Intelligent power system remote monitoring device and monitoring method thereof

Abstract

The invention discloses an intelligent power system remote monitoring device and a monitoring method thereof, relates to the technical field of intelligent power systems, and solves the problem that an original monitoring mode is not comprehensive and cannot identify whether the abnormity is caused by a voltage difference between power nodes. According to the method, voltage data of other power nodes associated with a current power node are analyzed, initial analysis is performed preferentially, whether voltage differences exist between the other power nodes and the current power node or not at the same moment is identified, then time delay confirmation is performed, and front-back translation adjustment is performed on voltage data lines associated with the other power nodes; on the basis of the specific adjustment process, the voltage difference data associated with the corresponding process is identified, so that whether the external abnormality is caused by the voltage difference or not is comprehensively evaluated, the processing mode of delay confirmation is adopted, the comprehensiveness of voltage data processing is guaranteed, the processing delay is considered, and the accuracy of data processing is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart power systems, and in particular to a smart power system remote monitoring device and a monitoring method thereof. Background Art

[0002] Smart power system is an intelligent power network that integrates modern information technology (such as the Internet of Things, big data, cloud computing, artificial intelligence, etc.) with traditional power systems; it can achieve comprehensive perception, automatic control, intelligent decision-making and efficient management of power production, transmission, distribution and use. For example, by installing smart sensors in various links such as power generation equipment, transmission lines, substations and user terminals, the system can obtain real-time operating parameters of the power system, such as voltage, current, power, frequency, equipment temperature and other information.

[0003] The application with publication number CN110336985B discloses a remote monitoring device for an electric power system in the field of electric power equipment monitoring technology, including a monitoring drone, a drone control terminal, a wireless image processing module, a video monitoring device, a processor, a memory, a display, a timer, a servo motor and an electric push rod. The first infrared high-definition camera can be triggered to start photographing the operator's appearance to improve safety. The second infrared high-definition camera can observe the working conditions of the equipment inside the electrical cabinet under the drive of the servo motor rotation and the extension of the electric push rod, and then the monitoring drone can be used to monitor the outside of the equipment. The device is simple to operate and rich in functions. It can provide timely maintenance reminders and has broad market application prospects.

