Island detection method and system based on Goertzel algorithm and quality factor entropy dispersion

Abstract

The invention discloses an island detection method and system based on a Goertzel algorithm and quality factor entropy dispersion. The method comprises the following steps: acquiring three-phase voltage and current signals of a distributed power generation system; performing double-domain leakage suppression processing on the signal to obtain an accurate fundamental wave parameter; carrying out heavy fundamental wave construction and harmonic signal separation on the accurate fundamental wave parameter to obtain a residual harmonic signal; a Goertzel algorithm is adopted to carry out directional spectrum analysis on specific harmonics in the residual harmonic signals, and the voltage amplitude and the current amplitude of the specific harmonics are obtained; calculating a harmonic impedance amplitude according to the voltage amplitude and the current amplitude of the specific harmonic, and obtaining a quality factor entropy dispersion corresponding to the specific harmonic based on the harmonic impedance amplitude; and carrying out island detection according to the quality factor entropy dispersion corresponding to the specific harmonic wave to obtain a detection result. The load quality factor distribution consistency is quantified by using the information entropy theory, the detection blind area can be reduced, and the detection efficiency and accuracy are improved.

Description

Technical Field

[0001] This invention relates to the technical field of grid-connected operation safety detection, and more specifically, to an island detection method and system based on the Goertzel algorithm and quality factor entropy dispersion. Background Technology

[0002] Islanding is a significant safety issue faced by distributed generation (DG) systems operating in grid-connected mode. When the grid is disconnected due to a fault or planned maintenance, the distributed generation continues to supply power to local loads, forming an independent power supply island uncontrolled by the grid. This island can endanger equipment safety and personnel safety, and hinder the grid's ability to restore power. Therefore, domestic and international standards such as IEEE Std 1547.1-2020 and GB / T 33593-2017 mandate that grid-connected inverters must have islanding detection capabilities, effectively detecting islanding and disconnecting the grid connection within 2 seconds.

[0003] Existing island detection methods mainly fall into the following three categories: 1. Communication-based method: Monitoring the status of circuit breakers on the power grid side via power line carrier communication or wireless communication. This method can theoretically achieve zero-blind-zone detection, but it is costly, depends on the reliability of the communication channel, and its real-time performance is difficult to guarantee in complex distribution network environments, limiting its large-scale application.

[0004] 2. Active Detection Method: This method involves injecting small-amplitude disturbances (such as active frequency drift, reactive power disturbances, harmonic injection, etc.) into the power grid and observing the grid's response to determine if islanding has occurred. This method effectively reduces the detection dead zone (NDZ), but the injected disturbances degrade power quality and increase the system's total harmonic distortion (THD). Especially in multi-inverter parallel systems, the disturbance signal may be diluted or interfere with each other, leading to detection failure or performance degradation.

[0005] 3. Passive Detection Method: This method detects islanding by monitoring abrupt changes in local electrical quantities (such as voltage, frequency, phase, and harmonics) at the point of common coupling (PCC). This method is simple, easy to implement, and does not affect power quality, making it the most widely used method currently. However, traditional over / under voltage (OVP / UVP) and over / under frequency (OFP / UFP) protection methods exhibit small voltage and frequency changes when the distributed power source output power is nearly balanced with the local load power, resulting in a significant detection blind zone and failing to meet standard requirements.

[0006] To overcome the shortcomings of traditional passive methods, researchers have proposed passive detection methods based on harmonic characteristics in recent years, mainly in two directions: 1. Harmonic Impedance Method: The principle is that during grid-connected operation, the harmonic impedance at the PCC point is composed of the grid impedance and the load impedance connected in parallel. The grid impedance is usually much smaller than the load impedance, thus playing a dominant role. After islanding occurs, the grid impedance disappears, and the harmonic impedance at the PCC point abruptly becomes the load impedance. Islanding can be detected by injecting specific harmonic disturbances and measuring the change in harmonic impedance at the PCC point. However, this method typically requires full-spectrum FFT analysis to handle the frequency variation characteristics of the load impedance, resulting in high computational complexity, poor real-time performance, and susceptibility to spectral leakage caused by frequency shifts, leading to decreased computational accuracy.

[0007] 2. Harmonic Voltage Method: This method directly utilizes the amplitude changes of characteristic subharmonic voltages (such as the 3rd, 5th, and 7th harmonics) at the PCC point after islanding occurs to construct the criterion. This method does not require the injection of disturbances, but it is susceptible to background harmonic interference from the power grid, and its dynamic response speed is affected by the filtering stage, making it unsuitable for weak power grid conditions.

