Photovoltaic sand control remote monitoring management method and system based on end-cloud cooperation

Abstract

This invention discloses a remote monitoring and management method and system for photovoltaic desertification control based on edge-cloud collaboration, belonging to the field of desertification control management technology. The method includes the following steps: collecting the electric field intensity variation curve and the original waveform of current sampling around the photovoltaic array; aligning them on the same time coordinate; extracting the transient current spike fragments corresponding to the electric field intensity surge intervals to form an electric field disturbance correlation fragment sequence; based on the electric field disturbance correlation fragment sequence, calculating the synchronous transition rhythm between the spatial electric field intensity variation amplitude and the current transient spike amplitude; marking the current anomaly intervals with spatial electric field abrupt change characteristics; and generating an electric field coupling feature sequence. This invention distinguishes between environmental electric field disturbances and actual current anomalies by introducing the electric field coupling feature sequence and the gradual release observation interval, avoiding accidental triggering of load reduction by transient spikes; and smooths the power change process by releasing power limiting commands in stages and partitions, reducing frequency impact at the grid connection point and improving grid-connected operation stability.

Description

Technical Field

[0001] This invention relates to the field of desertification control management technology, specifically to a remote monitoring and management method and system for photovoltaic desertification control based on edge-cloud collaboration. Background Technology

[0002] Remote monitoring and management of photovoltaic desertification control refers to the continuous online collection and centralized control of photovoltaic module operation status, support stability, combiner box electrical parameters, inverter output power, surface vegetation restoration status, soil moisture content, and wind and sand erosion intensity while constructing photovoltaic power stations in desert, Gobi, and desertified areas. Its role is to ensure the long-term stable output of the power generation system and reduce the risk of power attenuation caused by wind and sand burial, module hot spots, and array mismatch. On the other hand, it allows for real-time monitoring of vegetation coverage and soil improvement effects in desertification control areas, forming a synergistic control mechanism for both power generation revenue and ecological restoration, thereby improving the comprehensive land use efficiency in desert areas. Remote monitoring and management of photovoltaic desertification control based on edge-cloud collaboration refers to deploying edge sensing and data processing units at the front end to perform rapid local analysis and anomaly prediction on data such as component temperature, current and voltage curves, wind speed and direction, sand and dust deposition thickness, and irrigation water volume. At the same time, the compressed key data is uploaded to the cloud platform, where cross-power station comparative analysis, historical trend modeling, coupled calculation of the relationship between power generation and vegetation restoration, and strategy distribution are completed. Then, the edge side executes adjustment actions, forming a hierarchical collaborative operation mechanism of real-time edge response and global cloud decision-making, realizing refined operation control in large-scale photovoltaic desertification control scenarios.

[0003] The existing technology has the following shortcomings: During the approaching stage of a strong convective dust storm, the electric field intensity around the photovoltaic array fluctuates significantly within a short period due to the rapid accumulation of air charge and drastic changes in the electric field gradient. This fluctuation can easily couple to the current sampling circuit through the sensing link, resulting in transient glitches with sudden increases in amplitude on the sampling curve. Since the glitches resemble the abnormal current characteristics of a short circuit in the module, the edge-side judgment logic can easily identify them as short-circuit symptoms and trigger array load reduction commands, leading to a large-scale power reduction within seconds. This sudden power drop weakens the active power output at the grid connection point, thereby affecting the grid's frequency support capability. In scenarios with a high proportion of renewable energy integration, this can easily amplify frequency fluctuations and even trigger cascading protection actions.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a remote monitoring and management method and system for photovoltaic desertification control based on edge-cloud collaboration, so as to solve the problems in the background art mentioned above.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a remote monitoring and management method for photovoltaic desertification control based on edge-cloud collaboration, comprising the following steps: Step 1: Collect the electric field intensity variation curve and the original waveform of current sampling in the space surrounding the photovoltaic array, align them at the same time coordinate, extract the transient current spike fragments corresponding to the electric field intensity surge interval, and form a sequence of electric field disturbance related fragments. Step 2: Based on the electric field disturbance correlation segment sequence, calculate the synchronous transition rhythm between the spatial electric field intensity change amplitude and the current transient spike amplitude, mark the current anomaly interval with spatial electric field change characteristics, and generate an electric field coupling characteristic sequence. Step 3: In the edge determination process, the current abnormality interval within the spatial electric field intensity abrupt change window is included in the slow release observation interval by combining the electric field coupling feature sequence, and the load reduction trigger time window is extended. Step 4: Continuously track the current change trend around the slow-release observation range, compare the trajectory of the continuous rise of the current with the rhythm of the decline of the space electric field intensity, and only when the current anomaly continues to expand after the space electric field intensity declines will the corresponding anomaly range be switched to the load reduction preparation state. Step 5: Based on the abnormal interval of entering the load reduction preparation state, release power limiting commands step by step according to the photovoltaic array partition, and distribute the centrally triggered load reduction action to multiple time nodes.

[0007] Preferably, the steps for acquiring the electric field intensity variation curve and the original waveform of current sampling in the space surrounding the photovoltaic array include: The electric field intensity of the space around the photovoltaic array is continuously recorded at a fixed sampling period and arranged in a unified time mark order to form a spatial electric field intensity change curve. At the same time, the output current of the photovoltaic array is continuously sampled under the same time reference to form the original waveform of current sampling. A consistent time mark is written in each sampling period to establish a time mapping relationship. Under the time mapping relationship, the spatial electric field intensity change curve and the original current sampling waveform are aligned on the same time coordinate. The electric field intensity surge interval is determined based on the time period of continuous rise and the formation of abrupt inflection point. The original current sampling waveform within the corresponding time range is extracted on the same time coordinate as the current transient glitch segment to establish the correspondence. Each electric field intensity surge interval and corresponding current transient spike segment are sequentially numbered and arranged in ascending order of time. The start time, duration, end time and corresponding current transient spike segment information are stored side by side to form a continuous time correlation structure. All numbered electric field intensity surge intervals and corresponding current transient spike segments are integrated into an electric field disturbance associated segment sequence in chronological order, and the time interval and sequential relationship between adjacent sequence units are preserved to maintain temporal continuity.