[0004] When it comes to the specific monitoring of the internal parameters of the smart power system, it generally conducts a comprehensive assessment of abnormal items based on the specific numerical performance status, thereby generating corresponding abnormal signals for display, so that external personnel can perform maintenance and inspection. However, this assessment method is not comprehensive, because when the corresponding node has numerical fluctuations, it is not caused by the abnormality of the node itself, but by the voltage difference between the node and other power nodes, which causes the numerical value to fluctuate violently. If such voltage difference is not resolved, it will lead to an increase in line loss between the corresponding lines, resulting in an increase in temperature, and thus a power accident. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a smart power system remote monitoring device and a monitoring method thereof, which solves the problem that the original monitoring method is not comprehensive and cannot identify whether its abnormality is caused by the voltage difference between power nodes.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A remote monitoring method for a smart power system, comprising the following steps: Step 1: Perform real-time monitoring of different monitoring items associated with different power nodes in the smart power system, confirm whether the real-time monitoring data of the corresponding monitoring items are abnormal based on the set standard range, and calibrate the abnormal monitoring items based on the confirmation results. The specific sub-steps are: The real-time monitoring data of different monitoring items at different power nodes are calibrated as S i-k , where i represents different power nodes, k represents different monitoring items, and the monitored data S i-k Compare with the preset standard range of this monitoring item, where the standard range is the preset range. i-k ∈ standard range, no calibration is required, and the corresponding monitoring process continues. i-k ∉ Standard range, the monitoring items associated with this data are marked as abnormal monitoring items; Step 2: Based on the determined abnormal monitoring items, identify the monitoring data generated by other monitoring items of the corresponding power node at the corresponding time, and confirm the data association characteristics between this monitoring item and other monitoring items from the historical completion data, and identify whether this power node belongs to a comprehensive abnormality or an external abnormality. The specific sub-steps are: S21, mark the abnormal monitoring item determined by the corresponding power node as a characteristic item, mark the remaining other monitoring items as secondary characteristic items, mark the corresponding time as the abnormal time, mark the monitoring data associated with the characteristic item at the abnormal time as characteristic data, and mark the monitoring data associated with the secondary characteristic item at the abnormal time as secondary characteristic data; S22. A certain feature item is proposed as a traceability item, and several groups of secondary feature data associated with the feature data are identified from the historical completion data. The secondary feature data is the data generated by this traceability item, and the minimum value and the maximum value are selected from the several groups of secondary feature data as the standard interval of this feature item; Identify whether the sub-feature data of this feature item belongs to the standard interval. If so, mark this feature item as the standard item. If not, mark this feature item as the standard difference item. Then, the other sub-characteristic items are processed in the same way, and it is determined whether the other sub-characteristic items belong to the standard item or the standard deviation item; S23, identifying whether other sub-feature items associated with this feature item in the current process are all standard items. If other sub-feature items are all standard items, it means that the external abnormality of the power node is present. If there are standard deviation items in other sub-feature items, it means that the comprehensive abnormality of the power node is present. Step 3: If the power node is an external anomaly, determine other associated nodes with which the power node has power transmission, lock a set of limited periods based on the abnormal time associated with the abnormal monitoring item, confirm the voltage change data of other associated nodes within the limited period, and conduct a comprehensive analysis and confirmation of the voltage change data of other associated nodes within the limited period with the voltage change data of the power node to identify whether the power node is caused by a voltage difference. The specific sub-steps are as follows: S31, based on the abnormal moment associated with the abnormal monitoring item corresponding to this power node, use this abnormal moment as the middle moment of this limited period, and its limited period is a preset period, then confirm that there are other associated nodes associated with this power node, confirm the voltage data generated by other associated nodes within this limited period, and generate voltage change lines corresponding to other associated nodes according to the time sequence, and generate voltage change lines belonging to this power node based on different voltage data associated with this power node within the limited period; S32, placing the voltage change line of the power node and the voltage change lines of other associated nodes in the same two-dimensional coordinate system, taking the voltage change line of the power node as the main line, taking the voltage change lines of other associated nodes as secondary lines, determining the associated points whose voltage data at the same time is higher than the main line from the secondary lines, summing the voltage data of several groups of associated points, and determining the total value ZH t , and then confirm the voltage data DY associated with the main line at the corresponding moment t , where t represents different moments, using: ZH t -DY t =CZ t Locking voltage difference CZ t , identify several groups of voltage differences CZ confirmed at different times t Does it exist in: CZ t >Y1, where Y1 is a preset value. If it exists, a voltage difference signal is directly generated. If it does not exist, the subsequent steps are performed for analysis; S33, keeping the position of the main line in the two-dimensional coordinate system unchanged, and translating several different secondary lines horizontally forward and backward, with the maximum time difference of the translation being 3 seconds. For each translation process, the processing method in step S32 is adopted to confirm whether there is an evaluation condition for generating a voltage difference signal. If so, the voltage difference signal is directly generated and the translation process is stopped. If not, the translation process is continued, and the subsequent translation process cannot be repeated with the translation process that has already occurred. When all translation processes are completed and no voltage difference signal is generated, an analysis error signal is generated for display.

[0007] Preferably, it also includes: Step 4: If the power node is a comprehensive abnormality, the category of the abnormal monitoring item is confirmed according to the calibrated abnormal monitoring item, and an abnormal signal of the category is directly generated for display.