[0008] In summary, the existing technology has the following main drawbacks: 1. Traditional passive method (voltage / frequency): There is a huge detection blind zone (NDZ) under power balance, the threshold setting is difficult, and it is difficult to balance sensitivity and reliability.

[0009] 2. Active detection method: This method sacrifices power quality to reduce the blind zone. However, the injected disturbances can lead to an increase in THD, which is inconsistent with the increasingly stringent power quality requirements. In multi-inverter scenarios, there is a "disturbance dilution" effect, which reduces detection reliability.

[0010] 3. Communication detection method: High cost, complex system, dependent on the reliability of communication link, not suitable for all scenarios.

[0011] 4. Existing harmonic impedance methods rely on computationally intensive FFT full-spectrum analysis, which is inefficient. When the frequency shifts, asynchronous sampling can lead to severe spectral leakage, causing a sharp decrease in the accuracy of harmonic impedance measurements and thus affecting detection accuracy.

[0012] 5. Existing harmonic voltage method: Poor anti-interference capability; inherent background harmonics in the power grid can easily lead to misjudgment. The selection of characteristic frequency bands and the design of filters face a difficult trade-off between dynamic response speed and anti-interference capability. Summary of the Invention

[0013] To address the shortcomings of existing technologies, this invention provides an island detection method based on the Goertzel algorithm and quality factor entropy dispersion, which can overcome the technical defects of existing island detection methods.

[0014] The present invention adopts the following technical solution.

[0015] An island detection method based on the Goertzel algorithm and quality factor entropy dispersion includes the following steps: Step 1: Collect the three-phase voltage and current signals at the point of common coupling of the distributed generation system; Step 2: Perform dual-domain leakage suppression processing on the three-phase voltage and current signals, including time-domain processing and frequency-domain processing, to obtain accurate fundamental parameters; Step 3: Reconstruct the fundamental wave parameters and separate the harmonic signals to obtain the residual harmonic signals; Step 4: Use the Goertzel algorithm to perform directional spectrum analysis on specific harmonics in the residual harmonic signal to obtain the voltage and current amplitudes of specific harmonics. Step 5: Calculate the harmonic impedance amplitude based on the voltage and current amplitudes of the specific harmonic, and obtain the quality factor entropy dispersion corresponding to the specific harmonic based on the harmonic impedance amplitude. Step 6: Perform island detection based on the quality factor entropy dispersion corresponding to a specific harmonic to obtain the detection result.

[0016] Preferably, step 2 includes: Step 2-1, Time Domain Processing: The acquired three-phase voltage and current signals constitute the original signal. A three-stage cascaded adaptive filter is used to process the original signal. Perform time-domain filtering to obtain the fundamental frequency signal after time-domain filtering; Step 2-2, Frequency Domain Processing: The fundamental signal after time-domain filtering is precisely analyzed using the DFT frequency offset correction algorithm to obtain accurate fundamental parameters.

[0017] Preferably, the original signal is processed using a three-stage cascaded adaptive filter. The processing includes: The three-stage cascaded adaptive filter includes a first-order differential filter H1, a first-order integral filter H2, and a first-order integral filter H3; First-order differential filter H1, first-order integral filter H2 and first-order integral filter H3 are used to filter out DC components and even harmonics, third and integer multiples of 3 harmonics, and fifth and seventh characteristic harmonics in the original signal, respectively. The fundamental signal obtained after time-domain filtering is: S[n]*H1*H2*H3.

[0018] Preferably, the precise fundamental frequency parameter includes: the precise part of the fundamental frequency component. virtual part Amplitude and phase The method of obtaining it is as follows: Input signal at time t for:

[0019] in, The signal amplitude, The input signal is the fundamental frequency signal after time-domain filtering, where the initial phase angle is the fundamental frequency angle. The rated frequency of the power frequency. This is the frequency offset. Discretize the input signal, and assume that N points are sampled per cycle, with a sampling frequency of... The sampling interval is Then the sampled value at time k for:

[0020] The sampled value at time k The initial real part of the Discrete Fourier Transform (DFT) result after truncating with a rectangular window and performing the DFT. and the initial imaginary part They are respectively:

[0021] Zero-crossing frequency measurement is performed on the fundamental frequency signal to track the fundamental frequency in real time and obtain the current frequency value. Based on the current frequency value Obtain the rated frequency of the power frequency frequency offset for:

[0022] Combined with the sampled values ​​at time k and frequency difference For the initial real part and the initial imaginary part After preliminary correction, the real part is obtained. and the virtual part for:

[0023] Correction Amplitude for:

[0024] The final corrected real part is obtained by combining the correction system. and the virtual part for:

[0025] in, , , , , , , and All are correction factors.

