Power analysis and maintenance method and system based on PSASP software

Abstract

The invention discloses an electric power analysis and maintenance method and system based on PSASP software, and relates to the technical field of electric power analysis. According to the electric power analysis and maintenance method and system based on the PSASP software, fault current data feature analysis, stable current accurate extraction and segmentation processing and real-time monitoring of the operation state of bus current are carried out; the bus short-circuit fault can be predicted in advance and effectively prevented; the method effectively makes up for the hysteresis quality and intelligence deficiency of a traditional PSASP system in the aspect of fault pre-judgment, improves the accuracy and response speed of fault recognition by constructing a multi-level fault data analysis mechanism, and provides timely early warning guarantee for the operation safety of a power system; in addition, a stable interval is determined in combination with historical data, the system can sensitively capture the abnormal change trend of the bus current, and it is ensured that rapid early warning and efficient prevention and control are achieved before a fault occurs. According to the overall scheme, the risk caused by cascading failures is remarkably reduced, and the stability of a power grid and the continuity of power supply are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power analysis, and in particular to a power analysis and maintenance method and system based on PSASP software. Background Art

[0002] In modern power systems, the stability and safety of the power grid are directly related to the continuous supply of electricity and the normal power consumption of users. However, due to the complexity of the power system and its operating characteristics under high voltage and high current conditions, a short circuit fault in the system (such as a busbar short circuit) will have a serious impact on the entire power grid and may trigger a chain reaction, resulting in a large-scale power outage. Therefore, the fault prediction and prevention of the power system has become a key link in the safety assurance of the power system.

[0003] At present, power analysis and simulation software, such as PSASP (Power System Analysis Software Package), is widely used in the modeling and analysis of power systems. PSASP has a variety of power system analysis functions, and can perform power flow calculation, short-circuit calculation, transient stability analysis, etc. However, the existing analysis methods based on PSASP have certain limitations in the early judgment of faults such as busbar short circuit, which are mainly manifested in the following aspects:

[0004] 1. Lack of effective advance judgment mechanism: In the existing PSASP analysis, potential fault hazards are usually discovered through regular maintenance inspections. However, this method often has a lag and cannot achieve real-time advance judgment of bus short circuits. Since bus short circuit failures may cause chain failures of other equipment in a very short time, this lagging judgment method is difficult to meet actual needs.

[0005] 2. Lack of intelligent analysis methods: As the complexity of the power system increases, traditional analysis methods often have difficulty coping with the rapid changes in system status. The existing PSASP software lacks intelligent analysis modules based on artificial intelligence, machine learning and other technologies, and cannot conduct in-depth learning and trend prediction of the operating status of the power system bus, so as to effectively predict and control risks before a fault occurs. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a power analysis and maintenance method and system based on PSASP software, which solves the problems in the background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: A power analysis and maintenance method based on PSASP software, comprising:

[0008] Step 1: Collect or upload several historical records of the busbar fault current data. The PSASP software analyzes the fluctuation trend characteristics based on the historical records of the busbar fault current data and determines the upper and lower range values ​​of the corresponding historical records of the fault current data.

[0009] Step 2: According to the upper and lower interval values ​​of the fault current data in the corresponding historical records, the fault current data in the historical records are analyzed in sections, and the best section is determined from the separated data sections, and the stable current data corresponding to the fault current data in the historical records is determined from the best section;

[0010] Step 3: Summarize the steady current data of the fault current data in several historical records, sort them in descending order, determine the largest steady current data and the smallest steady current data, and use the two as the steady current interval;

[0011] Step 4: Use PSASP software to detect the current of the current bus in real time to determine whether the bus current in the working state exceeds the stable current range, and evaluate the working state of the bus based on the judgment result.