[0008] Preferably, a remote monitoring device for a smart power system comprises: The real-time monitoring terminal monitors different monitoring items associated with different power nodes in the smart power system in real time; The abnormal calibration end confirms whether the real-time monitoring data of the corresponding monitoring item is abnormal based on the set standard range, and calibrates the abnormal monitoring item based on the confirmation result; The abnormality determination end, based on the determined abnormal monitoring item, identifies the monitoring data generated by other monitoring items of the corresponding power node at the corresponding time, and confirms the data association characteristics between this monitoring item and other monitoring items from the historical completion data, and identifies whether this power node belongs to a comprehensive abnormality or an external abnormality; The voltage difference analysis end, if this power node is an external abnormality, determines other associated nodes with which this power node has power transmission, locks a set of limited periods based on the abnormal time associated with the abnormal monitoring item, confirms the voltage change data of other associated nodes within the limited period, and performs a comprehensive analysis and confirmation on the voltage change data of other associated nodes within the limited period and the voltage change data of this power node, to identify whether this power node is caused by a voltage difference; At the category signal generating end, if this power node belongs to a comprehensive abnormality, the category to which this abnormal monitoring item belongs is confirmed according to the calibrated abnormal monitoring item, and an abnormal signal of this category is directly generated for display.

[0009] The present invention provides a remote monitoring device and a monitoring method for a smart power system. Compared with the prior art, the present invention has the following beneficial effects: The present invention monitors the power parameters associated with different power nodes in the smart power system, and calibrates the abnormal monitoring items based on the monitored power parameters, and conducts a comprehensive analysis based on the calibrated abnormal monitoring items and the data characteristics of other monitoring items in the power station at the current moment, to assess whether the abnormal monitoring item is a single abnormality or there are other monitoring items with synchronous abnormalities. Based on this assessment method, the subsequent analysis and processing logic can be quickly and effectively determined to assess the specific cause of the abnormality; For anomalies caused by external factors, the voltage data of other power nodes associated with this power node are analyzed, with initial analysis given priority to identify whether there is a voltage difference between other power nodes and this power node at the same time, and then delayed confirmation is performed to adjust the voltage data lines associated with other power nodes forward and backward. Based on the specific adjustment process, the voltage difference data associated with the corresponding process is identified, so as to comprehensively assess whether the external anomaly is caused by the voltage difference. This delayed confirmation processing method is used to ensure the comprehensiveness of the voltage data processing, take the processing delay into account, and improve the accuracy of its data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 It is a schematic diagram of the process of the present invention; Figure 2 It is a schematic diagram of the principle framework of the present invention. DETAILED DESCRIPTION

[0011] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0012] First embodiment

