An electrical equipment energy-saving monitoring system based on a photovoltaic micro-grid

Abstract

The application discloses an electrical equipment energy-saving monitoring system based on a photovoltaic microgrid, which is connected with an inverter, an energy storage unit, a power distribution cabinet, a current collection box and a heat dissipation unit through a ring network optical fiber communication link, and comprises a node mapping and topology construction module, a data acquisition and time stamp alignment module, a normalization processing and index calculation module, a stage identification and pre-switching marking module, a priority buffer and sampling scheduling module, a self-healing switching and energy-saving control module and a feedback correction and threshold updating module. The system synchronously collects equipment operation data and link state data, generates link health indexes and equipment energy efficiency deviation indexes, divides operation stages and generates pre-switching marks according to the indexes, buffers and schedules data to be transmitted according to priorities, executes self-healing switching and issues energy-saving control instructions when the link is abnormal, and finally dynamically updates thresholds and sampling strategies through feedback correction, so that the communication reliability and energy-saving control continuity are improved.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic microgrid communication and energy-saving control technology, and in particular to an energy-saving monitoring system for electrical equipment based on a photovoltaic microgrid. Background Technology

[0002] Photovoltaic microgrids typically consist of inverters, energy storage units, distribution cabinets, combiner boxes, and heat dissipation equipment. The equipment is widely distributed and operates in a complex environment, requiring high real-time performance and reliability from the monitoring system.

[0003] Existing monitoring solutions primarily focus on equipment parameter acquisition and remote centralized display, often employing standard industrial Ethernet or star networks for communication. These solutions suffer from the following shortcomings: First, when link degradation, port failure, or fiber optic attenuation occurs, it's difficult to quickly locate the source of the anomaly and switch over in a timely manner. Second, communication delays or packet loss cause monitoring data to lag behind actual operating conditions, affecting the timeliness of energy-saving control. Third, equipment status assessment and communication status assessment are independent, failing to reflect their coupling relationship and easily leading to misjudgments or omissions. Fourth, data transmission lacks hierarchical buffering and priority scheduling, making protection alarms and control commands easily blocked by ordinary monitoring data. Fifth, thresholds and sampling strategies often rely on fixed empirical values, lacking dynamic correction based on operating samples, making it difficult to adapt to equipment aging and environmental changes.

[0004] Therefore, existing technologies still have problems in energy-saving monitoring scenarios for photovoltaic microgrids, such as inaccurate location of communication link anomalies, delayed link switching, easy blockage of key data, discontinuous energy-saving control response, and insufficient threshold adaptability. Summary of the Invention

[0005] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0006] Therefore, to solve the above-mentioned technical problems, the present invention provides the following technical solution: an energy-saving monitoring system for electrical equipment based on a photovoltaic microgrid, the system comprising: The node mapping and topology construction module is used to establish a correspondence between field devices, node numbers, and communication links, and to generate a topology table and a node mapping table. The topology table is used to record the adjacent nodes, primary link port identifiers, backup link port identifiers, and link directions of each node. The node mapping table is used to record the device number, device type, and installation location. The data acquisition and timestamp alignment module is used to synchronously acquire device operation data and link status data, and to attach a unified timestamp to the acquired data. The initialization learning and baseline generation module is used to generate a stable operating reference baseline by continuously collecting data when the system is first put into operation or restarted after a long period of shutdown. The normalization and index calculation module is used to normalize the collected multidimensional data and generate link health index and equipment energy efficiency deviation index. The phase identification and pre-switching tag module is used to identify the system operation phase based on the link health index and the device energy efficiency deviation index, and generate a pre-switching tag. The priority caching and sampling scheduling module is used to dynamically adjust the priority caching queue and sampling strategy of the data to be transmitted according to the pre-switching flag. The self-healing switching and energy-saving control module is used to execute the self-healing switching of the ring network optical fiber communication link and issue energy-saving control commands when the link health index is lower than the preset threshold or the predicted value meets the preset switching conditions. The feedback correction and threshold update module is used to evaluate the execution results and update the link threshold, device threshold and sampling strategy after the handover action is completed.

[0007] As a preferred embodiment of the energy-saving monitoring system for electrical equipment based on a photovoltaic microgrid as described in this invention, the equipment operation data includes at least the inverter output power, energy storage state of charge, temperature, voltage, and current of key parts of the equipment; the link status data includes at least the received optical power, bit error rate, link delay, packet loss rate, and original queue backlog value.

[0008] As a preferred embodiment of the energy-saving monitoring system for electrical equipment based on a photovoltaic microgrid according to the present invention, the normalization processing and index calculation module normalizes the original values ​​of received optical power, bit error rate, link delay, and queue backlog, and generates the link health index based on the weighted sum of the normalization results; and normalizes the deviation of inverter output power, the deviation of temperature of key parts of equipment, and the deviation of energy storage state of charge, and generates the equipment energy efficiency deviation index based on the weighted sum of the normalization results.

[0009] As a preferred embodiment of the energy-saving monitoring system for electrical equipment based on a photovoltaic microgrid according to the present invention, the stage identification and pre-switching marker module divides the system operating status into a normal stage, an early warning stage, a risk stage, and a switching preparation stage; wherein, the stage identification and pre-switching marker module calculates the predicted value of link health and the predicted value of equipment energy efficiency deviation based on the changing trends of link health indicators and equipment energy efficiency deviation indicators. When the predicted value of link health is lower than the link switching threshold or the predicted value of equipment energy efficiency deviation is higher than the equipment control threshold, a pre-switching marker is generated to prepare for link switching and energy-saving control in advance.

