An intelligent photovoltaic power distribution monitoring method and system based on energy consumption optimization

Abstract

The application discloses an intelligent photovoltaic power distribution monitoring method and system based on energy consumption optimization, and the method comprises the following steps: acquiring electrical topology data and equipment connection relationship data of a target photovoltaic power distribution area, constructing a photovoltaic power distribution network topology graph, identifying a path, and forming a path monitoring network; collecting current waveform change data of each power distribution path in a preset time period based on the monitoring network, identifying transient surge current waveform and steady-state current waveform, and constructing reference current waveform data of the power distribution path; on the basis, real-time current waveform monitoring is performed on the target area to determine current fluctuation; and the uploading frequency of real-time current waveform data is dynamically determined according to the current fluctuation, so that high-precision monitoring of the operation state of the photovoltaic power distribution system and intelligent regulation and control of data transmission energy consumption are realized, the equipment operation efficiency is improved, and the system energy consumption is reduced.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic power distribution monitoring technology, and in particular to an intelligent photovoltaic power distribution monitoring method and system based on energy consumption optimization. Background Technology

[0002] With the continuous expansion of photovoltaic power generation, the distribution network structure in photovoltaic power plants and distributed photovoltaic systems is becoming increasingly complex. Their operation involves the coordinated work of numerous branches, combiner boxes, inverters, and various load devices. To ensure the safe and stable operation of photovoltaic systems, real-time monitoring of current, power flow, and equipment status in each path of the distribution network is essential. However, traditional photovoltaic distribution monitoring systems often rely on fixed-period data acquisition and uploading methods, failing to dynamically adjust monitoring strategies based on current waveform changes. This results in continuous high-frequency data uploading during steady-state operation, while monitoring accuracy struggles to improve in a timely manner during transient disturbances, making it difficult to balance overall monitoring efficiency with energy consumption control.

[0003] On the other hand, existing monitoring methods are typically based on device-level monitoring, paying insufficient attention to the topology, path relationships, and electrical connections between nodes in the power distribution network, lacking a monitoring system built upon real electrical paths. In complex photovoltaic power distribution scenarios, operations such as branch switching and load switching can cause significant transient surge currents and current waveform distortions. If the transient waveforms cannot be accurately distinguished from steady-state waveforms, false alarms and missed alarms will occur, affecting the monitoring system's ability to identify abnormal states. Furthermore, the data redundancy caused by the fixed sampling frequency not only increases the communication load but also raises the energy consumption of monitoring equipment, increasing the overall operating cost of the photovoltaic system.

[0004] Therefore, there is an urgent need for an intelligent monitoring method that can combine the topology of photovoltaic power distribution networks, real-time status of pathways, and current waveform characteristics. By accurately identifying current fluctuations, it can achieve dynamic adaptive adjustment of data upload frequency, thereby significantly reducing energy consumption in data acquisition and communication while ensuring monitoring accuracy, and improving the intelligence, energy efficiency, and reliability of photovoltaic power distribution system operation. Summary of the Invention

[0005] To address at least one of the aforementioned technical problems, this invention proposes an intelligent photovoltaic power distribution monitoring method and system based on energy consumption optimization.

[0006] The first aspect of this invention provides an intelligent photovoltaic power distribution monitoring method based on energy consumption optimization, comprising:

[0007] Obtain electrical topology data and equipment connection relationship data of the target photovoltaic power distribution area, construct a photovoltaic power distribution network topology map, perform path identification on the photovoltaic power distribution network topology map, and construct a path monitoring network for the target photovoltaic power distribution area;

[0008] The current waveform change data of each power distribution path in the target photovoltaic power distribution area within a preset time period are obtained according to the path monitoring network, and the transient surge current waveform and steady-state current waveform of each power distribution path are identified.

[0009] The transient surge current waveform and steady-state current waveform are used to construct the reference current waveform data for each power distribution path. Based on the reference current waveform data, the target photovoltaic power distribution area is monitored in real time to identify current fluctuations.

[0010] The upload frequency of real-time current waveform data is determined based on the current fluctuation, and the photovoltaic distribution cabinet is monitored and its energy consumption is optimized based on the upload frequency to form an energy consumption optimization strategy.

[0011] In this solution, the step of acquiring electrical topology data and equipment connection relationship data of the target photovoltaic power distribution area, and constructing a photovoltaic power distribution network topology map, specifically involves:

[0012] Obtain electrical topology data radiating from the distribution cabinet to the load end from the distribution distribution map of the target photovoltaic distribution area. The electrical topology data includes the logical number of each output branch switch in the distribution cabinet, the identifier of the lower-level distribution unit and load equipment connected to each branch;

[0013] Obtain device connection relationship data, which specifically describes the connection sequence between each device and the connection logic between lines, including the connection between the output terminal of the distribution cabinet and the cable, the connection between the cable and the input terminal of the combiner box, the connection between the output terminal of the combiner box and the downstream line, and the connection relationship between the line and the terminal of the final load device.

[0014] Based on the electrical topology data and equipment connection relationship data, a tree-structured topology model is constructed with the distribution cabinet as the root node and each end load as the leaf node. In the topology model, a unique node identifier is assigned to each electrical connection node, and the upstream and downstream node sets connected to the node are recorded to form a node connection relationship table.

[0015] The topology information, including path identifiers, device node sequences, and corresponding line connection relationships, is integrated based on the node connection relationship table to construct a complete photovoltaic power distribution network topology map.