[0013] See also Figure 1 , the present application provides a remote monitoring method for a smart power system, comprising the following steps: Step 1: Perform real-time monitoring on different monitoring items associated with different power nodes in the smart power system, confirm whether the real-time monitoring data of the corresponding monitoring items is abnormal based on the set standard range, and calibrate the abnormal monitoring items based on the confirmation results. Specifically, in a power system, there are a large number of different power nodes, which can be monitoring nodes, substations and other related equipment. The power nodes are marked in advance in the power system by relevant operators. The monitoring items include: voltage, current, power, temperature and other related parameters. Different monitoring items of different nodes have different setting standards, and the setting standards correspond to the specified standard range to conduct specific evaluation of numerical abnormalities; Among them, the specific sub-steps for calibrating abnormal monitoring items are: The real-time monitoring data of different monitoring items at different power nodes are calibrated as S i-k , where i represents different power nodes, k represents different monitoring items, and the monitored data S i-kThe standard range is the preset range, which is prepared by the relevant operators in advance based on experience. The endpoint values ​​of the standard range are all preset values. i-k ∈ standard range, no calibration is required, and the corresponding monitoring process continues. i-k ∉Standard range, the monitoring item associated with this data is calibrated as an abnormal monitoring item. Specifically, under normal circumstances, when the corresponding monitored data belongs to the corresponding standard range, it means that the corresponding power node is in normal operation. When it does not belong to the corresponding range, it means that the data of the corresponding power node on the corresponding monitoring item must be abnormal, and it is necessary to perform abnormal calibration and subsequent related processing simultaneously; Step 2: Based on the determined abnormal monitoring items, identify the monitoring data generated by other monitoring items of the corresponding power node at the corresponding time, and confirm the data association characteristics between this monitoring item and other monitoring items from the historical completion data, and identify whether this power node belongs to a comprehensive abnormality or an external abnormality. Specifically, the comprehensive abnormality is that the power node itself has a data abnormality problem, which causes the associated monitoring data to have a corresponding problem. The external abnormality is the interference or interference problem of the external power line, which causes the data of the corresponding monitoring item of the power node to change, thereby generating the corresponding abnormal monitoring item. Among them, the specific sub-steps for identifying what kind of abnormality this power node belongs to are: S21, mark the abnormal monitoring item determined by the corresponding power node as a characteristic item, mark the remaining other monitoring items as secondary characteristic items, mark the corresponding time as the abnormal time, mark the monitoring data associated with the characteristic item at the abnormal time as characteristic data, and mark the monitoring data associated with the secondary characteristic item at the abnormal time as secondary characteristic data; S22. A certain characteristic item is proposed as a traceability item. From the historical completed data, several groups of sub-characteristic data associated with the characteristic data are identified. The sub-characteristic data is the data generated by this traceability item. The minimum and maximum values ​​are selected from several groups of sub-characteristic data as the standard interval of this characteristic item. For example, the characteristic item currently confirmed is proposed to be current. The characteristic data corresponding to the current of this characteristic item is 5A. Then a group of sub-characteristic items is selected as the traceability item. The sub-characteristic item is proposed to be voltage. Then in the past historical completed data: because the power system itself has corresponding numerical fluctuations, a current of 5A may correspond to different voltage values ​​under different circumstances. By confirming the intervals of several groups of voltage values, the corresponding standard interval can be confirmed. When the corresponding data generated by the corresponding sub-characteristic item at the abnormal moment belongs to the standard interval, it means that the corresponding sub-characteristic items are all in normal conditions, and the abnormal item is single abnormal. Then the single abnormality is basically an external abnormality caused by other external line fluctuations or other conditions. Identify whether the sub-feature data of this feature item belongs to the standard interval. If so, mark this feature item as the standard item. If not, mark this feature item as the standard difference item. Then, the other sub-characteristic items are processed in the same way, and it is determined whether the other sub-characteristic items belong to the standard item or the standard deviation item; S23, identifying whether other sub-feature items associated with this feature item in the current process are all standard items. If other sub-feature items are all standard items, it represents an external abnormality of the power node (that is, only a single group of monitoring items is abnormal, and other monitoring items are in normal operation). If there is a standard error item in other sub-feature items, it represents a comprehensive abnormality of the power node (there may also be an external abnormality here, but when there are both comprehensive abnormalities and external abnormalities, it is treated as a comprehensive abnormality first. After the comprehensive abnormality is resolved, the external abnormality can be accurately determined, and the signal can be displayed in time for external relevant personnel to view and take countermeasures in time. The comprehensive abnormality can be understood as an internal abnormality opposite to the external abnormality, but this type of internal abnormality may also have an external abnormality, so it is named a comprehensive abnormality); Step 3: If the power node is externally abnormal, determine other associated nodes with which the power node has power transmission, and based on the abnormal time associated with the abnormal monitoring item, lock a set of limited periods, confirm the voltage change data of other associated nodes within the limited period, and