[0026] Preferably, step 3 specifically includes: Based on the precise fundamental wave parameters, the reconstructed fundamental wave time-domain signal is obtained. : =

[0027] In the formula, n refers to the nth data window; Based on the reconstructed fundamental time-domain signal and the original signal Obtaining residual harmonic signals : .

[0028] Preferably, the specific harmonics in step 4 are: the 5th harmonic, the 7th harmonic, and the 9th harmonic.

[0029] Preferably, in step 5, the calculation of the quality factor entropy dispersion corresponding to a specific harmonic is as follows: Calculate the harmonic impedance amplitude at the PCC point |Zpcc(jω) for each specific harmonic order h. h )|: |Zpcc(jω h )|=|Vh| / |Ih| Where Zpcc is the impedance at the point of common coupling, j is the imaginary unit, and ω h Let be the angular frequency of the h-th specific harmonic, and Vh and Ih be the harmonic voltage and current when the specific harmonic order is h, respectively. Obtain the impedance amplitude |Zpcc(jω1)| at the fundamental frequency, and calculate the squared load quality factor at harmonic order h. :

[0030] Treating the squared value of the load quality factor as a random variable, calculate the probability value corresponding to each harmonic:

[0031] in, This represents the probability value corresponding to the h-th harmonic; Calculate the Shannon entropy of this probability distribution. :

[0032] Normalized entropy dispersion calculation: Normalize the Shannon entropy to obtain the quality factor entropy dispersion corresponding to a specific harmonic. :

[0033] in, This indicates the number of a specific harmonic used in the calculation.

[0034] Preferably, step 6 specifically includes: Set a threshold σ, and set the quality factor entropy dispersion corresponding to a specific harmonic. The results are obtained by comparing the results with a threshold. like The system is determined to be in grid-connected status.

[0035] like If the situation is isolated, an islanding state is determined, and a disconnection command is immediately issued to disconnect the distributed power source from the power grid.

[0036] This invention proposes an island detection system based on the Goertzel algorithm and quality factor entropy dispersion, used to implement the island detection method based on the Goertzel algorithm and quality factor entropy dispersion, comprising: The signal acquisition module is used to acquire three-phase voltage and current signals at the point of common coupling of the distributed generation system. The leakage suppression module is used to perform fundamental frequency tracking and dual-domain leakage suppression processing on three-phase voltage and current signals to obtain accurate fundamental parameters. The reconstruction and separation module is used to reconstruct the fundamental wave parameters and separate the harmonic signals to obtain the residual harmonic signals. The frequency analysis module uses the Goertzel algorithm to perform directional spectrum analysis on specific harmonics in the residual harmonic signal, and obtains the voltage amplitude and current amplitude of the specific harmonics. The quality factor entropy dispersion calculation module is used to calculate the harmonic impedance amplitude based on the voltage and current amplitudes of a specific harmonic, and obtain the quality factor entropy dispersion corresponding to the specific harmonic based on the harmonic impedance amplitude. The detection module is used to detect islands based on the quality factor entropy dispersion corresponding to a specific harmonic and obtain the detection results.

[0037] This invention proposes a terminal, including a processor and a storage medium; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the island detection method based on the Goertzel algorithm and quality factor entropy dispersion.

[0038] This invention proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the island detection method based on the Goertzel algorithm and quality factor entropy dispersion.

[0039] The beneficial effects of this invention are that, compared with the prior art, the islanding detection method of this invention, at its core, obtains impedance information under each harmonic through high-precision harmonic analysis, then calculates the load quality factor, quantifies its distribution consistency using information entropy theory, and finally achieves islanding discrimination through the entropy dispersion index. This invention has at least the following beneficial effects: 1. Fully Passive Detection: Implements a passive detection method that requires no communication and does not inject active disturbances, ensuring good power quality. The entire detection process does not require the injection of any form of disturbance, has no impact on power quality, and avoids the disturbance dilution problem when multiple inverters are connected in parallel.