[0008] Preferably, the step of generating an electric field coupling feature sequence based on an electric field perturbation associated segment sequence includes: The electric field disturbance correlation segment sequence is unfolded one by one in chronological order. The spatial electric field intensity change curve segment and the current transient spike segment in each electric field disturbance correlation segment are placed in the same time coordinate frame for corresponding unfolding. The initial value, peak value and fall value of electric field intensity and the initial value, peak value and fall value of current are extracted, and the amplitude change trajectory comparison relationship is formed. The amplitude of spatial electric field intensity change and the amplitude of current transient spike are synchronously unfolded along the time axis around the correlation of amplitude change trajectory. The peak corresponding time point, common jump start point, common peak interval, and common fall interval are recorded to form a time rhythm sequence across multiple electric field disturbance correlation segments. The time rhythm sequence is mapped to the complete time axis of the original current sampling waveform. Current change segments that are consistent with the time rhythm sequence are marked to form current anomaly intervals with spatial electric field mutation characteristics, and the start time, duration and end time are recorded. The abnormal current intervals are integrated in chronological order, and the information on the amplitude of spatial electric field intensity changes, the amplitude of current transient spikes, and the rhythm of synchronous transitions are incorporated into the same recording unit to construct an electric field coupling feature sequence.

[0009] Preferably, the time rhythm sequence includes the time point corresponding to the common peak of the change amplitude of the spatial electric field intensity and the amplitude of the transient current spike, as well as the common fall interval. The marking of the current abnormal interval is determined according to the correspondence of the time rhythm sequence in the complete time axis of the original waveform of the current sampling, and the original time markers are kept continuously arranged in the electric field coupling characteristic sequence.

[0010] Preferably, the step of performing layered processing in conjunction with the electric field coupling feature sequence during edge determination includes: During the edge detection process, the electric field coupling feature sequence is retrieved, and the spatial electric field intensity mutation window in the electric field coupling feature sequence is mapped to the real-time detection time axis of the current sampling original waveform. The start time of the current abnormal interval is compared with the spatial electric field intensity mutation window to determine the current abnormal interval within the spatial electric field intensity mutation window and add a spatial electric field intensity mutation label. The judgment path is adjusted around the current anomaly interval marked by the sudden change of the additional spatial electric field intensity. The current anomaly interval is classified into the slow-release observation interval, and the complete time trajectory and amplitude change trajectory are retained in the slow-release observation interval to form a layered treatment structure. For the current anomaly range entering the slow-release observation range, the load reduction trigger time window is extended, and the load reduction trigger time window is kept in an extended state and the time correspondence is continuously recorded during the period covered by the sudden change window of the spatial electric field intensity. Based on the time markers in the electric field coupling characteristic sequence, the current anomaly interval within the slow-release observation interval is mapped to the standard judgment time axis. The continuously changing state is tracked according to the extended load reduction trigger time window to determine whether the load reduction preparation stage has been entered.

[0011] Preferably, the duration of the extended load reduction triggering time window corresponds to the duration of the space electric field intensity mutation window, and the end time of the space electric field intensity mutation window is used as the judgment boundary point to judge the state transition of the current abnormal interval in the slow release observation interval, so as to determine whether to enter the load reduction preparation stage.

[0012] Preferably, the steps for determining the trend around the sustained-release observation period include: Using the time marker of spatial electric field intensity change in the electric field coupling characteristic sequence, the original waveform of current sampling in the slow-release observation interval is expanded, and the start time, duration, and current time position of the current abnormal interval are located corresponding to the rhythm of spatial electric field intensity decline, forming a time comparison relationship in the same time coordinate. By continuously tracking the abnormal current range within the slow-release observation period based on the time-comparison relationship, the trajectory of the continuous rise in current and the rhythm of the decline in spatial electric field intensity are arranged synchronously on the time axis to present the direction and sequence of change. Based on the end time node of the spatial electric field intensity decline rhythm in the time axis, continue to track the change trajectory of the current anomaly interval, and add a continuous expansion mark to the current anomaly interval that continues to expand after the spatial electric field intensity decline is completed. The current anomaly range with the attached continuous expansion indicator will be switched to the load reduction preparation state, and the time comparison information formed in the slow release observation range will be retained. The current anomaly range that does not maintain the expansion trend will be kept in the slow release observation range state.

[0013] Preferably, when continuously tracking the current anomaly interval within the slow-release observation interval, the end time node of the spatial electric field intensity decline rhythm is used as the time boundary point. The current continuous rise trajectory before the end time node and the current change trajectory after the end time node are recorded separately. Only when the current anomaly interval after the end time node continues to expand will the corresponding current anomaly interval be switched to the load reduction preparation state.

[0014] Preferably, the step of releasing power limiting commands in stages according to photovoltaic array zones includes: Based on the start time and duration of the abnormal interval entering the load reduction preparation state in the complete time axis, the current operating power distribution of the photovoltaic array is unfolded over time. The power output area involved in the abnormal interval is divided according to the existing operating partitions, and the real-time power output value of each partition in the corresponding time period is recorded to form a power baseline sequence arranged by time. At the same time, the unified triggering time for centralized triggering of load reduction action is determined. Based on the power baseline sequence, the concentrated triggering of load reduction actions is divided into time segments. According to the photovoltaic array partitioning order, each partition is arranged to enter the power limiting state in sequence on the time axis, thereby decomposing the overall load reduction range into multiple segmented power limiting intervals. For segmented power limiting intervals, power limiting commands are released at each time node, and the power output change trajectory of each partition is continuously recorded to form an overall power change curve that unfolds progressively. The overall power change curve and the active power output rhythm at the grid connection point are unfolded on the same time axis to ensure that the active power output at the grid connection point evolves continuously with the progressive load reduction rhythm of the zones.