[0016] In this solution, the step of identifying pathways in the photovoltaic distribution network topology map and constructing a pathway monitoring network for the target photovoltaic distribution area specifically involves:

[0017] Real-time acquisition of switch status signals and current vector data of each load node and distribution node in the photovoltaic power distribution network. The switch status signals include the electrical status of the switch being open or closed, and the current vector data includes current amplitude and phase information.

[0018] On the photovoltaic power distribution network topology map, a depth-first search algorithm is used to traverse the photovoltaic power distribution network topology map with the power distribution cabinet as the root node, and the switch status signal of each downstream node is checked in turn.

[0019] If the downstream node is in the closed state, mark the node as a connected node and continue traversing downstream. If the downstream node is in the open state, stop further traversing to the branch where the node is located and backtrack to the upstream node.

[0020] After completing the depth-first traversal, a candidate path is formed by the sequence of nodes from the root node to each end load node where all switches are closed.

[0021] For each candidate path, the phase difference of the current vector between adjacent nodes on the path is calculated based on the current vector data of each node. If the phase difference is within the preset phase tolerance range, the power flow direction of the path is determined to be consistent, and the candidate path is marked as an active path. The identification information, node sequence and corresponding real-time power flow direction of all active paths are integrated to construct a path monitoring network for the target photovoltaic power distribution area.

[0022] In this solution, the step of obtaining current waveform change data of each distribution path in the target photovoltaic distribution area within a preset time period based on the path monitoring network, and identifying the transient surge current waveform and steady-state current waveform of each distribution path, specifically involves:

[0023] The current waveform change data of each power distribution path in the target photovoltaic power distribution area within a preset time period is obtained according to the path monitoring network. The current change rate between adjacent sampling points in the current waveform change data is calculated using a differential algorithm. A first change rate threshold is set to initially identify the current fast rising edge and mark it as the potential surge initiation point.

[0024] Based on the potential surge initiation point, a first preset time window is traced backward and a second preset time window is extended backward to capture candidate transient current waveform segments;

[0025] Wavelet transform decomposition is performed on the candidate transient current waveform segment to extract high-frequency component energy. If the high-frequency component energy exceeds a preset energy threshold and the duration of the waveform segment is less than a preset transient duration threshold, then the candidate transient current waveform segment is determined to be a transient surge current waveform caused by load switching under normal operating conditions.

[0026] Excluding the transient surge current waveform, the remaining current waveform data within the preset time period is divided into multiple continuous time intervals. The mean and standard deviation of the current value in each time interval are calculated. If the fluctuation range of the mean is less than the preset steady-state fluctuation tolerance and the standard deviation is lower than the preset steady-state deviation threshold, then the current waveform in that time interval is determined to be a steady-state current waveform.

[0027] In this scheme, the step of constructing reference current waveform data for each power distribution path using the transient surge current waveform and steady-state current waveform, and then performing real-time current waveform monitoring on the target photovoltaic power distribution area based on the reference current waveform data to identify current fluctuations, specifically involves:

[0028] Waveform features are extracted for the transient surge current waveform and steady-state current waveform of each power distribution path. The waveform features include the peak value, rise time and duration of the transient surge current waveform and the average amplitude and characteristic harmonic components of the steady-state current waveform.

[0029] Based on the waveform characteristics, reference current waveform data for each power distribution path is constructed. During the current waveform monitoring process in the target photovoltaic power distribution area, real-time current waveform data for each power distribution path is acquired. The real-time current waveform data is dynamically time-warped and matched with the reference current waveform data of the corresponding power distribution path, and the similarity between the real-time waveform and the reference current waveform is calculated.

[0030] A similarity threshold is set, and the Mahalanobis distance between the similarity and the similarity threshold is calculated. Based on the Mahalanobis distance, the degree of current fluctuation abnormality of each distribution path in the target photovoltaic distribution area is identified, and the current fluctuation of each distribution path is obtained.

[0031] In this solution, the step of determining the upload frequency of real-time current waveform data based on the current fluctuation, and then monitoring and optimizing the energy consumption of the photovoltaic distribution cabinet based on the upload frequency, constitutes an energy consumption optimization strategy, specifically as follows:

[0032] An abnormality threshold is set, and the abnormality of the current fluctuation in the power distribution path is determined to be greater than the abnormality threshold based on the current fluctuation situation. When the abnormality of the current fluctuation is greater than the abnormality threshold, the corresponding power distribution path is determined to be in a high fluctuation state.

[0033] The first upload frequency is calculated based on the matching difference between the real-time current waveform data and the reference current waveform data of the high-fluctuation power distribution path. The first upload frequency is positively correlated with the matching difference.

[0034] When the abnormality of the current fluctuation is not greater than the abnormality threshold, the corresponding power distribution path is determined to be in a low fluctuation state. The historical duration data of the steady-state current waveform of the low fluctuation state power distribution path is obtained, and the second upload frequency is calculated based on the historical duration data. The second upload frequency is negatively correlated with the historical duration.

[0035] The upload frequency of real-time current waveform data for each power distribution path is determined based on the first upload frequency or the second upload frequency. The working cycle of the data acquisition and communication module of the current monitoring sensor in the photovoltaic distribution cabinet is adjusted based on the upload frequency. The photovoltaic distribution cabinet is monitored and its energy consumption is optimized based on the working cycle, thus forming an energy consumption optimization strategy.