[0040] 2. Highly efficient and accurate harmonic analysis technology: The Goertzel algorithm is used to accurately extract specific harmonics in a targeted manner, replacing the full spectrum analysis of FFT, which greatly reduces the amount of computation and improves real-time performance. This invention uses the Goertzel algorithm to replace the traditional FFT, and only performs targeted calculations on the required 5th, 7th and 9th harmonics, which greatly reduces the computational burden, improves the detection speed, and makes it easy to implement in embedded processors.

[0041] 3. A novel leakage suppression strategy combining time-domain adaptive filtering and frequency-domain frequency offset compensation was proposed using dual-domain (time domain + frequency domain) leakage suppression technology. The time-domain cascaded filter effectively suppresses non-target harmonic interference; the frequency-domain DFT correction algorithm eliminates spectral leakage and picket-fence effects caused by asynchronous sampling, jointly ensuring ultra-high extraction accuracy of harmonic features (amplitude and phase), a prerequisite for accurate calculation. The two are not isolated but deeply integrated: the frequency-domain algorithm provides high-precision parameters for time-domain processing, while time-domain processing creates a clean signal environment for frequency-domain analysis. Working together, they provide a reliable data foundation for the criterion of 'quality factor entropy dispersion', ultimately achieving highly reliable island detection, overcoming spectral leakage caused by frequency offset, and improving the extraction accuracy of harmonic amplitude and phase features.

[0042] 4. Innovative Criterion Based on Physical Essence: This invention explores the consistency change pattern of the inherent physical attribute of load quality factor before and after islanding, constructing a novel and reliable criterion: entropy dispersion of the quality factor. This invention is the first to propose using entropy dispersion to measure quality factor consistency, utilizing the cross-band consistency of the load quality factor as the core basis for islanding detection. As an inherent physical parameter of the load, it exhibits characteristics independent of harmonic order under islanding conditions. This criterion has clear physical meaning, strong anti-interference capability, and significant advantages, especially under power balance conditions. 5. To address the detection blind zone problem when voltage and frequency do not change or change very little during power balance, this invention significantly reduces the detection blind zone by using dual-domain leakage suppression technology and employing quality factor entropy dispersion as a criterion for measuring quality factor consistency. It can still detect quickly and accurately, especially under the harsh conditions of source-load power balance, while also having good anti-interference ability and grid adaptability.

[0043] 6. Robust threshold setting: The constructed quality factor entropy dispersion index approaches 1 and 0 in grid-connected and islanded states, respectively, showing obvious polarization. It is also insensitive to factors such as load changes and grid disturbances, making the threshold σ very easy to tune and highly practical for engineering applications. Attached Figure Description

[0044] Figure 1 This is a flowchart of the island detection method based on the Goertzel algorithm and quality factor entropy dispersion in this invention; Figure 2 This is the equivalent model circuit diagram of the grid-connected inverter system in this invention; Figure 3 This is a flowchart of the dual-domain leakage suppression and harmonic extraction process in this invention; Figure 4 This invention relates to different frequency offset conditions. Simulation waveform diagram; Figure 5 This is a performance comparison chart between the method of this invention and the traditional DFT algorithm; Figure 6 This is a structural diagram of the island detection system based on the Goertzel algorithm and quality factor entropy dispersion in this invention. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, other embodiments obtained by those skilled in the art without creative effort are all within the protection scope of this invention.

[0046] like Figure 1 The present invention proposes an island detection method based on the Goertzel algorithm and quality factor entropy dispersion. This method is applicable to island detection methods such as... Figure 2 In the grid-connected inverter system shown, the method includes the following steps: Step 1: Collect the three-phase voltage and current signals at the point of common coupling of the distributed generation system; At the point of common coupling (PCC) of a distributed generation system, voltage transformers (PTs) and current transformers (CTs) are used to acquire three-phase voltage and current signals in real time.

[0047] The acquired analog signals are subjected to anti-aliasing filtering and synchronous sampling, and then converted into digital signal sequences. Sampling frequency It is usually set to an integer multiple of the power frequency (e.g., N=24 points / cycle).