[0015] The photovoltaic desertification control remote monitoring and management system based on edge-cloud collaboration includes an electric field sampling and correlation module, a coupling rhythm identification module, a slow release determination and control module, a trend comparison and discrimination module, and a zoned load reduction scheduling module. The electric field sampling and correlation module collects the electric field intensity variation curve and the original waveform of current sampling in the space surrounding the photovoltaic array. It performs alignment processing under the same time coordinate, extracts the transient current spike fragments corresponding to the electric field intensity surge interval, and forms an electric field disturbance correlation fragment sequence. The coupling rhythm identification module calculates the synchronous transition rhythm between the amplitude of spatial electric field intensity change and the amplitude of current transient spikes based on the electric field disturbance correlation segment sequence, marks the current abnormal interval with spatial electric field abrupt change characteristics, and generates an electric field coupling feature sequence. The slow-release determination and control module combines the electric field coupling feature sequence during the edge determination process to classify the current abnormal interval within the spatial electric field intensity change window into the slow-release observation interval and extend the load reduction trigger time window. The trend comparison and judgment module continuously tracks the current change trend around the slow-release observation range, compares the continuous current rise trajectory with the rhythm of the space electric field intensity decline, and only when the current anomaly continues to expand after the space electric field intensity declines will the corresponding anomaly range be switched to the load reduction preparation state. The partitioned load reduction scheduling module, based on the abnormal intervals that enter the load reduction preparation state, releases power limiting commands step by step according to the photovoltaic array partitions, distributing the centrally triggered load reduction actions to multiple time nodes.

[0016] The technical effects and advantages provided by the present invention in the above technical solution are as follows: This invention establishes a temporal correlation between the spatial electric field intensity change curve and the original waveform of current sampling, and introduces an electric field coupling characteristic sequence and a slow-release observation interval during the edge judgment process. This enables the differentiation and control of environmental electric field disturbances and real current anomalies, preventing transient current spikes from directly entering the load reduction process within the spatial electric field intensity change window. This reduces the probability of falsely triggering load reduction from the source and avoids large-scale power reduction of the array caused by short-term environmental interference, thereby improving the operational stability and control reliability of the photovoltaic array under strong convective sandstorm conditions.

[0017] This invention continuously tracks the current change trend around the slow-release observation interval, and releases power limiting commands step by step according to the photovoltaic array partition after entering the load reduction preparation state in the abnormal interval. This disperses the originally concentrated load reduction action to multiple time nodes, so that the power change curve presents a progressive transition state. It smooths the active power output change process from the time rhythm level, reduces the impact of sudden power drop at the grid connection point on the grid frequency support capability, and improves the grid connection operation safety and overall stability level in high-proportion renewable energy access scenarios. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0019] Figure 1 This is a flowchart of the method for remote monitoring and management of photovoltaic desertification control based on edge-cloud collaboration according to the present invention.

[0020] Figure 2 This is a schematic diagram of the modules of the photovoltaic desertification control remote monitoring and management system based on edge-cloud collaboration of the present invention. Detailed Implementation

[0021] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0022] This invention provides, for example Figure 1 The remote monitoring and management method for photovoltaic desertification control based on edge-cloud collaboration, as shown, includes the following steps: Step 1: Collect the electric field intensity variation curve and the original waveform of current sampling in the space surrounding the photovoltaic array, align them at the same time coordinate, extract the transient current spike fragments corresponding to the electric field intensity surge interval, and form a sequence of electric field disturbance related fragments. The specific implementation method for this step is as follows: While the photovoltaic array is continuously operating, the electric field intensity of the space surrounding the photovoltaic array is continuously recorded at a fixed sampling period. The collected electric field intensity values ​​are arranged in a unified time stamp order to form a continuous electric field intensity variation curve. At the same time, the output current of the photovoltaic array is continuously sampled under the same time reference to completely preserve the instantaneous change trajectory of the current and form the original waveform of the current sampling. During the acquisition process, the electric field intensity variation curve and the original waveform of the current sampling share the same time reference starting point. In each sampling period, completely consistent time stamps are written for the two types of data, which are sequentially increased from the starting time to ensure that any time node in the electric field intensity variation curve can be located in the original waveform of the current sampling, thereby forming a precise time mapping relationship between the two time series.

[0023] After establishing the time mapping relationship, the spatial electric field intensity variation curve and the original current sampling waveform are loaded onto the same time coordinate for alignment. During the alignment process, the time marker is used as the sole reference, and the time scale of the spatial electric field intensity variation curve is expanded point by point. The original current sampling waveform data under the corresponding time scale are also arranged synchronously, so that the two types of data are presented in a synchronous expansion state on the same time coordinate. In this synchronous expansion state, the spatial electric field intensity variation curve is continuously scanned, and the change process of spatial electric field intensity with time is analyzed segment by segment. When the spatial electric field intensity value shows a continuous jump and forms an abrupt inflection point within several consecutive sampling periods, the time period is defined as the electric field intensity surge interval, and the start time and end time of the electric field intensity surge interval are recorded. Subsequently, based on the start time and end time, the original current sampling waveform segment within the corresponding time range is extracted from the same time coordinate, and the current change trajectory within the time range is completely preserved. This current change trajectory is defined as the current transient glitch segment, thereby establishing a one-to-one correspondence between the electric field intensity surge interval and the current transient glitch segment on the time axis.

[0024] After extracting multiple electric field intensity surge intervals and corresponding current transient spike segments, each set of correspondences is sequentially numbered and arranged in chronological order. The start time, duration, and end time of each electric field intensity surge interval and the start time, duration, and end time of the corresponding current transient spike segment are stored side-by-side in the same recording unit. This ensures that each set of data simultaneously includes the local change trajectory of the spatial electric field intensity change curve and the transient waveform trajectory of the original current sampling waveform. Based on this, all numbered correspondences are concatenated in ascending chronological order, preserving the time interval information and occurrence order between each set. This ensures that the time distance between adjacent electric field intensity surge intervals and the time distance between adjacent current transient spike segments are fully preserved, thereby constructing a continuous time-related structure. This structure includes both the correspondence between a single electric field intensity surge interval and a current transient spike segment and reflects the continuous distribution of multiple electric field disturbance events on the time axis.