[0036] A second aspect of the present invention also provides an intelligent photovoltaic power distribution monitoring system based on energy consumption optimization. The system includes a memory and a processor. The memory includes a program for an intelligent photovoltaic power distribution monitoring method based on energy consumption optimization. When the processor executes the program, the program performs the following steps:

[0037] Obtain electrical topology data and equipment connection relationship data of the target photovoltaic power distribution area, construct a photovoltaic power distribution network topology map, perform path identification on the photovoltaic power distribution network topology map, and construct a path monitoring network for the target photovoltaic power distribution area;

[0038] The current waveform change data of each power distribution path in the target photovoltaic power distribution area within a preset time period are obtained according to the path monitoring network, and the transient surge current waveform and steady-state current waveform of each power distribution path are identified.

[0039] The transient surge current waveform and steady-state current waveform are used to construct the reference current waveform data for each power distribution path. Based on the reference current waveform data, the target photovoltaic power distribution area is monitored in real time to identify current fluctuations.

[0040] The upload frequency of real-time current waveform data is determined based on the current fluctuation, and the photovoltaic distribution cabinet is monitored and its energy consumption is optimized based on the upload frequency to form an energy consumption optimization strategy.

[0041] This invention discloses an intelligent photovoltaic power distribution monitoring method and system based on energy consumption optimization. By acquiring electrical topology data and equipment connection relationship data of the target photovoltaic power distribution area, a photovoltaic power distribution network topology map is constructed and path identification is performed to form a path monitoring network. Based on this monitoring network, current waveform change data of each power distribution path within a preset time period are collected, transient surge current waveforms and steady-state current waveforms are identified, and reference current waveform data of the power distribution path is constructed. On this basis, real-time current waveform monitoring is performed on the target area to determine current fluctuations. The upload frequency of real-time current waveform data is dynamically determined according to the current fluctuations, achieving high-precision monitoring of the photovoltaic power distribution system's operating status and intelligent control of data transmission energy consumption, thereby improving equipment operating efficiency and reducing system energy consumption. Attached Figure Description

[0042] Figure 1 A flowchart of an intelligent photovoltaic power distribution monitoring method based on energy consumption optimization according to the present invention is shown;

[0043] Figure 2 The flowchart illustrating the construction of a photovoltaic power distribution network topology map according to the present invention is shown;

[0044] Figure 3 A flowchart illustrating the present invention for identifying transient surge current waveforms and steady-state current waveforms in power distribution paths is shown.

[0045] Figure 4 A block diagram of an intelligent photovoltaic power distribution monitoring system based on energy consumption optimization according to the present invention is shown. Detailed Implementation

[0046] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0047] 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 therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0048] Figure 1 A flowchart of an intelligent photovoltaic power distribution monitoring method based on energy consumption optimization according to the present invention is shown.

[0049] like Figure 1 As shown, the first aspect of the present invention provides an intelligent photovoltaic power distribution monitoring method based on energy consumption optimization, comprising:

[0050] S102, acquire electrical topology data and equipment connection relationship data of the target photovoltaic power distribution area, construct a photovoltaic power distribution network topology map, perform path identification on the photovoltaic power distribution network topology map, and construct a path monitoring network for the target photovoltaic power distribution area;

[0051] S104, Based on the channel monitoring network, obtain the current waveform change data of each distribution channel in the target photovoltaic distribution area within a preset time period, and identify the transient surge current waveform and steady-state current waveform of each distribution channel;

[0052] S106, construct the reference current waveform data for each power distribution path from the transient surge current waveform and the steady-state current waveform, and perform real-time current waveform monitoring on the target photovoltaic power distribution area based on the reference current waveform data to identify current fluctuations;

[0053] S108, determine the upload frequency of real-time current waveform data based on the current fluctuation, and perform energy consumption optimization monitoring on the photovoltaic distribution cabinet based on the upload frequency to form an energy consumption optimization strategy.

[0054] It should be noted that by acquiring electrical topology data and equipment connection relationship data of the target photovoltaic distribution area, the structure of the distribution network and the connection relationships between various devices are reconstructed, thereby generating a complete photovoltaic distribution network topology map. This enables comprehensive identification of network paths and clear mapping of power flow between nodes, ensuring monitoring coverage of all active paths. Based on the constructed path monitoring network, current waveform change data of each distribution path within a preset time period are collected. This allows for the differentiation and identification of transient surge current and steady-state current waveforms, enabling accurate monitoring of short-term load disturbances and long-term stable operating conditions. By constructing transient surge current waveforms and steady-state current waveforms as reference current waveform data for each path, dynamic time warping matching and waveform similarity analysis can be performed during real-time monitoring to accurately determine the degree of current fluctuation, identify abnormal states, and quantify current fluctuation risks. Based on the current fluctuation data obtained from real-time monitoring, the upload frequency of current waveform data for each path is adaptively determined, enabling high-frequency monitoring of high-fluctuation paths and low-frequency monitoring of low-fluctuation paths. This effectively reduces data redundancy and communication load, thereby optimizing the monitoring energy consumption of the photovoltaic distribution cabinet and forming a dynamically adjusted energy consumption optimization strategy.

[0055] Figure 2 The flowchart illustrating the construction of a photovoltaic power distribution network topology map according to the present invention is shown.