conduct a comprehensive analysis and confirmation of the voltage change data of other associated nodes within the limited period and the voltage change data of the power node to identify whether the power node is caused by a voltage difference. Specifically, when there is a voltage difference, that is, the working voltage of other associated nodes is much higher than the working voltage of the power node, which will cause the corresponding operating parameters of the power node to be affected by other working voltages, resulting in abnormal voltage data. Therefore, in order to confirm such problems, it is necessary to confirm whether there is a voltage difference in the corresponding power node, and perform real-time confirmation; The specific sub-steps for identifying whether the power node is caused by a voltage difference are: S31. Based on the abnormal moment associated with the abnormal monitoring item corresponding to this power node, this abnormal moment is used as the middle moment of this limited period. The limited period is a preset period, and its specific value is determined by the operator based on experience. Generally, it is 2 minutes. Then, it is confirmed that there are other associated nodes associated with this power node (other associated nodes are also power nodes). The voltage data generated by other associated nodes in this limited period are confirmed, and according to the time sequence, the voltage change line corresponding to the other associated nodes is generated. Based on the different voltage data associated with this power node in the limited period, the voltage change line belonging to this power node is generated; S32, place the voltage change line of the power node and the voltage change lines of other associated nodes in the same two-dimensional coordinate system, take the voltage change line of the power node as the main line, take the voltage change lines of other associated nodes as secondary lines, determine the associated points whose voltage data at the same time (same horizontal coordinate) is higher than the main line from the secondary lines, sum the voltage data of several groups of associated points (voltage data with the same horizontal coordinate higher than the main line, such voltage data is located on the secondary line, for example: it is proposed that there are three secondary lines and one main line, at the position of time 4, the voltage data corresponding to the three secondary lines are 5V, 5V and 4V, and the voltage data of the main line is 4V, then the associated points are two groups, that is, 10V, and the points associated with 4V are not involved in the calculation), and confirm the total value ZH t , and then confirm the voltage data DY associated with the main line at the corresponding moment t , where t represents different moments, using: ZH t -DY t =CZ t Locking voltage difference CZ t , identify several groups of voltage differences CZ confirmed at different times t Does it exist in: CZ t >Y1, where Y1 is a preset value, and its specific value is determined by the operator based on experience, representing the maximum acceptable voltage difference. If it exists, a voltage difference signal is directly generated. If it does not exist, the subsequent steps are performed for analysis; S33. Keep the position of the main line unchanged in the two-dimensional coordinate system, and translate several different secondary lines forward and backward. The maximum time difference of the translation is 3 seconds (that is, the corresponding secondary line can move forward or backward for at most one second. Assuming that there is a point with coordinates (4, 5V) in a group of secondary lines, where 4 represents the time, then after the corresponding secondary line is moved, the coordinates of this point may change to (3, 5V), (2, 5V) or (5, 5V). Although the time delay of voltage and current in the line can be ignored, in the actual processing process, its data is collected by corresponding sensors. The response time of traditional electromagnetic voltage transformers may be between a few milliseconds and tens of milliseconds. When the voltage at point A changes, the sensor cannot accurately capture this change immediately, but there will be a short delay. This is because the electromagnetic induction process, signal amplification and analog-to-digital conversion inside the sensor all require It takes time to complete), and each translation process of a group of processes adopts the processing method in step S32 to confirm whether there are evaluation conditions for generating a voltage difference signal. If so, the voltage difference signal is directly generated and the translation process is stopped. If not, the translation process is continued. The subsequent translation process cannot be repeated with the translation process that has already occurred, that is, in each translation process, the comprehensive performance state of several groups of sub-lines in the two-dimensional coordinate system cannot be repeated. The so-called comprehensive performance state means that in each process, there will be no complete repetition. A single sub-line may not move, while other sub-lines may move, as long as it is ensured that all sub-lines in this coordinate system do not repeat in the overall performance state. Each sub-line can move forward or backward 6 times. When there are two groups of sub-lines, there are 36 translation processes. By analogy, the comprehensiveness of the voltage data processing can be guaranteed, the processing delay is taken into account, and the accuracy of its data processing is improved; When all translation processes are completed and no voltage difference signal is generated, an analysis error signal is generated for display. When an external operator sees the generation of an analysis error signal, it is necessary to determine whether such abnormal monitoring items are caused by fluctuations in values, or whether the corresponding sensor has a monitoring failure or other problems, and human intervention is required for fault analysis; Step 4. If this power node belongs to a comprehensive abnormality, confirm the category of this abnormal monitoring item according to the calibrated abnormal monitoring item, and directly generate an abnormal signal of this category for display (if the category belongs to voltage, directly generate a voltage abnormal signal; if the category is current, directly generate a current abnormal signal, etc.). Based on the displayed abnormal signal, external personnel perform fault processing on the voltage condition of this power node, identify the cause of such voltage abnormality, and perform fault processing in time to solve such problems. The abnormal monitoring item belongs to the abnormal item confirmed in the first time, so it is sufficient to directly generate an abnormal signal about this item.