[0012] As a further solution of the present invention: in the step 1, the specific method of performing fluctuation trend characteristic analysis on the fault current data in the historical records of the busbar and determining the upper and lower interval values ​​of the fault current data in the historical records is:

[0013] S1: Collect or upload fault current data in the historical records of n buses, where n is a preset value;

[0014] The fault current data in the historical records are represented by the fluctuation of the current data in the first T1 time period when the busbar fails, where T1 is a preset value;

[0015] S2: sequentially select the fault current data in the historical records of each bus as the target fault current data;

[0016] S3: for the target fault current data, generate a fault current data change curve in the T1 time period;

[0017] S4: Determine the turning points in the fault current data change curve one by one, wherein the turning point is represented by the opposite change trend of the related points before and after the turning point;

[0018] All turning points identified in the fault current data curve are marked as A. i , where i represents different turning points, and its maximum value A is determined from all turning points i max and minimum value A i min;

[0019] S5: Get A i max and A i min, through the formula D = (A i max-A i min) / gs determines the upper and lower interval values ​​D in the target fault current data, where gs is a preset value.

[0020] As a further solution of the present invention: after step S5, the method further includes:

[0021] S6: Repeat steps S3-S5: determine the upper and lower interval values ​​of the fault current data in all historical records, and mark them as Dj respectively; where j represents the upper and lower interval values ​​of the fault current data in the historical records, and 1≤j≤n.

[0022] As a further solution of the present invention: in the step 2, according to the upper and lower interval values ​​of the fault current data in the corresponding different historical records, the fault current data in the historical records are correspondingly analyzed in sections, and the best section is determined from the separated data sections, and the specific method of determining the stable current data from the best section is:

[0023] BS1: Select the fault current data in the historical records of each busbar in turn as the analysis fault current data, sort the analysis fault current data according to the time sequence of the corresponding fault current generation, and determine the data sequence;

[0024] BS2: Then, the correlation value of the difference between adjacent data in the data sequence is judged, and the data is segmented according to the correlation value judgment result:

[0025] BS3: According to the determined data segment, obtain the time difference Tm between the initial data and the corresponding time of the end data of the corresponding data segment k , where k represents different data segments;

[0026] Then determine the sum of the differences of the fault current data in the corresponding data segment and mark it as ZS k , and through the formula Qm k =(Tm k ×C1) / (ZS k ×C2) Determine the reference value Qm k , where C1 and C2 are both preset fixed coefficient factors;

[0027] BS3: Next, determine the reference value Qm for different data segments k , and select the largest reference value Qm kThe corresponding data segment is taken as the optimal segment, and all the fault current data in the optimal segment are determined, and the average value of all the fault current data in the optimal segment is calculated to obtain the stable current data.

[0028] As a further solution of the present invention: the specific method of segmenting data according to the correlation value judgment result is:

[0029] BS21: Obtain the first fault current data and the second fault current data of the data sequence, and calculate the difference between the two, where the difference = |previous fault current data - next fault current data|;

[0030] If the difference is less than or equal to the upper and lower intervals Dj, the first two fault current data are calibrated as relevant data;

[0031] BS21: then calculating the difference between the second fault current data and the third fault current data to determine whether they are related data, and so on, until the subsequent fault current data determined is not related data, and dividing the several fault current data before this fault current data into the first data segment;

[0032] Starting from this fault current data, the relevant values ​​are confirmed, processed step by step, and the subsequent fault current data are confirmed one by one.

[0033] As a further solution of the present invention: the specific method of determining the maximum steady current data and the minimum steady current data and taking the two as the steady current interval is:

[0034] The maximum stable current data is marked as QP max , mark the maximum stable current data as QP min , the stable current interval is expressed as [QP max ,QP min ].

[0035] As a further solution of the present invention: the specific method of judging whether the bus current in the working state exceeds the stable current interval and evaluating the working state of the bus according to the judgment result is as follows;

[0036] If the bus current in the working state exceeds the stable current range, it means that there is a probability of a short circuit in the bus, and a warning prompt is issued;

[0037] If the bus current in the working state does not exceed the stable current range, it means that the current bus current data is normal and no measures are taken.

