Power dispatching automation system troubleshooting system

Abstract

The invention relates to the technical field of troubleshooting, and discloses a power dispatching automation system troubleshooting method, which comprises the following steps: monitoring the operation of power equipment in real time and collecting operation data; identifying a state variable and a decision variable in the power system based on the running data, and integrating the state variable and the decision variable into a structured state vector; based on the state vector, carrying out topology modeling on the power network by adopting a graph theory method; analyzing the state change of the system by using the topology model, formulating a state transition rule, and constructing a state transition model; and transferring the model based on the state. By adopting the technical scheme of real-time data acquisition and monitoring, the effects of continuous tracking and early warning of the state of the power equipment are achieved, and compared with a common regular inspection method in the prior art, the scheme can capture potential faults of the equipment in time, the probability of fault occurrence is greatly reduced, and the overall reliability of the system is improved.

Description

Technical Field

[0001] This invention relates to the field of troubleshooting technology, specifically to a troubleshooting system for power dispatch automation systems. Background Technology

[0002] The power dispatch automation system is a key system integrating computer technology, communication technology, automatic control technology, and other advanced technologies for power system operation monitoring and dispatch management. It can extensively collect various real-time data from the power system, such as voltage, current, and power, and based on this data, conduct real-time monitoring and precise control of the power grid system. The power dispatch automation system is a crucial pillar for ensuring the safe, stable, and economical operation of the power grid, playing an indispensable role in the entire power production process. However, like any complex system, the power dispatch automation system may experience various faults during operation. Once a fault occurs, it may lead to abnormal operation of the power system, affecting the reliability and stability of power supply, and even causing large-scale power outages, severely impacting social production and people's lives.

[0003] Currently, the most common method for monitoring the condition of power equipment is periodic inspection. However, due to the relatively long inspection cycle, potential faults may occur between inspections but are difficult to detect in time, leading to the gradual development and deterioration of the faults and affecting the normal operation of the equipment. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method for troubleshooting power dispatch automation systems, which solves the problem that potential faults that may be discovered during regular inspections are difficult to detect in a timely manner, leading to the gradual development and deterioration of faults and affecting the normal operation of equipment.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a fault troubleshooting method for a power dispatch automation system, comprising the following steps:

[0006] Real-time monitoring of power equipment operation and collection of operational data;

[0007] Based on the data from the operation, state variables and decision variables in the power system are identified, and the state variables and decision variables are integrated into a structured state vector.

[0008] Based on the state vector, graph theory methods are used to perform topology modeling of the power network;

[0009] Using the aforementioned topology model, we analyze system state changes, formulate state transition rules, and construct a state transition model.

[0010] Based on the state transition model, the overall objective function is defined by integrating the state variables and decision variables of each device.

[0011] Based on the state variables, decision variables, topology model, and objective function, a fault recovery scheme is determined and its effectiveness is evaluated.

[0012] Preferably, the collected operational data includes the operating status, load, historical fault records, and maintenance logs of each device at different time periods.

[0013] Preferably, when identifying the state variables in the power system, the health status, load capacity, and fault prediction indicators of the equipment are determined by analyzing historical operating modes and fault records.

[0014] Preferably, the structured state vector consists of identifiers of state variables, device type, current operating mode, and load demand information.

[0015] Preferably, the topology modeling uses an adjacency matrix in graph theory to represent power equipment and the connection relationships between power equipment.

[0016] Preferably, when formulating state transition rules, a state transition probability model is constructed by combining the changes in equipment state under different fault modes.

[0017] Preferably, the fault recovery scheme includes resource allocation, load adjustment, and equipment scheduling, with specific operation steps including equipment restart, load redistribution, and system status monitoring.

[0018] Preferably, while implementing the fault recovery scheme, the system status changes are monitored in real time, including indicators such as load, voltage, and frequency, and the recovery strategy is automatically adjusted according to a pre-set threshold.

[0019] Preferably, during the evaluation, the system operation data and recovery efficiency after each fault recovery are analyzed, and a feedback report is generated.

[0020] Preferably, a fault troubleshooting system for a power dispatch automation system includes the following modules:

[0021] The data acquisition module is used to monitor the operating status of power equipment in real time and collect operating data such as current value, voltage value, frequency and equipment temperature;

[0022] The state recognition module is used to identify state variables and decision variables in the power system based on the collected operating data, and integrate them into a structured state vector.

[0023] The topology modeling module is used to perform topology modeling of the power network based on the state vector using graph theory methods.