[0014] Second embodiment

[0015] Combination Figure 2 , a smart power system remote monitoring device, comprising: The real-time monitoring terminal monitors different monitoring items associated with different power nodes in the smart power system in real time; The abnormal calibration end confirms whether the real-time monitoring data of the corresponding monitoring item is abnormal based on the set standard range, and calibrates the abnormal monitoring item based on the confirmation result; The abnormality determination end, based on the determined abnormal monitoring item, identifies the monitoring data generated by other monitoring items of the corresponding power node at the corresponding time, and confirms the data association characteristics between this monitoring item and other monitoring items from the historical completion data, and identifies whether this power node belongs to a comprehensive abnormality or an external abnormality; The voltage difference analysis end, if this power node is an external abnormality, determines other associated nodes with which this power node has power transmission, locks a set of limited periods based on the abnormal time associated with the abnormal monitoring item, confirms the voltage change data of other associated nodes within the limited period, and performs a comprehensive analysis and confirmation on the voltage change data of other associated nodes within the limited period and the voltage change data of this power node, to identify whether this power node is caused by a voltage difference; At the category signal generating end, if this power node belongs to a comprehensive abnormality, the category to which this abnormal monitoring item belongs is confirmed according to the calibrated abnormal monitoring item, and an abnormal signal of this category is directly generated for display.

[0016] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0017] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A remote monitoring method for a smart power system, characterized in that: The following steps are involved: Step 1: Perform real-time monitoring of different monitoring items associated with different power nodes in the smart power system, confirm whether the real-time monitoring data of the corresponding monitoring items are abnormal based on the set standard range, and calibrate the abnormal monitoring items based on the confirmation results; Step 2: Based on the determined abnormal monitoring items, identify the monitoring data generated by other monitoring items of the corresponding power node at the corresponding time, and confirm the data association characteristics between this monitoring item and other monitoring items from the historical completion data to identify whether this power node belongs to a comprehensive abnormality or an external abnormality; Step three: If this power node is an external abnormality, determine other associated nodes that have power transmission with this power node, lock a set of limited periods based on the abnormal time associated with the abnormal monitoring item, confirm the voltage change data of other associated nodes within the limited period, and conduct a comprehensive analysis and confirmation of the voltage change data of other associated nodes within this limited period and the voltage change data of this power node to identify whether this power node is caused by a voltage difference.

2. A remote monitoring method for a smart power system according to claim 1, characterized in that: In step 1, the specific sub-steps for calibrating the abnormal monitoring items are: The real-time monitoring data of different monitoring items at different power nodes are calibrated as S i-k , where i represents different power nodes, k represents different monitoring items, and the monitored data S i-k Compare with the preset standard range of this monitoring item, where the standard range is the preset range. i-k ∈ standard range, no calibration is required, and the corresponding monitoring process continues. i-k ∉Standard range, the monitoring items associated with this data are marked as abnormal monitoring items.

3. A remote monitoring method for a smart power system according to claim 1, characterized in that: In step 2, the specific sub-steps for identifying what kind of abnormality this power node belongs to are: S21, mark the abnormal monitoring item determined by the corresponding power node as a characteristic item, mark the remaining other monitoring items as secondary characteristic items, mark the corresponding time as the abnormal time, mark the monitoring data associated with the characteristic item at the abnormal time as characteristic data, and mark the monitoring data associated with the secondary characteristic item at the abnormal time as secondary characteristic data; S22. A certain feature item is proposed as a traceability item, and several groups of secondary feature data associated with the feature data are identified from the historical completion data. The secondary feature data is the data generated by this traceability item, and the minimum value and the maximum value are selected from the several groups of secondary feature data as the standard interval of this feature item; Identify whether the sub-feature data of this feature item belongs to the standard interval. If so, mark this feature item as the standard item. If not, mark this feature item as the standard difference item. Then, the other sub-characteristic items are processed in the same way, and it is determined whether the other sub-characteristic items belong to the standard item or the standard deviation item; S23. Identify whether other sub-feature items associated with this feature item in the current process are all standard items. If other sub-feature items are all standard items, it means that the external abnormality of this power node exists. If there are standard deviation items in other sub-feature items, it means that the comprehensive abnormality of this power node exists.

