Micro-grid on-grid and off-grid state detection method and system capable of resisting step disturbance influence

Abstract

The invention discloses a step disturbance influence resistant micro-grid on-grid and off-grid state detection method and system, and the method comprises the steps: carrying out the filtering and normalization processing of a discrete time sequence, and obtaining a standard signal; identifying mutation points in the standard signal, and constructing a mutation point index set; screening out a point column meeting a time closeness feature from the mutation point index set, and constructing a continuous candidate section; performing stability analysis, monotonicity judgment and fallback trend identification on each continuity candidate section to judge whether the continuity candidate section is a step change section or not; whether a real continuous pulse sequence is formed or not is judged according to the time interval, the amplitude change rate and the amplitude peak-valley characteristic for the remaining sections excluding the step change section in the continuous candidate sections; and outputting the disturbance type of each continuity candidate section. According to the method, the accuracy of disturbance identification and the robustness of system control are improved, and the operation safety and the control decision reliability of the system under the complex disturbance background are guaranteed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of micro-grid and off-grid state detection, and particularly relates to a micro-grid and off-grid state detection method and system capable of resisting step disturbance influence. BACKGROUND

[0002] The statements in this section merely provide background information related to the present application and do not necessarily constitute the prior art.

[0003] With the access of a large number of new energy and distributed power to the power grid, new power systems such as micro-grid and active distribution network gradually develop maturely; such systems generally have the ability of parallel and off-grid operation, and can be flexibly switched between island mode and grid-connected mode to adapt to different operation requirements and fault response scenarios.

[0004] However, as a key operation link, the parallel and off-grid switching is often accompanied by the appearance of transient disturbance signal (i.e. step disturbance). For example: when the inverter is switched from grid-connected to off-grid operation, the system voltage or current may have a step change in a very short time, which is manifested as a rapid increase or decrease in amplitude; under normal circumstances, if the transient disturbance amplitude is small and can decay quickly, it is generally unnecessary to be specially treated.

[0005] However, such disturbance usually has obvious mutation, and due to its short duration and large amplitude, it is easy to be confused with multiple continuous pulse disturbances in waveform performance.

[0006] In the prior art, the following schemes are mostly used for disturbance detection: (1) Pulse amplitude threshold method: when the mutation amplitude exceeds a certain set value, it is determined as a pulse; (2) Time interval analysis method: when multiple mutation points appear intensively in a short time window and the interval is less than a set time threshold, it is determined as a continuous pulse; (3) Quantity statistics method: when the number of mutation points in a unit time exceeds a certain number threshold, it is determined as a pulse sequence event.

[0007] The above methods have good adaptability in conventional fault disturbance scenarios, and can accurately identify continuous pulses and then realize disturbance detection. However, in the special case of processing step disturbance, there are significant defects, such as: the step disturbance is easy to be determined as a continuous mutation point at multiple sampling points due to its steep signal mutation, resulting in misidentification of a physical step as multiple pulses, causing misjudgment; misjudgment not only affects the accuracy of disturbance identification itself, but also directly interferes with key functions such as off-grid control and protection switching decision, such as: it may trigger unnecessary protection logic (such as off-grid, island switching), causing system instability, especially in unattended automated microgrids, which is more likely to cause misoperation, increasing the risk of actual operation of the power grid. In addition, most traditional algorithms rely on numerical features and lack analysis of the essential properties of the disturbance, without considering whether the signal after the step disturbance is stable; and the existing algorithms usually use fixed threshold and time window parameters, which are difficult to adapt to changes in different topologies, operating modes and device characteristics, and have poor robustness, making it difficult to adapt to multiple working conditions. SUMMARY

[0008] To solve the above problems, the present application proposes a microgrid parallel-off-grid state detection method and system that can effectively identify and exclude the pseudo-pulse characteristics of step disturbance in parallel-off-grid switching, improve the accuracy of disturbance identification and the robustness of system control, and prevent false triggering behavior in system switching, protection or control strategy.

[0009] In some embodiments, the following technical solutions are adopted: A microgrid parallel-off-grid state detection method that can resist the influence of step disturbance, comprising: Obtain three-phase voltage signals and reconstruct them to obtain a reconstructed discrete time sequence; Filter and normalize the discrete time sequence to obtain a standard signal; Identify the mutation points in the standard signal and record their positions and amplitudes to construct a mutation point index set; Select a point column that meets the time close feature from the mutation point index set to construct a continuity candidate section; For each continuity candidate section, perform stability analysis, monotonicity judgment and fall-back trend identification to determine whether it is a step change section; For the remaining section after excluding the step change section from the continuity candidate section, determine whether it constitutes a real continuous pulse sequence based on time interval, amplitude change rate and amplitude peak-valley characteristics; Based on the judgment result, output the disturbance type of each continuity candidate section.