[0010] As a preferred embodiment of the energy-saving monitoring system for electrical equipment based on a photovoltaic microgrid according to the present invention, the priority caching and sampling scheduling module divides the data to be transmitted into a first type of data, a second type of data, and a third type of data according to a pre-switching marker; wherein, the first type of data consists of protection alarm data and control command data, and is written into the highest priority cache queue; the second type of data consists of critical operating data, and is written into the medium priority cache queue; the third type of data consists of non-critical monitoring data and historical archived data, and is written into the low priority cache queue; and the priority caching and sampling scheduling module dynamically adjusts the sampling period of each type of data according to the current operating stage, so that the sampling period of the first type of data is less than the sampling period of the second type of data, and the sampling period of the second type of data is less than the sampling period of the third type of data.

[0011] As a preferred embodiment of the energy-saving monitoring system for electrical equipment based on photovoltaic microgrids described in this invention, the self-healing switching and energy-saving control module locates the affected nodes and their corresponding backup transmission paths by querying the topology table and the node mapping table. During the self-healing switching process, it generates energy-saving control instructions based on the equipment energy efficiency deviation index and the real-time load status of the system, and issues the energy-saving control instructions in the order of the priority cache queue.

[0012] As a preferred embodiment of the energy-saving monitoring system for electrical equipment based on a photovoltaic microgrid as described in this invention, the control commands include: adjusting the upper limit of the inverter output power; adjusting the charging power or discharging power of the energy storage unit; adjusting the start-stop duration, start-stop sequence, or speed of the heat dissipation unit; adjusting the working cycle of non-critical loads; and adjusting the wake-up sequence of standby devices, thereby achieving synchronous execution of system communication self-healing and energy-saving control.

[0013] As a preferred embodiment of the energy-saving monitoring system for electrical equipment based on a photovoltaic microgrid as described in this invention, the feedback correction and threshold update module, after completing the switch, statistically analyzes whether the switch was successful, the switch time, the execution status of control commands, the changes in equipment temperature and power fluctuations, and updates the link warning threshold, link switch threshold, equipment warning threshold and equipment control threshold using an exponential smoothing method; the sampling strategy is adjusted according to the continuous operation phase to achieve system self-adaptation.

[0014] As a preferred embodiment of the energy-saving monitoring system for electrical equipment based on photovoltaic microgrids described in this invention, the feedback correction and threshold update module updates each threshold using an exponential smoothing method, including: weighting and fusing the currently used threshold and the benchmark threshold obtained from recent stable operating samples according to a preset smoothing coefficient to obtain the updated threshold; wherein, the benchmark threshold is determined based on the statistical mean and standard deviation of the recent stable operating samples of the system.

[0015] As a preferred embodiment of the energy-saving monitoring system for electrical equipment based on photovoltaic microgrids described in this invention, the node mapping and topology construction module, data acquisition and timestamp alignment module, normalization processing and index calculation module, stage identification and pre-switching marking module, priority caching and sampling scheduling module, and self-healing switching and energy-saving control module are deployed on the edge controller, while the initialization learning and benchmark value generation module and the feedback correction and threshold update module are deployed on the central controller. All modules are interconnected through a ring network fiber optic communication link to balance real-time control and long-term statistical analysis capabilities.

[0016] The beneficial effects of this invention are: 1. This invention achieves rapid anomaly location through node mapping and topology table, and uniformly binds device number, physical location, and primary / backup link port. When communication is degraded or link is interrupted, it can accurately locate affected nodes and link segments, providing a spatial basis for self-healing switching, and solving the problem of inaccurate anomaly location in the prior art.

[0017] 2. This invention avoids misjudgment by synchronously acquiring data and using a unified timestamp. Device operation data and link status data are acquired synchronously under the same time reference, and link health indicators and device energy efficiency deviation indicators are generated through normalization processing. This unifies different physical quantities into comparable dimensionless quantities, solving the problem in existing technologies where communication status and device status cannot be jointly analyzed.

[0018] 3. This invention achieves proactive prevention through stage identification and pre-handover marking. Based on trend analysis and short-term prediction, a pre-handover mark is generated before actual communication interruption or significant deviation of equipment from operating conditions, allowing the device to enter the handover preparation state in advance, thus solving the problems of link handover lag and control window period existing in the prior art.

[0019] 4. This invention ensures the transmission of critical data through priority caching and sampling scheduling. Data is divided into three levels: protection alarm / control commands, critical operational data, and non-critical monitoring data, each corresponding to a different priority cache queue and sampling period. In cases of link instability, priority is given to ensuring the transmission of the most critical data, thus solving the problem of critical data being easily blocked in existing technologies.

[0020] 5. This invention reduces control delay by synchronously executing self-healing switching and energy-saving control. During the switching process, energy-saving control commands are generated and issued simultaneously, achieving closed-loop linkage between communication recovery and equipment energy-saving control, thus solving the problem of discontinuous energy-saving control response in existing technologies.

[0021] 6. This invention achieves long-term adaptability through feedback correction and threshold updates. Based on the switching execution results and continuous running samples, the threshold and sampling strategy are dynamically updated using an exponential smoothing method, avoiding frequent false alarms or slow response caused by fixed thresholds, and solving the problem of insufficient threshold adaptability in existing technologies. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein: Figure 1 This is a system architecture diagram of the present invention.

[0023] Figure 2 This is a flowchart illustrating the overall workflow of the present invention.

[0024] Figure 3 This is a sub-flowchart for calculating the link health index and equipment energy efficiency deviation index of the present invention.

[0025] Figure 4 This is a sub-flowchart for the stage identification and pre-switching marker generation of the present invention.

