Digital intelligent management early warning system based on power grid construction

Abstract

The invention discloses a digital intelligent management early warning system based on power grid construction, and relates to the technical field of power grid early warning management. The intelligent management early warning center is in communication connection with a data acquisition module, a fault feature analysis module, a fault monitoring module, a fault evaluation module, an early warning grading module, an early warning generation and release module and an emergency response module. According to the method, the electrical parameters and the environmental parameters in the operation of the power grid are monitored in real time, abnormal conditions in the power grid are rapidly identified, fault features are extracted from a large number of historical fault cases and real-time data, the design parameters and the operation specifications of the power grid are combined, the fault evaluation standard is formulated, the coping capacity for the power grid faults is improved, and the fault diagnosis accuracy is improved. When a fault occurs, a response is quickly made, and through timely issuing of early warning information, it is ensured that related personnel can take actions quickly, so that the power failure time is shortened, and the reliability of a power grid and the satisfaction degree of users are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid early warning management, and in particular to a digital intelligent management early warning system based on power grid construction. Background Art

[0002] With the rapid development of the national economy, the demand for electricity is growing, and the electricity load is also soaring. At the same time, the electricity structure, electricity load, electricity environment and electricity consumption have all undergone major changes. This change has made the operation of the power grid more complicated and has put forward higher requirements on the safety and stability of the power grid. During the operation of the power grid, power grid failure is one of the important safety hazards faced by the power grid. Therefore, it is necessary to use digital management technology to monitor the operation status of the power grid and promptly discover and resolve potential safety hazards.

[0003] For example, China Patent Publication No.: CN113205190A is a smart grid energy storage safety early warning system, which is used to solve the problem of intelligent early warning analysis of energy storage power stations in smart grids, including data acquisition module, early warning analysis module, early warning processing module, registration and login module, operation and maintenance management module and early warning platform.

[0004] In the prior art, an early warning analysis module is used to perform early warning analysis on the energy storage data of the energy storage power station, and finally generates instructions to be inspected and early warning instructions, which solves the problem that the energy storage power station in the smart grid needs to be further strengthened in terms of massive data analysis and processing, safety risk analysis and control, and cannot achieve intelligent early warning analysis of the energy storage power station. However, there are many types of power grid faults, and the manifestations and development processes of different fault types are complex and diverse. There is a lack of clear power grid fault assessment standards, which makes the connection between the various links of the early warning process of power grid faults not smooth, thereby affecting the efficiency of early warning. For this reason, a digital intelligent management early warning system based on power grid construction is proposed. Summary of the invention

[0005] The purpose of the present invention is to provide a digital intelligent management and early warning system based on power grid construction to solve the problems raised in the above-mentioned background technology.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] A digital intelligent management and early warning system based on power grid construction includes an intelligent management and early warning center, wherein the intelligent management and early warning center is communicatively connected with a data acquisition module, a fault feature analysis module, a fault monitoring module, a fault assessment module, an early warning classification module, an early warning generation and release module, and an emergency response module, wherein electrical signals are connected between the modules;

[0008] The data acquisition module is used to collect various data in the operation of the power grid, including electrical parameters and environmental parameter data, wherein the electrical parameter data includes current, voltage, frequency, power factor, and the environmental parameter data includes ambient temperature, humidity, and wind speed, and transmit the collected data to the smart management and early warning center through the communication network to ensure the timeliness and integrity of the data, and perform pre-processing operations such as cleaning, verification, and formatting on the collected data to eliminate data errors and abnormal values;

[0009] The fault feature analysis module extracts fault features that characterize different types of power grid faults from the preprocessed data, and formulates fault feature evaluation criteria based on a large amount of historical power grid fault case data, power grid design parameters, and operating specifications;

[0010] The fault monitoring module monitors the operation status of the power grid in real time, and identifies and marks fault conditions in the operation of the power grid in combination with the established fault feature evaluation standards;

[0011] The fault assessment module analyzes the impact of the fault on the power grid based on the identified fault and in combination with the structure and operation characteristics of the power grid;

[0012] The warning classification module sets different warning levels based on the fault assessment results, performs classification processing on the faults, and implements fault priority sorting;

[0013] The warning generation and release module generates corresponding warning information according to the warning level result of the fault, combined with the real-time situation of the power grid operation and the factors affecting the safety of the power grid, and releases the warning information to relevant personnel to ensure that the operation status and risk situation of the power grid can be quickly understood. The warning information is released in a variety of ways such as SMS, email, and APP push to improve the accessibility and timeliness of the information;

[0014] The emergency response module implements corresponding emergency response measures according to the generated warning information, combined with the warning level and the fault type.

[0015] A further improvement of the technical solution of the present invention is that: in the data acquisition module, the process of collecting various types of data during power grid operation includes:

[0016] Deploy sensors and measuring equipment in the power grid to monitor and record the electrical parameters and environmental parameter data of the power grid operation process in real time. The sensors include current sensors, voltage sensors, frequency sensors, power factor sensors, and ambient temperature, humidity, and wind speed sensors. The electrical parameter data include current, voltage, frequency, and power factor, and the environmental parameter data include ambient temperature, humidity, and wind speed.