[0048] Step 2: Perform fundamental frequency tracking and dual-domain leakage suppression processing on the three-phase voltage and current signals to obtain accurate fundamental parameters; like Figure 3 As shown, step 2 in this invention is a crucial step in ensuring the accuracy of subsequent harmonic analysis. A dual-domain leakage suppression architecture is employed, specifically including: Step 2-1, Time Domain Processing (Fundamental Wave Purification): A three-stage cascaded adaptive filter is used to process the original signal. Processing: The three-stage cascaded adaptive filter includes a first-order differential filter H1, a first-order integral filter H2, and a first-order integral filter H3; First-order differential filter H1, first-order integral filter H2 and first-order integral filter H3 are used to filter out DC components and even harmonics, third and integer multiples of 3 harmonics, and fifth and seventh characteristic harmonics in the original signal, respectively. The fundamental signal obtained after time-domain filtering is: S[n]*H1*H2*H3.

[0049] The center frequencies of each filter stage are obtained in real time through zero-crossing frequency measurement, which is the system fundamental frequency. Adaptive adjustments are made to ensure optimal filtering performance even with frequency shifts.

[0050] Step 2-2, Frequency Domain Processing (Frequency Offset Compensation): The fundamental signal after time-domain filtering is accurately analyzed using the DFT frequency offset correction algorithm.

[0051] The actual frequency is obtained by frequency measurement at the zero crossing point. The DFT calculation results are corrected using formulas (1)-(8) to obtain the precise part of the fundamental component. virtual part Amplitude and phase The specific process is as follows: Let the actual frequency of the current signal be:

[0052] The input signal is:

[0053] The rated power frequency is: The fundamental frequency of the input signal is The frequency offset is , This represents the phase of the fundamental component.

[0054] Discretize the signal: Assume N points are sampled per cycle, and the sampling frequency is... The sampling interval is: , Then the sampled value at time k is:

[0055] Signal After truncating with a rectangular window and performing a Discrete Fourier Transform (DFT), the expressions for the real and imaginary parts of the DFT are as follows:

[0056] By performing zero-crossing frequency measurement on the fundamental frequency signal and tracking the fundamental frequency in real time, the current frequency value can be obtained. The frequency difference is obtained as follows:

[0057] First, by combining equations (3-5), we obtain the preliminary corrected real and imaginary parts as follows:

[0058] Secondly, the correction amplitude is:

[0059] The final corrected real and imaginary parts are:

[0060] in: , , , , , , and These are all correction factors. The correction factors are preset by those skilled in the art.

[0061] Step 3: Reconstruct the fundamental wave parameters and separate the harmonic signals to obtain the residual harmonic signals; Using the precise fundamental wave parameters obtained in step 2, the pure fundamental wave time-domain signal is reconstructed according to Equation 9.

[0062]

[0063] In the formula, These are the fundamental frequency signal sampling points. It is the current fundamental frequency. It is the sampling frequency, and n refers to the nth data window.

[0064] Original signal Subtract the reconstructed fundamental signal The residual harmonic signal can then be obtained. The signal contains all harmonic components except the fundamental frequency, and fundamental frequency leakage has been greatly suppressed.

[0065] Step 4: Use the Goertzel algorithm to perform directional spectrum analysis on specific harmonics in the residual harmonic signal to obtain the voltage and current amplitudes of specific harmonics. Among them, the specific harmonics are the 5th, 7th and 9th harmonics, avoiding the 3rd harmonic which is isolated due to the transformer delta connection; The Goertzel algorithm is an efficient form of DFT, particularly suitable for calculating the spectral values ​​of individual frequency points, with a computational complexity far less than FFT. Through recursive calculations using formulas (10)-(15), the complex spectral value Yh of the target harmonic h can be extracted efficiently and accurately, thereby obtaining the voltage amplitude |Vh|, voltage amplitude |Ih|, and phase. The specific process is as follows: The Goertzel algorithm is used for the target harmonics (h=5, 7, 9). First, set the discrete frequency points. :

[0066] Where N is the number of sampling points per wave, For the frequency of a specific harmonic, For sampling frequency, It is a real number; Calculate the rotation factor :

[0067] The merging phase correction coefficients are used to obtain the merging coefficients. :

[0068] Recursive calculations yield the recursive sequence. :

[0069] Obtain vector :

[0070] in, , Recursive sequences The Nth Item 1 and the Nth 2 items.