[0010] As a further solution, the mutation points in the standard signal are identified, specifically: calculating a first-order differential signal of each standard signal, if the amplitude of the first-order differential signal is greater than a minimum change threshold of mutation recognition, then the sampling point corresponding to the standard signal is determined as a suspected mutation point; wherein the minimum change threshold of mutation recognition is set as a set multiple of the standard deviation of the first-order differential signal.

[0011] As a further scheme, after constructing the mutation point index set, further comprising: performing minimum interval filtering on the mutation points, if the interval between two adjacent mutation points is less than a set value , then retaining the mutation point with larger signal amplitude and removing the other mutation point.

[0012] As a further scheme, filtering out the point set satisfying the time close feature from the mutation point index set, constructing as a continuity candidate section, specifically: introducing a time interval threshold , if the interval between two adjacent mutation points is less than the threshold , then considering that the two adjacent mutation points belong to the same continuous section; starting from the starting point, traversing each adjacent mutation point pair and , if , then merging it into the current continuous section, otherwise, disconnecting the current continuous section, taking as the starting point of a new continuous section, and continuing to traverse; ultimately forming a set of several continuity candidate section sets .

[0013] As a further scheme, after obtaining the continuity candidate section set, further comprising: setting a minimum pulse point number threshold , and only retaining the sections whose point number is greater than the threshold .

[0014] As a further scheme, for each continuity candidate section, respectively performing stability analysis, monotonicity judgment and fall trend recognition to judge whether it is a step change section, specifically: judging the change amplitude between the maximum signal and the minimum signal in the observation window of a set length after each continuity candidate section, judging whether the change amplitude is less than the maximum allowed fluctuation amplitude; if yes, it satisfies the step disturbance characteristic; judging whether there is a consistent direction slope mutation before and after each continuity candidate section; if yes, it satisfies the step disturbance characteristic; judging whether the maximum fall amplitude after each continuity candidate section is greater than a set value or whether the peak-valley alternating feature appears; if not, it satisfies the step disturbance characteristic; If at least one of the three judgment conditions is satisfied, the continuous candidate section is determined as a step change section.

[0015] As a further solution, the remaining section after excluding the step change section from the continuous candidate section is determined whether to constitute a real continuous pulse sequence based on the time interval, the amplitude change rate and the amplitude peak-valley characteristic, specifically: The standard deviation of the pulse interval of each mutation point in the continuous candidate section is calculated, and if the standard deviation is not greater than the time consistency criterion threshold, the real continuous pulse sequence determination condition one is satisfied; The average amplitude mutation rate of each mutation point in the continuous candidate section is calculated, and if the average amplitude mutation rate is not less than the amplitude mutation threshold, the real continuous pulse sequence determination condition two is satisfied; The signal polarity of each mutation point in the continuous candidate section is calculated, the number of alternations is counted, the alternation ratio is calculated, and if the alternation ratio is not less than the alternation threshold, the real continuous pulse sequence determination condition three is satisfied; If a continuous candidate section satisfies the above three determination conditions at the same time, the continuous candidate section is determined as a real continuous pulse sequence.

[0016] As a further solution, based on the judgment result, the disturbance type of each continuous candidate section is output, specifically: Each continuous candidate section is classified and coded according to the judgment result, different flag bits represent different disturbance types, and finally a disturbance type code sequence is output; wherein the disturbance type includes: normal fluctuation, step disturbance and real pulse sequence.

[0017] In some other embodiments, the following technical solutions are adopted: A micro-grid anti-step disturbance influence and off-grid state detection system, comprising: A data acquisition module configured to acquire three-phase voltage signals and reconstruct to obtain a reconstructed discrete time sequence; A data processing module configured to filter and normalize the discrete time sequence to obtain a standard signal; A mutation point identification module configured to identify mutation points in the standard signal and record their positions and amplitudes, and construct a mutation point index set; A step change identification module configured to select a point column satisfying the time close feature from the mutation point index set to construct a continuous candidate section; for each continuous candidate section, stability analysis, monotonicity judgment and fall-back trend identification are performed to determine whether it is a step change section; The continuous pulse identification module is configured to determine whether the remaining segments after excluding the step change segments from the continuity candidate segments constitute a real continuous pulse sequence based on a time interval, an amplitude change rate and an amplitude peak-valley characteristic. The result output module is configured to output the disturbance type of each continuity candidate segment based on the determination result.

[0018] In some other embodiments, the following technical solutions are adopted: A terminal device comprises a processor and a memory, the processor is used to implement instructions, and the memory is used to store a plurality of instructions, the instructions are suitable for being loaded and executed by the processor to implement the micro-grid and off-grid state detection method against the influence of step disturbance.