[0025] Based on the aforementioned time-related structure, all electric field intensity surge intervals and their corresponding transient current spike segments are integrated into an electric field disturbance correlation segment sequence in chronological order. In this electric field disturbance correlation segment sequence, each sequence unit contains a segment of the spatial electric field intensity change curve within the corresponding time range and a segment of the original current sampling waveform, while maintaining the original time marker, so that any sequence unit can be traced back to a specific time point in the original time coordinate. During the sequence integration process, the time interval and sequential relationship between adjacent sequence units are preserved, so that the electric field disturbance correlation segment sequence presents complete temporal continuity. The electric field disturbance correlation segment sequence formed in this way reflects both the multiple surge processes of the spatial electric field intensity change curve during the approach stage of the strong convective dust storm and records the change trajectory of the transient spike segments that appear in the original current sampling waveform within the same time interval. This provides a continuous time reference basis for further judgment around the electric field coupling characteristic sequence, and maintains the correspondence between the spatial electric field intensity change curve and the original current sampling waveform under the same time coordinate.

[0026] Step 2: Based on the electric field disturbance correlation segment sequence, calculate the synchronous transition rhythm between the spatial electric field intensity change amplitude and the current transient spike amplitude, mark the current anomaly interval with spatial electric field change characteristics, and generate an electric field coupling characteristic sequence. The specific implementation method for this step is as follows: Based on the established sequence of electric field disturbance correlation segments, each segment is unfolded sequentially according to time. The corresponding spatial electric field intensity variation curve segment and current transient spike segment in each segment are placed in the same time coordinate frame for corresponding unfolding. During the unfolding process, the initial value, peak value, and fall value of the electric field intensity in the spatial electric field intensity variation curve segment are extracted, and the initial value, peak value, and fall value of the current in the current transient spike segment are extracted. These values ​​are kept in one-to-one correspondence with the original time markers, thus forming a correlation relationship of amplitude change trajectory within each electric field disturbance correlation segment. Based on this correlation relationship, the corresponding intervals of the spatial electric field intensity variation amplitude and the current transient spike amplitude are determined, providing a data source for the subsequent establishment of synchronous transition rhythm.

[0027] After establishing the amplitude change trajectory comparison relationship within each electric field disturbance correlation segment, the amplitude of spatial electric field intensity change and the amplitude of current transient spike are synchronously unfolded around the time axis. The rising phase, peak phase, and falling phase of the spatial electric field intensity change curve segment are compared with the corresponding phases in the current transient spike segment on a time-by-time basis. The time points corresponding to the peak values ​​of the spatial electric field intensity change amplitude and the current transient spike amplitude are recorded, and their relative positional relationship on the time axis is analyzed. The above comparison process is repeated in multiple electric field disturbance correlation segments. The common starting point, common peak interval, and common falling interval of the spatial electric field intensity change amplitude and the current transient spike amplitude on the time axis are statistically analyzed. These time correspondences are arranged in the order of occurrence to form a time rhythm sequence across multiple electric field disturbance correlation segments, thereby forming a synchronous transition rhythm expression between the spatial electric field intensity change amplitude and the current transient spike amplitude.

[0028] After obtaining the synchronous transition rhythm expression across multiple electric field disturbance correlation segments, the synchronous transition rhythm is mapped back to the complete time axis of the original current sampling waveform. The original current sampling waveform is continuously scanned, and current change segments consistent with the synchronous transition rhythm are searched on the time axis. When a certain interval in the original current sampling waveform corresponds to the jump stage of the spatial electric field intensity change amplitude in time, and the amplitude change trajectory is consistent with the jump trajectory of the current transient spike amplitude, the current interval is marked as a current anomaly interval with spatial electric field mutation characteristics. The start time, duration, and end time of the current anomaly interval in the complete time axis are recorded. At the same time, the current anomaly interval is associated with the corresponding spatial electric field intensity change curve segment, thereby forming a set of current anomaly segments marked with environmental disturbance characteristics.

[0029] After marking all current anomaly intervals, each current anomaly interval with spatial electric field abrupt change characteristics is integrated in chronological order. The corresponding spatial electric field intensity change amplitude information and current transient spike amplitude information are included in the same recording unit to construct an electric field coupling feature sequence. In this electric field coupling feature sequence, each sequence unit contains the time trajectory of the spatial electric field intensity change amplitude, the time trajectory of the current transient spike amplitude, and the synchronous transition rhythm information between the two, while keeping the original time identifier unchanged, so that the electric field coupling feature sequence presents a continuous arrangement on the time axis. The electric field coupling feature sequence generated in this way can fully reflect the temporal coupling relationship between the spatial electric field intensity change amplitude and the current transient spike amplitude, and provide a continuous time basis for introducing environmental disturbance reference in the subsequent edge determination process, so that the current anomaly intervals can be orderly distinguished in the context of environmental electric field disturbance.

[0030] Step 3: In the edge determination process, the current abnormality interval within the spatial electric field intensity change window is classified into the slow release observation interval by combining the electric field coupling feature sequence, and the load reduction trigger time window is extended to avoid the current transient spike directly entering the load reduction process. The specific implementation method for this step is as follows: During the edge detection process, the generated electric field coupling feature sequence is retrieved, and the information of each time period in the electric field coupling feature sequence is mapped to the real-time detection time axis of the current sampling original waveform. This ensures that the spatial electric field intensity mutation window in the electric field coupling feature sequence is consistent with the time scale of the real-time current sampling original waveform. Based on this unified time scale, when an abnormal current interval appears in the current sampling original waveform, the start time of the abnormal current interval is compared one by one with the spatial electric field intensity mutation window in the electric field coupling feature sequence. When the occurrence time of the abnormal current interval falls within the time range of the spatial electric field intensity mutation window, it is determined that the abnormal current interval is within the spatial electric field intensity mutation window. A spatial electric field intensity mutation identifier is added to the abnormal current interval in the real-time detection record, thereby correspondingly associating the abnormal current interval with the environmental electric field disturbance background in the time dimension.