[0026] Figure 5 This is a sub-flowchart of the present invention, showing the synchronous execution of self-healing switching and energy-saving control. Detailed Implementation

[0027] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0028] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0029] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention; the phrase "in an embodiment" appearing in different places in this specification does not all refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0030] Example 1 Reference Figure 1The first embodiment of the present invention provides an energy-saving monitoring system for electrical equipment based on a photovoltaic microgrid. This system is applied at the photovoltaic microgrid site and is used to monitor the operation of electrical equipment such as inverters, energy storage units, distribution cabinets, combiner boxes, and heat dissipation units, as well as to monitor communication links, perform self-healing switching, and control energy saving.

[0031] This system enables multi-node data interaction through a ring network fiber optic communication link. By combining the joint analysis of link health status and equipment energy efficiency status, it achieves communication self-healing and energy-saving control linkage, thereby solving problems such as inaccurate location of communication link anomalies, delayed link switching, easy blockage of key control data, and insufficient continuity of energy-saving control in existing photovoltaic microgrid systems.

[0032] like Figure 1 As shown, the system includes: The node mapping and topology construction module is used to establish a correspondence between field devices, node numbers, and communication links after system installation, and to generate a topology table and a node mapping table. The topology table records the adjacent nodes, primary / backup link port representations, and data flow direction of each node; the node mapping table records the device number, type, and installation location.

[0033] The data acquisition and timestamp alignment module is used to synchronously acquire device operation data and link status data, and append a unified timestamp to the above data. This module can compensate for missing data from individual sensors and record the data source status.

[0034] The initialization learning and benchmark generation module is used to establish a stable operating reference benchmark by continuously collecting data when the system is first put into operation or restarted after a long period of shutdown. This benchmark is then used for subsequent normalization processing and index calculation.

[0035] The normalization and index calculation module is used to normalize the collected multidimensional data and generate link index and equipment energy efficiency deviation index. This module can determine the weights based on the variance contribution rate or time integral contribution ratio of the samples during stable operation to ensure the unified processing of data with different dimensions.

[0036] The phase identification and pre-switching tag module is used to identify the system operation phase based on link health indicators and equipment energy efficiency deviation indicators, including normal, early warning, risk and switchover preparation phases. It also generates pre-switching tags by combining trend analysis and short-term forecasts to prepare for link switching and energy-saving control in advance.

[0037] The priority caching and sampling scheduling module is used to adjust data priority and sampling strategy according to the pre-switching flag. The data is divided into protection alarm / control command data, critical operation data and non-critical monitoring data according to their importance, corresponding to high, medium and low priority cache queues respectively. The sampling cycle is dynamically adjusted according to the operation stage to ensure priority transmission of critical data and continuity of energy-saving control.

[0038] The self-healing switching and energy-saving control module is used to perform ring network fiber self-healing switching when the link health index is lower than the link switching threshold or when the switching conditions are predicted to be met. At the same time, it generates and issues energy-saving control commands based on the equipment energy efficiency index. Through inter-module collaboration, communication switching and energy-saving control are executed synchronously, reducing the control downtime.

[0039] The feedback correction and threshold update module is used to evaluate the execution results and update the link and device thresholds and sampling strategies after the system completes pre-switching or self-healing switching. This module can dynamically adjust the thresholds using the exponential smoothing method and restore the default sampling strategy according to the continuous operating status, so as to achieve long-term adaptation of the system to environmental changes and equipment aging.

[0040] In this embodiment, the node mapping and topology construction module, data acquisition and timestamp alignment module, normalization processing and index calculation module, stage identification and pre-switching marking module, priority caching and sampling scheduling module, and self-healing switching and energy-saving control module are preferably deployed on the edge controller. The initialization learning and baseline value generation module, and feedback correction and threshold update module are preferably deployed on the central controller. All modules are interconnected via ring network fiber optic communication, balancing real-time control capabilities with long-term statistical analysis capabilities; each functional module can be implemented through software programs and executed by the controller.

[0041] This embodiment achieves intelligent energy-saving monitoring and communication self-healing control of photovoltaic microgrid equipment by constructing continuous counting links such as node mapping, link health assessment, equipment energy efficiency deviation analysis, pre-switching identification, cache scheduling, self-healing switching, and feedback updates. The data between each module is coordinated to ensure that the system can continue to control even when the link is abnormal or the equipment deviates from the operating condition, thus realizing end-to-end energy-saving monitoring.

[0042] Example 2 Reference Figures 2-5 This is the second embodiment of the present invention, which differs from the first embodiment in that it provides specific implementation steps for an energy-saving monitoring system for electrical equipment based on a photovoltaic microgrid, as follows: S1. Establish the node mapping relationship and topology table of the ring network optical fiber communication link; After the system is installed, a clear correspondence will be established between the field devices, node numbers, and communication links. This will facilitate the accurate location of abnormal nodes and links based on the collected data. The specific steps are as follows: A1. Establish the correspondence between devices and nodes; Inverters, energy storage units, distribution cabinets, combiner boxes, and heat dissipation units are each designated as different nodes, and the equipment number, installation location, and equipment type of each node are recorded. The installation location is derived from the engineering configuration table formed during the on-site construction and commissioning phase, for example, an inverter is located near a certain battery array, and a combiner box is located on a certain circuit branch. The equipment number is derived from the equipment nameplate, asset number, or construction ledger. The equipment type is derived from the equipment's factory parameters or the information entered during commissioning.

[0043] A2. Record the physical connection sequence of the optical fibers in the ring network; Based on the completed fiber optic connection sequence, the upstream node, downstream node, primary transmission direction, and backup transmission direction of each node are clearly identified. Simultaneously, the actual physical ports of the industrial Ethernet switches or communication processors used for the primary and backup transmission directions at each node are registered to form a port binding table. The port binding table must include at least: node number, port sequence number, port MAC address, connected fiber optic cable number, and the corresponding transmission direction (primary / backup) for that port.