[0019] Compared with the prior art, the present application has the following advantages: (1) The present application constructs a mutation point index set based on a normalized standard signal, screens a plurality of continuity candidate segments from the mutation point index set, and determines whether each continuity candidate segment belongs to a step disturbance through stability analysis, monotonicity judgment and fall-back trend identification. Further, whether it belongs to a real continuous pulse sequence is determined through a time interval, an amplitude change rate and an amplitude peak-valley characteristic. The present application can accurately identify whether the mutation type is a step disturbance or a real continuous pulse, avoid misidentifying a step change as a continuous pulse in a parallel-off-grid switching scenario, prevent mis-triggering behavior in system switching, protection or control strategy, improve the accuracy of disturbance identification and the robustness of system control, and ensure the operation safety and control decision reliability of the system under complex disturbance background.

[0020] (2) The present application can more accurately determine whether a suspected continuous pulse segment belongs to a step disturbance by introducing stability analysis, monotonicity judgment and fall-back trend identification. The typical feature of a step disturbance is that the signal enters a new steady state after mutation, rather than continuous oscillation. Therefore, the signal form of "single mutation and then stable" can be identified through stability analysis; the step of "single direction jump" can be distinguished from the pulse of "positive and negative alternation" through monotonicity judgment; and the disturbance segment with obvious peak-valley alternation characteristics can be eliminated through fall-back trend identification. These methods start from the dynamic evolution characteristics of the signal, avoid the misjudgment problem caused by the traditional algorithm which only relies on numerical threshold, and thus improve the identification accuracy of the step disturbance.

[0021] (3) The application further judges whether the candidate section is a real continuous pulse by introducing time interval consistency, amplitude change rate and amplitude peak-valley characteristics. Real pulse groups are relatively uniform in time distribution, while noise or accidental glitches lack regularity, so the time interval consistency criterion can effectively eliminate random interference. The introduction of the amplitude change rate enables the algorithm to distinguish between real pulses with high amplitude mutations and background fluctuations with low amplitude, thereby reducing false positives. The amplitude peak-valley characteristic takes advantage of the fact that real pulses often exhibit a positive-negative alternating oscillation structure, which is distinct from a monotonic step. Through analysis of these three characteristics, the essential properties of the pulse can be fully reflected, making the recognition result more reliable.

[0022] Compared with the traditional method, the application not only relies on numerical size and statistical threshold, but also combines the dynamic change process after the disturbance occurs, considering whether the signal enters a steady state and its inherent regularity in time, amplitude and waveform structure. This method effectively solves the problem of the prior art "lack of analysis of the essential properties of the disturbance, and does not consider whether the signal is stable after the step disturbance", thereby significantly improving the accuracy and robustness of disturbance recognition, avoiding misoperation or refusal to operate in critical scenarios such as grid-connected and off-grid switching, and ensuring the safety and stability of system operation.

[0023] (4) The application identifies the mutation point based on the adjacent signal amplitude change and the minimum change threshold for mutation recognition, and the minimum change threshold for mutation recognition is calculated based on the standard deviation of the adjacent signal amplitude change; The threshold values for stability analysis, the threshold values for monotonicity judgment and the threshold values for fall-back trend recognition in the application are all calculated based on the local noise level before the mutation point; These threshold values are all dynamic threshold values related to signal points, which can dynamically change with changes in different topologies, operating modes and device characteristics, fully adapt to the needs of different working condition scenarios, and improve the robustness of the system.

[0024] Other features and advantages of the additional aspects of the application will be partially given in the following description, partially will become apparent from the following description, or will be understood by practicing the present application. BRIEF DESCRIPTION OF DRAWINGS

[0025] Figure 1 Flow chart of the micro-grid grid-connected and off-grid state detection method for resisting the influence of step disturbance in the embodiment of the application. DETAILED DESCRIPTION

[0026] It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the application. Unless otherwise indicated, all technical and scientific terms used in the application have the same meaning as understood by a person skilled in the art.

[0027] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0028] Example 1 In one or more embodiments, a method for detecting the on-grid / off-grid status of a microgrid that resists step disturbances is disclosed, combined with... Figure 1 Specifically, it includes the following processes: S101: Acquire the three-phase voltage signal and reconstruct it to obtain the reconstructed discrete time series.

[0029] In this embodiment, a phase-locked loop (PLL) is used to track the phase and frequency of the grid voltage in real time during grid-connection / off-grid switching, ensuring that the inverter output is synchronized with the grid. Before grid connection, it achieves phase alignment to prevent grid connection shock; during operation, the PLL provides a phase reference to support smooth switching control. When the grid is abnormal, the PLL's phase or frequency estimation changes abruptly, which can be used to quickly identify off-grid conditions.

[0030] Phase error is extracted using Clarke and Park transforms, and the phase is obtained by adjusting the frequency and integrating using a PI controller, thus achieving synchronous signal reconstruction and phase-locked tracking. The three-phase voltage signal is... , , , , and For the reconstructed Coordinate components.