[0071] Combining equations (23-27), we obtain the target harmonic vector as follows: :

[0072] Step 5: Calculate the harmonic impedance amplitude based on the voltage and current amplitudes of the specific harmonic, and obtain the quality factor entropy dispersion corresponding to the specific harmonic based on the harmonic impedance amplitude. Step 5-1: Calculate the harmonic impedance amplitude at the PCC point for each target harmonic order h according to Ohm's law. |Zpcc(jω h )|=|Vh| / |Ih| Where Zpcc is the impedance of the common connection point, j is the imaginary unit, ωh is the angular frequency of the h-th specific harmonic, and Vh and Ih are the harmonic voltage and current when the specific harmonic order is h, respectively. Simultaneously, the impedance amplitude |Zpcc(jω1)| at the fundamental frequency is obtained. The impedance amplitude at the fundamental frequency can be calculated from the fundamental voltage and current amplitudes. Substituting the above values ​​into the following formula, the squared load quality factor values ​​under each harmonic are calculated. :

[0073] Step 5-2, quality factor entropy dispersion ( Indicator Calculation: Probability distribution calculation: Calculate the 5th, 7th, and 9th harmonics. Treating it as a random variable, calculate the probability value corresponding to each harmonic:

[0074]

[0075] Shannon entropy calculation: Calculate the Shannon entropy of this probability distribution to measure the degree of disorder in the distribution.

[0076]

[0077] Normalized entropy dispersion calculation: To facilitate threshold tuning, the Shannon entropy is normalized to obtain the final criterion index:

[0078] For example, when the specific harmonics are the 5th, 7th, and 9th harmonics, m=3, then:

[0079] Step 6: Perform island detection based on the quality factor entropy dispersion corresponding to a specific harmonic to obtain the detection result.

[0080] Set a threshold σ (usually tuned to 0.15). Continuously calculate. The value is compared with the threshold: if The system is determined to be in grid-connected status.

[0081] if If the situation is isolated, an islanding state is determined, and a disconnection command is immediately issued to disconnect the distributed power source from the power grid.

[0082] After islanding occurs, the RLC parallel resonant circuit of the load becomes dominant, and its quality factor... It is an inherent property of the load and is independent of the harmonic order; therefore, the values ​​calculated for each harmonic order are independent of the harmonic order. The values ​​are highly consistent, their probability distribution is concentrated, and the Shannon entropy is... It approaches the maximum value ln(3), thus leading to Approaching 0. During grid connection, the introduction of grid impedance disrupts this consistency, making... Dispersed distribution Decrease Increase. Through monitoring A jump from a high value to a low value can reliably detect islands.

[0083] like Figure 6 As shown, this invention also proposes an island detection system based on the Goertzel algorithm and quality factor entropy dispersion, used to implement the aforementioned island detection method based on the Goertzel algorithm and quality factor entropy dispersion. The system includes: The signal acquisition module is used to acquire three-phase voltage and current signals at the point of common coupling of the distributed generation system. The leakage suppression module is used to perform fundamental frequency tracking and dual-domain leakage suppression processing on three-phase voltage and current signals to obtain accurate fundamental parameters. The reconstruction and separation module is used to reconstruct the fundamental wave parameters and separate the harmonic signals to obtain the residual harmonic signals. The frequency analysis module uses the Goertzel algorithm to perform directional spectrum analysis on specific harmonics in the residual harmonic signal, and obtains the voltage amplitude and current amplitude of the specific harmonics. The quality factor entropy dispersion calculation module is used to calculate the harmonic impedance amplitude based on the voltage and current amplitudes of a specific harmonic, and obtain the quality factor entropy dispersion corresponding to the specific harmonic based on the harmonic impedance amplitude. The detection module is used to detect islands based on the quality factor entropy dispersion corresponding to a specific harmonic and obtain the detection results.

[0084] To verify the beneficial effects of this invention, a simulation experiment was conducted using the method proposed in this invention. The simulation results are as follows: Figure 4 As shown, it can be seen that: Before islanding occurs (in grid-connected state), the quality factor entropy dispersion The value stabilizes around 0.4; after islanding occurs, the value rapidly decreases and stabilizes below 0.1. Whether in near-synchronous (Δf=0.002Hz) power balance or under conditions with large frequency offsets (Δf=-0.427Hz, +0.027Hz), it accurately crosses the threshold (σ=0.15), achieving reliable detection. In contrast, traditional DFT algorithms, due to spectral leakage, exhibit large fluctuations in calculated values ​​exceeding the threshold under frequency offset conditions, potentially leading to detection failure.

