Power grid situation abnormity monitoring method and system based on power distribution equipment

Abstract

The invention discloses a power grid situation abnormity monitoring method and system based on power distribution equipment, relates to the technical field of power grid monitoring, and aims to monitor the running state of a power grid in real time, timely discover and early warn abnormal conditions, acquire electrical parameters in real time and construct a time sequence through an intelligent electric meter integrated with the power distribution equipment, and realize real-time monitoring of the power grid situation abnormity. Analyzing the operation state of the power distribution equipment, and calculating a fluctuation coefficient to judge whether the power distribution equipment is abnormal; secondary analysis is further carried out on the equipment, the occurrence frequency and duration of the abnormity are counted, and an operation abnormity index is evaluated; generating a power grid situation abnormal index by integrating the operation abnormal index of each device and combining with a device weight coefficient, and informing an operator of maintenance by continuously monitoring the change trend of the index, judging the power grid situation state and outputting abnormal power distribution device information; the system comprises a situation awareness module, an abnormity quantification module and a situation deduction module, the whole-process monitoring from data acquisition to abnormity early warning is realized, and the safety and reliability of power grid operation are effectively improved.

Description

Technical Field

[0001] This invention belongs to the field of power grid monitoring technology, specifically relating to a method and system for monitoring abnormal power grid conditions based on power distribution equipment. Background Technology

[0002] The stable operation of the power grid is directly related to the continuity of industrial production, the convenience of residents' lives, and the normal operation of various infrastructures.

[0003] Existing power grid monitoring technologies mostly rely on periodic inspections, preset fixed threshold alarms, or isolated judgments of single real-time parameters. Their monitoring methods are relatively passive and lagging, lacking in-depth time-series analysis of the operating status of power distribution equipment and comprehensive deduction of macro-situation. Existing technologies usually only focus on whether electrical parameters exceed static thresholds, while ignoring the fluctuation characteristics of equipment within the normal range and their trends over time. They cannot effectively identify early abnormal states that have not yet exceeded limits but have already shown significant deviations from normal fluctuation patterns. Meanwhile, existing methods often use a binary approach to anomaly assessment (normal or faulty), lacking a continuous and quantifiable indicator to characterize the frequency and duration of abnormal equipment operation. This makes it difficult for maintenance personnel to distinguish between occasional fluctuations and continuous degradation, and to accurately assess the actual impact weight of individual devices on the overall stability of the power grid. At the system level, existing technologies often simply present alarm information from various devices side by side, lacking a comprehensive index that can integrate the degree of anomaly in all devices and reflect the deterioration or improvement trend of the overall power grid operation. This makes the assessment of the overall health status of the power grid dependent on the personal experience of maintenance personnel, resulting in insufficient foresight in early warning. Furthermore, when multiple devices are abnormal, it is difficult to quickly locate the most critical and priority root cause. Traditional methods typically lack the ability to automatically associate abnormal situations with a list of specific abnormal devices and sort them, leading to inaccurate and inefficient issuance of maintenance instructions.

[0004] To address the aforementioned problems, this invention proposes a method and system for monitoring abnormal power grid conditions based on power distribution equipment. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method and system for monitoring abnormal power grid conditions based on power distribution equipment, which solves the problems of one-sided power grid condition assessment, delayed early warning, and inaccurate fault location in existing technologies.

[0006] The objective of this invention can be achieved through the following technical solutions: A method for monitoring power grid anomalies based on power distribution equipment, the method comprising: Step 1: Based on the smart meters integrated with each power distribution device in the target power grid, the electrical parameters of each power distribution device and their time-series correlation are acquired in real time, and an electrical parameter sequence corresponding to the monitoring cycle is constructed. Step 2: Analyze the electrical parameter sequences of each power distribution device to determine the operating status of the power distribution device within the monitoring period; A secondary analysis of the power distribution equipment is conducted to determine the number of times the equipment experienced abnormal operating conditions within the assessment period and the duration of each occurrence, thereby assessing the operating abnormality index of the power distribution equipment. Step 3: Comprehensively evaluate the operational anomaly index of each power distribution device during the assessment period to generate the power grid status anomaly index; Continuously monitor the power grid status anomaly index, assess the changing trend of the power grid status anomaly index, determine the power grid status based on the changing trend, and output the abnormal power distribution equipment corresponding to the abnormal power grid status, and notify the operators to carry out maintenance.