[0031] The specific refactoring process is as follows: Let the three-phase voltage signal at the current sampling time be... , , Convert it to Orthogonal components in coordinate system: ; ; Using rotational coordinate transformation Signal projection onto the current phase-locked angle corresponding In coordinate system: ; ; in, For the synchronization error signal, the ideal phase-locked state is 0.

[0032] The phase error is converted to an angular frequency increment by a PI controller : ; ; where, is the error integral term, is the sampling period, and are the PI controller parameters. represents the current angular frequency increment output by the PI controller.

[0033] The frequency offset is added to the nominal frequency to form the current estimated frequency : ; The frequency is then integrated to obtain the current phase : ; where, represents the nominal angular frequency of the power grid.

[0034] The synchronous component in the coordinate is inverse-transformed back to the coordinate to realize synchronous filtering signal reconstruction: ; ; is the direct-axis component of the synchronous rotating coordinate system; is the quadrature-axis component of the synchronous rotating coordinate system.

[0035] It is then recovered to a three-phase signal through inverse Clarke transformation: ; ; ; where, and are the reconstructed coordinate components, , and are the reconstructed coordinate components.

[0036] ​The system original signal is a discrete time sequence, denoted as: ; wherein, is the signal value at the kth sampling time (x(k), x(k), or x(k) ) ; ; is the total number of sampling points in the current analysis window.

[0037] S102: filtering and normalizing the discrete time sequence to obtain a standard signal.

[0038] In this embodiment, in order to eliminate system high-frequency noise and sampling burr, the original signal is first subjected to low-pass filtering, and the moving average filtering is realized by using the following formula: ; wherein, is the filtered signal; is the sliding average window length, which is taken as 4; represents the signal value at the n-kth sampling time.

[0039] In order to ensure the uniformity of signal amplitude scale, the filtered signal is subjected to normalization processing to obtain a standard signal : ; wherein, is the signal mean value; is the signal standard deviation; is the normalized signal, which is used for subsequent mutation point extraction.

[0040] In order to enhance the responsiveness to signal mutation, the first-order difference signal is calculated for subsequent mutation point detection: ; wherein, is the amplitude change at the kth time, i.e. the first-order difference signal; the boundary condition is .

[0041] S103: identifying the mutation points in the standard signal and recording their positions and amplitudes to construct a mutation point index set.

[0042] In this embodiment, the minimum change threshold for mutation identification is set as , and when the following formula is satisfied, the kth point can be determined as a suspected mutation point: ; wherein, ​​​​​is set as 1.5 times of the standard deviation of the normalized signal, i.e. , is the standard deviation of the change rate sequence , representing the natural fluctuation range of the whole signal.

[0043] The change rate threshold of the embodiment is obtained based on the standard deviation of the signal amplitude change amount sequence, and can dynamically change with the change of the signal amplitude change amount, thereby improving the dynamic adaptability of the abrupt change point identification method.

[0044] All points satisfying the above conditions form an abrupt change point index set: ; The set is the suspected abrupt change point set, which is used for subsequent continuity judgment and step exclusion logic.

[0045] As an optional implementation, to improve stability, the abrupt change point set is further subjected to minimum interval filtering, i.e., if the interval between adjacent abrupt change points is less than a set value , only the abrupt change point with a larger amplitude is retained to eliminate the influence of repetition or jitter.

[0046] S104: A point sequence satisfying the time closeness feature is selected from the abrupt change point index set to form a continuity candidate section.

[0047] In the embodiment, the abrupt change point set obtained in S103 is: ; Among them, represents the index of the i-th sampling point identified as an abrupt change, satisfying , and k is the number of abrupt change points.

[0048] A time interval threshold (unit: sampling points) is introduced to determine whether the abrupt change points are close enough to form continuity; specifically, if , it is considered that and belong to the same continuous section.

[0049] The abrupt change point set is traversed, and is segmented according to the following rules: (1) the starting point of a new section is initialized as ; (2) each pair of adjacent points , in the set is traversed, and its interval is determined: if , it is merged into the current section, if , the current section is disconnected, and the section is recorded, and start point of a new segment; (3) finally form several candidate segment sets wherein: ; indicates a mutation point in a segment.

[0050] As an optional embodiment, to eliminate short segments caused by accidental disturbance, the embodiment introduces a minimum pulse point threshold to determine whether each segment has analysis value, only the candidate segments meeting are retained, as an example, 4 is set, which is used to eliminate false segments formed by accidental disturbance or burr.

[0051] S105: For each continuity candidate segment, stability analysis, monotonicity judgment and fall-back trend identification are performed respectively to determine whether it is a step change segment.

[0052] In this embodiment, the stability analysis is used to determine whether the signal after mutation remains in a new amplitude interval for a long time (such as within 20 sampling points, the change does not exceed the set value); if so, it means that the signal remains stable and has minimal fluctuation for a period of time (such as 20 sampling points) after mutation, which meets the steady-state characteristics after single jump of step disturbance, and therefore can be determined as step change.