[0024] The state analysis module is used to analyze system state changes using the topology model, formulate state transition rules, and construct a state transition model.

[0025] The objective function definition module is used to define the overall objective function based on the state transition model and by integrating the state variables and decision variables of each device.

[0026] The recovery decision module is used to determine fault recovery schemes and evaluate their effectiveness based on the defined state variables, decision variables, topology model, and objective function.

[0027] This invention provides a method for troubleshooting faults in a power dispatch automation system. It has the following beneficial effects:

[0028] 1. By adopting a real-time data acquisition and monitoring technology solution, this invention achieves the effect of continuous tracking and early warning of the status of power equipment. Compared with the common periodic inspection methods in the prior art, this solution can capture potential equipment faults in a timely manner, greatly reduce the probability of fault occurrence, and improve the overall reliability of the system.

[0029] 2. This invention integrates state variables and decision variables by utilizing a state recognition module to determine the health status of equipment. Compared with the existing method of analyzing data in a decentralized manner, this solution can effectively improve the accuracy of fault prediction, ensure timely and effective countermeasures, and reduce the risk of equipment failure from the source.

[0030] 3. This invention uses graph theory to perform topology modeling, which achieves the goal of intuitively presenting the power network structure. Compared with traditional model description methods, the new topology provides an intuitive view of the mutual influence between devices, making resource allocation more reasonable and optimizing the formulation of fault recovery strategies.

[0031] 4. By combining state analysis and effect evaluation in the recovery decision module, this invention provides a scientific basis for fault recovery. Compared with experience-based decision-making in the prior art, this data-driven method can provide targeted recovery solutions, improve the efficiency and accuracy of the recovery process, and ensure that the system can quickly return to normal operation. Attached Figure Description

[0032] Figure 1 This is a flowchart of a fault troubleshooting method for a power dispatch automation system according to the present invention;

[0033] Figure 2 This is a system architecture diagram of a fault troubleshooting system for a power dispatch automation system according to the present invention. Detailed Implementation

[0034] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0035] Please see the appendix Figure 1 This invention provides a fault troubleshooting method for a power dispatch automation system, comprising the following steps:

[0036] Real-time monitoring of power equipment operation and collection of operational data;

[0037] Based on operational data, identify state variables and decision variables in the power system, and integrate the state variables and decision variables into a structured state vector;

[0038] Based on state vectors, graph theory methods are used to perform topology modeling of power networks;

[0039] Using a topological model, analyze system state changes, formulate state transition rules, and construct a state transition model;

[0040] Based on the state transition model, the overall objective function is defined by integrating the state variables and decision variables of each device.

[0041] Based on state variables, decision variables, topology model, and objective function, a fault recovery scheme is determined and its effectiveness is evaluated.

[0042] Specifically, firstly, real-time monitoring and collection of key parameters of power equipment are performed, including real-time current values ​​reflecting the load status of the power equipment. Voltage level of power equipment operation The frequency of the power system Temperature of electrical equipment operation Next, data analysis tools are used to process the collected data. By analyzing historical operating patterns, state variables and decision variables in the power system are identified. The state variables mainly include the current operating state of the power equipment. For example, normal or faulty conditions, load status. The health status of equipment is assessed by factors such as temperature and historical fault records. Decision variables include resource allocation Load adjustment Equipment scheduling These variables are integrated into a structured state vector, represented as:

[0043] ;

[0044] Subsequently, graph theory methods are used to perform topological modeling of the power network, constructing an adjacency matrix A to represent the connection relationships between devices. This matrix is ​​used for efficient analysis of the network structure, helping to identify key devices and their connection states. Based on this, the topological model is used to analyze the state changes of the power system, and state transition rules reflecting device state changes under different fault modes are formulated. These rules are used to establish a state transition probability model to predict the current state. Next state The transition probability is expressed as:

[0045] ;

[0046] in, Representing the previously defined structured state vector, reflecting the current operating status of the device, after obtaining the state transition model, and integrating the state variables and decision variables of each device, the overall objective function is defined as follows:

[0047] ;

[0048] in, This indicates the system's operating efficiency and reflects the equipment's performance. This indicates the fault response time, aiming to minimize the recovery time after equipment failure. The operating cost is represented to ensure the economic effectiveness of the solution. By optimizing this objective function, the efficiency and feasibility of the solution are ensured. Next, after clarifying the state variables, decision variables, topology model, and objective function, a detailed fault recovery plan is formulated. This includes reasonable resource allocation (R), load adjustment (D), and equipment scheduling (S) strategies. During implementation, the system's state changes will be monitored in real time to ensure the effectiveness of adjustment measures. Finally, the effect of fault recovery is evaluated by monitoring the system operation data and recovery efficiency after each recovery and generating feedback reports. These reports will analyze recovery time, system stability, and fault occurrence frequency to guide subsequent fault management and improvement measures through this series of steps.