[0007] As a further aspect of the present invention, the specific method for constructing the electrical parameter sequence corresponding to the monitoring cycle in step one is as follows: Identify the target power grid; Obtain the smart meters pre-configured by the operator and integrated with each power distribution device in the target power grid, count the total number, and denote it as j, where j equals the total number of power distribution devices; Arrange j power distribution devices randomly, and denote the power distribution device sequence A1, A2, ..., Aj; Get any power distribution device Ai, where i is the counting index, and its value ranges from 1 to j; Extract the monitoring period, count the total number of moments within the monitoring period, and denot it as m. The duration of the monitoring period is preset by the operator. Taking the current time as the start time of the monitoring cycle, the apparent power of the power distribution equipment Ai is continuously monitored for m time periods and arranged in time order, denoted as the apparent power sequence P1, P2, ..., Pm, where the apparent power sequence is the electrical parameter sequence; Similarly, determine the electrical parameter sequence of each power distribution device within any monitoring period.

[0008] As a further aspect of the present invention, the specific method for analyzing the electrical parameter sequence of each power distribution device to determine the operating status of the power distribution device within the monitoring period in step two is as follows: Based on the apparent power sequence P1, P2, ..., Pm, calculate the apparent power standard deviation σP_i and apparent power average μP_i of the power distribution equipment Ai during the monitoring period; Extract the rated apparent power P'_i of the power distribution equipment Ai; use: γ_i=(σP_i / μP_i)×(1+(|μP_i-P'_i| / P'_i)); Calculate the fluctuation coefficient γ_i of the power distribution equipment Ai during the monitoring period, where (σP_i / μP_i) evaluates the apparent power fluctuation and (1+(|μP_i-P'_i| / P'_i)) evaluates the apparent power level deviation. The larger the fluctuation coefficient γ_i, the more the operating state of the power distribution equipment Ai deviates from the normal operating state; conversely, the smaller the fluctuation coefficient γ_i, the closer the operating state of the power distribution equipment Ai is to the normal operating state. Compare the volatility coefficient γ_i with the volatility coefficient threshold γ_yu preset by the operator; If γ_i≥γ_yu, the power distribution equipment Ai is determined to be in an abnormal operating state during the monitoring period; If γ_i < γ_yu, it is determined that the power distribution equipment Ai is in normal operating condition during the monitoring period; Similarly, determine the operating status of each power distribution device in the power distribution equipment sequence A1, A2, ..., Aj within the monitoring period.

[0009] As a further aspect of the present invention, the specific method for performing secondary analysis on the power distribution equipment in step two is as follows: Get the Ai of any power distribution device; The Ai continuously monitors the power distribution equipment for one evaluation cycle, where one evaluation cycle consists of X monitoring cycles, where X is a preset integer by the operator and X≥3; During the assessment period, the fluctuation coefficient γ_i of the power distribution equipment Ai in each monitoring period is continuously determined, and its operating status is judged. The number of times k of abnormal operating states of power distribution equipment Ai occurs within the evaluation period is counted. One occurrence of abnormal operating state indicates the duration of continuous abnormal operating state of power distribution equipment Ai for one monitoring period. The abnormal duration of power distribution equipment Ai in abnormal operation during the evaluation period is calculated as Tk = k × m.

[0010] As a further aspect of the present invention, the specific method for evaluating the operational anomaly index of the power distribution equipment in step two is as follows: For power distribution equipment Ai, the abnormal operation index C_i of power distribution equipment Ai during the evaluation period is calculated using C_i=(k / X)*[1+β*(Tk / (X*m))], where β is the weighting coefficient of abnormal duration preset by the operator; The larger the value of the operational anomaly index C_i, the more unstable the operating status of the power distribution equipment Ai is during the evaluation period; conversely, the smaller the value, the more stable the operating status. Similarly, calculate the operational anomaly index of each power distribution device in the power distribution equipment sequence A1, A2, ..., Aj within the evaluation period, and arrange them according to the power distribution equipment sequence, denoted as the operational anomaly index sequence C_1, C_2, ..., C_j.

[0011] As a further aspect of the present invention, in step two, if any one of the power distribution equipment Ai in the power distribution equipment sequence A1, A2, ..., Aj does not experience any abnormal operating state during the evaluation period, then the operating abnormality index C_i of power distribution equipment Ai is 0, and it is removed from the operating abnormality index sequence C_1, C_2, ..., C_j.

[0012] As a further aspect of the present invention, the specific method for generating the power grid status anomaly index by comprehensively evaluating the operational anomaly index of each power distribution device within the evaluation period in step three is as follows: Extract the power distribution equipment sequence A1, A2, ..., Aj; Obtain the weighting coefficients W1, W2, ..., Wj set by the operator based on the importance of each power distribution device in the target power grid, where the weighting coefficient of Ai is Wi, and W1+W2+...+Wj=1; Using S=[∑ j i=1 [(Ai×Wi)] / (∑ j i=1 Wi) Calculate the power grid status anomaly index S of the target power grid during the assessment period; The larger the value of the power grid status anomaly index S, the more the status of the target power grid deviates from the normal state.