[0053] The monotonicity judgment is used to analyze whether there is a consistent direction of slope mutation before and after the mutation segment, if so, it means that the signal is a single direction change (rise / fall), rather than a pulse (oscillation), and therefore can be determined as one-time step change.

[0054] The fall-back trend identification is used to check whether the signal after mutation rapidly falls back or has peak-valley alternation characteristics, if there is rapid fall-back or significant oscillation structure after mutation, it means that the signal after mutation is not stable but has significant fall-back, which is a pulse rather than a step; if there is no rapid fall-back or significant oscillation structure after mutation, it can be determined as step change.

[0055] As a specific implementation, the specific determination process is as follows: (1) Stability analysis: determine whether the change amplitude between the maximum signal and the minimum signal in the observation window of a set length after each continuity candidate segment meets the step disturbance characteristics.

[0056] Specifically, assuming is the end point of a certain continuity candidate segment, is the normalized signal. Calculate the maximum amplitude change range in the time window after mutation: ​ ; wherein, is the length of observation window after mutation (take 20 sampling points); is the sequence number of the last sampling point of the candidate section; is the length of stability observation window after mutation, take 20 in this embodiment.

[0057] Calculate the local noise level before mutation point (background standard deviation): ; wherein, denotes the standard deviation, denotes the interval of sampling points before the mutation point.

[0058] The adaptive judgment condition is as follows: ; wherein, is the maximum allowable fluctuation amplitude (take 0.03, normalized unit), denoted as: ; wherein, is the empirical adjustment coefficient, take 2.5.

[0059] If the adaptive judgment condition is met, it means that the signal after the continuous candidate section enters a stable state, meeting the step disturbance characteristics.

[0060] (2) Monotonicity judgment: judge whether there is a consistent direction of slope mutation before and after each continuity candidate section, if yes, it meets the step disturbance characteristics; Specifically, the average slope of the first and last ends of a certain continuity candidate section is defined as follows: ; ; wherein, is the average slope of the first end (i.e. before the continuity candidate section), is the average slope of the tail end (i.e. after the continuity candidate section); is the slope calculation window length; is the normalized signal amplitude of the starting point of the continuity candidate section; is the normalized signal amplitude of the termination point of the continuity candidate section; is the signal amplitude of sampling points before the starting point of the continuity candidate section, is the signal amplitude of sampling points after the termination point of the continuity candidate section.

[0061] determine whether the slopes are changing in the same direction, i.e. whether the following condition is satisfied: ; ; wherein, is an adaptive threshold for the end slope to be gentle, , is an adjustment coefficient for the latter slope to be stable, and is taken as 1.5; is the local noise level before the mutation point, is a sign function for determining the positivity or negativity of a real number, if > 0, then ; if < 0, then .

[0062] If the slopes before and after the mutation segment (i.e. the continuity candidate segment) are consistent in direction, and the end slope is close to 0, it is indicated that it is a stepwise rise or fall, rather than a pulse oscillation.

[0063] (3) Fall-back trend identification: determine whether the maximum fall-back amplitude after each continuity candidate segment is greater than a set value or a peak-valley alternation feature appears; if not, it satisfies the step disturbance characteristic; Specifically, after the mutation point , the points are observed: ; The maximum fall-back amplitude is calculated: ; The adaptive judgment condition is: ; wherein, represents the minimum value among the points after the mutation point , and is a fall-back strength threshold factor, taken as 3.

[0064] If it is true, it is indicated that the signal after the mutation is not stable but has a significant fall-back, which is a pulse non-step; if it is not true, it is determined to satisfy the step disturbance characteristic.

[0065] The specific judgment rules are as follows: For each continuity candidate segment , if any of the following (a), (b), and (c) conditions is satisfied, it is determined that the continuity candidate segment is a step change: (a) the stability analysis condition is satisfied; (b) the monotonicity judgment condition is satisfied; (c) No significant falling trend.

[0066] The candidate segment is then rejected from the continuity candidate segments and marked as a "step disturbance".

[0067] In this embodiment, the threshold value for stability analysis, the threshold value for monotonicity judgment, and the threshold value for falling trend identification are all calculated based on the local noise level before the mutation point. These threshold values are all dynamic threshold values related to signal points, which can dynamically change with changes in different topologies, operating modes, and device characteristics, fully adapting to the needs of different working condition scenarios and improving system robustness.

[0068] S106: For the remaining segments after excluding the step change segments in the continuity candidate segments, determine whether they constitute a real continuous pulse sequence based on time interval, amplitude change rate, and amplitude peak-valley characteristics.

[0069] In this embodiment, the time interval consistency judgment process is as follows: Continuous pulse sequences have the characteristics of stable pulse interval and density.

[0070] For a certain candidate segment , define its pulse interval sequence: ; Calculate the standard deviation as a consistency index: ; The judgment condition is: ; Where, is the average pulse interval, i.e. the average value. is the time consistency criterion threshold (take 2 sampling points), if it is true, it means that the pulse interval is stable, supporting the judgment of real continuous pulses.

