Microgrid island detection method and system, electronic device and storage medium

Abstract

The application discloses a micro-grid island detection method and system, electronic equipment and a storage medium, comprising: phase-locked processing of a three-phase voltage signal, obtaining an instantaneous phase sequence and constructing a phase increment fluctuation sequence; the numerical distribution probability of the phase increment fluctuation sequence in a preset sliding time window is counted, and a phase fluctuation dynamic entropy value is calculated. The active power signal of the point of common coupling is extracted, the active power fluctuation rate sequence is constructed, and the power fluctuation dynamic entropy value is calculated based on the sliding time window; according to the moving average value of the entropy value in the historical time period, the phase fluctuation determination threshold and the power fluctuation determination threshold are updated. When the voltage amplitude and frequency of the point of common coupling are both in the preset normal operation interval, if the phase fluctuation dynamic entropy value is greater than the phase fluctuation determination threshold or the power fluctuation dynamic entropy value is greater than the power fluctuation determination threshold, it is determined that the island effect occurs, and a trip control signal is output. The application can eliminate the power balance blind area and has strong noise resistance.

Description

Technical Field

[0001] This invention relates to methods, systems, electronic devices, and storage media for detecting microgrid islanding, particularly methods for detecting microgrid islanding in power system microgrid control and protection. Background Technology

[0002] Microgrid islanding detection is a key technology for ensuring the safe operation of distribution networks. Existing passive detection methods mainly include over / under voltage methods, over / under frequency methods, and voltage phase change detection methods. Among them, the voltage phase change detection method identifies islanding by monitoring the step change in voltage phase at the moment of grid disconnection.

[0003] However, under conditions where the output power of distributed generation is highly matched with the local load power (i.e., power equilibrium), the frequency and amplitude of the microgrid voltage often remain within the normal range after the grid is disconnected. Furthermore, due to the influence of load impedance characteristics, the voltage phase may not exhibit a significant step change. In this situation, traditional phase change detection methods have a large non-detection zone (NDZ), preventing timely clearing of islanded faults. In addition, existing detection methods based on fixed thresholds are ill-suited to changes in grid background noise, making them prone to false trips or failures to trip. While active detection methods can reduce the NDZ, they inject disturbance signals into the grid, reducing power quality, and pose a risk of disturbance cancellation in scenarios with multiple inverters in parallel. Summary of the Invention

[0004] Objectives of the Invention: The first objective of this invention is to provide a microgrid islanding detection method that eliminates power balance state blind zones without the need for disturbance injection and has strong noise immunity; the second objective of this invention is to provide a passive microgrid islanding detection system that eliminates power balance state blind zones without the need for disturbance injection and has strong noise immunity; the third objective of this invention is to provide an electronic device for performing the above method; and the fourth objective of this invention is to provide a computer-readable storage medium for storing the above method.

[0005] Technical solution: The microgrid island detection method of the present invention includes: (1) real-time acquisition of three-phase voltage signals at the common connection point of the microgrid; (2) phase-locked processing of the three-phase voltage signals to obtain an instantaneous phase sequence, and constructing a phase increment fluctuation sequence based on the instantaneous phase sequence; (3) statistical analysis of the numerical distribution probability of the phase increment fluctuation sequence within a preset sliding time window, and calculation of the phase fluctuation dynamic entropy value.

[0006] Prior to, step (3) includes: (31) extracting N-1 data points from the current time and previous times to form a data set; (32) determining the maximum and minimum values ​​of the data set, and dividing the numerical range between the maximum and minimum values ​​into... (33) Count the number of data points falling into each interval of equal width. Calculate the probability (34) Based on probability Substituting the phase increment fluctuation sequence into the Shannon entropy formula, the dynamic entropy value of the phase fluctuation is calculated. Substituting the active power fluctuation rate sequence into the Shannon entropy formula, the dynamic entropy value of power fluctuation is calculated. The Shannon entropy formula is:

[0007] .

[0008] Prior to step (34), the method further includes: (4) when executing steps (2) and (3), simultaneously extracting the active power signal of the common connection point, constructing the active power fluctuation rate sequence, and calculating the dynamic entropy value of power fluctuation based on a preset sliding time window; (5) updating the phase fluctuation judgment threshold and the power fluctuation judgment threshold according to the moving average of the entropy value in the historical time period.

[0009] Preferred, step (5) includes: calculating past time Internal phase fluctuation dynamic entropy value moving average and power fluctuation dynamic entropy value moving average ; Calculate the phase fluctuation judgment threshold : Calculate the power fluctuation judgment threshold ,in, The first tuning coefficient, This is the second tuning coefficient.

