power based on the voltage-current phase difference;

[0069] In some embodiments, step S3 includes:

[0070] Detecting the number of zero-crossing points in the first voltage sequence, and obtaining a first reference frequency as a measurement frequency based on a time length corresponding to the first voltage sequence and the number of zero-crossing points;

[0071] Based on the obtained measurement frequency, discrete Fourier transform is performed on the first voltage sequence and the first current sequence respectively to obtain the voltage phase and the current phase at the same moment;

[0072] The difference between the voltage phase and the current phase obtained at the same moment is taken as the voltage-current phase difference;

[0073] Based on the measured voltage and current RMS values, the active power is obtained by combining the voltage and current phase difference:

[0074] P=U*I*sin△φ

[0075] Where: P is active power, U is the effective value of voltage, I is the effective value of current, and △φ is the phase difference between voltage and current.

[0076] In this way, the overall program writing is easier, and the frequency is measured using the first voltage sequence, so there is no need to perform a secondary calculation of the current, but it will result in low operating efficiency.

[0077] In this embodiment, step S3 includes:

[0078] Detecting the number of zero-crossing points in the first voltage sequence, and obtaining a first reference frequency based on a time length corresponding to the first voltage sequence and the number of zero-crossing points;

[0079] Detecting the number of zero-crossing points in the first current sequence, and obtaining a second reference frequency based on a time length corresponding to the first current sequence and the number of zero-crossing points;

[0080] obtaining a second reference frequency based on the first reference frequency and the second reference frequency;

[0081] Based on the obtained measurement frequency, discrete Fourier transform is performed on the first voltage sequence and the first current sequence respectively to obtain the voltage phase and the current phase at the same moment;

[0082] The difference between the voltage phase and the current phase obtained at the same moment is taken as the voltage-current phase difference;

[0083] Based on the measured voltage and current RMS values, the active power is obtained by combining the voltage and current phase difference:

[0084] P=U*I*sin△φ

[0085] Where: P is active power, U is the effective value of voltage, I is the effective value of current, and △φ is the phase difference between voltage and current.

[0086] Furthermore, in this embodiment, detecting the number of zero-crossing points in the first voltage sequence and obtaining the first reference frequency based on the time length corresponding to the first voltage sequence and the number of zero-crossing points includes:

[0087] Initialize the first statistical set to an empty set;

[0088] Selecting the first element in the first voltage sequence;

[0089] Product calculation steps: calculate the product of the currently selected element and the next element;

[0090] Determine whether the calculated product is greater than 0. If so, perform an iterative determination step. Otherwise, add a data pair consisting of the currently selected element and the next element in the first voltage sequence to the first statistical set as an element of the first statistical set, and perform an iterative determination step.

[0091] Iterative judgment step: judge whether the unselected elements in the first voltage sequence exceed 1. If so, calculate the first reference frequency according to the elements in the first statistical set. Otherwise, select the next element in the first voltage sequence and return to the product calculation step.

[0092] The first voltage sequence and the first current sequence are used to calculate the first reference frequency and the second reference frequency respectively. Whether there is a zero crossing point is determined by judging whether the product is less than 0. In this way, the zero crossing point can be calculated with less computing power on the basis of low-frequency sampling, and the problem of excessive measurement frequency error caused by inaccurate actual time of the estimated zero crossing point can be minimized.

[0093] In addition, in this embodiment, calculating the first reference frequency according to the elements in the first statistical set includes:

[0094] Select the first and last elements in the first statistical set;

[0095] Calculate the mean of the sampling times of the two voltage values ​​in the first element as the start time, and the mean of the sampling times of the two voltage values ​​in the last element as the end time;

[0096] A first reference frequency is obtained based on the difference between the end time and the start time, and the number of elements in the first statistical set.

[0097] By using the mean of the sampling moments of two voltage values ​​contained in a single element in the first statistical set as a calculation basis, the requirement for the sampling frequency is reduced, and the calculation accuracy of the measurement frequency is improved on the basis of low-frequency sampling.

[0098] Step S4: obtaining a reference frequency and a reference power based on the measured frequencies and active powers at multiple measurement moments;

[0099] In this embodiment, the reference frequency and the reference power are obtained by maximum likelihood estimation.

[0100] Step S5: Based on the reference frequency and the reference power, the equivalent inertia is obtained in combination with the measured frequency and the active power at the measurement moment.

[0101] Step S6: Determine whether the calculated equivalent inertia is within a pre-configured threshold range. If not, issue an alarm signal.