[0017] Preprocess the collected electrical parameter and environmental parameter data, including data cleaning, data verification and data formatting steps, perform preliminary screening on the received data, remove invalid data, duplicate data or obviously erroneous data, use data cleaning algorithms to fill in missing data, verify the cleaned data to ensure data accuracy and consistency, and convert the verified data into a unified format for subsequent data analysis and processing;

[0018] The data acquisition module packages the pre-processed data to ensure the integrity and accuracy of the data, and uses the wireless network to transmit the packaged data to the smart management and early warning center in real time. During the transmission process, encryption and verification mechanisms are used to ensure the security and integrity of the data.

[0019] After the data arrives at the smart management and early warning center, the data warehouse built in the smart management and early warning center is used to store the pre-processed electrical parameter and environmental parameter data.

[0020] A further improvement of the technical solution of the present invention is that: in the fault feature analysis module, the fault feature extraction process includes:

[0021] Screening out fault features related to power grid faults from the preprocessed electrical parameter and environmental parameter data, wherein the fault features of electrical parameters include abnormal current features, abnormal voltage features, abnormal frequency features, and abnormal power factor features, and the fault features of environmental parameters include abnormal ambient temperature features, abnormal humidity features, and abnormal wind speed features;

[0022] Collect and analyze a large amount of historical power grid fault case data, extract fault features, analyze the relationship between fault features and fault types, and comprehensively extract each fault feature data to obtain a fault feature data set;

[0023] In combination with grid design parameters and operating specifications, fault characteristic evaluation standards are developed, and thresholds for the normal range of each fault characteristic evaluation are set.

[0024] A further improvement of the technical solution of the present invention is that: in the fault monitoring module, the identification and marking process of the fault situation in the operation of the power grid includes:

[0025] Combined with the preprocessed electrical parameter and environmental parameter data, the fault characteristics of the electrical parameters and environmental parameters in the fault characteristic data set are analyzed;

[0026] Compare the collected electrical parameter data with the preset threshold of the normal range, calculate the difference between the actual value of each electrical parameter and the threshold of the normal range, assign a deviation measurement value to each electrical parameter according to the size of the difference, and summarize the deviation measurement values ​​of all electrical parameters to obtain the abnormal electrical evaluation index, which quantifies the degree of deviation of each feature in the electrical parameter;

[0027] Compare the collected environmental parameter data with the preset threshold of the normal range, calculate the difference between the actual value of each environmental parameter and the threshold of the normal range, assign a deviation measurement value to each environmental parameter according to the size of the difference, and summarize the deviation measurement values ​​of all environmental parameters to obtain the abnormal environment assessment index, and analyze the impact of the fault characteristics of the environmental parameters on the operation of the power grid;

[0028] Based on the calculated abnormal electrical assessment index and abnormal environmental assessment index, the parameters that deviate from the normal range are identified, and combined with the analysis results of the fault feature data set, the fault conditions in the power grid operation are identified and marked.

[0029] A further improvement of the technical solution of the present invention is that the calculation expression of the abnormal electrical evaluation index is:

[0030]

[0031] R i =T i -M i ;

[0032] Where D is the abnormal electrical evaluation index, i is the index of the electrical parameter, representing current, voltage, frequency, power factor, P i is the actual measured value of the ith electrical parameter, T i is the reference value of the normal range of the i-th electrical parameter, M i is the minimum value of the normal range of the ith electrical parameter, R i is the difference between the reference value and the minimum value of the normal range of the i-th electrical parameter, w i is the weight of the ith electrical parameter. The value range of D is between 0 and 1. The higher the value, the greater the degree to which the electrical parameter deviates from the normal state, and the higher the possibility of electrical faults in the power grid;

[0033] The calculation expression of the abnormal environment assessment index is:

[0034]

[0035] F = log 10 (1+|P j -T j |);

[0036] Among them, H is the abnormal environment assessment index, j is the index of environmental parameters, representing environmental temperature, humidity, wind speed, P j is the actual measured value of the jth environmental parameter, T j is the reference value of the normal range of the jth environmental parameter, M j is the minimum value of the normal range of the jth environmental parameter, X j is the maximum value of the normal range of the jth environmental parameter, F is the deviation metric adjustment function, and H ranges from 0 to 1. The higher the value, the greater the degree to which the environmental parameter deviates from the normal state.