[0056] According to an embodiment of the present invention, the step of obtaining electrical topology data and equipment connection relationship data of the target photovoltaic power distribution area and constructing a photovoltaic power distribution network topology map specifically includes:

[0057] Obtain electrical topology data radiating from the distribution cabinet to the load end from the distribution distribution map of the target photovoltaic distribution area. The electrical topology data includes the logical number of each output branch switch in the distribution cabinet, the identifier of the lower-level distribution unit and load equipment connected to each branch;

[0058] Obtain device connection relationship data, which specifically describes the connection sequence between each device and the connection logic between lines, including the connection between the output terminal of the distribution cabinet and the cable, the connection between the cable and the input terminal of the combiner box, the connection between the output terminal of the combiner box and the downstream line, and the connection relationship between the line and the terminal of the final load device.

[0059] Based on the electrical topology data and equipment connection relationship data, a tree-structured topology model is constructed with the distribution cabinet as the root node and each end load as the leaf node. In the topology model, a unique node identifier is assigned to each electrical connection node, and the upstream and downstream node sets connected to the node are recorded to form a node connection relationship table.

[0060] The topology information, including path identifiers, device node sequences, and corresponding line connection relationships, is integrated based on the node connection relationship table to construct a complete photovoltaic power distribution network topology map.

[0061] It should be noted that by acquiring electrical topology data and equipment connection relationship data of the photovoltaic power distribution area, and constructing a tree topology model and node connection relationship table based on this data, the power distribution network structure is modeled. This clearly describes each power path from the distribution cabinet to the end load and its node sequence, accurately records the upstream and downstream connection relationships between each node, and finally integrates path identifiers, equipment node sequences, and line connection relationships to form a complete photovoltaic power distribution network topology map. The end load refers to the final electrical equipment or electrical unit that directly uses electrical energy in the power distribution network, such as lighting, air conditioning, and motors.

[0062] According to an embodiment of the present invention, the step of identifying pathways in the photovoltaic distribution network topology map and constructing a pathway monitoring network for the target photovoltaic distribution area specifically involves:

[0063] Real-time acquisition of switch status signals and current vector data of each load node and distribution node in the photovoltaic power distribution network. The switch status signals include the electrical status of the switch being open or closed, and the current vector data includes current amplitude and phase information.

[0064] On the photovoltaic power distribution network topology map, a depth-first search algorithm is used to traverse the photovoltaic power distribution network topology map with the power distribution cabinet as the root node, and the switch status signal of each downstream node is checked in turn.

[0065] If the downstream node is in the closed state, mark the node as a connected node and continue traversing downstream. If the downstream node is in the open state, stop further traversing to the branch where the node is located and backtrack to the upstream node.

[0066] After completing the depth-first traversal, a candidate path is formed by the sequence of nodes from the root node to each end load node where all switches are closed.

[0067] For each candidate path, the phase difference of the current vector between adjacent nodes on the path is calculated based on the current vector data of each node. If the phase difference is within the preset phase tolerance range, the power flow direction of the path is determined to be consistent, and the candidate path is marked as an active path. The identification information, node sequence and corresponding real-time power flow direction of all active paths are integrated to construct a path monitoring network for the target photovoltaic power distribution area.

[0068] It should be noted that in photovoltaic power distribution systems, the distribution network is typically a tree or mesh structure, consisting of distribution cabinets, combiner boxes, branch lines, and end loads. The network has many nodes, complex branches, and frequently changing loads. During operation and maintenance, the distribution network may develop decommissioned circuits or branches that have been unused for a long time. Although these circuits exist in the topology diagram, they do not actually carry current or power flow in operation. Without path identification, the monitoring system will include the magnetic induction caused by adjacent circuits in these decommissioned or inactive branches within the scope of data acquisition and analysis, leading to wasted monitoring resources and data redundancy. By real-time acquisition of switch status signals and current vector data of each load node and distribution node in the photovoltaic distribution network, and combined with a depth-first search algorithm, distribution paths are determined. Switch status signals directly reflect the on / off status of each node, thus determining whether a node participates in current transmission. Current vector data provides current amplitude and phase information between nodes, which can be used to verify the consistency of power flow direction, ensuring that candidate paths are actually active paths. The depth-first search algorithm starts from the root node of the distribution cabinet and systematically traverses downstream nodes along the topology graph, dynamically marking connected nodes according to switch status and generating a complete node sequence. Through a traversal backtracking mechanism, all potential paths can be comprehensively covered. Combined with phase difference verification, active paths with consistent power flow direction can be screened, thereby constructing a precise path monitoring network. This path monitoring network is constructed by installing current sensors inside the photovoltaic distribution cabinet to monitor each active path.

[0069] Figure 3 A flowchart illustrating the present invention for identifying transient surge current waveforms and steady-state current waveforms in power distribution paths is shown.

[0070] According to an embodiment of the present invention, the step of acquiring current waveform change data of each distribution path in the target photovoltaic distribution area within a preset time period based on the path monitoring network, and identifying the transient surge current waveform and steady-state current waveform of each distribution path, specifically includes:

[0071] The current waveform change data of each power distribution path in the target photovoltaic power distribution area within a preset time period is obtained according to the path monitoring network. The current change rate between adjacent sampling points in the current waveform change data is calculated using a differential algorithm. A first change rate threshold is set to initially identify the current fast rising edge and mark it as the potential surge initiation point.