[0044] Preferably, the ring network optical fiber communication link is a bidirectional redundant ring structure, that is, any node has at least two data transmission paths in different directions.

[0045] A3. Generate the topology table and node mapping table; By merging the information from the two steps above, a topology table and a node mapping table are generated: The topology table includes at least: node number, neighboring node number (upstream node and downstream node), primary link port identifier (data transmission port number for the current primary transmission direction), backup link port identifier (data transmission port number for the backup transmission direction), and link direction marker (marking the preferred and backup directions of the data flow).

[0046] The node mapping table includes at least: device number, device type, physical location (installation location coordinates or site number), and corresponding node number. All subsequent communication decisions, path switching, and control command issuance are based on the above topology table and node mapping table.

[0047] In this step, when communication degradation, link interruption, or self-healing handover occurs, the system can directly look up a table to accurately locate the affected physical device, fiber segment, and specific switch port, thereby completing data flow redirection in a very short time and avoiding control actions from missing the target due to missing information.

[0048] S2. Synchronously collect device operation data and link status data, and attach a unified timestamp; After the topology table is established, the system enters the continuous data acquisition phase. During this phase, device and link data are acquired synchronously under the same data benchmark, ensuring a consistent time reference for subsequent decision-making steps. The specific steps are as follows: A1. Collect equipment operation data; During each sampling period, each node synchronously collects equipment operation data, including at least: Inverter output power Energy storage state of charge Temperature of key parts of the equipment Voltage U, current I; wherein, the energy storage state of charge This represents the battery's state of charge or equivalent remaining energy storage ratio, and its normalized value range is... Its original value comes from the battery management system; when the battery's state of charge... When the battery is fully charged, it indicates that the energy storage unit is in a fully charged state; when the battery is in a state of charge... When this time, it indicates that the energy storage unit is close to its minimum usable state.

[0049] A2. Collect link status data; Within the same sampling period, the communication processor also collects link status data, including at least the received optical power. Bit error rate (BER), link latency (D), packet loss rate and queue backlog raw values Wherein, the original value of the queue backlog It is directly read from the transmit queue counter inside the communication processor and is used to indicate the number of data frames or bytes to be sent.

[0050] A3. Add a unified timestamp; After data collection is completed, the edge controller adds a timestamp to the device operation data and link status data and writes them to the device data buffer and link data buffer respectively. If individual sensors are missing data at a certain moment, the last valid value of the previous cycle is used for compensation, and the missing compensation status is recorded in the data marker so that the data source can be identified in subsequent calculations.

[0051] In this step, if only the device power is considered, the system may misjudge the data lag caused by communication delay as a device malfunction; if only the link indicators are considered, the system may overlook that the device has deviated from the energy-saving operation state. This step improves the accuracy of the system's malfunction judgment by collecting the two types of data in parallel and incorporating the communication status and energy efficiency status into the same judgment framework.

[0052] S3. Normalize the raw data stream and calculate link health metrics. Deviation index of equipment energy efficiency ; It should be noted that when the system is first put into operation or restarted after a long period of shutdown, due to the lack of 72 hours of stable historical operating data, it needs to enter a preset initialization learning phase. The rules for this phase are as follows: The system defaults to the warning phase, collecting and recording all data, but does not trigger self-healing switching or forced energy-saving control. When the system detects that the fluctuation range of various indicators in the continuous operating data is within the allowable range of the equipment's technical parameters, and there are no communication interruption alarms, and the fluctuation amplitude of parameters such as active power and temperature on the equipment side is less than the preset initial fluctuation threshold, this period is defined as the quasi-steady-state period. After the quasi-steady-state period has accumulated to 72 hours, the system automatically calculates the average value of each parameter during this period as the initial reference benchmark. like Figure 3 As shown, this step is used to map and unify data with different units and dimensions to a unified standard. The dimensionless interval is used to avoid dimensional conflicts during subsequent threshold comparisons. All reference values ​​involved in normalization are derived from the statistical average of continuous sampling of each parameter during the 72 hours of fault-free stable operation after system commissioning. , , , The system then calculates the corresponding standard deviation for initial threshold calibration. After initialization, the system automatically exits the learning phase and enters normal closed-loop monitoring.

[0053] The specific steps are as follows: A1. Normalize the link status parameters; For link status parameters and device operating parameters, first perform unified normalization processing, and then limit the results to a certain range. Between these points, the truncation function is defined as follows: ; A2. Calculate the normalized health value of the received optical power. ; The calculation formula is: ; in, The reference received optical power is measured during the stable operation of the ring network fiber optic communication link, and is derived from the average value obtained from the system trial phase. This is the minimum effective received optical power allowed by the optical module, derived from the equipment's technical parameters or the lower limit of on-site acceptance.

[0054] The larger the value, the closer the current received optical power is to the ideal level, and the better the quality of the link optical layer.

[0055] when At that time, the system directly ordered 1 is added as a protective measure to avoid invalidating the denominator.

[0056] A3. Calculate the normalized degradation index of bit error rate. ; The calculation formula is: ; in, To achieve the average bit error rate during stable operation, The acceptable bit error rate upper limit is defined in industry standards or equipment manuals, such as the commonly used upper limit for Gigabit Ethernet. Bit error rate normalized bad value The larger the value, the worse the link.

[0057] A4. Calculate the normalized degradation index of link latency. ; The calculation formula is as follows: ; in, This refers to the average end-to-end delay of the ring network during the aforementioned stable operation period. This is the maximum allowable delay determined based on the real-time requirements of the control system; for example, 10ms is often used in the field of industrial automation.