[0037] A further improvement of the technical solution of the present invention is that: in the fault assessment module, the analysis process of the impact of the fault situation on the power grid includes:

[0038] Extract the identified fault conditions from the fault monitoring module, including the time, location, and preliminary fault type information of the fault, and extract the abnormal electrical evaluation index and abnormal environmental evaluation index, and extract the fault characteristics from the electrical parameter data and environmental parameter data;

[0039] Analyze the abnormal electrical evaluation index, clarify the degree to which the electrical parameters deviate from the normal range, evaluate the abnormal degree of current, voltage, frequency, and power factor, analyze the abnormal environmental evaluation index, clarify the degree of influence of environmental parameters on the operation of the power grid, and evaluate the potential impact of abnormal ambient temperature, humidity, and wind speed on power grid equipment;

[0040] Combined with the structural characteristics of the line layout and equipment configuration in the power grid, analyze the impact of fault characteristics on the line transmission capacity and equipment operating status. Combined with the operating characteristics of load distribution and power supply demand in the power grid, evaluate the impact of fault characteristics on the power supply capacity and load balance of the power grid. Comprehensively analyze the potential threat of fault characteristics to the stability of the power grid, and evaluate the risk of power grid fluctuations and power outages that are easily caused by faults.

[0041] The fault assessment coefficient is calculated by combining the abnormal electrical assessment index and the abnormal environmental assessment index. According to the size of the fault assessment coefficient, the impact and interference of the fault situation on the power grid are comprehensively analyzed.

[0042] A further improvement of the technical solution of the present invention is that the calculation expression of the fault assessment coefficient is:

[0043]

[0044] Among them, FC is the fault assessment coefficient, D is the abnormal electrical assessment index, which quantifies the degree of deviation of each feature in the electrical parameters, and H is the abnormal environmental assessment index, which quantifies the degree of deviation of each feature in the environmental parameters. The value range of FC is between 0 and 1. The higher the FC value, the greater the severity of the power grid fault and the more urgent the response measures to be taken.

[0045] A further improvement of the technical solution of the present invention is that: in the warning classification module, the warning level classification process includes:

[0046] Receive the fault assessment coefficient and related information from the fault assessment module, and set different warning levels according to the fault assessment results, namely, the first warning level, the second warning level, and the third warning level, wherein the severity of the warning level increases from the first level to the third level;

[0047] Based on the calculation results of the fault assessment coefficient, the corresponding fault warning threshold is matched for each warning level in combination with historical data, grid design parameters and operation specifications;

[0048] According to the size of the fault assessment coefficient, the impact and interference of the fault on the power grid are comprehensively analyzed, and the faults are prioritized to determine the order of processing, with the first-level warning being given priority, followed by the second-level and third-level warnings;

[0049] According to the warning level and fault assessment coefficient, formulate corresponding emergency response measures to quickly and effectively handle the fault and reduce the impact on the stability of the power grid. The emergency response measures include adjusting the power grid operation mode, isolating the fault area, mobilizing backup power supplies, and notifying relevant maintenance personnel.

[0050] A further improvement of the technical solution of the present invention is that: the multiple warning levels correspond to the multiple fault warning thresholds, wherein the fault warning thresholds include an upper threshold and a lower threshold;

[0051] The multiple warning levels and the multiple fault warning thresholds satisfy the following relationship:

[0052] Level 1 Warning 0 <FC≤FC O ;

[0053] Level 2 warning level FC O <FC≤FC T ;

[0054] Level 3 warning level FC T <FC<1;

[0055] Where FC is the fault assessment coefficient, FC O is the lower threshold corresponding to the second-level warning level and the upper threshold corresponding to the first-level warning level, FC T is the lower threshold corresponding to the third-level warning level and the upper threshold corresponding to the second-level warning level, FC O =0.5, FC T =0.8.

[0056] A further improvement of the technical solution of the present invention is that in the warning generation and release module, the generation and release process of the warning information includes:

[0057] Receive the fault warning level results from the warning classification module, including the first-level warning level, the second-level warning level and the third-level warning level, access the power grid monitoring system, obtain the power grid operation data in real time, and analyze the current status of the power grid, including load conditions, equipment operation status, and power supply demand;

[0058] Combine the fault warning level and the real-time operation status of the power grid to assess the impact of the fault on the power grid security and analyze the consequences of the fault-induced power grid fluctuations, power outage risks, and equipment damage;

[0059] Generate corresponding warning information based on the warning level and grid security impact assessment, including warning level, fault type, impact range, expected duration, and recommended measures;

[0060] Early warning information is released to relevant personnel, including power grid operation and maintenance personnel, management personnel, and dispatching personnel, through SMS, email, and APP push, to ensure that relevant personnel can receive early warning information in a timely manner, understand the operating status and risk situation of the power grid, and monitor the sending status of early warning information to ensure that the information is successfully sent and record the sending results.

[0061] Due to the adoption of the above technical solution, the present invention has the following technical advances compared with the prior art:

[0062] 1. The present invention provides a digital intelligent management and early warning system based on power grid construction. By real-time monitoring of electrical parameters and environmental parameters in power grid operation, abnormal conditions in the power grid can be quickly identified. Fault characteristics can be extracted from a large number of historical fault cases and real-time data. In combination with the design parameters and operation specifications of the power grid, fault assessment standards can be formulated to improve the ability to respond to power grid faults. When a fault occurs, a rapid response can be made. Through the timely release of early warning information, relevant personnel can be ensured to take prompt action, thereby reducing power outage time and improving the reliability of the power grid and user satisfaction.