[0031] After completing time mapping and labeling, the judgment path that would normally directly enter the load reduction process is diverted around the current anomaly interval that has been marked as being within the window of sudden change in spatial electric field intensity. This current anomaly interval is then classified into a slow-release observation interval, and its complete time trajectory and amplitude change trajectory are kept unchanged within the slow-release observation interval. At the same time, its duration and fluctuation rhythm within the slow-release observation interval are recorded. When classifying it into the slow-release observation interval, the original data of the current anomaly interval is not changed. Only its processing order is adjusted in the judgment process, so that the current anomaly interval does not trigger the load reduction process temporarily, but enters the slow-release observation interval to wait for further trend judgment. This forms a hierarchical handling structure that prioritizes the identification of environmental disturbances during the edge judgment process.

[0032] After the current anomaly range enters the slow-release observation range, the original load reduction trigger time window is dynamically extended. The time threshold originally used to trigger the load reduction process is extended during the coverage of the spatial electric field intensity mutation window, so that the current anomaly range has an extended judgment time range within the slow-release observation range. Within this extended load reduction trigger time window, the time correspondence between the change trajectory of the current anomaly range and the spatial electric field intensity mutation window is continuously maintained. When the spatial electric field intensity mutation window is still ongoing, the load reduction trigger time window is kept in an extended state, so that the current transient spike will not directly enter the load reduction process due to a short-time amplitude jump under the background of spatial electric field intensity mutation, thus isolating the direct triggering relationship between the current transient spike and the load reduction process on a time scale.

[0033] After the end of the sudden change window of the spatial electric field intensity, the current anomaly interval within the slow-release observation interval is remapped to the standard judgment time axis based on the time marker in the electric field coupling characteristic sequence. The continuous change state of the current anomaly interval is then tracked according to the extended load reduction trigger time window. If the current anomaly interval does not continue to expand after the end of the sudden change window of the spatial electric field intensity, it is released from the slow-release observation interval and does not enter the load reduction process. If the current anomaly interval continues to expand after the end of the sudden change window of the spatial electric field intensity, it enters the subsequent load reduction preparation stage according to the extended load reduction trigger time window conditions. Through this layered handling method that combines the electric field coupling characteristic sequence in the edge judgment process, the current anomaly interval within the sudden change window of the spatial electric field intensity is classified into the slow-release observation interval, and the load reduction trigger time window is extended. From the time control perspective, this prevents current transient spikes from directly entering the load reduction process.

[0034] Step 4: Continuously track the current change trend around the slow-release observation range, compare the trajectory of the continuous rise of the current with the rhythm of the decline of the space electric field intensity, and only when the current anomaly continues to expand after the space electric field intensity declines will the corresponding anomaly range be switched to the load reduction preparation state. The specific implementation method for this step is as follows: After the current anomaly interval is included in the slow-release observation interval, the time marker of the spatial electric field intensity change recorded in the electric field coupling characteristic sequence is used to continuously expand the original waveform of the current sampling within the slow-release observation interval. The start time, duration, and current time position of the current anomaly interval are located in a unified time coordinate. At the same time, the time segment of the spatial electric field intensity decline rhythm is extracted synchronously, and the entire process of the spatial electric field intensity transitioning from the peak value to the decline value is marked on the time axis. This makes the current anomaly interval and the spatial electric field intensity decline rhythm in the slow-release observation interval correspond to each other in the same time coordinate, thereby establishing a time comparison basis between the continuous current rise trajectory and the spatial electric field intensity decline rhythm.

[0035] After completing the basic construction of the time-based comparison, the current anomaly interval within the slow-release observation period is continuously tracked. The current anomaly interval is unfolded sequentially at each time point within the slow-release observation period, and the progressive change process of the current value is recorded. This progressive change process is synchronized with the time process of the spatial electric field intensity decline rhythm. When the spatial electric field intensity decline rhythm enters the continuous decline stage, it is observed at the same time coordinate whether the current anomaly interval synchronously enters the decline stage or maintains the original amplitude change state. Through this parallel arrangement, the continuous rise trajectory of the current and the decline rhythm of the spatial electric field intensity form a dynamic comparison relationship on the time axis, thus clearly showing the direction and sequence of change of the two within the same time window.

[0036] Based on the above dynamic comparison, we focus on observing the end time of the decline in the spatial electric field intensity. After the decline in the spatial electric field intensity completes the entire process from peak value to decline value, we mark the time when the decline in the spatial electric field intensity is completed on the time axis. We then use this time as the dividing point to continue tracking the change trajectory of the current anomaly interval. If the current anomaly interval continues to increase or expand after the decline in the spatial electric field intensity is completed, we extract the current anomaly interval from the slow-release observation interval and add a continuous expansion mark to the judgment record. This makes the anomaly interval form a before-and-after comparison with the decline in the spatial electric field intensity in time, and clarifies that the expansion behavior of the current anomaly interval occurs after the decline in the spatial electric field intensity.