[0085] Combination Figure 5 The indication, Figure 5 This is a performance comparison chart between the method of this invention and the traditional DFT algorithm. It can be seen that under frequency shift, the traditional DFT algorithm obtains the following results ( Figure 4 (Figure a) Due to spectrum leakage The calculation results are unstable and exceed the threshold, while the results obtained by the method of this invention ( Figure 4 Figure b) shows that the invention is stable and reliable, highlighting the advantages of the dual-domain leakage suppression technology.

[0086] Through theoretical analysis, simulation verification, and comparison with traditional methods, the method proposed in this invention demonstrates the following significant advantages and effects: 1. Minimal Detection Blind Zone: Since the consistency law still holds under power balance, this invention significantly reduces the non-detection zone (NDZ) of traditional passive methods. Simulations show that even in extreme cases with a source-load power matching degree as high as 99.9%, islanding protection can still be accurately triggered.

[0087] 2. Fast detection speed: The entire algorithm is computationally efficient, and the process from data acquisition to decision output can be completed within 200 milliseconds, which is far better than the standard requirement of 2 seconds, providing a greater margin for system security.

[0088] 3. High detection accuracy and reliability: Dual-domain leakage suppression technology ensures the accuracy of harmonic measurement. The criterion is based on profound physical principles and has strong immunity to interference such as power grid fluctuations and load switching, effectively avoiding false operation and failure to operate.

[0089] 4. Power quality friendly: It is a purely passive detection method that does not generate any form of harmonics or power disturbances, and has a low output current THD, thus ensuring the power quality of grid connection.

[0090] 5. High computational efficiency: The Goertzel algorithm significantly reduces the amount of computation compared to FFT, saving up to 70% or more of the computational resources, reducing the performance requirements of the hardware processor, and helping to reduce costs and power consumption.

[0091] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0092] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0093] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0094] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0095] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. An island detection method based on the Goertzel algorithm and quality factor entropy dispersion, characterized in that, Includes the following steps: Step 1: Collect the three-phase voltage and current signals at the point of common coupling of the distributed generation system; Step 2: Perform dual-domain leakage suppression processing on the three-phase voltage and current signals, including time-domain processing and frequency-domain processing, to obtain accurate fundamental parameters; Step 3: Reconstruct the fundamental wave parameters and separate the harmonic signals to obtain the residual harmonic signals; Step 4: Use the Goertzel algorithm to perform directional spectrum analysis on specific harmonics in the residual harmonic signal to obtain the voltage and current amplitudes of specific harmonics. Step 5: Calculate the harmonic impedance amplitude based on the voltage and current amplitudes of the specific harmonic, and obtain the quality factor entropy dispersion corresponding to the specific harmonic based on the harmonic impedance amplitude. Step 6: Perform island detection based on the quality factor entropy dispersion corresponding to a specific harmonic to obtain the detection result.

2. The island detection method based on Goertzel algorithm and quality factor entropy dispersion as described in claim 1, characterized in that, Step 2 includes: Step 2-1, Time Domain Processing: The acquired three-phase voltage and current signals constitute the original signal. A three-stage cascaded adaptive filter is used to process the original signal. Perform time-domain filtering to obtain the fundamental frequency signal after time-domain filtering; Step 2-2, Frequency Domain Processing: The fundamental signal after time-domain filtering is precisely analyzed using the DFT frequency offset correction algorithm to obtain accurate fundamental parameters.

3. The island detection method based on the Goertzel algorithm and quality factor entropy dispersion as described in claim 2, characterized in that, The original signal is processed using a three-stage cascaded adaptive filter. The processing includes: The three-stage cascaded adaptive filter includes a first-order differential filter H1, a first-order integral filter H2, and a first-order integral filter H3. First-order differential filter H1, first-order integral filter H2 and first-order integral filter H3 are used to filter out DC components and even harmonics, third and integer multiples of 3 harmonics, and fifth and seventh characteristic harmonics in the original signal, respectively. The fundamental signal obtained after time-domain filtering is: S[n]*H1*H2*H3.