[0013] As a further aspect of the present invention, the specific method for continuously monitoring the power grid status anomaly index and assessing the changing trend of the power grid status anomaly index in step three is as follows: The power grid status anomaly indices of o target power grids are continuously monitored and denoted as S1, S2, ..., So in time sequence; A two-dimensional coordinate system is constructed with the timeline as the horizontal axis and the value of the power grid anomaly index as the vertical axis. S1, S2, ..., So are plotted in the two-dimensional coordinate system as data points in time sequence, resulting in o data points. The power grid anomaly index change curve L is obtained by curve fitting. Calculate the slope of each point in the power grid status anomaly index change curve L, and count the proportion of the part with a slope ≥ 0 R1 and the proportion of the part with a slope < 0 R2, where R1 + R2 = 1; If R1≥R2, then the trend of the power grid status anomaly index is determined to be a deteriorating trend, and the power grid status anomaly is output. Conversely, if the trend is positive, the output power grid is considered to be in a normal state.

[0014] As a further aspect of the present invention, in step three, when the power grid status is abnormal, all power distribution equipment with an abnormal operation index of not 0 within the corresponding evaluation period is extracted, and arranged in descending order according to the value of the abnormal operation index, recorded as an abnormal power distribution equipment sequence, and transmitted to the operator to notify the operator to carry out maintenance.

[0015] A power grid status anomaly monitoring system based on power distribution equipment, the system comprising: The situational awareness module, based on smart meters integrated with each power distribution device in the target power grid, acquires the electrical parameters of each power distribution device in real time and time-series correlation, and constructs an electrical parameter sequence corresponding to the monitoring cycle; The anomaly quantification module analyzes the electrical parameter sequences of each power distribution device to determine the operating status of the power distribution device within the monitoring period; A secondary analysis of the power distribution equipment is conducted to determine the number of times the equipment experienced abnormal operating conditions within the assessment period and the duration of each occurrence, thereby assessing the operating abnormality index of the power distribution equipment. The situation simulation module comprehensively evaluates the operational anomaly index of each power distribution device during the assessment period and generates the power grid situation anomaly index. Continuously monitor the power grid status anomaly index, assess the changing trend of the power grid status anomaly index, determine the power grid status based on the changing trend, and output the abnormal power distribution equipment corresponding to the abnormal power grid status, and notify the operators to carry out maintenance.

[0016] The beneficial effects of this invention are: This invention achieves refined and continuous monitoring of the power grid status by collecting time-series electrical parameters in real time and constructing a sequence using smart meters integrated into power distribution equipment. Its core benefit lies in upgrading traditional passive response to proactive early warning. By analyzing the electrical parameter sequence to quantitatively evaluate the equipment operation anomaly index, and combining the indices of each equipment to generate a power grid status anomaly index, the anomaly detection becomes more scientific and objective. It can dynamically assess the overall health status of the power grid, promptly identify potential risk trends, and thus effectively prevent the escalation of faults. In addition, this method accurately locates abnormal power distribution equipment and automatically notifies maintenance, improving the operation and maintenance response speed and location accuracy, reducing power outage time and maintenance costs, and promoting the transformation of power grid management towards intelligence and data-driven approaches. This invention achieves comprehensive and continuous monitoring of all power distribution equipment in a target power grid by systematically constructing electrical parameter sequences. Its advantages include real-time data acquisition starting from the current moment, ensuring the timeliness and accuracy of monitoring; integration of all smart meters and random arrangement of devices effectively avoids biases caused by monitoring order, improving data objectivity; and the standardization of monitoring cycles and data formats ensures high consistency and comparability of electrical parameter sequences across devices, providing a reliable and complete data foundation for subsequent analysis and facilitating accurate understanding of the power grid's operating status and trends. This invention calculates the fluctuation coefficient based on the apparent power sequence to achieve real-time determination of the operating status of power distribution equipment in a single monitoring cycle. It introduces an evaluation cycle for secondary analysis, statistically analyzes the number and duration of anomalies, and comprehensively calculates the operational anomaly index, thereby forming a multi-level dynamic monitoring system from short-term to long-term. The advantages are that it objectively and accurately assesses the operational stability of equipment through quantitative indicators, reduces subjective misjudgments, and improves monitoring efficiency by combining automated analysis. It also enables early identification of potential equipment risks through long-term tracking, optimizes maintenance strategies, and enhances preventive management capabilities. This invention achieves quantitative assessment and dynamic monitoring of power grid operation status by constructing a weighted comprehensive power grid status anomaly index. Its advantages lie in integrating scattered equipment anomaly data into intuitive status indicators and introducing a weighting mechanism to accurately reflect the differences in the impact of different equipment on overall power grid security. Through continuous monitoring and trend analysis, it can automatically identify deteriorating trends and provide early warnings, giving maintenance personnel more time to respond. Furthermore, when anomalies occur, it can automatically filter and prioritize key abnormal equipment, improving the targeting and efficiency of fault location and repair work. Attached Figure Description

[0017] The invention will now be further described with reference to the accompanying drawings.