[0102] In addition, in this embodiment, the slave acquisition terminal 7 is also configured with a temperature and humidity acquisition module. The temperature and humidity acquisition module adopts a DHT11 module, and uses CC2530 to configure current data and serve as its main control. The DHT11 module has a built-in semiconductor humidity sensor and an NTC thermistor to sense the humidity and temperature in the environment respectively. The data line is pulled low for at least 18 milliseconds and then released to initialize the DHT11, and the DHT11 is requested to process the data after receiving the request for about 20-80 milliseconds, and then the data is sent out through the data line. The analog output voltage is connected to the CC2530 microcontroller and converted by its internal 12-bit high-precision ADC to obtain the temperature and humidity values. Similarly, an alarm can be issued when the temperature and humidity are abnormal.

[0103] The communication module uses ZigBee communication circuitry to communicate from the slave to the master. The CC2530 converts information received from the sensor into a wireless transmission format, combines the encoded digital signal with a high-frequency carrier signal to form an analog signal, and uses an antenna to convert the electrical signal into radio waves. To ensure good and clear signal transmission, a power amplifier can be used to increase the signal power, allowing it to cover a wider range.

[0104] If the above functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

Claims

1. A multi-source microgrid inertial support detection method, characterized in that: include: Step S1: collecting discretized original voltage time series and original current time series; Step S2: extracting a first voltage sequence and a first current sequence corresponding to a measurement moment based on the original voltage time series sequence and the original current time series sequence, respectively, wherein the first voltage sequence and the first current sequence have the same length and the sampling moments of the same elements are the same; Step S3: obtaining a measurement frequency and a voltage-current phase difference based on the first voltage sequence and the first current sequence, and obtaining active power based on the voltage-current phase difference; Step S4: obtaining a reference frequency and a reference power based on the measured frequencies and active powers at multiple measurement moments, wherein the reference frequency and the reference power are obtained by maximum likelihood estimation; Step S5: Based on the reference frequency and the reference power, the equivalent inertia is obtained in combination with the measured frequency and the active power at the measurement time; The step S3 comprises: Detecting the number of zero-crossing points in the first voltage sequence, and obtaining a first reference frequency based on a time length corresponding to the first voltage sequence and the number of zero-crossing points, includes: Initialize the first statistical set to an empty set; Selecting the first element in the first voltage sequence; Product calculation steps: calculate the product of the currently selected element and the next element; Determine whether the calculated product is greater than 0. If so, perform an iterative determination step. Otherwise, add a data pair consisting of the currently selected element and the next element in the first voltage sequence to the first statistical set as an element of the first statistical set, and perform an iterative determination step. Iterative determination step: determining whether the number of unselected elements in the first voltage sequence exceeds 1; if so, calculating the first reference frequency based on the elements in the first statistical set; otherwise, selecting the next element in the first voltage sequence and returning to the product calculation step; The calculating the first reference frequency according to the elements in the first statistical set includes: Select the first and last elements in the first statistical set; Calculate the mean of the sampling times of the two voltage values ​​in the first element as the start time, and the mean of the sampling times of the two voltage values ​​in the last element as the end time; A first reference frequency is obtained based on the difference between the end time and the start time, and the number of elements in the first statistical set.

2. A multi-source microgrid inertial support detection method according to claim 1, characterized in that: The step S3 further includes: Detecting the number of zero-crossing points in the first current sequence, and obtaining a second reference frequency based on a time length corresponding to the first current sequence and the number of zero-crossing points; Obtaining a measurement frequency based on a first reference frequency and a second reference frequency; Based on the obtained measurement frequency, discrete Fourier transform is performed on the first voltage sequence and the first current sequence respectively to obtain the voltage phase and the current phase at the same moment; The difference between the voltage phase and the current phase obtained at the same moment is taken as the voltage-current phase difference; Based on the measured voltage and current RMS values, the active power is obtained by combining the voltage and current phase difference: P=U*I*sin△φ Where: P is active power, U is the effective value of voltage, I is the effective value of current, and △φ is the phase difference between voltage and current.

3. A multi-source microgrid inertial support detection method according to claim 1, characterized in that: The first voltage sequence and the first current sequence are both normalized.

4. A multi-source microgrid inertial support detection method according to claim 1, characterized in that: The method further comprises: Step S6: Determine whether the calculated equivalent inertia is within a pre-configured threshold range. If not, issue an alarm signal.

5. An electronic device comprising a memory, a processor, and a program stored in the memory, wherein: When the processor executes the program, the method according to any one of claims 1 to 4 is implemented.

6. A storage medium having a program stored thereon, characterized in that: When the program is executed, the method according to any one of claims 1 to 4 is implemented.

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