[0072] Based on the potential surge initiation point, a first preset time window is traced backward and a second preset time window is extended backward to capture candidate transient current waveform segments;

[0073] Wavelet transform decomposition is performed on the candidate transient current waveform segment to extract high-frequency component energy. If the high-frequency component energy exceeds a preset energy threshold and the duration of the waveform segment is less than a preset transient duration threshold, then the candidate transient current waveform segment is determined to be a transient surge current waveform caused by load switching under normal operating conditions.

[0074] Excluding the transient surge current waveform, the remaining current waveform data within the preset time period is divided into multiple continuous time intervals. The mean and standard deviation of the current value in each time interval are calculated. If the fluctuation range of the mean is less than the preset steady-state fluctuation tolerance and the standard deviation is lower than the preset steady-state deviation threshold, then the current waveform in that time interval is determined to be a steady-state current waveform.

[0075] It should be noted that in photovoltaic distribution networks, operations such as load switching and branch switching can cause short-term transient surge currents, while the current waveform is relatively stable during steady-state operation. These are normal current fluctuations. By distinguishing between the short-term transient fluctuations caused by load switching or branch switching and the steady-state current changes under normal system operation, we can avoid misjudging normal disturbances as anomalies and improve the reliability of anomaly detection. At the same time, by analyzing the mean and standard deviation of the steady-state current waveform, we can provide data support for constructing a reference current waveform, enabling real-time current monitoring to accurately reflect the actual operating status of each path.

[0076] According to an embodiment of the present invention, the step of constructing reference current waveform data for each power distribution path from the transient surge current waveform and the steady-state current waveform, and performing real-time current waveform monitoring on the target photovoltaic power distribution area based on the reference current waveform data to identify current fluctuations, specifically includes:

[0077] Waveform features are extracted for the transient surge current waveform and steady-state current waveform of each power distribution path. The waveform features include the peak value, rise time and duration of the transient surge current waveform and the average amplitude and characteristic harmonic components of the steady-state current waveform.

[0078] Based on the waveform characteristics, reference current waveform data for each power distribution path is constructed. During the current waveform monitoring process in the target photovoltaic power distribution area, real-time current waveform data for each power distribution path is acquired. The real-time current waveform data is dynamically time-warped and matched with the reference current waveform data of the corresponding power distribution path, and the similarity between the real-time waveform and the reference current waveform is calculated.

[0079] A similarity threshold is set, and the Mahalanobis distance between the similarity and the similarity threshold is calculated. Based on the Mahalanobis distance, the degree of current fluctuation abnormality of each distribution path in the target photovoltaic distribution area is identified, and the current fluctuation of each distribution path is obtained.

[0080] It should be noted that by extracting waveform features such as peak value, rise time, duration, average amplitude, and characteristic harmonics from the transient surge current waveform and steady-state current waveform of each distribution path, accurate reference current waveform data is constructed, enabling dynamic monitoring of real-time current waveforms in the photovoltaic distribution area. By dynamically time-warping and matching the real-time current waveform with the reference waveform and calculating the similarity, and then using Mahalanobis distance to quantify the degree of deviation, the degree of current fluctuation anomalies in each path can be accurately identified. This allows for precise judgment of the current operating status of the distribution path, rapid detection of abnormal fluctuations, and improved accuracy and reliability of current monitoring.

[0081] According to an embodiment of the present invention, the step of determining the upload frequency of real-time current waveform data based on the current fluctuation, and optimizing the energy consumption of the photovoltaic distribution cabinet based on the upload frequency to form an energy consumption optimization strategy, specifically includes:

[0082] An abnormality threshold is set, and the abnormality of the current fluctuation in the power distribution path is determined to be greater than the abnormality threshold based on the current fluctuation situation. When the abnormality of the current fluctuation is greater than the abnormality threshold, the corresponding power distribution path is determined to be in a high fluctuation state.

[0083] The first upload frequency is calculated based on the matching difference between the real-time current waveform data and the reference current waveform data of the high-fluctuation power distribution path. The first upload frequency is positively correlated with the matching difference.

[0084] When the abnormality of the current fluctuation is not greater than the abnormality threshold, the corresponding power distribution path is determined to be in a low fluctuation state. The historical duration data of the steady-state current waveform of the low fluctuation state power distribution path is obtained, and the second upload frequency is calculated based on the historical duration data. The second upload frequency is negatively correlated with the historical duration.

[0085] The upload frequency of real-time current waveform data for each power distribution path is determined based on the first upload frequency or the second upload frequency. The working cycle of the data acquisition and communication module of the current monitoring sensor in the photovoltaic distribution cabinet is adjusted based on the upload frequency. The photovoltaic distribution cabinet is monitored and its energy consumption is optimized based on the working cycle, thus forming an energy consumption optimization strategy.