[0037] Once it is confirmed that the current anomaly interval continues to expand after the spatial electric field intensity decreases, the corresponding anomaly interval is switched to a load reduction preparation state. When switching to the load reduction preparation state, all time comparison information formed within the slow-release observation interval is retained, including the complete time period of the current continuous rise trajectory and the complete time period of the spatial electric field intensity decline rhythm. This ensures that the load reduction preparation state is established based on the fact that the current change trend and the rhythm of the environmental electric field change have been separated. For current anomaly intervals that do not continue to expand after the spatial electric field intensity decreases, they are kept within the slow-release observation interval until they end naturally and do not enter the load reduction preparation state. By continuously tracking the current change trend around the slow-release observation interval and comparing the current continuous rise trajectory with the rhythm of the spatial electric field intensity decline, the corresponding anomaly interval is switched to the load reduction preparation state only when the current anomaly continues to expand after the spatial electric field intensity decreases. This distinguishes between transient current spikes caused by environmental electric field disturbances and the actual current anomaly expansion behavior in the time dimension.

[0038] Step 5: Based on the abnormal interval of entering the load reduction preparation state, release power limiting instructions step by step according to the photovoltaic array partition, and distribute the centrally triggered load reduction action to multiple time nodes in order to smooth the power change curve and stabilize the active power output rhythm of the grid connection point. The specific implementation method for this step is as follows: After confirming that the abnormal interval has entered the load reduction preparation state, the current operating power distribution of the photovoltaic array is unfolded over time according to the start time and duration of the abnormal interval in the complete timeline. The power output area involved in the abnormal interval is divided according to the existing operating partitions, and the real-time power output value of each partition in the corresponding time period of the abnormal interval is recorded sequentially to form a power baseline sequence arranged by time. Based on the power baseline sequence, the time point when the abnormal interval may have a concentrated impact on the overall power output is determined. This time point is used as the unified trigger time for the original concentrated triggering of load reduction actions. Before this unified trigger time, the power output rhythm of each partition is pre-arranged to lay the time foundation for the subsequent release of power limiting instructions by photovoltaic array partition.

[0039] After the power baseline sequence is constructed, the originally concentrated load reduction actions are split into time segments. Based on the time position of the abnormal interval in the load reduction preparation state, the overall load reduction range is decomposed into segmented power limitation intervals corresponding to multiple partitions. According to the spatial arrangement order of the photovoltaic array partitions or the existing operation order, the time nodes for each partition to enter the power limitation state are arranged sequentially on the time axis. This ensures that the power limitation command of each partition is released at different time nodes, rather than being executed simultaneously at the same time. During the release process, the time interval between each partition is kept fixed and traceable, so that the power limitation command forms a progressive unfolding state on the time axis, thereby dispersing the originally concentrated load reduction actions to multiple time nodes.

[0040] After being distributed across multiple time points, the power output change trajectory of each partition is continuously recorded around the partition power limiting command corresponding to each time point. The partition power change trajectory is then superimposed onto the overall power change curve in chronological order. The continuity and smoothness of the overall power change curve are observed on the time axis. When the first partition enters the power limiting state, the overall power change curve shows the first stage of decline. Subsequently, the power limiting command is released again at the time point corresponding to the second partition, and the overall power change curve enters the second stage of decline. Through this phased progressive approach, the overall power change curve presents a multi-stage progressive decline pattern, rather than a steep drop pattern at a single time point, thereby reconstructing the power change rhythm in the time dimension.

[0041] After the power limiting commands for all zones are released step by step, the overall power change curve formed by the progressive division is unfolded in accordance with the active power output rhythm of the grid connection point on the same time axis. The active power output of the grid connection point is kept in a continuous progressive state with the overall power change curve, so that the active power output rhythm of the grid connection point and the progressive load reduction rhythm of the zones are kept in sync. Within the corresponding time range of the entire abnormal interval, concentrated power drop is avoided at a single time node. By releasing power limiting commands step by step according to the photovoltaic array zones, the concentrated load reduction action is distributed to multiple time nodes, so that the power change curve is kept in a continuous progressive state. From the time control level, the power change curve is smoothed and the active power output rhythm of the grid connection point is stabilized, thereby reducing the frequency fluctuation risk caused by concentrated load reduction.

[0042] Beneficial effect 1: This invention establishes a temporal correlation between the spatial electric field intensity change curve and the original waveform of current sampling, and introduces an electric field coupling characteristic sequence and a slow-release observation interval during the edge judgment process. This enables the differentiation and control of environmental electric field disturbances and real current anomalies, preventing transient current spikes from directly entering the load reduction process within the spatial electric field intensity change window. This reduces the probability of falsely triggering load reduction from the source and avoids large-scale power reduction of the array caused by short-term environmental interference, thereby improving the operational stability and control reliability of the photovoltaic array under strong convective sandstorm conditions.

[0043] Benefit 2: This invention continuously tracks the current change trend around the slow-release observation interval, and releases power limiting commands step by step according to the photovoltaic array partition after entering the load reduction preparation state in the abnormal interval. This disperses the originally concentrated load reduction action to multiple time nodes, so that the power change curve presents a progressive transition state. It smooths the active power output change process from the time rhythm level, reduces the impact of sudden power drop at the grid connection point on the grid frequency support capability, and improves the grid connection operation safety and overall stability level in high-proportion renewable energy access scenarios.

[0044] This invention provides, for example Figure 2 The photovoltaic desertification control remote monitoring and management system based on edge-cloud collaboration shown includes an electric field sampling and correlation module, a coupling rhythm identification module, a slow release determination and control module, a trend comparison and discrimination module, and a zone load reduction scheduling module. The electric field sampling and correlation module collects the electric field intensity variation curve and the original waveform of current sampling in the space surrounding the photovoltaic array. It performs alignment processing under the same time coordinate, extracts the transient current spike fragments corresponding to the electric field intensity surge interval, and forms an electric field disturbance correlation fragment sequence. The coupling rhythm identification module calculates the synchronous transition rhythm between the amplitude of spatial electric field intensity change and the amplitude of current transient spikes based on the electric field disturbance correlation segment sequence, marks the current abnormal interval with spatial electric field abrupt change characteristics, and generates an electric field coupling feature sequence. The slow-release determination and control module combines the electric field coupling feature sequence during the edge determination process to classify the current abnormal interval within the spatial electric field intensity change window into the slow-release observation interval and extend the load reduction trigger time window. The trend comparison and judgment module continuously tracks the current change trend around the slow-release observation range, compares the continuous current rise trajectory with the rhythm of the space electric field intensity decline, and only when the current anomaly continues to expand after the space electric field intensity declines will the corresponding anomaly range be switched to the load reduction preparation state. The partitioned load reduction scheduling module, based on the abnormal intervals that enter the load reduction preparation state, releases power limiting commands step by step according to the photovoltaic array partitions, distributing the centrally triggered load reduction actions to multiple time nodes.