[0010] Prior to step (5), the method further includes: (6) if the voltage amplitude or frequency of the common coupling point is not within the preset normal operating range, then step 1 is executed; when the voltage amplitude and frequency of the common coupling point are both within the preset normal operating range, if the phase fluctuation dynamic entropy value is greater than the phase fluctuation judgment threshold, or the power fluctuation dynamic entropy value is greater than the power fluctuation judgment threshold, then an islanding effect is determined to occur, and a trip control signal is output.

[0011] Preferably, in step (6), the preset normal operating range is: , , U is the rated voltage, U is the voltage amplitude, and f is the frequency.

[0012] Preferably, the phase increment fluctuation sequence is a first-order difference absolute value sequence of instantaneous angular frequency deviation.

[0013] The microgrid islanding detection system of the present invention is used to execute the method described in any of the above-mentioned methods, comprising: a signal acquisition module configured to acquire three-phase voltage signals at the microgrid's point of common coupling (PCC) in real time; a phase processing module configured to perform phase-locked loop processing on the three-phase voltage signals to obtain an instantaneous phase sequence, and construct a phase increment fluctuation sequence based on the instantaneous phase sequence; an entropy calculation module configured to statistically analyze the numerical distribution probability of the phase increment fluctuation sequence within a preset sliding time window, and calculate the dynamic entropy value of the phase fluctuation; a threshold adaptive module configured to synchronously extract the active power signal at PCC, construct an active power fluctuation rate sequence, and calculate the dynamic entropy value of the power fluctuation based on a preset sliding time window; dynamically update the phase fluctuation judgment threshold and the power fluctuation judgment threshold according to the moving average of the entropy value over a historical time period; and a logic judgment and execution module configured to determine that an islanding effect has occurred and output a trip control signal when the voltage amplitude and frequency at PCC are both within a preset normal operating range, and if the dynamic entropy value of the phase fluctuation is greater than the phase fluctuation judgment threshold or the dynamic entropy value of the power fluctuation is greater than the power fluctuation judgment threshold. The system is integrated into the digital signal processor (DSP) or field-programmable gate array (FPGA) control unit of the distributed power inverter.

[0014] An electronic device according to the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of any of the methods described herein.

[0015] The present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described herein.

[0016] Beneficial Effects: Compared with existing technologies, this invention has the following significant advantages: 1. Elimination of Detection Blind Zone. Under power balance conditions where neither voltage nor frequency exceeds limits, traditional passive methods fail, while this invention achieves accurate detection by capturing the "disorderliness" of phase increment fluctuations. 2. Fast Response Speed. Utilizing the Shannon entropy algorithm within a sliding time window, it only takes 20ms to identify microscopic phase instability caused by islanding, far faster than traditional methods that rely on frequency drift. 3. Strong Environmental Adaptability. The adaptive threshold mechanism allows the judgment criteria to change dynamically with the operating conditions, avoiding frequent false triggering of fixed thresholds in noisy environments while ensuring sensitivity when islanding occurs. Attached Figure Description

[0017] Figure 1 This is a flowchart of the present invention;

[0018] Figure 2 This is a schematic diagram illustrating the calculation principle of the phase increment fluctuation sequence entropy value in this invention. Detailed Implementation

[0019] As shown in the figure, this invention proposes a microgrid island detection method, including: in step 1, using a value not less than... The sampling frequency is set, and signals are acquired using voltage transformers and current transformers. Anti-aliasing analog filters, circuit analog switches, and a 16-bit high-speed ADC are used to acquire three-phase voltage signals in real time. and three-phase current signal The sampling frequency was set to 2560Hz to meet the requirements of high-frequency sampling and ensure the capture of phase fluctuation details. A synchronous rotating coordinate system phase-locked loop (SRF-PLL) algorithm was used to track the fundamental voltage phase in real time and output a discretized instantaneous phase sequence. ,in Sampling sequence number, sampling period s, the rated angular frequency of the power grid rad / s.

[0020] The specific implementation method for constructing the phase increment fluctuation sequence in step 2 is as follows: using the sampling frequency Discrete sampling of the voltage signal yields the instantaneous phase at discrete time k. Based on instantaneous phase Calculate the instantaneous angular frequency deviation :

[0021] ,in, The sampling period is The rated angular frequency of the power grid; based on the instantaneous angular frequency deviation. Calculate the phase increment fluctuation sequence : This sequence reflects the second-order dynamic characteristics of phase change, namely the phase jitter intensity. Its magnitude is directly related to the system's synchronization stiffness and forms the basis for subsequent entropy analysis.