[0018] Figure 1 This is a schematic diagram of the system described in this invention; Figure 2 This is a flowchart illustrating the method described in Embodiment 3 of the present invention; Figure 3 This is a flowchart illustrating the method described in Embodiment 4 of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] like Figure 1 As shown, this application provides a method and system for monitoring abnormal power grid conditions based on power distribution equipment; As an embodiment 1 of this application, it specifically includes: The situational awareness module, based on smart meters integrated with each power distribution device in the target power grid, acquires the electrical parameters of each power distribution device in real time and time-series correlation, and constructs an electrical parameter sequence corresponding to the monitoring cycle; The anomaly quantification module analyzes the electrical parameter sequences of each power distribution device to determine the operating status of the power distribution device within the monitoring period; A secondary analysis of the power distribution equipment is conducted to determine the number of times the equipment experienced abnormal operating conditions within the assessment period and the duration of each occurrence, thereby assessing the operating abnormality index of the power distribution equipment. The situation simulation module comprehensively evaluates the operational anomaly index of each power distribution device during the assessment period and generates the power grid situation anomaly index. Continuously monitor the power grid status anomaly index, assess the changing trend of the power grid status anomaly index, determine the power grid status based on the changing trend, and output the abnormal power distribution equipment corresponding to the abnormal power grid status, and notify the operators to carry out maintenance.

[0021] Example 2 This embodiment further discloses the specific steps involved in the situational awareness module based on embodiment 1, including the following: First, the target power grid is determined. The target power grid can be a physically or logically independent power supply area, such as the distribution network of a factory or the low-voltage power grid of a residential community. The target power grid must be able to be integrated with the SCADA system or each power distribution device in the target power grid must be equipped with a smart meter to collect relevant electrical parameters. This embodiment uses the latter method to illustrate the process. First, the smart meters integrated with the power distribution equipment in the target power grid are obtained (considered as a power distribution equipment equipped with a separate smart meter). The total number of smart meters is counted and recorded as j, that is, the total number of power distribution equipment is j. Then, the j power distribution devices are randomly arranged to obtain a sequence, denoted as power distribution device sequence A1, A2, ..., Aj; Then, extract any one power distribution device Ai from the determined power distribution device sequence A1, A2, ..., Aj, where i is the counting index, with a value range of 1 to j. The 1 to j in A1, A2, ..., Aj represent logical counting indices, not the inherent number of the power distribution device. Then, obtain the monitoring cycle preset by the operator, and count the total number of moments within a monitoring cycle, denoted as m; The current time is obtained and used as the start time of a monitoring cycle. The apparent power of the power distribution equipment Ai is continuously monitored for m time periods (i.e., one monitoring cycle) starting from the start time. Finally, m apparent power values ​​corresponding to the m time periods can be obtained. The m apparent power values ​​are arranged in chronological order to obtain a data sequence, denoted as the apparent power sequence P1, P2, ..., Pm of the power distribution equipment Ai. It should be noted that the electrical parameter sequence of the power distribution equipment Ai is the apparent power sequence.

[0022] Following the same method used for processing power distribution equipment Ai, all power distribution equipment in the sequence A1, A2, ..., Aj are processed in the same and synchronous manner to ultimately determine the electrical parameter sequence of each power distribution equipment within any monitoring period.

[0023] Example 3 This embodiment further discloses the specific steps involved in the anomaly quantification module based on embodiment 2, such as... Figure 2 As shown, it includes the following: First, assess the operating status of power distribution equipment Ai within a single monitoring cycle, and simultaneously assess the operating status of other power distribution equipment within the same monitoring cycle. Extract the apparent power sequence P1, P2, ..., Pm of the power distribution equipment Ai obtained in Example 2, and calculate the apparent power standard deviation σP_i and the apparent power average μP_i of the power distribution equipment Ai during the monitoring period based on the apparent power sequence P1, P2, ..., Pm. The apparent power standard deviation σP_i is used to measure the degree of dispersion of the apparent power value of the power distribution equipment Ai around its average value during the monitoring period. If the larger σP_i is, the more severe the load fluctuation and the more unstable the operation. The apparent power average value μP_i represents the average load level of the power distribution equipment Ai during the monitoring period; Next, extract the rated apparent power P'_i of the power distribution equipment Ai. The rated apparent power P'_i is the upper limit of the long-term safe operating capacity of the power distribution equipment Ai, which is a fixed nameplate parameter. By using: γ_i=(σP_i / μP_i)×(1+(|μP_i-P'_i| / P'_i)); Calculate the fluctuation coefficient γ_i of the power distribution equipment Ai during the monitoring period; In the formula, (σP_i / μP_i) is essentially the coefficient of variation, which eliminates the influence of absolute numerical values, allowing for a fair comparison and assessment of the apparent power fluctuation of devices with different capacities. For example, the severity of instability is different for a device with an average power of 100kW that fluctuates by 10kW and for a device with an average power of 10kW that fluctuates by 10kW. The coefficient of variation can accurately reflect this. (1+(|μP_i-P'_i| / P'_i)) is used to evaluate the extent to which the apparent power level deviates from the rated capacity. The "+1" design ensures that the minimum value of this term is not less than 1. When μP_i=P'_i (i.e. the average load is exactly equal to the rated value), this term is 1, and the fluctuation coefficient γ_i is completely determined by the volatility.