[0049] The collected data includes the operating status, load, historical fault records, and maintenance logs of each device at different time periods.

[0050] Specifically, by collecting operational data, including the operating status, load, historical fault records, and maintenance logs of each device at different time periods, comprehensive and detailed data analysis of this data enables the monitoring and management of the entire power system to promptly identify potential faults, optimize equipment maintenance plans, improve system reliability and security, ensure that every link is properly monitored, and help achieve the efficient operation of the power dispatch automation system.

[0051] When identifying state variables in a power system, the health status, load capacity, and fault prediction indicators of equipment are determined by analyzing historical operating modes and fault records.

[0052] Specifically, firstly, a comprehensive database is established to summarize and organize historical operating data for each device, including parameters such as current (I), voltage (U), frequency (f), and temperature (T). Once these parameters are available, the operating characteristics of the device over different time periods can be plotted. During use, this data is updated periodically, and each historical record provides a fundamental basis for fault analysis. Data mining techniques are used to analyze the device's health status (H), which is derived by comparing real-time and historical data. For example, if the current temperature (T) is within a safe range, but historical records show multiple failures under similar conditions, this indicates a potential problem with the device. By comparing data, a logical judgment is formed. To better understand the device's load capacity (L), a formula is used.

[0053] ;

[0054] High-frequency monitoring enables real-time acquisition of load capacity. The Fault Prediction Index (FPI) is based on historical fault records (HFR) and reflects the potential failure risk of the equipment. It is calculated using the following formula:

[0055] ;

[0056] Through data modeling, we can predict the likelihood of future equipment failures and take corresponding measures in advance. Finally, all these state variables are integrated into a state vector, represented as...

[0057] ;

[0058] This integration enables the system to comprehensively reflect the health status of equipment, while also aiding in fault prediction and decision-making. This improves the accuracy of fault diagnosis, optimizes operational and maintenance decisions, effectively shortens equipment failure response time, and reduces maintenance costs.

[0059] The structured state vector consists of the identifier of the state variable, the device type, the current operating mode, and the load demand information.

[0060] Specifically, this state vector consists of multiple elements, including identifiers of state variables, equipment type, current operating mode, and load demand information. First, this structured state vector is constructed to comprehensively reflect various important indicators of the equipment. This vector will contain identifiers of state variables, equipment type, current operating mode, and load demand. This integration of information provides an important data foundation for fault monitoring and decision-making. The state variable identifier is used to uniquely identify the state of each piece of equipment. This identification ensures that the state of each piece of equipment in the system can be accurately tracked and recorded. The equipment type indicates the category of the equipment; for example, generators, transformers, and other types of equipment have their own classifications. Different equipment types will have different operating parameters, so they are listed separately in the vector to facilitate subsequent analysis. The current operating mode refers to the operating state of the equipment at the current moment. Possible modes include normal, warning, and fault. Understanding the current mode of the equipment allows for real-time monitoring of equipment operation and the identification of potential risks. In addition, load demand represents the power required by the equipment. Using this structured state vector, the monitoring system can quickly respond to different operating states. When the equipment is in a warning or fault state, the relevant team can intervene in a timely manner to prevent more serious faults from occurring.

[0061] Topology modeling uses an adjacency matrix from graph theory to represent power equipment and the connections between them.

[0062] Specifically, first, a graph model of the power system is constructed. Each power device is considered a node in the graph, and the connections between devices are considered edges. These connections can be unidirectional, such as from a generator to a substation, or bidirectional, such as the connection between a substation and a user. When designing the adjacency matrix, a square matrix is ​​constructed based on the number of devices. In this matrix, the values ​​of the matrix elements represent the connection status of the devices. The adjacency matrix is ​​represented as follows:

[0063] ;

[0064] If there is a connection between device i and device j, then the corresponding matrix elements The value is 1 if there is no connection, and 0 if there is no connection. This allows the topology of the power network to be represented intuitively in the matrix. With the help of the adjacency matrix in graph theory, the connection relationship of power equipment can be clearly depicted, which improves the visualization of the power system and enhances the efficiency of information transmission.

[0065] When formulating state transition rules, a state transition probability model is constructed by combining the changes in equipment state under different fault modes.