4. A remote monitoring method for a smart power system according to claim 3, characterized in that: In step 3, the specific sub-steps for identifying whether the power node is caused by a voltage difference are: S31, based on the abnormal moment associated with the abnormal monitoring item corresponding to this power node, use this abnormal moment as the middle moment of this limited period, and its limited period is a preset period, then confirm that there are other associated nodes associated with this power node, confirm the voltage data generated by other associated nodes within this limited period, and generate voltage change lines corresponding to other associated nodes according to the time sequence, and generate voltage change lines belonging to this power node based on different voltage data associated with this power node within the limited period; S32, placing the voltage change line of the power node and the voltage change lines of other associated nodes in the same two-dimensional coordinate system, taking the voltage change line of the power node as the main line, taking the voltage change lines of other associated nodes as secondary lines, determining the associated points whose voltage data at the same time is higher than the main line from the secondary lines, summing the voltage data of several groups of associated points, and determining the total value ZH t , and then confirm the voltage data DY associated with the main line at the corresponding moment t , where t represents different moments, using: ZH t -DY t =CZ t Locking voltage difference CZ t , identify several groups of voltage differences CZ confirmed at different times t Does it exist in: CZ t >Y1, where Y1 is a preset value. If it exists, a voltage difference signal is directly generated. If it does not exist, the subsequent steps are performed for analysis; S33, keeping the position of the main line in the two-dimensional coordinate system unchanged, and translating several different secondary lines horizontally forward and backward, with the maximum time difference of the translation being 3 seconds. For each translation process, the processing method in step S32 is adopted to confirm whether there is an evaluation condition for generating a voltage difference signal. If so, the voltage difference signal is directly generated and the translation process is stopped. If not, the translation process is continued, and the subsequent translation process cannot be repeated with the translation process that has already occurred. When all translation processes are completed and no voltage difference signal is generated, an analysis error signal is generated for display.

5. A remote monitoring method for a smart power system according to claim 3, characterized in that: Also includes: Step 4: If the power node is a comprehensive abnormality, the category of the abnormal monitoring item is confirmed according to the calibrated abnormal monitoring item, and an abnormal signal of the category is directly generated for display.

6. A remote monitoring device for a smart power system, the monitoring device operates according to a remote monitoring method for a smart power system according to any one of claims 1 to 5, characterized in that: include: The real-time monitoring terminal monitors different monitoring items associated with different power nodes in the smart power system in real time; The abnormal calibration end confirms whether the real-time monitoring data of the corresponding monitoring item is abnormal based on the set standard range, and calibrates the abnormal monitoring item based on the confirmation result; The abnormality determination end, based on the determined abnormal monitoring item, identifies the monitoring data generated by other monitoring items of the corresponding power node at the corresponding time, and confirms the data association characteristics between this monitoring item and other monitoring items from the historical completion data, and identifies whether this power node belongs to a comprehensive abnormality or an external abnormality; The voltage difference analysis end, if this power node is an external abnormality, determines other associated nodes with which this power node has power transmission, locks a set of limited periods based on the abnormal time associated with the abnormal monitoring item, confirms the voltage change data of other associated nodes within the limited period, and performs a comprehensive analysis and confirmation on the voltage change data of other associated nodes within the limited period and the voltage change data of this power node, to identify whether this power node is caused by a voltage difference; At the category signal generating end, if this power node belongs to a comprehensive abnormality, the category to which this abnormal monitoring item belongs is confirmed according to the calibrated abnormal monitoring item, and an abnormal signal of this category is directly generated for display.

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