[0038] A power analysis and maintenance system based on PSASP software, comprising:

[0039] The interval value determination module is used to collect or upload the fault current data in several historical records of the busbar. The PSASP software performs fluctuation trend characteristic analysis based on the fault current data in the historical records of the busbar and determines the upper and lower interval values ​​of the fault current data in the corresponding historical records;

[0040] A steady current module is used to perform segmented analysis on the fault current data in the historical records according to the upper and lower interval values ​​of the fault current data in different historical records, determine the best segment from the separated data segments, and determine the steady current data corresponding to the fault current data in the historical records from the best segment;

[0041] A steady current interval determination module is used to summarize the steady current data of the fault current data in a number of historical records, sort them in order from large to small, and determine the largest steady current data and the smallest steady current data, and use the two as the steady current interval;

[0042] The real-time current evaluation module is used to detect the current of the current bus in real time through the PSASP software, determine whether the bus current in the working state exceeds the stable current range, and evaluate the working state of the bus based on the judgment result.

[0043] The present invention provides a power analysis and maintenance method and system based on PSASP software. Compared with the prior art, it has the following beneficial effects:

[0044] The present invention realizes the early prediction and effective prevention of busbar short-circuit faults through characteristic analysis of fault current data, accurate extraction and segmented processing of steady current, and real-time monitoring of the operating status of busbar current. This method effectively makes up for the lag and lack of intelligence in the traditional PSASP system in fault prediction. By building a multi-level fault data analysis mechanism, it improves the accuracy and response speed of fault identification, and provides timely early warning guarantee for the safe operation of the power system.

[0045] In addition, by combining historical data to determine the stable interval, the system can sensitively capture the abnormal change trend of the bus current, ensuring rapid early warning and efficient prevention and control before the failure. This overall solution not only significantly reduces the risk caused by cascading failures, but also improves the stability of the power grid and the continuity of power supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The present invention will be further described below in conjunction with the accompanying drawings.

[0047] Figure 1 A flowchart of the steps of a power analysis and maintenance method based on PSASP software of the present invention;

[0048] Figure 2The present invention is a structural framework diagram of a power analysis and maintenance system based on PSASP software. DETAILED DESCRIPTION

[0049] 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.

[0050] Embodiment 1

[0051] See also Figure 1 ,The present invention provides a power analysis and maintenance method based on PSASP software, comprising:

[0052] Step 1: Collect or upload several historical records of the busbar fault current data. The PSASP software analyzes the fluctuation trend characteristics based on the historical records of the busbar fault current data and determines the upper and lower range values ​​of the corresponding historical records of the fault current data.

[0053] It should be noted that the fault current data in the historical records is further explained. When a busbar short circuit occurs, the current data for a period of time before the short circuit occurs is obtained for analysis to observe the real-time change of the current and determine the abnormality of the current. The current data for a period of time before the short circuit occurs is called the fault current data in the historical records.

[0054] The specific method of performing fluctuation trend characteristic analysis based on the fault current data in the historical records of the busbar and determining the upper and lower range values ​​of the fault current data in the historical records is:

[0055] S1: Collect or upload fault current data in the historical records of n buses, where n is a preset value, which is selected by professional staff. In this example, n is 30;

[0056] The fault current data in the historical records are represented by the fluctuation of the current data in the first T1 time period when the busbar fails, wherein T1 is a preset value, and the T1 time period is specifically set by professional staff;

[0057] S2: sequentially select the fault current data in the historical records of each bus as the target fault current data;

[0058] S3: for the target fault current data, generate a fault current data change curve in the T1 time period;

[0059] Specifically, the current of the busbar has different current values ​​at different times, that is, the relevant coordinate points can be determined in the two-dimensional coordinate system, and then the relevant coordinate points can be interconnected to determine the fault current data change curve of the T1 time period. The horizontal coordinate of the curve is time, and the vertical coordinate is the specific fault current data;

[0060] S4: Determine the turning points in the fault current data change curve one by one, and the turning point is represented by the opposite change trend of the related points before and after the point; for example: there are 3 fault current data points, namely b1, b2 and b3, with specific values ​​of 110, 113 and 112, the change trend from b1 to b2 is rising, and the change trend from b2 to b3 is falling, then b2 is the turning point;