[0058] A5. Calculate the normalized degradation index for queue backlog. ; The calculation formula is: ; in, The current number of frames or bytes backlogged in the queue. The maximum acceptable queue capacity for the communication node switch cache is given in the device specification.

[0059] A6. Calculate link health indicators ; Link health metrics The calculation formula is: ; in, , , , Let be the weighting coefficient, and satisfy: , ; Link health metrics The higher the value, the healthier the ring network fiber optic communication link.

[0060] In a preferred embodiment, the initial values ​​of each weight coefficient are based on the aforementioned 72-hour stable operation sample and can preferably be determined through the variance contribution rate. Specifically, the fluctuation variance of each normalized degradation indicator (such as received optical power, bit error rate, link delay, and queue backlog) is analyzed, and the proportion of the fluctuation variance of each indicator to the total variance is calculated. This proportion is used as its initial weight. This rule ensures that the weight allocation objectively reflects the fluctuation importance of each indicator during stable operation.

[0061] For example, if the sum of the variance contribution rates of optical power and bit error rate measured by this rule exceeds 60%, a set of preferred initial weighting coefficients can be obtained accordingly. (Received optical power) (Bit error rate) (Delay) (Queue backlog); If the site is in a strong electromagnetic interference area, the variance of the bit error rate fluctuation will increase, so the weight corresponding to the bit error rate can be appropriately increased. The weight corresponding to the received optical power is adjusted accordingly. This enables the indicators to adapt to environmental changes.

[0062] A7. Calculate the energy efficiency deviation index of equipment. ; The calculation steps are as follows: Active power deviation: ; Active power deviation: ; Energy storage deviation: ; in, This data is the reference active power of the corresponding equipment under stable and energy-saving operating conditions. It is derived from the 72-hour stable operating baseline after the equipment is put into operation. The reference temperature for the corresponding equipment under stable energy-saving operating conditions and the reference active power data are derived from the same baseline; and These are the minimum reference lower limits for power and temperature, respectively, used to avoid the denominator being too small in extreme cases; for example, for conventional photovoltaic inverters, 10W can be taken. A value of 0.1℃ can be used; this set of values ​​is only for use as the lower limit of protection.

[0063] The equipment energy efficiency deviation index is calculated using the following formula. : ; in, , , Let be the weighting coefficient, and satisfy: , ; Equipment energy efficiency deviation index The larger the value, the more significantly the electrical equipment deviates from its energy-saving operating state.

[0064] In a preferred embodiment, the weighting coefficient is determined according to the following rules: similar to the link weighting, it is based on 72 hours of stable operation data, analyzing normal operating conditions such as normal start-up and shutdown, cloud cover, and load changes. , , The time series of fluctuations in the three deviations are used. Weights are assigned by calculating the time integral contribution of each deviation to the overall operating efficiency of the equipment. For example, active power deviation has the highest impact on energy efficiency (approximately 45%) in most non-steady-state conditions; the state of charge of energy storage is directly related to the system's uptime (approximately 30%); and temperature rise lags behind (approximately 25%). Based on this, a set of optimal initial weighting coefficients is obtained. (Active power) (temperature), (State of charge of energy storage); if a certain type of equipment places greater emphasis on temperature rise sensitivity, the threshold can be appropriately increased. The value can be increased appropriately if a certain type of equipment places greater emphasis on the state of charge of energy storage. The value of .

[0065] A8. After the above processing, the system outputs the link health index and the device energy efficiency deviation index.

[0066] In this step, through the processing of the above steps, all quantities involved in the subsequent judgment are dimensionless values, avoiding unit conflicts when comparing thresholds later; at the same time, each parameter can be traced back to a specific sensor, a specific communication counter or a specific baseline data, avoiding the existence of empty parameters.

[0067] S4. Based on link health indicators and equipment energy efficiency deviation index Perform stage identification and generate the aforementioned switching marker; like Figure 4 As shown, the specific steps are as follows: A1. Operational phase division; The system divides its operating status into normal phase, early warning phase, risk phase, and switchover preparation phase.

[0068] The specific division rules are as follows: When link health metrics The data exceeds the link warning threshold, and the equipment energy efficiency deviation index is also higher. When the temperature drops below the device's warning threshold, the system is considered to be in a normal state. When link health metrics The energy efficiency index drops below the link warning threshold, or the energy efficiency deviation index of the equipment drops below the threshold. When the temperature rises above the device's warning threshold, the system is determined to enter the warning phase. When link health metrics If the value continues to decline and the predicted link health value may be lower than the link switching threshold within the preset prediction window, or if the predicted equipment energy efficiency deviation value continues to rise and is predicted to exceed the equipment control threshold, the system is determined to enter the risk stage. When link health metrics When the link switching threshold is approaching and the risk status continues to exceed the preset confirmation event window, the system is determined to enter the switching preparation stage.

[0069] Furthermore, the preferred link warning threshold is 0.75~0.85, with an example value of 0.80; this range corresponds to the link health index. A drop to 75% to 85% of the ideal state indicates a significant degradation in at least one key indicator (such as received optical power or bit error rate) but the system is still operational, allowing it to prepare for self-healing switching in advance.

[0070] The preferred link switching threshold is 0.25~0.35, with an example value of 0.30; when the link health index... A value below 0.35 indicates that several key indicators have deteriorated severely and the link is unreliable; a value above 0.25 reserves a certain amount of switching margin to prevent switching from being performed only after the link is completely interrupted.

[0071] The preferred equipment control threshold is 0.45~0.55, with an example value of 0.50; this preferred range corresponds to the equipment energy efficiency deviation index. Reaching half of its permissible deviation indicates that the combined deviation of the equipment's active power, temperature, or energy storage state of charge has deviated from the ideal energy-saving operating conditions by approximately 50%, a severe level requiring immediate mandatory energy-saving intervention to avoid equipment damage or downtime.