[0086] It should be noted that when the abnormal current fluctuation exceeds the abnormality threshold, it indicates that the power distribution path is in a highly volatile state, either unstable or a precursor to a potential fault. In this case, the first upload frequency is calculated based on the difference between the real-time waveform and the reference waveform, and the upload frequency is increased as the difference increases. This allows for more intensive and timely monitoring of abnormal paths, improving fault warning and safety protection capabilities. When the abnormal current fluctuation is not greater than the threshold, it indicates that the path is in a stable, low-fluctuation state. In this case, the second upload frequency is calculated based on the historical duration of the steady-state current waveform, and the upload frequency is decreased as the steady-state duration increases. This effectively reduces the energy consumption caused by redundant data uploads and frequent sensor operation while ensuring monitoring accuracy. By using an adaptive upload frequency, a dynamic balance between monitoring accuracy and energy consumption is achieved, reducing the overall energy consumption of the monitoring system and improving the operating efficiency of the photovoltaic distribution cabinet and the utilization rate of monitoring resources. The energy consumption optimization strategy involves uploading current fluctuation data and current fluctuation status data to the management terminal according to the corresponding upload frequency, enabling management personnel to detect and identify power distribution anomalies.

[0087] According to an embodiment of the present invention, it further includes:

[0088] Obtain the candidate path set of the photovoltaic power distribution network initially generated based on the depth-first search algorithm and the real-time switching status signal of each node. Based on the real-time switching status signal, select the candidate path composed of nodes containing signal transition history from the candidate path set and mark it as a ghost path to be verified.

[0089] The historical power data of the end load node of the ghost path to be verified within a preset verification time window is obtained, the average value of the historical power data is calculated as the reference power level, and a low power test command is sent to the starting control node of the ghost path to be verified. The low power test command is used to drive the starting control node to generate a small power disturbance waveform with specific identification characteristics.

[0090] Simultaneously monitor real-time power data at the end load node, extract waveform features from the real-time power data, calculate the matching degree between the extracted waveform features and the expected features of the small power disturbance waveform, and if the matching degree exceeds a preset threshold, determine that the ghost path to be verified is a real physical connection path and formally include it in the path monitoring network.

[0091] If the matching degree does not exceed the preset threshold and the baseline power level of the end load node remains zero within the verification time window, the ghost path to be verified is determined to be a false path and is removed from the candidate path set.

[0092] According to an embodiment of the present invention, sending a low-power test command to the starting control node of the ghost path to be verified specifically includes:

[0093] Based on the photovoltaic power distribution network topology map, the starting control node of the ghost path to be verified is determined. The starting control node is an intelligent circuit breaker or contactor capable of switching the path.

[0094] The low-power test instruction is generated, which contains a specific sequence of actions. The sequence of actions is used to control the starting control node to perform a millisecond-level micro-opening and closing operation in a very short time. The power disturbance amplitude generated by this operation is much lower than the impact of normal load start-up and shutdown, but is sufficient to be detected on the load side.

[0095] Before sending the low-power test command, the real-time current of the branch where the starting control node is located is detected to ensure that the micro-opening and closing operation will not cause overcurrent or impact the upper-level system. The low-power test command is sent to the starting control node and executed, thereby generating a micro-power disturbance waveform with unique timing and amplitude characteristics on the distribution line.

[0096] It should be noted that during automated network topology identification, sensor communication delays or transient signal interference can lead to false alarms regarding switch states, potentially creating ghost paths (false paths that are not actually energized). This problem contaminates the accuracy of the path monitoring network, causing the system to monitor invalid paths and optimize energy consumption, wasting resources and affecting the reliability of state judgments. By proactively injecting a safe and controllable small feature disturbance signal into suspicious paths and performing synchronous detection and feature matching at a remote end, the physical connectivity of the paths can be accurately verified without affecting the normal operation of the system. This improves the authenticity and robustness of topology identification, effectively eliminates the interference of ghost paths on the monitored network structure, and avoids the unnecessary waste of computing resources and communication bandwidth.

[0097] Figure 4A block diagram of an intelligent photovoltaic power distribution monitoring system based on energy consumption optimization according to the present invention is shown.

[0098] A second aspect of the present invention also provides an intelligent photovoltaic power distribution monitoring system based on energy consumption optimization. The system includes a memory 401, a processor 402, and a communication interface 403. The memory includes a program for an intelligent photovoltaic power distribution monitoring method based on energy consumption optimization. The communication interface is used for data connection and communication between the memory and the processor. When the processor executes the program for the intelligent photovoltaic power distribution monitoring method based on energy consumption optimization, it performs the following steps:

[0099] Obtain electrical topology data and equipment connection relationship data of the target photovoltaic power distribution area, construct a photovoltaic power distribution network topology map, perform path identification on the photovoltaic power distribution network topology map, and construct a path monitoring network for the target photovoltaic power distribution area;

[0100] The current waveform change data of each power distribution path in the target photovoltaic power distribution area within a preset time period are obtained according to the path monitoring network, and the transient surge current waveform and steady-state current waveform of each power distribution path are identified.

[0101] The transient surge current waveform and steady-state current waveform are used to construct the reference current waveform data for each power distribution path. Based on the reference current waveform data, the target photovoltaic power distribution area is monitored in real time to identify current fluctuations.

[0102] The upload frequency of real-time current waveform data is determined based on the current fluctuation, and the photovoltaic distribution cabinet is monitored and its energy consumption is optimized based on the upload frequency to form an energy consumption optimization strategy.