[0022] In step 3, the number of data points corresponding to the sliding time window The value range is from 40 to 100, and the number of values ​​in the interval is... The value range is 8 to 12. The sliding time window length is set to... Each sampling point corresponds to a sliding time window of approximately 25ms. At the current moment... Extract the phase increment fluctuation sequence within the sliding time window The larger the dynamic entropy value of phase fluctuation, the more disordered the phase fluctuation and the more unstable the system. Data points specifically include the rate of change of instantaneous angular frequency deviation (i.e., phase jitter intensity) or the instantaneous rate of change of active power. For example, for a phase path, the system does not directly use the original value of the voltage phase φ (e.g., 1.57 radians), but rather uses the phase increment fluctuation value obtained after second-order difference operations (e.g., 0.1 radians / second²). This data point reflects the synchronization stiffness disturbance of the system at a microscopic time scale and is the fundamental physical quantity for subsequent probability distribution statistics and Shannon entropy calculations. Purpose of Construction: Since this invention uses Shannon entropy as the criterion for island detection, and the calculation of entropy values ​​depends on the probability distribution characteristics of the data, a single data point cannot reflect the probability distribution. Therefore, it is necessary to extract φ−1 data points from the current moment and previous moments to form a 'data set' (i.e., a sample pool) containing φ samples.

[0023] In step 4, The typical value is 5 seconds. Use the same sliding window length as in step 3. Number of intervals Calculate the dynamic entropy value of power fluctuation. This value is used to quantify the randomness of power fluctuations and serves as an auxiliary criterion for island detection.

[0024] In step 5, the system continuously maintains historical entropy data for the past 5 seconds and calculates the moving averages of dynamic entropy values ​​for phase fluctuations and power fluctuations, respectively. and . The first tuning coefficient, This is the second tuning coefficient. and The values ​​range from 1.3 to 1.5 and can be slightly adjusted according to the actual engineering situation. They are used to distinguish between normal operating condition fluctuations and significant instability caused by islanding.

[0025] Step 5: Dual-entropy fusion criterion logic. Real-time monitoring of the voltage amplitude U and frequency at the point of common coupling. Islanding protection is triggered when the following two conditions are met: (a) Voltage and frequency are within the preset normal operating range: (b) Either the dynamic entropy of phase fluctuation or the dynamic entropy of power fluctuation exceeds its corresponding threshold: If the above conditions are met, the system outputs a trip control signal to the grid-connected circuit breaker to achieve rapid disconnection of islanded protection. Figure 2 As shown, in this embodiment, the construction of the phase increment fluctuation sequence and the entropy calculation process are implemented inside the DSP, which has the characteristics of high efficiency, strong real-time performance and low resource consumption.

[0026] This embodiment verifies the effectiveness of the microgrid island detection method proposed in this invention through simulation testing. The test system configuration is as follows: Figure 1 As shown in Table 1, the main parameters are as follows.

[0027] Table 1. Test System Parameter Table

[0028]

[0029] The test simulated the entire process of the system transitioning from grid-connected to islanded state during a power outage (islanding) at t=0s. Data from three typical time points—T-1 seconds (stable grid connection), T+0.02 seconds (initial islanding), and T+0.1 seconds (islanding stabilization)—were recorded in detail.

[0030] Table 2. Test Data for Dual Entropy Fusion Detection

[0031]

[0032] 1. Under grid-connected conditions: The system is clamped by the large power grid, resulting in minimal phase jitter and low entropy.

[0033] Well below the dynamic threshold The system will not malfunction.

[0034] 2. Islanding occurs within 20ms: At the instant the power grid disconnects, although the voltage frequency has not yet exceeded the limit, the disorder of the phase increment fluctuation sequence begins to increase, and the phase entropy... The value instantly jumped to 0.65, exceeding the threshold of 0.49. Based on the "dual-entropy fusion criterion," the system immediately identified the anomaly but took no action.

[0035] Islanding 100ms: The system enters full islanding operation, phase and power fluctuations intensify, and both entropy values ​​are significantly higher than the threshold, ensuring the reliability of protection actions.

[0036] Table 3. Comparison Test Table of Different Island Detection Methods

[0037]

[0038] Based on the above test data, it can be seen that the present invention has the following significant substantive features and advancements:

[0039] 1. Elimination of detection blind zone: Under power balance conditions where neither voltage nor frequency exceeds the limit, traditional passive methods fail, while this invention achieves accurate detection by capturing the "disorder" of phase increment fluctuations.