[0045] The remote monitoring and management method for photovoltaic desertification control based on edge-cloud collaboration provided in this invention is implemented through the aforementioned remote monitoring and management system for photovoltaic desertification control based on edge-cloud collaboration. For details of the specific methods and processes of the remote monitoring and management system for photovoltaic desertification control based on edge-cloud collaboration, please refer to the embodiments of the aforementioned remote monitoring and management method for photovoltaic desertification control based on edge-cloud collaboration, which will not be repeated here.

[0046] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A photovoltaic sand control remote monitoring and management method based on end-cloud collaboration, characterized in that, Includes the following steps: Step 1: Collect the electric field intensity variation curve and the original waveform of current sampling in the space surrounding the photovoltaic array, align them at the same time coordinate, extract the transient current spike fragments corresponding to the electric field intensity surge interval, and form a sequence of electric field disturbance related fragments. Step 2: Based on the electric field disturbance correlation segment sequence, calculate the synchronous transition rhythm between the spatial electric field intensity change amplitude and the current transient spike amplitude, mark the current anomaly interval with spatial electric field change characteristics, and generate an electric field coupling characteristic sequence. Step 3: In the edge determination process, the current abnormality interval within the spatial electric field intensity abrupt change window is included in the slow release observation interval by combining the electric field coupling feature sequence, and the load reduction trigger time window is extended. Step 4: Continuously track the current change trend around the slow-release observation range, compare the trajectory of the continuous rise of the current with the rhythm of the decline of the space electric field intensity, and only when the current anomaly continues to expand after the space electric field intensity declines will the corresponding anomaly range be switched to the load reduction preparation state. Step 5: Based on the abnormal interval of entering the load reduction preparation state, release power limiting commands step by step according to the photovoltaic array partition, and distribute the centrally triggered load reduction action to multiple time nodes.

2. The photovoltaic sand control remote monitoring and management method based on end-cloud cooperation according to claim 1, characterized in that, The steps for acquiring the electric field intensity variation curve and the original waveform of current sampling in the space surrounding the photovoltaic array include: The electric field intensity of the space around the photovoltaic array is continuously recorded at a fixed sampling period and arranged in a unified time mark order to form a spatial electric field intensity change curve. At the same time, the output current of the photovoltaic array is continuously sampled under the same time reference to form the original waveform of current sampling. A consistent time mark is written in each sampling period to establish a time mapping relationship. Under the time mapping relationship, the spatial electric field intensity change curve and the original current sampling waveform are aligned on the same time coordinate. The electric field intensity surge interval is determined based on the time period of continuous rise and the formation of abrupt inflection point. The original current sampling waveform within the corresponding time range is extracted on the same time coordinate as the current transient glitch segment to establish the correspondence. Each electric field intensity surge interval and corresponding current transient spike segment are sequentially numbered and arranged in ascending order of time. The start time, duration, end time and corresponding current transient spike segment information are stored side by side to form a continuous time correlation structure. All numbered electric field intensity surge intervals and corresponding current transient spike segments are integrated into an electric field disturbance associated segment sequence in chronological order, while retaining the time interval and sequential relationship between adjacent sequence units.

3. The method for remote monitoring and management of photovoltaic desertification control based on edge-cloud collaboration according to claim 2, characterized in that, The steps for generating an electric field coupling feature sequence based on an electric field perturbation-correlated fragment sequence include: The electric field disturbance correlation segment sequence is unfolded one by one in chronological order. The spatial electric field intensity change curve segment and the current transient spike segment in each electric field disturbance correlation segment are placed in the same time coordinate frame for corresponding unfolding. The initial value, peak value and fall value of electric field intensity and the initial value, peak value and fall value of current are extracted, and the amplitude change trajectory comparison relationship is formed. The amplitude of spatial electric field intensity change and the amplitude of current transient spike are synchronously unfolded along the time axis around the correlation of amplitude change trajectory. The peak corresponding time point, common jump start point, common peak interval, and common fall interval are recorded to form a time rhythm sequence across multiple electric field disturbance correlation segments. The time rhythm sequence is mapped to the complete time axis of the original current sampling waveform. Current change segments that are consistent with the time rhythm sequence are marked to form current anomaly intervals with spatial electric field mutation characteristics, and the start time, duration and end time are recorded. The abnormal current intervals are integrated in chronological order, and the information on the amplitude of spatial electric field intensity changes, the amplitude of current transient spikes, and the rhythm of synchronous transitions are incorporated into the same recording unit to construct an electric field coupling feature sequence.

4. The method for remote monitoring and management of photovoltaic desertification control based on edge-cloud collaboration according to claim 3, characterized in that, The time rhythm sequence includes the time point corresponding to the common peak of the change amplitude of the spatial electric field intensity and the amplitude of the transient current spike, as well as the common fall interval. The marking of the current abnormal interval is determined according to the correspondence of the time rhythm sequence in the complete time axis of the original current sampling waveform, and the original time markers are kept continuously arranged in the electric field coupling characteristic sequence.