4. The island detection method based on the Goertzel algorithm and quality factor entropy dispersion as described in claim 3, characterized in that, The precise fundamental frequency parameters include: the precise part of the fundamental frequency component. virtual part Amplitude and phase The method of obtaining it is as follows: Input signal at time t for: in, The signal amplitude, The input signal is the fundamental frequency signal after time-domain filtering, where the initial phase angle is the fundamental frequency angle. The rated frequency of the power frequency. This is the frequency offset; Discretize the input signal, and assume that N points are sampled per cycle, with a sampling frequency of... The sampling interval is Then the sampled value at time k for: The sampled value at time k The initial real part of the Discrete Fourier Transform (DFT) result after truncating with a rectangular window and performing the DFT. and the initial imaginary part They are respectively: Zero-crossing frequency measurement is performed on the fundamental frequency signal to track the fundamental frequency in real time and obtain the current frequency value. Based on the current frequency value Obtain the rated frequency of the power frequency frequency offset for: Combined with the sampled values ​​at time k and frequency difference For the initial real part and the initial imaginary part After preliminary correction, the real part is obtained. and the virtual part for: Correction Amplitude for: The final corrected real part is obtained by combining the correction system. and the virtual part for: in, , , , , , , and All are correction factors.

5. The island detection method based on the Goertzel algorithm and quality factor entropy dispersion as described in claim 4, characterized in that, Step 3 specifically includes: Based on the precise fundamental wave parameters, the reconstructed fundamental wave time-domain signal is obtained. : = In the formula, n refers to the nth data window; Based on the reconstructed fundamental time-domain signal and the original signal Obtaining residual harmonic signals : 。 6. The island detection method based on Goertzel algorithm and quality factor entropy dispersion according to claim 1, characterized in that, The specific harmonics in step 4 are: the 5th harmonic, the 7th harmonic, and the 9th harmonic.

7. The island detection method based on Goertzel algorithm and quality factor entropy dispersion as described in claim 1, characterized in that, In step 5, the quality factor entropy dispersion corresponding to a specific harmonic is calculated as follows: Calculate the harmonic impedance amplitude at the PCC point |Zpcc(jω) for each specific harmonic order h. h )|: |Zpcc(jω h )|=|H| / |Yes| Where Zpcc is the impedance at the point of common coupling, j is the imaginary unit, and ω h Let be the angular frequency of the h-th specific harmonic, and Vh and Ih be the harmonic voltage and current when the specific harmonic order is h, respectively. Obtain the impedance amplitude |Zpcc(jω1)| at the fundamental frequency, and calculate the squared load quality factor at harmonic order h. : Treating the squared value of the load quality factor as a random variable, calculate the probability value corresponding to each harmonic: in, This represents the probability value corresponding to the h-th harmonic; Calculate the Shannon entropy for this probability value. : Normalized entropy dispersion calculation involves normalizing the Shannon entropy to obtain the quality factor entropy dispersion corresponding to a specific harmonic. : in, This indicates the number of a specific harmonic used in the calculation.

8. The island detection method based on Goertzel algorithm and quality factor entropy dispersion according to claim 1, characterized in that, Step 6 specifically includes: Set a threshold σ, and set the quality factor entropy dispersion corresponding to a specific harmonic. The results are obtained by comparing the results with a threshold. like The system is determined to be in grid-connected status. like If the situation is isolated, an islanding state is determined, and a disconnection command is immediately issued to disconnect the distributed power source from the power grid.

9. An island detection system based on the Goertzel algorithm and quality factor entropy dispersion, used to implement the island detection method based on the Goertzel algorithm and quality factor entropy dispersion as described in any one of claims 1-8, characterized in that, include: The signal acquisition module is used to acquire three-phase voltage and current signals at the point of common coupling of the distributed generation system. The leakage suppression module is used to perform fundamental frequency tracking and dual-domain leakage suppression processing on three-phase voltage and current signals to obtain accurate fundamental parameters. The reconstruction and separation module is used to reconstruct the fundamental wave parameters and separate the harmonic signals to obtain the residual harmonic signals. The frequency analysis module uses the Goertzel algorithm to perform directional spectrum analysis on specific harmonics in the residual harmonic signal, and obtains the voltage amplitude and current amplitude of the specific harmonics. The quality factor entropy dispersion calculation module is used to calculate the harmonic impedance amplitude based on the voltage and current amplitudes of a specific harmonic, and obtain the quality factor entropy dispersion corresponding to the specific harmonic based on the harmonic impedance amplitude. The detection module is used to detect islands based on the quality factor entropy dispersion corresponding to a specific harmonic and obtain the detection results.

10. A terminal, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the island detection method according to any one of claims 1-8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the island detection method according to any one of claims 1-8.

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