[0072] The aforementioned range is used to create a buffer zone between early warning and switching, preventing the system from frequently operating due to short-term fluctuations.

[0073] A2. Calculate the trend quantity; To avoid false triggering by single-point fluctuations, the system uses the difference quotient between adjacent periods to represent the trend. The specific calculation steps are as follows: ; ; in, This is the current sampling sequence number. The number of sampling points within the trend calculation window. The sampling period.

[0074] like This indicates that the link health indicator is trending downwards; if This indicates that the equipment energy efficiency deviation index is on the rise.

[0075] A3. Implement short-term forecasts; The system performs linear extrapolation for the next prediction window and calculates the following predicted values: Link health prediction value: ; Predicted energy efficiency deviation of equipment: ; in, The prediction window is preferably set to 1 to 10 seconds, or 1 to 5 times the shortest control response time on site.

[0076] A4. Generate pre-switching markers; The pre-switching flag indicates that the system has entered a state where path switching and frame skipping need to be prepared in advance. This step links the stage division rules of A1 with the predicted values ​​of A3, and generates the following rules: When the link health prediction value When the value is below the link switching threshold, or when the device energy efficiency deviation prediction value is... When the value exceeds the device control threshold, the system generates a pre-switching flag; once the pre-switching flag is generated, it indicates that the system has entered the switching preparation state.

[0077] In this step, the above settings transform passive recovery into proactive prevention, enabling the system to prepare buffers, sampling, and control logic in advance before communication is actually interrupted or the equipment deviates significantly from its operating conditions, thereby reducing the control window during the handover process.

[0078] S5. Reorder the priority cache queue based on the pre-switching mark and adjust the sampling strategy; This step is executed immediately when a pre-switch marker is generated, or when the system enters a warning or risk phase; the specific steps are as follows: A1. Classify the data to be transmitted; The edge controller categorizes the data to be transmitted into the following three types: The first category is protection alarm data and control command data, including switching commands, shutdown commands, power limiting commands, and heat dissipation enhancement commands; The second category is key operating data, including inverter output power. Energy storage state of charge and the temperature of key parts of the equipment Voltage U, current I; The third category is non-critical monitoring data, including environmental auxiliary data, non-critical equipment status, and historical archived data.

[0079] A2. Reorder the priority cache queue; Specifically as follows: The first type of data enters the highest priority buffer queue and is sent in a straight-through manner, and bandwidth is not allowed to be occupied by non-critical data. The second type of data enters the medium-priority cache queue to maintain short-cycle sampling and real-time uploading. The third type of data enters the low-priority cache queue and is allowed to be temporarily stored in the edge cache area when the link status is unstable. It will be uploaded in batches after the link is restored.

[0080] A3. Adjust the sampling period according to the link health status; When the system is in normal operation, all data uses the default cycle. When the system is in the early warning stage, shorten the sampling period for the first and second types of data, and extend the sampling period for the third type of data. When the system is in a risky phase, the real-time uploading of the third type of data is suspended.

[0081] In a preferred embodiment, the sampling period satisfies the following condition: ; in, Corresponding to the first type of data, Corresponding to the second type of data, This corresponds to the third type of data.

[0082] In this step, communication resources are reclassified according to the importance of the data to energy-saving control, with the most critical data being transmitted with priority, thereby ensuring uninterrupted energy-saving control.

[0083] S6. Perform self-healing switching of the ring network fiber optic communication link and issue energy-saving control instructions; like Figure 5 As shown, when a pre-switching flag has been generated, or when the system detects current link health indicators... When the threshold for link switching is reached, the edge controller and the central controller work together to immediately perform the following actions: A1. Locate the faulty node and determine the backup path; The edge controller first queries and identifies the health indicators of the current link based on the topology table and node mapping table established in step S1. The specific faulty link segment and its corresponding affected node are identified; then, a healthy and usable backup transmission path is determined for the affected node from the preset ring network protection path; the backup path is a predefined, redundant link route within the ring network in step S1, which can ensure that data continues to be transmitted from the source node to the central controller without loss after switching; this backup path is determined during the engineering commissioning phase and stored inside the controller for quick retrieval.

[0084] A2. Self-healing switching execution; The edge controller immediately switches the data stream of the affected nodes from the current primary transmission path to the backup path; this switching process ensures no data loss and simultaneously performs the following operations: The critical data frames to be sent are temporarily stored in the highest priority buffer queue established by S5; the data transmission in the current transmission direction is interrupted and the backup link port is immediately activated; after confirming that the backup link is connected, the data is retrieved from the buffer queue in the original data packet order and sent; the switching status is monitored in real time to confirm that the data has successfully arrived at the next node.

[0085] Through the above steps, the link self-healing switching and data transmission remain continuous, avoiding delays or loss of critical control commands due to link degradation or interruption.

[0086] A3. Generate energy-saving control commands and issue them simultaneously; While initiating the self-healing switch, the central controller bases its decisions on the current equipment energy efficiency deviation index. Based on the numerical values ​​and real-time load status of the system, specific energy-saving control commands are generated upon startup switching. Utilizing the priority caching mechanism established by S5, these commands are immediately sent via the backup or detour path after the switch. The purpose of issuing these control commands is to proactively pull the equipment back from a deviated state to energy-saving operation.

[0087] The energy-saving control commands include, but are not limited to: adjusting the upper limit of inverter output power; adjusting the charging or discharging power of energy storage units; adjusting the start-stop duration, start-stop sequence, or speed of heat dissipation units; adjusting the duty cycle of non-critical loads; and adjusting the wake-up sequence of some standby devices.