[0071] The process of amplitude change rate judgment is as follows: Calculate the amplitude mutation rate corresponding to each mutation point in the segment: ; Calculate the average amplitude mutation rate: ; The judgment condition is: ; Where, is the amplitude mutation threshold, used to exclude pulse groups with insufficient amplitude that may be noise.

[0072] The process of peak-valley feature structure analysis is as follows: The pulse sequence presents consistent interval, positive and negative alternation, and sharp change in waveform. The signal polarity corresponding to each mutation point in the segment is extracted: ; is a symbol function, used to determine the positive and negative of a real number. If > 0, then ; if < 0, then .

[0073] The number of alternations is counted : ; where is an indicator function. The alternation ratio is defined as: ; The judgment condition is: ; wherein is the set alternation ratio threshold.

[0074] If the judgment condition is met, it means that the direction of the mutation point has strong alternation, which meets the peak-valley alternation structure of the typical pulse group.

[0075] The specific judgment rules are as follows: If a candidate segment satisfies all the following conditions (a) (b) (c) at the same time: (a) ;(b) ; (c) ; , then it is confirmed as a real continuous pulse sequence, and is recorded in the final event set: . Otherwise, it is rejected.

[0076] After two rounds of discrimination (step disturbance and real continuous pulse), the remaining is normal fluctuation.

[0077] S107: Based on the judgment result, the disturbance type of each continuous candidate segment is output.

[0078] In this embodiment, after completing the steps of signal mutation point detection, continuous candidate segment extraction, step disturbance exclusion and real pulse confirmation, the recognition result is further structured and output, which is used for subsequent processing of the system disturbance identification module.

[0079] In this embodiment, after completing the steps of signal mutation point detection, continuous candidate segment extraction, step disturbance exclusion and real pulse confirmation, the recognition result is further structured and output, which is used for subsequent processing of the system disturbance identification module.

[0080] Each candidate disturbance section is classified and encoded according to its identification result in the form of a disturbance type flag bit, so as to provide decision basis for function modules such as grid-connected and off-grid control, relay protection, disturbance recording and operation and maintenance monitoring.

[0081] The embodiment sets a disturbance type flag bit The value and meaning of which are shown in Table 1: Table 1

[0082] For each identified candidate disturbance section , the type thereof is judged through the aforementioned feature analysis, and the corresponding flag bit is output, forming a disturbance type sequence: .

[0083] The method can accurately identify whether the mutation type is a step disturbance or a real continuous pulse, avoid misidentifying a step change as a continuous pulse in a grid-connected and off-grid switching scenario, prevent false triggering behavior in system switching, protection or control strategy, improve the accuracy of disturbance identification and the robustness of system control, and ensure the operation safety and control decision reliability of the system under a complex disturbance background.

[0084] Embodiment two In one or more embodiments, an anti-step disturbance influence microgrid grid-connected and off-grid state detection system is disclosed, comprising: A data acquisition module configured to acquire three-phase voltage signals and reconstruct them to obtain a reconstructed discrete time sequence; A data processing module configured to filter and normalize the discrete time sequence to obtain a standard signal; A mutation point identification module configured to identify mutation points in the standard signal and record their positions and amplitudes, and construct a mutation point index set; A step change identification module configured to filter out a point column satisfying the time closeness feature from the mutation point index set, and construct a continuity candidate section; for each continuity candidate section, stability analysis, monotonicity judgment and fall-back trend identification are performed respectively to determine whether it is a step change section; A continuous pulse identification module configured to determine whether the remaining section after excluding the step change section from the continuity candidate section constitutes a real continuous pulse sequence based on time interval, amplitude change rate and amplitude peak-valley characteristics; A result output module configured to output the disturbance type of each continuity candidate section based on the judgment result.

[0085] As a further scheme, the mutation points in the standard signal are identified, specifically: calculating a first-order differential signal of each standard signal, if the amplitude of the first-order differential signal is greater than a minimum change threshold of mutation recognition, determining the sampling point of the corresponding standard signal as a suspected mutation point; Wherein, the minimum change threshold of mutation recognition is set as a set multiple of the standard deviation of the first-order differential signal.

[0086] As a further solution, after constructing the mutation point index set, it also includes: If the interval between the two adjacent mutation points is less than a set value , the mutation point with larger signal amplitude is retained, and the other mutation point is removed.

[0087] As a further solution, from the mutation point index set, a point column satisfying the time close feature is selected, which is constructed as a continuity candidate section, specifically: Introducing a time interval threshold If the interval between the two adjacent mutation points is less than the threshold , it is considered that the two adjacent mutation points belong to the same continuous section; From the starting point, traverse each adjacent mutation point pair And If , it is merged into the current continuous section, otherwise, the current continuous section is disconnected, and is taken as the starting point of a new continuous section, and the traversal continues; Finally, a set of continuity candidate section sets .