[0024] If the load of the power distribution equipment Ai is close to the rated value and the fluctuation is small, the fluctuation coefficient γ_i value will be very small. The closer the operating state of the power distribution equipment Ai is to the normal operating state, the healthier it is. If the average load of the power distribution equipment Ai deviates significantly from the rated value or fluctuates violently, it will lead to an increase in the fluctuation coefficient γ_i value. The larger the fluctuation coefficient γ_i value, the more the operating state of the power distribution equipment Ai deviates from the normal operating state. If the power distribution equipment Ai has both severe load offset and violent fluctuation, the fluctuation coefficient γ_i value will be significantly amplified, thus being sensitively captured by the system. Finally, the calculated volatility coefficient γ_i is compared with the volatility coefficient threshold γ_yu preset by the operator. If the comparison result is: γ_i≥γ_yu, it is determined that the power distribution equipment Ai is in an abnormal operating state during the monitoring period; If the comparison result is: γ_i < γ_yu, it is determined that the power distribution equipment Ai is in normal operation during the monitoring period; By repeating the above steps and performing the same synchronous processing on each power distribution device in the power distribution equipment sequence A1, A2, ..., Aj, the operating status of each power distribution device during the monitoring period can be determined.

[0025] As mentioned above, the operational status of the power distribution equipment Ai has been assessed within a single monitoring cycle. Next, the perspective will be expanded from an isolated monitoring cycle to a longer assessment cycle that includes multiple monitoring cycles, in order to observe the continuous behavioral patterns of the power distribution equipment Ai, i.e., to conduct a secondary analysis of the power distribution equipment. First, obtain any power distribution device Ai from the power distribution device sequence A1, A2, ..., Aj; The system continuously monitors the power distribution equipment using AI for one evaluation cycle. The evaluation cycle is a higher-level time analysis dimension, consisting of X monitoring cycles, where X is a preset integer for the operator, and X ≥ 3. During this evaluation period, the fluctuation coefficient γ_i of the power distribution equipment Ai is continuously determined in each monitoring period, and its operating status is judged. The number of times k of abnormal operating states of power distribution equipment Ai occurs within the evaluation period is counted. One occurrence of abnormal operating state indicates the duration of continuous abnormal operating state of power distribution equipment Ai for one monitoring period. The number of occurrences k reflects the frequency of unstable operation of power distribution equipment Ai. Then, the abnormal duration value Tk of the power distribution equipment Ai in abnormal operation state during the evaluation period is calculated using Tk=k×m, and the total time of the power distribution equipment Ai in abnormal state is quantified.

[0026] Next, based on the monitoring results of the assessment cycle, the power distribution equipment will be further analyzed to determine the abnormal operation index of the power distribution equipment. Extract the occurrence number k and the abnormal duration value Tk of the power distribution equipment Ai. It should be noted that both the occurrence number k and the abnormal duration value Tk are dimensionless values. The abnormal operation index C_i of the power distribution equipment Ai during the evaluation period is calculated by using: C_i=(k / X)*[1+β*(Tk / (X*m))], where β is the weighting coefficient of the abnormal duration preset by the operator; (k / X) represents the proportion of time that the power distribution equipment Ai is in an abnormal state during the assessment period (with the monitoring period as the unit). The value range of (k / X) is [0,1], which reflects the frequency of abnormality. In the weighted severity of anomaly [1+β*(Tk / (X*m))], Tk / (X*m) is the precise proportion of the anomaly time to the total assessment time, which is conceptually consistent with (k / X), but here it is used to make a weighted correction for the frequency ratio; The abnormal duration weighting coefficient β is an adjustable parameter. The larger β is, the higher the weight of the duration of the abnormal event in the evaluation. Operators can adjust β according to the operation and maintenance strategy. For example, if more attention is paid to short-term impacts, a small β can be set; if more attention is paid to long-term overload and other persistent hidden dangers, a large β can be set.