[0040] 2. Fast response speed: Utilizing the Shannon entropy algorithm within a sliding time window, it only takes 20ms to identify micro-phase instability caused by islands, which is much faster than traditional methods that rely on frequency drift.

[0041] 3. Strong environmental adaptability: Adaptive threshold mechanism ( This allows the judgment criteria to change dynamically with the operating conditions, which avoids frequent false triggering of fixed thresholds in noisy environments and ensures sensitivity when islanding occurs.

Claims

1. A method for detecting isolated microgrids, characterized in that, include: (1) Real-time acquisition of three-phase voltage signals at the microgrid common connection point; (2) Perform phase-locked processing on the three-phase voltage signal to obtain the instantaneous phase sequence, and construct a phase increment fluctuation sequence based on the instantaneous phase sequence; (3) Statistically analyze the numerical distribution probability of the phase increment fluctuation sequence within a preset sliding time window, and calculate the dynamic entropy value of the phase fluctuation.

2. The microgrid island detection method according to claim 1, characterized in that, Step (3) includes: (31) Extract the current time and the N-1 data points before it to form a data set; (32) Determine the maximum and minimum values ​​of the data set, and divide the numerical range between the maximum and minimum values ​​into... A number of equal-width intervals; (33) Count the number of data points falling into each interval. Calculate the probability ; (34) Based on probability Substituting the phase increment fluctuation sequence into the Shannon entropy formula, the dynamic entropy value of the phase fluctuation is calculated. Substituting the active power fluctuation rate sequence into the Shannon entropy formula, the dynamic entropy value of power fluctuation is calculated. The Shannon entropy formula is: 。 3. The microgrid island detection method according to claim 2, characterized in that, Following step (34), the following is also included: (4) When performing steps (2) and (3), the active power signal of the common connection point is extracted synchronously, the active power fluctuation rate sequence is constructed, and the dynamic entropy value of power fluctuation is calculated based on the preset sliding time window. (5) Update the phase fluctuation judgment threshold and power fluctuation judgment threshold based on the moving average of entropy values ​​over the historical time period.

4. The microgrid island detection method according to claim 3, characterized in that, Step (5) includes: Calculate past time Internal phase fluctuation dynamic entropy value moving average and power fluctuation dynamic entropy value moving average ; Calculate the phase fluctuation judgment threshold : , Calculate the power fluctuation judgment threshold , in, The first tuning coefficient, This is the second tuning coefficient.

5. The microgrid island detection method according to claim 4, characterized in that, Following step (5), the following is also included: (6) If the voltage amplitude or frequency of the common coupling point is not within the preset normal operating range, then step (1) is executed; when the voltage amplitude and frequency of the common coupling point are both within the preset normal operating range, if the phase fluctuation dynamic entropy value is greater than the phase fluctuation judgment threshold, or the power fluctuation dynamic entropy value is greater than the power fluctuation judgment threshold, then it is determined that an islanding effect has occurred, and a trip control signal is output.

6. The microgrid island detection method according to claim 5, characterized in that, In step (6), the preset normal operating range is: , , U is the rated voltage, U is the voltage amplitude, and f is the frequency.

7. The microgrid island detection method according to claim 1, characterized in that, The phase increment fluctuation sequence is a first-order difference absolute value sequence of instantaneous angular frequency deviation.

8. A microgrid island detection system, characterized in that, For performing the method according to any one of claims 1 to 7, comprising: The signal acquisition module is configured to acquire the three-phase voltage signal at the microgrid's common connection point in real time. The phase processing module is configured to perform phase-locked processing on the three-phase voltage signal, obtain an instantaneous phase sequence, and construct a phase increment fluctuation sequence based on the instantaneous phase sequence; The entropy calculation module is configured to statistically analyze the numerical distribution probability of the phase increment fluctuation sequence within a preset sliding time window and calculate the dynamic entropy value of the phase fluctuation. The threshold adaptive module is configured to synchronously extract the active power signal of the common connection point, construct the active power fluctuation rate sequence, and calculate the dynamic entropy value of power fluctuation based on a preset sliding time window; and dynamically update the phase fluctuation judgment threshold and the power fluctuation judgment threshold according to the moving average of the entropy value over a historical time period. The logic judgment and execution module is configured to determine that an islanding effect has occurred and output a trip control signal when the voltage amplitude and frequency of the common connection point are both within the preset normal operating range, and the phase fluctuation dynamic entropy value is greater than the phase fluctuation judgment threshold or the power fluctuation dynamic entropy value is greater than the power fluctuation judgment threshold.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 7.

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