[0061] All turning points identified in the fault current data curve are marked as A. i , where i represents different turning points, and its maximum value A is determined from all turning points i max and A i min;

[0062] S5: Get A i max and A i min, through the formula D = (A i max-A i min) / gs determines the upper and lower interval values ​​D of the fault conditions in the target fault current data, where gs is a preset value, which is specifically set by professional staff. In this implementation, gs is 2;

[0063] S6: repeating steps S3-S5: determining the upper and lower interval values ​​of the fault current data in all historical records, and marking them as Dj respectively; wherein j represents the upper and lower interval values ​​of the fault current data in the historical records, and 1≤j≤n;

[0064] Step 2: According to the upper and lower interval values ​​of the fault current data in the corresponding historical records, the fault current data in the historical records are analyzed in sections, and the best section is determined from the separated data sections, and the stable current data corresponding to the fault current data in the historical records is determined from the best section;

[0065] It should be noted that the steady current data is the most stable data among the values ​​of the corresponding fault current data. The current value will fluctuate before the bus fault occurs. Determining the steady current data can obtain the most stable current value for a period of time before the bus fault occurs.

[0066] The specific method of performing segmented analysis on the fault current data in the historical records according to the upper and lower interval values ​​of the fault current data in the corresponding historical records, determining the best segment from the separated data segments, and determining the stable current data from the best segment is as follows:

[0067] BS1: Select the fault current data in the historical records of each busbar as the analysis fault current data in sequence, sort the analysis fault current data according to the time sequence of the corresponding fault current generation, and determine the data sequence;

[0068] BS2: Then, the correlation value of the difference between adjacent data in the data sequence is judged, and the data is segmented according to the correlation value judgment result:

[0069] The specific method of segmenting data according to the correlation value judgment result is:

[0070] BS21: Obtain the first fault current data and the second fault current data of the data sequence, and calculate the difference between the two, where the difference = |previous fault current data - next fault current data|;

[0071] If the difference is less than or equal to the upper and lower intervals Dj, the first two fault current data are calibrated as relevant data;

[0072] BS21: then calculating the difference between the second fault current data and the third fault current data to determine whether they are related data, and so on, until the subsequent fault current data determined is not related data, and dividing the several fault current data before this fault current data into the first data segment;

[0073] Starting from this fault current data, the relevant values ​​are confirmed, and the processing is carried out step by step, and the subsequent fault current data are confirmed one by one in sections;

[0074] BS3: According to the determined data segment, obtain the time difference Tm between the initial data and the corresponding time of the end data of the corresponding data segment k , where k represents different data segments;

[0075] Then determine the sum of the differences of the fault current data in the corresponding data segment and mark it as ZS k , and through the formula Qm k =(Tm k ×C1) / (ZS k ×C2) Determine the reference value Qm k , where C1 and C2 are preset fixed coefficient factors, and the specific parameter values ​​are determined by professional staff. k The smaller the value, the greater the time difference, and the greater the baseline value generated;

[0076] BS3: Next, determine the reference value Qm for different data segments k , and select the largest reference value Qm k The corresponding data segment is taken as the best segment, and all the fault current data in the best segment are determined, and the average value of all the fault current data in the best segment is calculated to obtain the stable current data;

[0077] The goal of segmented analysis is to find the stable current state before the bus fault and provide a benchmark reference for the current data. By selecting the data segment with the smallest fluctuation as the stable current, this method can remove the interference of abnormal fluctuations and make the results more reliable. The determination of the best segment not only helps to more accurately reflect the normal operating state of the bus, but also makes the subsequent monitoring and judgment basis more stable, improves the ability to identify the safe state before the bus fault occurs, and helps to provide early warning.