[0088] As a further solution, after obtaining the continuity candidate section set, it also includes: Set a minimum pulse point number threshold Only keep the sections whose point number is greater than the threshold .

[0089] As a further solution, for each continuity candidate section, stability analysis, monotonicity judgment and fall trend recognition are performed respectively to judge whether it is a step change section, specifically: Judging the change amplitude between the maximum signal and the minimum signal in the observation window of a set length after each continuity candidate section, judging whether the change amplitude is less than the maximum allowed fluctuation amplitude; if yes, it satisfies the step disturbance characteristic; Judging whether there is a consistent direction slope mutation before and after each continuity candidate section, if yes, it satisfies the step disturbance characteristic; Judging whether the maximum fall amplitude after each continuity candidate section is greater than a set value or whether the peak-valley alternating feature appears; if not, it satisfies the step disturbance characteristic; If at least one of the three judgment conditions is satisfied, the continuous candidate section is determined as a step change section.

[0090] As a further solution, the remaining section after excluding the step change section from the continuous candidate section is determined whether to constitute a real continuous pulse sequence based on the time interval, the amplitude change rate and the amplitude peak-valley characteristic, specifically: The standard deviation of the pulse interval of each mutation point in the continuous candidate section is calculated, and if the standard deviation is not greater than the time consistency criterion threshold, the real continuous pulse sequence determination condition one is satisfied. The average amplitude mutation rate of each mutation point in the continuous candidate section is calculated, and if the average amplitude mutation rate is not less than the amplitude mutation threshold, the real continuous pulse sequence determination condition two is satisfied. The signal polarity of each mutation point in the continuous candidate section is calculated, the number of alternations is counted, the alternation ratio is calculated, and if the alternation ratio is not less than the alternation threshold, the real continuous pulse sequence determination condition three is satisfied. If a continuous candidate section satisfies the above three determination conditions at the same time, the continuous candidate section is determined as a real continuous pulse sequence.

[0091] As a further solution, based on the judgment result, the disturbance type of each continuous candidate section is output, specifically: Each continuous candidate section is classified and coded according to the judgment result, different flag bits represent different disturbance types, and finally a disturbance type code sequence is output; wherein the disturbance type includes: normal fluctuation, step disturbance and real pulse sequence.

[0092] It should be noted that the specific implementation of each module is exactly the same as that in Embodiment One, and will not be described in detail.

[0093] Embodiment Three In one or more embodiments, a terminal device is disclosed, which includes a processor and a memory, the processor is used to implement instructions; the memory is used to store a plurality of instructions, the instructions are suitable for being loaded and executed by the processor to implement the micro-grid and off-grid state detection method against the influence of step disturbance described in Embodiment One.

[0094] It should be understood that in the present embodiment, the processor can be a central processing unit CPU, and the processor can also be other general-purpose processors, digital signal processors DSP, application-specific integrated circuits ASIC, ready-to-program gate arrays FPGA or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0095] The memory can include read-only memory and random access memory, and provide instructions and data to the processor, a portion of the memory can also include non-volatile random access memory. For example, the memory can also store device type information.

[0096] In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in the processor or the instructions in the form of software.

[0097] Although the specific embodiments of the application are described above with reference to the drawings, the description is not a limitation on the scope of protection of the application, and those skilled in the art should understand that various modifications or changes made by those skilled in the art on the basis of the technical solutions of the application without creative labor are still within the protection scope of the application.

Claims

1. A method for detecting the off-grid state of a microgrid against the influence of step disturbances, characterized in that, The method comprises the following steps: acquiring three-phase voltage signals and reconstructing to obtain a reconstructed discrete-time sequence; filtering and normalizing the discrete-time sequence to obtain a standard signal; identifying mutation points in the standard signal and recording their positions and amplitudes to construct a mutation point index set; selecting point columns that meet the time closeness feature from the mutation point index set to construct a continuity candidate section; for each continuity candidate section, performing stability analysis, monotonicity judgment and falling trend identification to determine whether it is a step change section; for the remaining sections after excluding the step change sections from the continuity candidate section, determining whether they constitute a real continuous pulse sequence based on time interval, amplitude change rate and amplitude peak-valley characteristics; based on the judgment result, outputting the disturbance type of each continuity candidate section.

2. The method of claim 1, wherein the microgrid is an anti-step disturbance impact microgrid and the off-grid state detection method is characterized in that, The method of identifying mutation points in the standard signal comprises the following steps: calculating the first-order differential signal of each standard signal, and if the amplitude of the first-order differential signal is greater than the minimum change threshold for mutation recognition, the sampling point of the corresponding standard signal is determined as a suspected mutation point; wherein the minimum change threshold for mutation recognition is set as a set multiple of the standard deviation of the first-order differential signal.