[0027] When the total duration of the anomaly Tk is larger, the value of [1+β*(Tk / (X*m))] is larger, which amplifies the frequency ratio (k / X) and makes the final C_i value higher. The larger the value of the operational anomaly index C_i, the more unstable the operating status of the power distribution equipment Ai is during the evaluation period; conversely, the smaller the value, the more stable the operating status. Repeat the above steps to calculate the operational anomaly index of each power distribution device in the power distribution equipment sequence A1, A2, ..., Aj during the evaluation period, and arrange them according to the power distribution equipment sequence, denoted as the operational anomaly index sequence C_1, C_2, ..., C_j.

[0028] It should be noted that when calculating the operation anomaly index, if any power distribution equipment Ai in the power distribution equipment sequence A1, A2, ..., Aj does not experience an abnormal operating state during the evaluation period, then the operation anomaly index C_i of power distribution equipment Ai is 0, and its operation anomaly index C_i is removed from the operation anomaly index sequence C_1, C_2, ..., C_j. In other words, if the abnormal operation index C_1 of the power distribution equipment A1 is 0, then the abnormal operation index sequence is: C_2,...,C_j. At this time, there are only j-1 values ​​in the abnormal operation index sequence C_2,...,C_j.

[0029] Example 4 This embodiment further discloses the specific steps involved in the situation simulation module based on embodiment 3, such as... Figure 3 As shown, it includes the following: This embodiment constructs a macro-level assessment and dynamic early warning system for power grid operation status. First, by weighted fusion of the operational anomaly indices of all distribution equipment, a power grid status anomaly index S representing the overall health level of the entire network is generated. Next, by continuously monitoring this index and analyzing its changing trends, a forward-looking judgment on whether the power grid operation status is improving or deteriorating is achieved. Finally, when a deterioration is determined, key abnormal equipment is automatically located and prioritized, providing maintenance personnel with a precise action list, as detailed below: Extract the sequence of power distribution equipment A1, A2, ..., Aj from the target power grid; The weight coefficients of each power distribution device are set by the operators according to the importance of each power distribution device in the power grid, and the power distribution devices are arranged in the order of A1, A2, ..., Aj as W1, W2, ..., Wj, where the weight coefficient of any power distribution device Ai is Wi, and W1 + W2 + ... + Wj = 1; Next, by adopting: S=[∑ j i=1 [(Ai×Wi)] / (∑ j i=1 Wi) Calculate the power grid status anomaly index S of the target power grid during the assessment period; It should be noted that in this embodiment, the example processing uses j running anomaly indices in the running anomaly index sequence C_1, C_2, ..., C_j as an example. If the total number of running anomaly indices in the running anomaly index sequence C_1, C_2, ..., C_j is less than j, the calculation index of the calculation formula will be adaptively adjusted.

[0030] The power grid status anomaly index S is a comprehensive quantitative indicator whose value directly represents the overall operational instability or health deterioration of the target power grid during the assessment period. If the power grid status anomaly index S is close to 0, it means that all power distribution equipment is operating smoothly, or only a few non-critical equipment have minor anomalies, and the power grid status is healthy. The larger the value of the power grid status anomaly index S, the more unstable power distribution equipment there are in the power grid or the more serious the instability of key power distribution equipment. The deeper the power grid deviates from a safe, stable, and optimal operating state, the more attention it needs to attract at a higher level.

[0031] Next, monitor continuously for o assessment cycles to determine the grid status anomaly index of o target power grids, and record them as S1, S2, ..., So in chronological order; A two-dimensional coordinate system is constructed with the timeline as the horizontal axis (the time span is o evaluation periods) and the value of the power grid status anomaly index as the vertical axis. S1, S2, ..., So are plotted in the two-dimensional coordinate system as data points in time sequence, resulting in o data points. The power grid status anomaly index change curve L is obtained by curve fitting. Calculate the instantaneous slope of any point in the power grid status anomaly index change curve L, and count the proportion of the part with a slope ≥ 0 R1 and the proportion of the part with a slope < 0 R2, where R1 + R2 = 1. The portion with a slope ≥ 0 means that at the corresponding time, the power grid status anomaly index is flat or rising. A rise indicates that the overall instability of the power grid is increasing and the status is deteriorating. The portion with a slope less than 0 means that at the corresponding time, the power grid status anomaly index is decreasing, indicating that the overall instability of the power grid is decreasing and the status is improving; If R1≥R2, then the trend of the power grid status anomaly index is determined to be a deteriorating trend, and the power grid status anomaly is output. Conversely, if the trend is positive, the output power grid status is considered normal. It should be noted that if the value of the power grid status anomaly index at any time is greater than the power grid status anomaly index threshold preset by the operator, then regardless of the slope value at the corresponding time, it will be directly regarded as belonging to the proportion R1.