[0063] 2. The present invention provides a digital intelligent management and early warning system based on power grid construction. By comprehensively analyzing the abnormal electrical evaluation index and the abnormal environmental evaluation index, the fault assessment coefficient is calculated to provide a quantitative assessment coefficient for the power grid fault. The system can accurately assess the severity of the fault and prioritize the faults according to the size of the fault assessment coefficient to ensure that the most serious faults can be dealt with first. This helps to formulate reasonable emergency response measures, quickly isolate the fault area, and prevent the fault from spreading, thereby protecting the power grid from wider damage and enhancing the overall security and stability of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0065] Figure 1 It is a module diagram of the present invention;

[0066] Figure 2 A flow chart for identifying and marking fault conditions during operation of a power grid according to the present invention;

[0067] Figure 3 The present invention is a flowchart for analyzing the impact of a fault condition on a power grid;

[0068] Figure 4 This is a flow chart of the classification of warning levels of the present invention. DETAILED DESCRIPTION

[0069] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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.

[0070] Embodiment 1, as Figure 1 , Figure 2 As shown, the present invention provides a digital intelligent management and early warning system based on power grid construction, including an intelligent management and early warning center, the intelligent management and early warning center is communicatively connected with a data acquisition module, a fault feature analysis module, a fault monitoring module, a fault assessment module, an early warning classification module, an early warning generation and release module, and an emergency response module, wherein the modules are connected by electrical signals;

[0071] The data acquisition module is used to collect various data in the operation of the power grid, including electrical parameters and environmental parameter data. The electrical parameter data includes current, voltage, frequency, and power factor, and the environmental parameter data includes ambient temperature, humidity, and wind speed. The collected data is transmitted to the intelligent management and early warning center through the communication network to ensure the timeliness and integrity of the data. The collected data is cleaned, verified, and formatted for preprocessing to eliminate data errors and abnormal values. Sensors and measuring equipment are deployed in the power grid to monitor and record the electrical parameters and environmental parameter data of the power grid operation process in real time. The sensors include current sensors, voltage sensors, frequency sensors, power factor sensors, and ambient temperature, humidity, and wind speed sensors. The electrical parameter data includes current, voltage, frequency, and power factor, and the environmental parameter data includes ambient temperature, humidity, and wind speed. The electrical parameter and environmental parameter data are preprocessed, including the steps of data cleaning, data verification and data formatting. The received data is preliminarily screened to remove invalid data, duplicate data or obviously erroneous data. The missing data is filled with data cleaning algorithms. The cleaned data is verified to ensure the accuracy and consistency of the data, and the verified data is converted into a unified format for subsequent data analysis and processing. The data acquisition module packages the preprocessed data to ensure the integrity and accuracy of the data, and uses the wireless network to transmit the packaged data to the smart management early warning center in real time. During the transmission process, encryption and verification mechanisms are used to ensure the security and integrity of the data. After the data arrives at the smart management early warning center, the data warehouse constructed in the smart management early warning center is used to store the preprocessed electrical parameter and environmental parameter data;

[0072] The fault feature analysis module extracts fault features that characterize different types of power grid faults from the preprocessed data, and formulates fault feature evaluation standards in combination with a large amount of historical power grid fault case data, power grid design parameters, and operating specifications, and screens out fault features related to power grid faults from the preprocessed electrical parameter and environmental parameter data, wherein the fault features of electrical parameters include abnormal current features, abnormal voltage features, abnormal frequency features, and abnormal power factor features, and the fault features of environmental parameters include abnormal ambient temperature features, abnormal humidity features, and abnormal wind speed features. A large amount of historical power grid fault case data is collected and analyzed to extract fault features, analyze the correlation between fault features and fault types, and comprehensively extract each fault feature data to obtain a fault feature data set. In combination with power grid design parameters and operating specifications, fault feature evaluation standards are formulated, and thresholds of the normal range of each fault feature evaluation are set;

[0073] The fault monitoring module monitors the operation status of the power grid in real time, identifies and marks fault conditions in the operation of the power grid in combination with the established fault feature evaluation standards, analyzes the fault characteristics of the electrical parameters and environmental parameters in the fault feature data set in combination with the pre-processed electrical parameter and environmental parameter data, compares the collected electrical parameter data with the preset threshold value of the normal range, calculates the difference between the actual value of each electrical parameter and the threshold value of the normal range, assigns a deviation measurement value to each electrical parameter according to the size of the difference, and summarizes the deviation measurement values ​​of all electrical parameters to obtain an abnormal electrical evaluation index, quantifies the degree of deviation of each feature in the electrical parameter, and thus determines whether there is an electrical fault in the power grid. The higher the abnormal electrical evaluation index, the more the electrical parameter deviates from the normal state. The greater the degree, the higher the possibility of electrical faults in the power grid. The collected environmental parameter data is compared with the preset threshold of the normal range, and the difference between the actual value of each environmental parameter and the threshold of the normal range is calculated. According to the size of the difference, a deviation measurement value is assigned to each environmental parameter, and the deviation measurement values ​​of all environmental parameters are summarized to obtain the abnormal environment assessment index. The impact of the fault characteristics of the environmental parameters on the operation of the power grid is analyzed. The higher the abnormal environment assessment index, the greater the negative impact of the environmental parameters on the operation of the power grid, which may cause higher fault risks. According to the calculated abnormal electrical assessment index and abnormal environment assessment index, the parameters that deviate from the normal range are identified, and combined with the analysis results of the fault feature data set, the fault conditions in the operation of the power grid are identified and marked;