[0103] This invention discloses an intelligent photovoltaic power distribution monitoring method and system based on energy consumption optimization. By acquiring electrical topology data and equipment connection relationship data of the target photovoltaic power distribution area, a photovoltaic power distribution network topology map is constructed and path identification is performed to form a path monitoring network. Based on this monitoring network, current waveform change data of each power distribution path within a preset time period are collected, transient surge current waveforms and steady-state current waveforms are identified, and reference current waveform data of the power distribution path is constructed. On this basis, real-time current waveform monitoring is performed on the target area to determine current fluctuations. The upload frequency of real-time current waveform data is dynamically determined according to the current fluctuations, achieving high-precision monitoring of the photovoltaic power distribution system's operating status and intelligent control of data transmission energy consumption, thereby improving equipment operating efficiency and reducing system energy consumption.

[0104] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0105] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0106] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0107] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0108] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0109] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for intelligent photovoltaic power distribution monitoring based on energy consumption optimization, characterized in that, The method comprises the following steps: Obtain electrical topology data and device connection relationship data of a target photovoltaic power distribution area, construct a photovoltaic power distribution network topology map, identify paths of the photovoltaic power distribution network topology map, and construct a path monitoring network of the target photovoltaic power distribution area; Obtain current waveform change data of each power distribution path of the target photovoltaic power distribution area in a preset time period according to the path monitoring network, and identify transient surge current waveform and steady-state current waveform of each power distribution path; Construct reference current waveform data of each power distribution path from the transient surge current waveform and the steady-state current waveform, perform real-time current waveform monitoring on the target photovoltaic power distribution area according to the reference current waveform data, and identify current fluctuation conditions; Determine the uploading frequency of real-time current waveform data according to the current fluctuation conditions, monitor and optimize the energy consumption of the photovoltaic power distribution cabinet according to the uploading frequency, and construct an energy consumption optimization strategy, specifically as follows: Set an abnormality degree threshold, determine whether the current fluctuation abnormality degree of the power distribution path is greater than the abnormality degree threshold according to the current fluctuation conditions, and determine that the corresponding power distribution path is in a high fluctuation state when the current fluctuation abnormality degree is greater than the abnormality degree threshold; Calculate a first uploading frequency according to the matching difference amount of real-time current waveform data and reference current waveform data of the high fluctuation state power distribution path, and the first uploading frequency is in a positive correlation with the matching difference amount; When the current fluctuation abnormality degree is not greater than the abnormality degree threshold, it is determined that the corresponding power distribution path is in a low fluctuation state, the historical duration data of the steady-state current waveform of the low fluctuation state power distribution path is obtained, and a second uploading frequency is calculated according to the historical duration data, and the second uploading frequency is in a negative correlation with the historical duration; Determine the uploading frequency of real-time current waveform data of each power distribution path according to the first uploading frequency or the second uploading frequency, adjust the working period of the data acquisition and communication module of the current monitoring sensor in the photovoltaic power distribution cabinet based on the uploading frequency, and monitor and optimize the energy consumption of the photovoltaic power distribution cabinet according to the working period to construct an energy consumption optimization strategy.

2. The intelligent photovoltaic power distribution monitoring method based on energy consumption optimization according to claim 1, characterized in that, The method for obtaining electrical topology data and device connection relationship data of a target photovoltaic power distribution area and constructing a photovoltaic power distribution network topology map comprises the following steps: Obtain electrical topology data radiating from a power distribution cabinet to a load end from a power distribution distribution map of the target photovoltaic power distribution area, and the electrical topology data comprises logical numbers of each output branch switch in the power distribution cabinet, identifiers of each branch connected to a lower power distribution unit and a load device; Obtain device connection relationship data, and the device connection relationship specifically represents the connection order between devices and the connection logic between lines, including the connection between a power distribution cabinet output terminal and a cable, the connection between the cable and a busbar input terminal, the connection between a busbar output terminal and a lower line, and the connection relationship between the line and a terminal of a final load device; Based on the electrical topology data and the device connection relationship data, a tree structure topology model is constructed with the power distribution cabinet as a root node and each end load as a leaf node, in which each electrical connection node is assigned a unique node identifier and the upstream and downstream node sets connected to the node are recorded to form a node connection relationship table; According to the node connection relationship table, the topology information including the path identifier, the device node sequence and the corresponding line connection relationship is integrated to construct a complete photovoltaic power distribution network topology map. 3.The intelligent photovoltaic power distribution monitoring method based on energy consumption optimization of claim 1, wherein, The photovoltaic power distribution network topology map is subjected to path identification to construct a path monitoring network of the target photovoltaic power distribution area, specifically: Real-time acquisition of switch state signals and current vector data of each load node and power distribution node in the photovoltaic power distribution network, wherein the switch state signals include the electrical state of switch opening or closing, and the current vector data includes the current amplitude and phase information; On the photovoltaic power distribution network topology map, the photovoltaic power distribution network topology map is subjected to traversal operation based on a depth-first search algorithm with the power distribution cabinet as a root node, and the switch state signals of each downstream node are sequentially checked; If the switch state of the downstream node is in the closed state, the node is marked as a connected node and the downstream traversal is continued, and if the switch state of the downstream node is in the open state, the further traversal of the branch where the node is located is stopped and the upstream node is traced back; After completing the depth-first traversal, a node sequence in which all switch states from the root node to each end load node are in the closed state constitutes a candidate path; For each candidate path, the phase difference between the current vectors of adjacent nodes on the path is calculated according to the current vector data of each node, and if the phase difference is within a preset phase tolerance range, it is determined that the power flow direction of the path is consistent, the candidate path is marked as an active path, and the identification information, node sequence and corresponding real-time power flow direction of all active paths are integrated to construct a path monitoring network of the target photovoltaic power distribution area.