[0078] Step 3: Summarize the steady current data of the fault current data in several historical records, sort them in descending order, determine the largest steady current data and the smallest steady current data, and use the two as the steady current interval;

[0079] The specific method of determining the maximum steady current data and the minimum steady current data and taking the two as the steady current interval is:

[0080] The maximum stable current data is marked as QP max , mark the maximum stable current data as QP min , the stable current interval is expressed as [QP max ,QP min ];

[0081] Step 4: Use PSASP software to detect the current of the current bus in real time to determine whether the bus current in the working state exceeds the stable current range, and evaluate the working state of the bus based on the result of the determination;

[0082] The specific method of judging whether the bus current in the working state exceeds the stable current range and evaluating the working state of the bus according to the judgment result is as follows;

[0083] If the bus current in the working state exceeds the stable current range, it means that there is a probability of a short circuit in the bus, and a warning prompt is issued;

[0084] If the bus current in the working state does not exceed the stable current range, it means that the current bus current data is normal and no measures are taken.

[0085] Embodiment 2

[0086] See also Figure 2 In the specific implementation process of this embodiment, based on the first embodiment, this embodiment provides a power analysis and maintenance system based on PSASP software, including:

[0087] The interval value determination module is used to collect or upload the fault current data in several historical records of the busbar. The PSASP software performs fluctuation trend characteristic analysis based on the fault current data in the historical records of the busbar and determines the upper and lower interval values ​​of the fault current data in the corresponding historical records;

[0088] The steady current module is used to perform segmented analysis on the fault current data in the historical records according to the upper and lower interval values ​​of the fault current data in the corresponding historical records, determine the best segment from the separated data segments, and determine the steady current data corresponding to the fault current data in the historical records from the best segment;

[0089] A steady current interval determination module is used to summarize the steady current data of the fault current data in a number of historical records, sort them in order from large to small, and determine the largest steady current data and the smallest steady current data, and use the two as the steady current interval;

[0090] The real-time current evaluation module is used to detect the current of the current bus in real time through the PSASP software, determine whether the bus current in the working state exceeds the stable current range, and evaluate the working state of the bus based on the judgment result.

[0091] Embodiment 3

[0092] The specific implementation process of this embodiment includes the entire implementation process of the above two groups of embodiments.

[0093] 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.

[0094] 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 power analysis and maintenance method based on PSASP software, characterized in that: include: Step 1: Collect or upload several historical records of the busbar fault current data. The PSASP software analyzes the fluctuation trend characteristics based on the historical records of the busbar fault current data and determines the upper and lower range values ​​of the corresponding historical records of the fault current data. Step 2: According to the upper and lower interval values ​​of the fault current data in the corresponding historical records, the fault current data in the historical records are segmented and analyzed accordingly, and the best segment is determined from the separated data segments, and the stable current data corresponding to the fault current data in the historical records is determined from the best segment; Step 3: Summarize the steady current data of the fault current data in several historical records, sort them in descending order, determine the largest steady current data and the smallest steady current data, and use the two as the steady current interval; Step 4: Use PSASP software to detect the current of the current bus in real time to determine whether the bus current in the working state exceeds the stable current range, and evaluate the working state of the bus based on the judgment result.

2. The power analysis and maintenance method based on PSASP software according to claim 1, characterized in that: In the step 1, the specific method of performing fluctuation trend characteristic analysis on the fault current data in the historical records of the busbar and determining the upper and lower interval values ​​of the fault current data in the historical records is: S1: Collect or upload fault current data in the historical records of n buses, where n is a preset value; The fault current data in the historical records are represented by the fluctuation of the current data in the first T1 time period when the busbar fails, where T1 is a preset value; S2: sequentially select the fault current data in the historical records of each bus as the target fault current data; S3: for the target fault current data, generate a fault current data change curve in the T1 time period; S4: Determine the turning points in the fault current data change curve one by one, wherein the turning point is represented by the opposite change trend of the related points before and after the turning point; All turning points identified in the fault current data curve are marked as A. i , where i represents different turning points, and its maximum value A is determined from all turning points i max and minimum value A i min; S5: Get A i max and A i min, through the formula D = (A i max-A i min) / gs determines the upper and lower interval values ​​D in the target fault current data, where gs is a preset value.