[0074] Furthermore, the calculation expression of the abnormal electrical evaluation index is:

[0075]

[0076] R i =T i -M i ;

[0077] Where D is the abnormal electrical evaluation index, i is the index of the electrical parameter, representing current, voltage, frequency, power factor, P i is the actual measured value of the ith electrical parameter, T i is the reference value of the normal range of the i-th electrical parameter, M i is the minimum value of the normal range of the ith electrical parameter, R i is the difference between the reference value and the minimum value of the normal range of the i-th electrical parameter, w i is the weight of the i-th electrical parameter, indicating the relative importance of the parameter in the evaluation. The sum of all weights is 1. i -T i| represents the absolute difference between the actual value of the i-th electrical parameter and the reference value within the normal range. The larger the difference, the greater the degree to which the parameter deviates from the normal state. -(·) It is used to convert the deviation measurement value into a value between 0 and 1. This value decreases as the deviation measurement value increases, so that the output value of the entire formula increases as the degree of deviation increases. When the actual values ​​of all electrical parameters are within the normal range, D is close to 0, indicating that the power grid is operating normally. When the actual value of at least one electrical parameter deviates from the normal range, D increases, indicating that there is a risk of electrical fault in the power grid. The higher the value of D, the greater the degree of deviation of the electrical parameter from the normal state, and the higher the possibility of electrical fault in the power grid. The value range of D is between 0 and 1. The higher the value, the greater the degree of deviation of the electrical parameter from the normal state, and the higher the possibility of electrical fault in the power grid.

[0078] The calculation expression of abnormal environment assessment index is:

[0079]

[0080] F = log 10 (1+|P j -T j |);

[0081] Among them, H is the abnormal environment assessment index, j is the index of environmental parameters, representing environmental temperature, humidity, wind speed, P j is the actual measured value of the jth environmental parameter, T j is the reference value of the normal range of the jth environmental parameter, M j is the minimum value of the normal range of the jth environmental parameter, X j is the maximum value of the normal range of the jth environmental parameter, F is the deviation metric adjustment function, represents the weighted difference between the actual value of the jth environmental parameter and the reference value within the normal range, where M j reflects the impact of the minimum value on the deviation, log 10 (1+|P j -T j |) is used to adjust the sensitivity of the deviation metric. As the degree of deviation increases, the logarithmic function grows slowly, which helps to maintain a high sensitivity when the degree of deviation is small. When the actual values ​​of all environmental parameters are within the normal range, H is close to 0, indicating that the power grid is operating normally. When the actual value of at least one environmental parameter deviates from the normal range, H increases, indicating that the possibility of a fault risk in the power grid increases. The higher the value of H, the greater the degree of deviation of the environmental parameter from the normal state, and the higher the possibility of a fault risk in the power grid. The value range of H is between 0 and 1. The higher the value, the greater the degree of deviation of the environmental parameter from the normal state, and the higher the possibility of a fault risk in the power grid.

[0082] The fault assessment module analyzes the impact of the fault on the power grid based on the identified fault conditions and the structure and operation characteristics of the power grid;

[0083] The early warning classification module sets different early warning levels based on the fault assessment results, handles the faults in a graded manner, and prioritizes the faults.

[0084] The warning generation and release module generates corresponding warning information based on the warning level of the fault, combined with the real-time operation of the power grid and the factors affecting the safety of the power grid, and releases the warning information to relevant personnel to ensure that they can quickly understand the operation status and risk situation of the power grid. The warning information is released through SMS, email, and APP push to improve the accessibility and timeliness of the information;

[0085] The emergency response module implements corresponding emergency response measures based on the generated warning information, combined with the warning level and fault type.

[0086] Embodiment 2, as Figure 3 , Figure 4 As shown, based on Example 1, the present invention provides a technical solution: Preferably, in the fault assessment module, the analysis process of the impact of the fault situation on the power grid includes:

[0087] Extract the identified fault conditions from the fault monitoring module, including the time, location, and preliminary fault type information of the fault, and extract the abnormal electrical assessment index and abnormal environmental assessment index, extract fault characteristics from the electrical parameter data and environmental parameter data, analyze the abnormal electrical assessment index, clarify the degree of deviation of the electrical parameters from the normal range, evaluate the abnormal degree of current, voltage, frequency, and power factor, analyze the abnormal environmental assessment index, clarify the degree of influence of environmental parameters on the operation of the power grid, and evaluate the potential influence of abnormal ambient temperature, humidity, and wind speed on power grid equipment. Combined with the structural characteristics of the line layout and equipment configuration in the power grid, analyze the influence of fault characteristics on the line transmission capacity and equipment operating status, and combined with the operating characteristics of load distribution and power supply demand in the power grid, evaluate the influence of fault characteristics on the power supply capacity and load balance of the power grid, comprehensively analyze the potential threat of fault characteristics to the stability of the power grid, evaluate the risk of power grid fluctuation and power outage caused by the fault, combine the abnormal electrical assessment index and the abnormal environmental assessment index, calculate the fault assessment coefficient, and comprehensively analyze the influence and interference of the fault situation on the power grid according to the size of the fault assessment coefficient;