5. The method for remote monitoring and management of photovoltaic desertification control based on edge-cloud collaboration according to claim 3, characterized in that, The steps for layered processing in edge detection, combining electric field coupling feature sequences, include: During the edge detection process, the electric field coupling feature sequence is retrieved, and the spatial electric field intensity mutation window in the electric field coupling feature sequence is mapped to the real-time detection time axis of the current sampling original waveform. The start time of the current abnormal interval is compared with the spatial electric field intensity mutation window to determine the current abnormal interval within the spatial electric field intensity mutation window and add a spatial electric field intensity mutation label. The judgment path is adjusted around the current anomaly interval marked by the sudden change of the additional spatial electric field intensity. The current anomaly interval is included in the slow-release observation interval, and the complete time trajectory and amplitude change trajectory are retained in the slow-release observation interval to form a layered treatment structure. For the current anomaly range entering the slow-release observation range, the load reduction trigger time window is extended, and the load reduction trigger time window is kept in an extended state and the time correspondence is continuously recorded during the period covered by the sudden change window of the spatial electric field intensity. Based on the time markers in the electric field coupling characteristic sequence, the current anomaly interval within the slow-release observation interval is mapped to the standard judgment time axis. The continuously changing state is tracked according to the extended load reduction trigger time window to determine whether the load reduction preparation stage has been entered.

6. The method for remote monitoring and management of photovoltaic desertification control based on edge-cloud collaboration according to claim 5, characterized in that, The duration of the extended load reduction trigger time window corresponds to the duration of the space electric field intensity mutation window. When the space electric field intensity mutation window ends, the end time is used as the judgment boundary point to judge the state transition of the current abnormal interval within the slow release observation interval, so as to determine whether to enter the load reduction preparation stage.

7. The method for remote monitoring and management of photovoltaic desertification control based on edge-cloud collaboration according to claim 5, characterized in that, The steps for determining the trend around the sustained-release observation period include: Using the time marker of spatial electric field intensity change in the electric field coupling characteristic sequence, the original waveform of current sampling in the slow-release observation interval is expanded, and the start time, duration, and current time position of the current abnormal interval are located corresponding to the rhythm of spatial electric field intensity decline, forming a time comparison relationship in the same time coordinate. By continuously tracking the abnormal current range within the slow-release observation period based on the time-comparison relationship, the trajectory of the continuous rise in current and the rhythm of the decline in spatial electric field intensity are synchronously arranged on the time axis. Based on the end time node of the spatial electric field intensity decline rhythm in the time axis, continue to track the change trajectory of the current anomaly interval, and add a continuous expansion mark to the current anomaly interval that continues to expand after the spatial electric field intensity decline is completed. The current anomaly range with the attached continuous expansion indicator will be switched to the load reduction preparation state, and the time comparison information formed in the slow release observation range will be retained. The current anomaly range that does not maintain the expansion trend will be kept in the slow release observation range state.

8. The method for remote monitoring and management of photovoltaic desertification control based on edge-cloud collaboration according to claim 7, characterized in that, When continuously tracking the current anomaly range within the slow-release observation period, the end time node of the spatial electric field intensity decline rhythm is used as the time boundary point. The current continuous rise trajectory before the end time node and the current change trajectory after the end time node are recorded separately. Only when the current anomaly range after the end time node continues to expand will the corresponding current anomaly range be switched to the load reduction preparation state.

9. The method for remote monitoring and management of photovoltaic desertification control based on edge-cloud collaboration according to claim 7, characterized in that, The steps for releasing power limiting commands in stages according to photovoltaic array zones include: Based on the start time and duration of the abnormal interval entering the load reduction preparation state in the complete time axis, the current operating power distribution of the photovoltaic array is unfolded over time. The power output area involved in the abnormal interval is divided according to the existing operating partitions, and the real-time power output value of each partition in the corresponding time period is recorded to form a power baseline sequence arranged by time. At the same time, the unified triggering time for centralized triggering of load reduction action is determined. Based on the power baseline sequence, the concentrated triggering of load reduction actions is divided into time segments. According to the photovoltaic array partitioning order, each partition is arranged to enter the power limiting state in sequence on the time axis, and the overall load reduction range is decomposed into multiple segmented power limiting intervals. For segmented power limiting intervals, power limiting commands are released at each time node, and the power output change trajectory of each partition is continuously recorded to form an overall power change curve that unfolds progressively. The overall power change curve and the active power output rhythm at the grid connection point are unfolded on the same time axis to ensure that the active power output at the grid connection point evolves continuously with the progressive load reduction rhythm of the zone.

10. A remote monitoring and management system for photovoltaic desertification control based on edge-cloud collaboration, used to implement the remote monitoring and management method for photovoltaic desertification control based on edge-cloud collaboration as described in any one of claims 1-9, characterized in that, It includes an electric field sampling and correlation module, a coupling rhythm recognition module, a slow release determination and control module, a trend comparison and discrimination module, and a zoned load reduction scheduling module; The electric field sampling and correlation module collects the electric field intensity variation curve and the original waveform of current sampling in the space surrounding the photovoltaic array. It performs alignment processing under the same time coordinate, extracts the transient current spike fragments corresponding to the electric field intensity surge interval, and forms an electric field disturbance correlation fragment sequence. The coupling rhythm identification module calculates the synchronous transition rhythm between the amplitude of spatial electric field intensity change and the amplitude of current transient spikes based on the electric field disturbance correlation segment sequence, marks the current abnormal interval with spatial electric field abrupt change characteristics, and generates an electric field coupling feature sequence. The slow-release determination and control module combines the electric field coupling feature sequence during the edge determination process to classify the current abnormal interval within the spatial electric field intensity change window into the slow-release observation interval and extend the load reduction trigger time window. The trend comparison and judgment module continuously tracks the current change trend around the slow-release observation range, compares the continuous current rise trajectory with the rhythm of the space electric field intensity decline, and only when the current anomaly continues to expand after the space electric field intensity declines will the corresponding anomaly range be switched to the load reduction preparation state. The partitioned load reduction scheduling module, based on the abnormal intervals that enter the load reduction preparation state, releases power limiting commands step by step according to the photovoltaic array partitions, distributing the centrally triggered load reduction actions to multiple time nodes.

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