[0088] A4. Execute the distribution order according to the priority cache queue; To ensure that core control actions are not blocked during switching moments when link bandwidth may fluctuate, the central controller strictly follows the priority cache queue that has been rearranged in S5 and issues data and instructions in sequence: first, it sends and executes the first type of protection alarm and control instruction data, then processes the second type of critical operational data, and finally processes the third type of non-critical monitoring data.

[0089] Through this step, the system achieves atomic synchronous execution of communication self-healing and energy-saving control: instead of initiating control after communication is restored, energy-saving commands are issued at the same moment the switch is decided, and transmission is guaranteed according to priority. This avoids control downtime, enabling the system to continuously maintain energy-saving control over electrical equipment even when communication status changes dynamically. It also provides accurate statistical data for S7 feedback correction, including whether the switch was successful, the duration, and the equipment status recovery.

[0090] S7. Perform feedback correction and update the threshold table and sampling strategy; After a complete pre-switching or switching action, the system needs to evaluate the execution effect and, based on this evaluation, perform closed-loop corrections to the judgment threshold and sampling strategy, enabling the system to adapt to slow changes in the field environment. The specific steps are as follows: A1. Statistics on the execution results after the switch; The system records and compiles statistics on whether the self-healing switch was successful, whether the switch time met the set requirements, whether the control commands were executed on time, whether the equipment temperature dropped, and whether the power fluctuation decreased.

[0091] A2. Based on statistical results, the system adjusts the threshold according to clearly defined rules; Specifically, the system updates the link warning threshold, link switching threshold, device warning threshold, and device control threshold; Preferably, the threshold update adopts the exponential smoothing method, which is based on a slow correction method, so that the threshold slowly approaches the recent benchmark value, thereby filtering out short-term disturbances; The corrected formula is: ; in, The threshold currently in use is the threshold used in the previous control cycle or feedback cycle (such as link warning threshold, link switching threshold, or device control threshold). This is a new baseline threshold obtained from recent stable operating samples; the calculation steps are as follows: take the most recent continuous and normal operating interval (e.g., a period of no alarms in the last 72 hours), and calculate the energy efficiency deviation index of all equipment within this interval. or link health indicators Statistical analysis was performed on the sampled values; for thresholds such as link warnings and other downward breakthroughs, the following values ​​were taken: For thresholds that exceed the limits for equipment control and other upward breakthroughs, take... ;in, This is the sample mean for that interval. Standard deviation, The regularity coefficient is used to determine the strictness of the baseline threshold relative to the statistical distribution; to ensure uniformity and avoid arbitrariness, it is based on the three sigma criterion in statistics, i.e., under the assumption of normality, the sample falls within the range of... The probability of any exception is less than 0.3%. Therefore, in this embodiment, the rule coefficient is [value missing] for all thresholds. The value of is fixed at 3. Using this as a benchmark ensures that the new threshold is a reliable estimate of the system's normal state boundary after excluding low-probability extreme fluctuations. Furthermore, in practical applications, it can be adjusted within the range of 2 to 4 based on the field's tolerance for false alarms / missed alarms. The value of .

[0092] It should be noted that if the system is unable to obtain a continuous normal operating range that meets the above conditions for an extended period of time (e.g., more than 168 hours) due to reasons such as a continuously adverse external environment, the system will suspend the active correction of the threshold, maintain the current threshold unchanged, and issue a "Adaptive threshold update paused" prompt message to the operation and maintenance personnel to avoid incorrect corrections due to a lack of valid data; once the system obtains at least one continuous normal operating range that meets the conditions again, the above smooth correction process will be automatically resumed.

[0093] The smoothing coefficient is used to control the threshold update speed and prevent the threshold from being over-corrected due to instantaneous fluctuations. Its preferred value is 0.7 to 0.9. The formula for calculating this smoothing coefficient is as follows: ;in, This represents the equivalent smoothing period, i.e., the time it takes for the desired threshold to gradually approach the new benchmark; preferably, in this embodiment... The example value is 5, meaning that it is desired that the threshold basically reflects the new operating condition after about 5 feedback cycles. At this time, it is... This value strikes a balance between the speed of response to real changes and the smoothness of resistance to short-term fluctuations; if less sensitivity to changes is desired, it can be increased. Up to 10, at this time ; The larger the threshold, the slower the threshold update, resulting in a smoother process and reducing the risk of false alarms. The smaller the threshold, the faster the response, but it may be too sensitive to short-term fluctuations.

[0094] The above processing allows the threshold to change slowly with the on-site operating status, rather than changing abruptly.

[0095] A3. Update the sampling strategy; If the system is in the normal stage for multiple consecutive statistical periods, the system will gradually restore the sampling strategy to the default sampling strategy. If the system enters the early warning stage for multiple consecutive statistical periods, the system will appropriately tighten the threshold and shorten the sampling period for key data. If the switchover preparation state appears frequently but no actual interruption occurs, a prompt to check link attenuation, loose ports, or abnormal equipment load should be issued first to investigate whether there are physical causes such as loose fiber optic connectors or abnormal equipment load, rather than simply repeating the switchover.

[0096] Through the above settings, the system can gradually correct parameters according to the actual operating status, avoiding the simultaneous occurrence of false alarms and delayed alarms, and realizing long-term adaptability of energy-saving monitoring of photovoltaic microgrid electrical equipment.