[0088] Furthermore, the calculation expression of the fault assessment coefficient is:

[0089]

[0090] Among them, FC is the fault assessment coefficient, D is the abnormal electrical assessment index, which quantifies the degree of deviation of each feature in the electrical parameters, and H is the abnormal environmental assessment index, which quantifies the degree of deviation of each feature in the environmental parameters. When both D and H are close to 0, it means that the electrical parameters and environmental parameters are within the normal range. FC is close to 0, which means that the power grid is operating normally. When either D or H increases, the FC value increases, indicating that the possibility of power grid failure increases. The value range of FC is between 0 and 1. The higher the FC value, the greater the severity of the power grid failure and the more urgent the response measures to be taken.

[0091] In the warning classification module, the warning level classification process includes:

[0092] Receive the fault assessment coefficient and related information from the fault assessment module, and set different warning levels according to the fault assessment results, namely, the first warning level, the second warning level, and the third warning level, wherein the warning levels increase in severity from the first level to the third level, match the corresponding fault warning threshold for each warning level based on the calculation result of the fault assessment coefficient, combine historical data, power grid design parameters and operation specifications, comprehensively analyze the impact and interference of the fault situation on the power grid according to the size of the fault assessment coefficient, prioritize the faults to determine the order of processing, give priority to the first warning, and then the second and third warnings, formulate corresponding emergency response measures according to the warning level and the fault assessment coefficient, so as to quickly and effectively handle the fault and reduce the impact on the stability of the power grid, and the emergency response measures include adjusting the operation mode of the power grid, isolating the fault area, mobilizing the backup power supply, and notifying the relevant maintenance personnel;

[0093] Further, the multiple warning levels correspond to multiple fault warning thresholds, wherein the fault warning threshold includes an upper threshold and a lower threshold;

[0094] Multiple warning levels and multiple fault warning thresholds satisfy the following relationship:

[0095] Level 1 Warning 0 <FC≤FC O The least serious means that the electrical and environmental parameters of the power grid have slightly deviated, and the stability of the power grid is slightly affected. A lower-level emergency response plan is initiated to monitor and warn the fault area, prepare maintenance teams and spare equipment to respond quickly when the fault escalates, issue warnings to relevant maintenance personnel, prepare for troubleshooting and repair, and continuously monitor the power grid status to ensure that the fault does not develop further.

[0096] Level 2 warning level FC O <FC≤FC T; It indicates that there are significant deviations between the electrical parameters and environmental parameters of the power grid, and the stability of the power grid is threatened. Activate a higher-level emergency response plan, isolate the fault area and conduct partial power outages, mobilize backup power sources and maintenance teams, conduct emergency repairs on the fault area, issue warnings to users in the affected areas, notify the possible power outage and power restoration times, monitor the power grid status, and prepare further emergency measures to prevent the fault from escalating;

[0097] The third-level warning level FC T <FC<1; The most serious, indicating that the electrical parameters and environmental parameters of the power grid seriously deviate from the normal range, and the stability of the power grid is extremely threatened. Immediately activate the highest-level emergency response plan, including cutting off the power supply in the fault area to prevent the spread of the fault, mobilize all available backup power sources and emergency teams, restore power supply to key areas as soon as possible, notify all relevant departments and personnel, including maintenance teams, safety supervision agencies and affected users, conduct a thorough inspection and repair of the fault area, and ensure that the power supply is restored only after the fault is completely eliminated;

[0098] Among them, FC is the fault assessment coefficient, FC O is the lower threshold corresponding to the second-level warning level and the upper threshold corresponding to the first-level warning level, FC T is the lower threshold corresponding to the third-level warning level and the upper threshold corresponding to the second-level warning level, FC O = 0.5, FC T = 0.8;

[0099] In the warning generation and release module, the process of generating and releasing warning information includes:

[0100] Receive the warning level results of the fault from the warning classification module, including the first-level warning level, the second-level warning level, and the third-level warning level. Connect to the power grid monitoring system, obtain the real-time operation data of the power grid, analyze the current state of the power grid, including the load situation, equipment operation status, and power supply demand. Combine the fault warning level and the real-time situation of the power grid operation, evaluate the impact degree of the fault on the power grid safety, analyze the consequences of the power grid fluctuations, power outage risks, and equipment damage caused by the fault. According to the warning level and the power grid safety impact assessment, generate corresponding warning information. The warning information includes the warning level, fault type, impact range, expected duration, and recommended measures. Send the warning information to relevant personnel, including power grid operation and maintenance personnel, management personnel, and dispatching personnel, through multiple methods such as text messages, emails, and APP push, to ensure that relevant personnel can receive the warning information immediately, understand the operation status and risk situation of the power grid, and monitor the sending status of the warning information to ensure that the information is successfully sent and record the sending results.