4. The intelligent photovoltaic power distribution monitoring method based on energy consumption optimization according to claim 1, characterized in that, According to the path monitoring network, the current waveform change data of each power distribution path in the target photovoltaic power distribution area within a preset time period is obtained, and the transient surge current waveform and the steady-state current waveform of each power distribution path are identified, specifically: According to the path monitoring network, the current waveform change data of each power distribution path in the target photovoltaic power distribution area within a preset time period is obtained, and the current change rate between adjacent sampling points in the current waveform change data is calculated using a difference algorithm, a first change rate threshold is set to preliminarily identify the current rapid rising edge, which is marked as a potential surge starting point; The first preset time window is traced back from the potential surge starting point and the second preset time window is extended backward, and a candidate transient current waveform segment is obtained by intercepting; The candidate transient current waveform segment is subjected to wavelet transform decomposition to extract the high-frequency component energy, and if the high-frequency component energy exceeds a preset energy threshold and the waveform segment duration is less than a preset transient duration threshold, it is determined that the candidate transient current waveform segment is a transient surge current waveform caused by load switching in the normal operating state; After excluding the transient surge current waveform, the current waveform data remaining in the preset time period is divided into multiple continuous time intervals, the mean and standard deviation of the current value in each time interval are calculated, and if the fluctuation range of the mean is less than the preset steady-state fluctuation tolerance and the standard deviation is lower than the preset steady-state deviation threshold, it is determined that the current waveform in the time interval is a steady-state current waveform.

5. The intelligent photovoltaic power distribution monitoring method based on energy consumption optimization according to claim 1, characterized in that, The transient surge current waveform and the steady-state current waveform are used to construct the reference current waveform data of each power distribution path, and real-time current waveform monitoring is performed on the target photovoltaic power distribution area according to the reference current waveform data to identify the current fluctuation condition, specifically as follows: Waveform characteristics of the transient surge current waveform and the steady-state current waveform of each power distribution path are extracted, and the waveform characteristics include the peak value, rise time and duration of the transient surge current waveform, and the average amplitude and characteristic harmonic component of the steady-state current waveform; Based on the waveform characteristics, the reference current waveform data of each power distribution path is constructed, and in the current waveform monitoring process of the target photovoltaic power distribution area, real-time current waveform data of each power distribution path is obtained, and dynamic time warping matching is performed between the real-time current waveform data and the reference current waveform data of the corresponding power distribution path, and the similarity between the real-time waveform and the reference current waveform is calculated; A similarity threshold is set, the Mahalanobis distance between the similarity and the similarity threshold is calculated, and the current fluctuation abnormality degree of each power distribution path of the target photovoltaic power distribution area is identified according to the Mahalanobis distance to obtain the current fluctuation condition of each power distribution path.

6. An intelligent photovoltaic power distribution monitoring system based on energy consumption optimization, characterized in that, The intelligent photovoltaic power distribution monitoring system based on energy consumption optimization includes a storage and a processor, the storage includes an intelligent photovoltaic power distribution monitoring method program based on energy consumption optimization, and the intelligent photovoltaic power distribution monitoring method program based on energy consumption optimization is executed by the processor to realize the following steps: Obtain the electrical topology data and equipment connection relationship data of the target photovoltaic power distribution area, construct a photovoltaic power distribution network topology map, identify the paths of the photovoltaic power distribution network topology map, and construct a path monitoring network of the target photovoltaic power distribution area; According to the path monitoring network, obtain the current waveform change data of each power distribution path of the target photovoltaic power distribution area in a preset time period, and identify the transient surge current waveform and the steady-state current waveform of each power distribution path; The transient surge current waveform and the steady-state current waveform are used to construct the reference current waveform data of each power distribution path, and real-time current waveform monitoring is performed on the target photovoltaic power distribution area according to the reference current waveform data to identify the current fluctuation condition, specifically as follows: According to the current fluctuation condition, determine the upload frequency of the real-time current waveform data, monitor and optimize the energy consumption of the photovoltaic power distribution cabinet according to the upload frequency, and form an energy consumption optimization strategy, specifically as follows: An abnormality degree threshold is set, and whether the current fluctuation abnormality degree of the power distribution path is greater than the abnormality degree threshold is determined according to the current fluctuation condition, and when the current fluctuation abnormality degree is greater than the abnormality degree threshold, it is determined that the corresponding power distribution path is in a high fluctuation state; According to the matching difference between the real-time current waveform data of the high fluctuation state power distribution path and the reference current waveform data, a first uploading frequency is calculated, and the first uploading frequency is positively correlated with the matching difference; When the current fluctuation abnormality degree is not greater than the abnormality degree threshold value, it is determined that the corresponding power distribution path is in a low fluctuation state, historical duration data of the steady-state current waveform of the low fluctuation state power distribution path is obtained, a second uploading frequency is calculated according to the historical duration data, and the second uploading frequency is negatively correlated with the historical duration; According to the first uploading frequency or the second uploading frequency, the uploading frequency of the real-time current waveform data of each power distribution path is determined, the working cycle of the data acquisition and communication module of the current monitoring sensor in the photovoltaic power distribution cabinet is adjusted based on the uploading frequency, the photovoltaic power distribution cabinet is monitored and energy consumption optimization is performed according to the working cycle, and an energy consumption optimization strategy is formed.

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