[0097] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An energy-saving monitoring system for electrical equipment based on a photovoltaic microgrid, characterized in that, The system includes: The node mapping and topology construction module is used to establish a correspondence between field devices, node numbers, and communication links, and to generate a topology table and a node mapping table. The topology table is used to record the adjacent nodes, primary link port identifier, backup link port identifier, and link direction of each node. The node mapping table is used to record the device number, device type, and installation location. The data acquisition and timestamp alignment module is used to synchronously acquire device operation data and link status data, and to attach a unified timestamp to the acquired data. The initialization learning and baseline generation module is used to generate a stable operating reference baseline by continuously collecting data when the system is first put into operation or restarted after a long period of shutdown. The normalization and index calculation module is used to normalize the collected multidimensional data and generate link health index and equipment energy efficiency deviation index. The phase identification and pre-switching tag module is used to identify the system operation phase based on link health indicators and equipment energy efficiency deviation indicators, and generate pre-switching tags. The priority caching and sampling scheduling module is used to dynamically adjust the priority caching queue and sampling strategy of the data to be transmitted according to the pre-switching flag; The self-healing switching and energy-saving control module is used to execute the self-healing switching of the ring network fiber optic communication link and issue energy-saving control commands when the link health index is lower than the preset threshold or the predicted value meets the preset switching conditions. The feedback correction and threshold update module is used to evaluate the execution results and update the link threshold, device threshold and sampling strategy after the handover action is completed.

2. The energy-saving monitoring system for electrical equipment based on a photovoltaic microgrid as described in claim 1, characterized in that: The equipment operation data includes at least the inverter output power, energy storage state of charge, temperature, voltage and current of key parts of the equipment; the link status data includes at least the received optical power, bit error rate, link delay, packet loss rate and original queue backlog value.

3. The energy-saving monitoring system for electrical equipment based on a photovoltaic microgrid as described in claim 2, characterized in that: The normalization and index calculation module normalizes the original values ​​of received optical power, bit error rate, link delay and queue backlog, and generates the link health index based on the weighted sum of the normalization results. Furthermore, the deviations in the inverter output power, the temperature deviations in key parts of the equipment, and the deviations in the energy storage state of charge are normalized respectively, and the energy efficiency deviation index of the equipment is generated by weighted summation of the normalization results.

4. The energy-saving monitoring system for electrical equipment based on a photovoltaic microgrid as described in claim 3, characterized in that: The phase identification and pre-switching marker module divides the system operation status into normal phase, early warning phase, risk phase, and switching preparation phase. The phase identification and pre-switching marker module calculates the predicted value of link health and the predicted value of equipment energy efficiency deviation based on the changing trends of link health indicators and equipment energy efficiency deviation indicators. When the predicted value of link health is lower than the link switching threshold or the predicted value of equipment energy efficiency deviation is higher than the equipment control threshold, a pre-switching marker is generated to prepare for link switching and energy-saving control in advance.

5. The energy-saving monitoring system for electrical equipment based on a photovoltaic microgrid as described in claim 4, characterized in that: The priority caching and sampling scheduling module divides the data to be transmitted into three categories based on the pre-switching marker: a first category of data, a second category of data, and a third category of data. The first category of data consists of protection alarm data and control command data, which is written to the highest priority cache queue. The second category of data consists of critical operational data, which is written to the medium priority cache queue. The third category of data consists of non-critical monitoring data and historical archived data, which is written to the low priority cache queue. Furthermore, the priority caching and sampling scheduling module dynamically adjusts the sampling period of each type of data according to the current operational stage, ensuring that the sampling period of the first category of data is shorter than that of the second category of data, and that the sampling period of the second category of data is shorter than that of the third category of data.

6. The energy-saving monitoring system for electrical equipment based on a photovoltaic microgrid as described in claim 5, characterized in that: The self-healing switching and energy-saving control module locates the affected nodes and their corresponding backup transmission paths by querying the topology table and the node mapping table. During the self-healing switching process, it generates energy-saving control instructions based on the device energy efficiency deviation index and the real-time load status of the system, and issues the energy-saving control instructions in the order of the priority cache queue.

7. The energy-saving monitoring system for electrical equipment based on a photovoltaic microgrid as described in claim 6, characterized in that: The control commands include: adjusting the upper limit of inverter output power; adjusting the charging or discharging power of energy storage unit; adjusting the start-stop duration, start-stop sequence, or speed of heat dissipation unit; adjusting the duty cycle of non-critical loads; and adjusting the wake-up sequence of standby devices, thereby enabling the system communication self-healing and energy-saving control to be executed simultaneously.

8. The energy-saving monitoring system for electrical equipment based on a photovoltaic microgrid as described in claim 7, characterized in that: After completing the switch, the feedback correction and threshold update module statistically analyzes whether the switch was successful, the switch time, the execution status of control commands, the changes in equipment temperature and power fluctuations, and updates the link warning threshold, link switch threshold, equipment warning threshold and equipment control threshold using the exponential smoothing method; it also adjusts the sampling strategy according to the continuous operation phase to achieve system self-adaptation.

9. The energy-saving monitoring system for electrical equipment based on a photovoltaic microgrid as described in claim 8, characterized in that: The feedback correction and threshold update module updates each threshold using an exponential smoothing method, including: weighting and fusing the currently used threshold and the benchmark threshold obtained from recent stable operating samples according to a preset smoothing coefficient to obtain the updated threshold; wherein, the benchmark threshold is determined based on the statistical mean and standard deviation of the system's recent stable operating samples.

10. The energy-saving monitoring system for electrical equipment based on a photovoltaic microgrid as described in claim 9, characterized in that: The node mapping and topology construction module, data acquisition and timestamp alignment module, normalization processing and index calculation module, stage identification and pre-switching marking module, priority caching and sampling scheduling module, and self-healing switching and energy-saving control module are deployed on the edge controller, while the initialization learning and benchmark value generation module and feedback correction and threshold update module are deployed on the central controller. All modules are interconnected through a ring network fiber optic communication link to balance real-time control and long-term statistical analysis capabilities.

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