[0101] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A digital intelligent management and early warning system based on power grid construction, including an intelligent management and early warning center, characterized in that: The smart management warning center is connected to the data acquisition module, the fault feature analysis module, the fault monitoring module, the fault assessment module, the warning classification module, the warning generation and release module and the emergency response module, wherein the electrical signals between the modules are connected; The data acquisition module is used to collect various data during the operation of the power grid, including electrical parameters and environmental parameters; The fault feature analysis module extracts fault features representing different types of power grid faults from the preprocessed data; The fault monitoring module monitors the operation status of the power grid in real time, identifies and marks fault conditions in the operation of the power grid; The fault assessment module analyzes the impact of the fault on the power grid based on the identified fault; The warning classification module sets different warning levels based on the fault assessment results; The warning generation and release module generates corresponding warning information according to the warning level result of the fault, and releases the warning information to relevant personnel; The emergency response module implements corresponding emergency response measures according to the generated warning information, combined with the warning level and the fault type.

2. According to claim 1, a digital intelligent management early warning system based on power grid construction is characterized by: In the data acquisition module, the process of collecting various data during power grid operation includes: Deploy sensors and measuring equipment in the power grid to monitor and record the electrical parameters and environmental parameter data of the power grid operation process in real time. The sensors include current sensors, voltage sensors, frequency sensors, power factor sensors, and ambient temperature, humidity, and wind speed sensors. The electrical parameter data include current, voltage, frequency, and power factor, and the environmental parameter data include ambient temperature, humidity, and wind speed. Preprocess the collected electrical parameter and environmental parameter data, including data cleaning, data verification and data formatting steps, perform preliminary screening on the received data, remove invalid data, duplicate data or obviously erroneous data, verify the cleaned data, and convert the verified data into a unified format; The data acquisition module packages the pre-processed data and transmits the packaged data to the smart management and early warning center in real time using the wireless network; After the data arrives at the smart management and early warning center, the data warehouse built in the smart management and early warning center is used to store the pre-processed electrical parameter and environmental parameter data.

3. According to claim 2, a digital intelligent management early warning system based on power grid construction is characterized in that: In the fault feature analysis module, the fault feature extraction process includes: Screening out fault features related to power grid faults from the preprocessed electrical parameter and environmental parameter data, wherein the fault features of electrical parameters include abnormal current features, abnormal voltage features, abnormal frequency features, and abnormal power factor features, and the fault features of environmental parameters include abnormal ambient temperature features, abnormal humidity features, and abnormal wind speed features; Collect and analyze historical power grid fault case data, extract fault features, analyze the relationship between fault features and fault types, and comprehensively extract each fault feature data to obtain a fault feature data set; In combination with grid design parameters and operating specifications, fault characteristic evaluation standards are developed, and thresholds for the normal range of each fault characteristic evaluation are set.

4. According to claim 3, a digital intelligent management and early warning system based on power grid construction is characterized in that: In the fault monitoring module, the process of identifying and marking fault conditions during power grid operation includes: Combined with the preprocessed electrical parameter and environmental parameter data, the fault characteristics of the electrical parameters and environmental parameters in the fault characteristic data set are analyzed; Compare the collected electrical parameter data with the preset threshold of the normal range, calculate the difference between the actual value of each electrical parameter and the threshold of the normal range, assign a deviation measurement value to each electrical parameter according to the size of the difference, and summarize the deviation measurement values ​​of all electrical parameters to obtain the abnormal electrical evaluation index, which quantifies the degree of deviation of each feature in the electrical parameter; Compare the collected environmental parameter data with the preset threshold of the normal range, calculate the difference between the actual value of each environmental parameter and the threshold of the normal range, assign a deviation measurement value to each environmental parameter according to the size of the difference, and summarize the deviation measurement values ​​of all environmental parameters to obtain the abnormal environment assessment index, and analyze the impact of the fault characteristics of the environmental parameters on the operation of the power grid; Based on the calculated abnormal electrical assessment index and abnormal environmental assessment index, the parameters that deviate from the normal range are identified, and combined with the analysis results of the fault feature data set, the fault conditions in the power grid operation are identified and marked.