[0032] When the power grid status is abnormal, all power distribution equipment with an abnormal operation index of non-zero within the corresponding assessment period is extracted, sorted in descending order of the abnormal operation index value, recorded as an abnormal power distribution equipment sequence, and transmitted to the operator to notify the operator to carry out maintenance.

[0033] All data in the formulas described above have been calculated with dimensions removed. Furthermore, any content not described in detail in this specification is existing technology known to those skilled in the art.

[0034] The above description is merely an example and illustration of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

[0035] It should be stated that all user data collected in this application was collected with the user's consent and authorization. Furthermore, the uses of user data are legal and compliant, and the use and processing of user data comply with the relevant laws, regulations, and standards of the relevant regions.

Claims

1. A method for monitoring power grid anomalies based on power distribution equipment, characterized in that, The method includes: Step 1: Based on the smart meters integrated with each power distribution device in the target power grid, the electrical parameters of each power distribution device and their time-series correlation are acquired in real time, and an electrical parameter sequence corresponding to the monitoring cycle is constructed. Step 2: Analyze the electrical parameter sequences of each power distribution device to determine the operating status of the power distribution device within the monitoring period; A secondary analysis of the power distribution equipment is conducted to determine the number of times the equipment experienced abnormal operating conditions within the assessment period and the duration of each occurrence, thereby assessing the operating abnormality index of the power distribution equipment. Step 3: Comprehensively evaluate the operational anomaly index of each power distribution device during the assessment period to generate the power grid status anomaly index; Continuously monitor the power grid status anomaly index, assess the changing trend of the power grid status anomaly index, determine the power grid status based on the changing trend, and output the abnormal power distribution equipment corresponding to the abnormal power grid status, and notify the operators to carry out maintenance.

2. The method according to claim 1, characterized in that, In step one, the specific method for constructing the electrical parameter sequence corresponding to the monitoring cycle is as follows: Identify the target power grid; Obtain the smart meters pre-configured by the operator and integrated with each power distribution device in the target power grid, count the total number, and denote it as j, where j equals the total number of power distribution devices; Arrange j power distribution devices randomly, and denote the power distribution device sequence A1, A2, ..., Aj; Get any power distribution device Ai, where i is the counting index, and its value ranges from 1 to j; Extract the monitoring period, count the total number of moments within the monitoring period, and denot it as m. The duration of the monitoring period is preset by the operator. Taking the current time as the start time of the monitoring cycle, the apparent power of the power distribution equipment Ai is continuously monitored for m time periods and arranged in time order, denoted as the apparent power sequence P1, P2, ..., Pm, where the apparent power sequence is the electrical parameter sequence; Similarly, determine the electrical parameter sequence of each power distribution device within any monitoring period.

3. The method according to claim 2, characterized in that, In step two, the specific method for analyzing the electrical parameter sequences of each power distribution device to determine the operating status of the power distribution device within the monitoring period is as follows: Based on the apparent power sequence P1, P2, ..., Pm, calculate the apparent power standard deviation σP_i and apparent power average μP_i of the power distribution equipment Ai during the monitoring period; Extract the rated apparent power P'_i of the power distribution equipment Ai; use: γ_i=(σP_i / μP_i)×(1+(|μP_i-P'_i| / P'_i)); Calculate the fluctuation coefficient γ_i of the power distribution equipment Ai during the monitoring period, where (σP_i / μP_i) evaluates the apparent power fluctuation and (1+(|μP_i-P'_i| / P'_i)) evaluates the apparent power level deviation. The larger the fluctuation coefficient γ_i, the more the operating state of the power distribution equipment Ai deviates from the normal operating state; conversely, the smaller the fluctuation coefficient γ_i, the closer the operating state of the power distribution equipment Ai is to the normal operating state. Compare the volatility coefficient γ_i with the volatility coefficient threshold γ_yu preset by the operator; If γ_i≥γ_yu, the power distribution equipment Ai is determined to be in an abnormal operating state during the monitoring period; If γ_i < γ_yu, it is determined that the power distribution equipment Ai is in normal operating condition during the monitoring period; Similarly, determine the operating status of each power distribution device in the power distribution equipment sequence A1, A2, ..., Aj within the monitoring period.