3. The method of claim 1, wherein the microgrid is a hybrid AC / DC microgrid. After constructing the mutation point index set, the method further comprises the following steps: If the interval between two adjacent mutation points is less than a set value, the mutation point with a larger signal amplitude is reserved and the other one is removed. If the interval between two adjacent mutation points is less than a set value, the mutation point with a larger signal amplitude is reserved and the other one is removed.

4. The method of claim 1, wherein the microgrid is an anti-step disturbance method for detecting a grid-connected state of a microgrid, characterized in that, selecting point columns that meet the time closeness feature from the mutation point index set to construct a continuity candidate section; Introducing a time interval threshold If the distance between two adjacent mutation points is less than the threshold , the two adjacent mutation points are considered to belong to the same continuous segment. From the start, traverse each adjacent mutation point pair and if then incorporate into the current contiguous segment, otherwise break the current contiguous segment and continue traversing with as the start of the new contiguous segment. a number of sets of continuity candidate segments are finally formed .

5. The method of claim 4, wherein the microgrid is an anti-step disturbance method, and the off-grid state detection method is characterized in that, After obtaining the continuity candidate section set, the method further comprises the following steps: Setting a minimum pulse dot count threshold , only keeping segments with continuity candidate segment dot count greater than the threshold .

6. The method of claim 1, wherein the microgrid is an anti-step disturbance method for detecting a grid-connected state of a microgrid, characterized in that, for each continuity candidate section, performing stability analysis, monotonicity judgment and falling trend identification to determine whether it is a step change section; judging the change amplitude between the maximum signal and the minimum signal in the observation window of a set length after each continuity candidate section, and determining whether the change amplitude is less than the maximum allowed fluctuation amplitude; if yes, it meets the step disturbance feature; judging whether there is a consistent direction slope mutation before and after each continuity candidate section; if yes, it meets the step disturbance feature; judging whether the maximum falling amplitude after each continuity candidate section is greater than a set value or whether the peak-valley alternating feature appears; if no, it meets the step disturbance feature; if at least one of the above three judgment conditions meets the step disturbance feature for a certain continuity candidate section, the continuity candidate section is determined as a step change section.

7. The method of claim 1, wherein the microgrid is an anti-step disturbance impact microgrid and the off-grid state detection method is characterized by, For the remaining sections after excluding the step change sections from the continuity candidate section, determining whether they constitute a real continuous pulse sequence based on time interval, amplitude change rate and amplitude peak-valley characteristics, which comprises the following steps: calculating the standard deviation of the pulse interval of each mutation point in the continuity candidate section, and if the standard deviation is not greater than the time consistency criterion threshold, it meets the first real continuous pulse sequence determination condition; calculating the average amplitude mutation rate of each mutation point in the continuity candidate section, and if the average amplitude mutation rate is not less than the amplitude mutation threshold, it meets the second real continuous pulse sequence determination condition; calculating the signal polarity of each mutation point in the continuity candidate section, counting the number of alternations, calculating the alternation ratio, and if the alternation ratio is not less than the alternation threshold, it meets the third real continuous pulse sequence determination condition; if a certain continuity candidate section meets the above three determination conditions at the same time, it is determined as a real continuous pulse sequence.

8. The method of claim 1, wherein the microgrid is an anti-step disturbance impact microgrid and the off-grid state detection method is characterized by, Based on the judgment result, the disturbance type of each continuity candidate segment is output, specifically: The continuity candidate segments are classified and coded according to the judgment result, different flag bits represent different disturbance types, and finally the disturbance type code sequence is output; wherein the disturbance type includes: normal fluctuation, step disturbance and real pulse sequence.

9. A microgrid anti-step disturbance impact system for detecting off-grid state, characterized in that, Comprise: The data acquisition module is configured to acquire three-phase voltage signals and reconstruct to obtain a reconstructed discrete time sequence; The data processing module is configured to filter and normalize the discrete time sequence to obtain a standard signal; The mutation point identification module is configured to identify the mutation points in the standard signal and record their positions and amplitudes, and construct a mutation point index set; The step change identification module is configured to filter out the point column meeting the time close feature from the mutation point index set to construct a continuity candidate segment; for each continuity candidate segment, stability analysis, monotonicity judgment and fall trend identification are performed to determine whether it is a step change segment; The continuous pulse identification module is configured to determine whether the remaining segment after excluding the step change segment from the continuity candidate segment constitutes a real continuous pulse sequence based on the time interval, amplitude change rate and amplitude peak-valley characteristics; The result output module is configured to output the disturbance type of each continuity candidate segment based on the judgment result.

10. A terminal device comprising a processor and a memory, the processor configured to implement instructions; the memory configured to store a plurality of instructions, the terminal device characterized by, The instructions are suitable for being loaded and executed by the processor to perform the anti-step disturbance influence microgrid and off-grid state detection method of any one of claims 1-8.

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