5. According to claim 4, a digital intelligent management early warning system based on power grid construction is characterized in that: The calculation expression of the abnormal electrical evaluation index is: R i =T i -M i ; Where D is the abnormal electrical evaluation index, i is the index of the electrical parameter, representing current, voltage, frequency, power factor, P i is the actual measured value of the ith electrical parameter, T i is the reference value of the normal range of the i-th electrical parameter, M i is the minimum value of the normal range of the ith electrical parameter, R i is the difference between the reference value and the minimum value of the normal range of the i-th electrical parameter, w i is the weight of the ith electrical parameter, and the value range of D is between 0 and 1. The higher the value, the greater the degree to which the electrical parameter deviates from the normal state; The calculation expression of the abnormal environment assessment index is: F=log 10 (1+|P j -T j |); Among them, H is the abnormal environment assessment index, j is the index of environmental parameters, representing environmental temperature, humidity, wind speed, P j is the actual measured value of the jth environmental parameter, T j is the reference value of the normal range of the jth environmental parameter, M j is the minimum value of the normal range of the jth environmental parameter, X j is the maximum value of the normal range of the jth environmental parameter, F is the deviation metric adjustment function, and H ranges from 0 to 1. The higher the value, the greater the degree to which the environmental parameter deviates from the normal state.

6. According to claim 5, a digital intelligent management early warning system based on power grid construction is characterized in that: In the fault assessment module, the analysis process of the impact of the fault situation on the power grid includes: Extract the identified fault conditions from the fault monitoring module, including the time, location, and preliminary fault type information of the fault, and extract the abnormal electrical evaluation index and abnormal environmental evaluation index, and extract the fault characteristics from the electrical parameter data and environmental parameter data; Analyze the abnormal electrical evaluation index, clarify the degree to which the electrical parameters deviate from the normal range, evaluate the abnormal degree of current, voltage, frequency, and power factor, analyze the abnormal environmental evaluation index, clarify the degree of influence of environmental parameters on the operation of the power grid, and evaluate the potential impact of abnormal ambient temperature, humidity, and wind speed on power grid equipment; Combined with the structural characteristics of the line layout and equipment configuration in the power grid, analyze the impact of fault characteristics on the line transmission capacity and equipment operating status. Combined with the operating characteristics of load distribution and power supply demand in the power grid, evaluate the impact of fault characteristics on the power supply capacity and load balance of the power grid. Comprehensively analyze the potential threat of fault characteristics to the stability of the power grid, and evaluate the risk of power grid fluctuations and power outages that are easily caused by faults. The fault assessment coefficient is calculated by combining the abnormal electrical assessment index and the abnormal environmental assessment index. According to the size of the fault assessment coefficient, the impact and interference of the fault situation on the power grid are comprehensively analyzed.

7. A digital intelligent management and early warning system based on power grid construction according to claim 6, characterized in that: The calculation expression of the fault assessment coefficient is: Among them, FC is the fault assessment coefficient, D is the abnormal electrical assessment index, H is the abnormal environmental assessment index, and the value range of FC is between 0 and 1. The higher the FC value, the greater the severity of the power grid fault.

8. A digital intelligent management and early warning system based on power grid construction according to claim 7, characterized in that: In the warning classification module, the warning level classification process includes: Receive the fault assessment coefficient and related information from the fault assessment module, and set different warning levels according to the fault assessment results, namely, the first warning level, the second warning level, and the third warning level, wherein the severity of the warning level increases from the first level to the third level; Based on the calculation results of the fault assessment coefficient, the corresponding fault warning threshold is matched for each warning level in combination with historical data, grid design parameters and operation specifications; According to the size of the fault assessment coefficient, the impact and interference of the fault situation on the power grid are comprehensively analyzed, and the faults are prioritized; According to the warning level and fault assessment coefficient, formulate corresponding emergency response measures, which include adjusting the grid operation mode, isolating the fault area, mobilizing backup power supply, and notifying relevant maintenance personnel.

9. A digital intelligent management and early warning system based on power grid construction according to claim 8, characterized in that: The plurality of warning levels correspond to the plurality of fault warning thresholds, wherein the fault warning thresholds include an upper threshold and a lower threshold; The multiple warning levels and the multiple fault warning thresholds satisfy the following relationship: Level 1 warning 0 <FC≤FC O ; Level 2 warning level FC O <FC≤FC T ; Level 3 warning level FC T <FC<1; Where FC is the fault assessment coefficient, FC O is the lower threshold corresponding to the second-level warning level and the upper threshold corresponding to the first-level warning level, FC T is the lower threshold corresponding to the third-level warning level and the upper threshold corresponding to the second-level warning level, FC O =0.5, FC T =0.

8.

10. A digital intelligent management and early warning system based on power grid construction according to claim 9, characterized in that: In the warning generation and release module, the generation and release process of warning information includes: Receive the fault warning level results from the warning classification module, including the first-level warning level, the second-level warning level and the third-level warning level, access the power grid monitoring system, obtain the power grid operation data in real time, and analyze the current status of the power grid, including load conditions, equipment operation status, and power supply demand; Combine the fault warning level and the real-time operation status of the power grid to assess the impact of the fault on the power grid security and analyze the consequences of the fault-induced power grid fluctuations, power outage risks, and equipment damage; Generate corresponding warning information based on the warning level and grid security impact assessment, including warning level, fault type, impact range, expected duration, and recommended measures; The warning information is released to relevant personnel, including power grid operation and maintenance personnel, management personnel, and dispatching personnel, through SMS, email, and APP push, and the sending status of the warning information is monitored and the sending results are recorded.

Images