4. The method according to claim 3, characterized in that, In step two, the specific method for performing secondary analysis on the power distribution equipment is as follows: Get the Ai of any power distribution device; The Ai continuously monitors the power distribution equipment for one evaluation cycle, where one evaluation cycle consists of X monitoring cycles, where X is a preset integer by the operator and X≥3; During the assessment period, the fluctuation coefficient γ_i of the power distribution equipment Ai in each monitoring period is continuously determined, and its operating status is judged. The number of times k of abnormal operating states of power distribution equipment Ai occurs within the evaluation period is counted. One occurrence of abnormal operating state indicates the duration of continuous abnormal operating state of power distribution equipment Ai for one monitoring period. The abnormal duration of power distribution equipment Ai in abnormal operation during the evaluation period is calculated as Tk = k × m.

5. The method according to claim 4, characterized in that, In step two, the specific method for assessing the operational anomaly index of the power distribution equipment is as follows: For power distribution equipment Ai, the abnormal operation index C_i of power distribution equipment Ai during the evaluation period is calculated using C_i=(k / X)*[1+β*(Tk / (X*m))], where β is the weighting coefficient of abnormal duration preset by the operator; The larger the value of the operational anomaly index C_i, the more unstable the operating status of the power distribution equipment Ai is during the evaluation period; conversely, the smaller the value, the more stable the operating status. Similarly, calculate the operational anomaly index of each power distribution device in the power distribution equipment sequence A1, A2, ..., Aj within the evaluation period, and arrange them according to the power distribution equipment sequence, denoted as the operational anomaly index sequence C_1, C_2, ..., C_j.

6. The method according to claim 5, characterized in that, In step two, if any power distribution device Ai in the power distribution device sequence A1, A2, ..., Aj does not experience any abnormal operating state during the evaluation period, then the operating abnormality index C_i of power distribution device Ai is 0, and it is removed from the operating abnormality index sequence C_1, C_2, ..., C_j.

7. The method according to claim 6, characterized in that, In step three, the specific method for comprehensively evaluating the operational anomaly index of each power distribution device during the assessment period and generating the power grid status anomaly index is as follows: Extract the power distribution equipment sequence A1, A2, ..., Aj; Obtain the weighting coefficients W1, W2, ..., Wj set by the operator based on the importance of each power distribution device in the target power grid, where the weighting coefficient of Ai is Wi, and W1+W2+...+Wj=1; Using S=[∑ j i=1 [(Ai×Wi)] / (∑ j i=1 Wi) Calculate the power grid status anomaly index S of the target power grid during the assessment period; The larger the value of the power grid status anomaly index S, the more the status of the target power grid deviates from the normal state.

8. The method according to claim 7, characterized in that, In step three, the specific method for continuously monitoring the power grid status anomaly index and assessing its changing trend is as follows: The power grid status anomaly indices of o target power grids are continuously monitored and denoted as S1, S2, ..., So in time sequence; A two-dimensional coordinate system is constructed with the timeline as the horizontal axis and the value of the power grid anomaly index as the vertical axis. S1, S2, ..., So are plotted in the two-dimensional coordinate system as data points in time sequence, resulting in o data points. The power grid anomaly index change curve L is obtained by curve fitting. Calculate the slope of each point in the power grid status anomaly index change curve L, and count the proportion of the part with a slope ≥ 0 R1 and the proportion of the part with a slope < 0 R2, where R1 + R2 = 1; If R1≥R2, then the trend of the power grid status anomaly index is determined to be a deteriorating trend, and the power grid status anomaly is output. Conversely, if the trend is positive, the output power grid is considered to be in a normal state.

9. The method according to claim 8, characterized in that, In step three, when the power grid status is abnormal, all power distribution equipment with an abnormal operation index of not 0 within the corresponding evaluation period is extracted, and arranged in descending order of the abnormal operation index value, recorded as the abnormal power distribution equipment sequence, and transmitted to the operator to notify the operator to carry out maintenance.

10. A power grid status anomaly monitoring system based on power distribution equipment, characterized in that, The system includes: The situational awareness module, based on smart meters integrated with each power distribution device in the target power grid, acquires the electrical parameters of each power distribution device in real time and time-series correlation, and constructs an electrical parameter sequence corresponding to the monitoring cycle; The anomaly quantification module analyzes the electrical parameter sequences of each power distribution device to determine the operating status of the power distribution device within the monitoring period; A secondary analysis of the power distribution equipment is conducted to determine the number of times the equipment experienced abnormal operating conditions within the assessment period and the duration of each occurrence, thereby assessing the operating abnormality index of the power distribution equipment. The situation simulation module comprehensively evaluates the operational anomaly index of each power distribution device during the assessment period and generates the power grid situation anomaly index. Continuously monitor the power grid status anomaly index, assess the changing trend of the power grid status anomaly index, determine the power grid status based on the changing trend, and output the abnormal power distribution equipment corresponding to the abnormal power grid status, and notify the operators to carry out maintenance.

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