3. The power analysis and maintenance method based on PSASP software according to claim 2 is characterized in that: After step S5, the method further includes: S6: Repeat steps S3-S5: determine the upper and lower interval values ​​of the fault current data in all historical records, and mark them as Dj respectively; where j represents the upper and lower interval values ​​of the fault current data in the historical records, and 1≤j≤n.

4. The power analysis and maintenance method based on PSASP software according to claim 3 is characterized in that: In the step 2, according to the upper and lower interval values ​​of the fault current data in the corresponding historical records, the fault current data in the historical records are analyzed in sections, the best section is determined from the separated data sections, and the specific method of determining the stable current data from the best section is: BS1: Select the fault current data in the historical records of each busbar in turn as the analysis fault current data, sort the analysis fault current data according to the time sequence of the corresponding fault current generation, and determine the data sequence; BS2: Then, the correlation value of the difference between adjacent data in the data sequence is judged, and the data is segmented according to the correlation value judgment result: BS3: According to the determined data segment, obtain the time difference Tm between the initial data and the corresponding time of the end data of the corresponding data segment k , where k represents different data segments; Then determine the sum of the differences of the fault current data in the corresponding data segment and mark it as ZS k , and through the formula Qm k =(Tm k ×C1) / (ZS k ×C2) Determine the reference value Qm k , where C1 and C2 are both preset fixed coefficient factors; BS3: Next, determine the reference value Qm for different data segments k , and select the largest reference value Qm k The corresponding data segment is taken as the optimal segment, and all the fault current data in the optimal segment are determined, and the average value of all the fault current data in the optimal segment is calculated to obtain the stable current data.

5. The power analysis and maintenance method based on PSASP software according to claim 4 is characterized in that: The specific method of segmenting data according to the correlation value judgment result is: BS21: Obtain the first fault current data and the second fault current data of the data sequence, and calculate the difference between the two, where the difference = |previous fault current data - next fault current data|; If the difference is less than or equal to the upper and lower intervals Dj, the first two fault current data are calibrated as relevant data; BS21: then calculating the difference between the second fault current data and the third fault current data to determine whether they are related data, and so on, until the subsequent fault current data determined is not related data, and dividing the several fault current data before this fault current data into the first data segment; Starting from this fault current data, the relevant values ​​are confirmed, processed step by step, and the subsequent fault current data are confirmed one by one.

6. The power analysis and maintenance method based on PSASP software according to claim 1 is characterized in that: The specific method of determining the maximum steady current data and the minimum steady current data and taking the two as the steady current interval is: The maximum stable current data is marked as QP max , mark the maximum stable current data as QP min , the stable current interval is expressed as [QP max ,QP min ].

7. The power analysis and maintenance method based on PSASP software according to claim 1, characterized in that: The specific method of judging whether the bus current in the working state exceeds the stable current range and evaluating the working state of the bus according to the judgment result is as follows; If the bus current in the working state exceeds the stable current range, it means that there is a probability of a short circuit in the bus, and a warning prompt is issued; If the bus current in the working state does not exceed the stable current range, it means that the current bus current data is normal and no measures are taken.

8. A power analysis and maintenance system based on PSASP software, characterized in that: include: The interval value determination module is used to collect or upload the fault current data in several historical records of the busbar. The PSASP software performs fluctuation trend characteristic analysis based on the fault current data in the historical records of the busbar and determines the upper and lower interval values ​​of the fault current data in the corresponding historical records; A steady current module is used to perform segmented analysis on the fault current data in the historical records according to the upper and lower interval values ​​of the fault current data in different historical records, determine the best segment from the separated data segments, and determine the steady current data corresponding to the fault current data in the historical records from the best segment; A steady current interval determination module is used to summarize the steady current data of the fault current data in a number of historical records, sort them in order from large to small, and determine the largest steady current data and the smallest steady current data, and use the two as the steady current interval; The real-time current evaluation module is used to detect the current of the current bus in real time through the PSASP software, determine whether the bus current in the working state exceeds the stable current range, and evaluate the working state of the bus based on the judgment result.

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