[0066] Specifically, based on the operating status, load conditions, fault type, and time of fault occurrence of power equipment, the changes in equipment state under different fault modes are analyzed. For example, when the equipment is in a normal state, it may transition to a warning state or a fault state. In a fault state, the equipment may transition to a maintenance state. For state changes, a state transition diagram is constructed to describe the transition relationships between equipment. The state transition probability model can be expressed as follows:

[0067] ;

[0068] in Indicates starting from the current state Transition to the next state The probability of a device transitioning between different states can be represented by establishing a state transition probability matrix. For example, suppose the state transition of a certain power device can be represented by the following transition matrix. express

[0069] ;

[0070] in, Indicates from state Transition to state The probability is calculated based on historical fault records and actual equipment operating data. It can reflect the real state changes of the equipment under different fault modes. The system can monitor the state changes of the equipment in real time and make corresponding decisions. When the equipment state changes, this model can be used to quickly assess the fault risk and formulate maintenance and scheduling plans in advance.

[0071] The fault recovery plan includes resource allocation, load adjustment, and equipment scheduling. Specific operational steps include equipment restart, load redistribution, and system status monitoring.

[0072] Specifically, upon receiving a fault alarm, the system assesses the current resource status to determine how to allocate resources. After resource allocation is complete, the next step is to adjust the load. The load allocation formula can be expressed as follows:

[0073] ;

[0074] in This is the new load value. It is the original load. This involves redistributing load increments to ensure the power system doesn't become overloaded due to equipment failures, thus preventing new faults. Subsequent power equipment scheduling includes deciding which equipment needs to be restarted and which needs to be temporarily shut down. When equipment is restarted, the system first performs a status check to ensure it's in good condition before putting it back into use. Based on the status monitoring results, the system determines which equipment to restart, using the following status check formula to evaluate the equipment.

[0075] ;

[0076] R represents the decision to restart the device, H is the health status of the device, and U is the real-time load status of the device. Restarting can only be performed when the health status and load status of the device are within safe limits. Based on the current network operating characteristics and newly allocated resources, the system will update the load allocation in real time. By monitoring the operating status, load status, and fault records of power equipment in real time, the system ensures that it remains under control during the recovery process. Through a series of steps such as resource allocation, load adjustment, and equipment scheduling, the normal operation of the power system can be effectively restored. With the help of real-time monitoring and dynamic adjustment, the ability of the power system to cope with sudden failures is improved, providing strong support for ensuring power supply and system security.

[0077] While implementing the fault recovery plan, the system status changes are monitored in real time, including load, voltage and frequency indicators, and the recovery strategy is automatically adjusted according to the pre-set thresholds.

[0078] Specifically, the system needs to establish a real-time monitoring mechanism, covering load (L), voltage (U), and frequency (f). These indicators provide a comprehensive view of the equipment status, helping administrators make timely decisions. Real-time monitoring data can be expressed using the following formula.

[0079] ;

[0080] in, Represents the moment This involves a collection of monitoring data, which needs to be continuously updated to enable the system to analyze data in real time. Each monitoring indicator has a corresponding safety threshold, such as the maximum load threshold. Minimum voltage threshold Maximum frequency threshold When a certain indicator exceeds its set threshold, the system will activate an automatic adjustment mechanism to implement an adjustment strategy. The adjustment strategy can be expressed through the following rules:

[0081]

[0082] in, This indicates an adjustment to the strategy. It is based on the monitoring data set and preset thresholds. The calculated adjustment function, if the monitored load Exceed The system will automatically reduce the load or adjust the operating status of the power supply equipment. Once the monitoring data triggers an alert, the system will quickly adjust the recovery strategy according to the severity of the assessment.

[0083] (1) Strengthen power distribution and give priority to ensuring the power needs of important users;

[0084] (2) Reconfigure the load and transfer part of the load to the standby equipment;

[0085] (3) If the voltage drops to If this happens, an alarm must be issued immediately and measures must be taken quickly to increase the voltage.

[0086] Through timely monitoring and adjustments, the power system can not only react quickly when a fault occurs, but also prioritize the safety of critical loads, thereby improving the stability and reliability of the power system.

[0087] During the evaluation, the system operation data and recovery efficiency after each fault recovery are analyzed, and a feedback report is generated.

[0088] Specifically, after each fault recovery, the system needs to collect relevant operational data, including recovery time, load before and after recovery, voltage, and frequency. The recovery efficiency can be calculated using the following formula.

[0089] ;

[0090] in, This is the scheduled recovery time after the fault occurs. This refers to the actual time taken for recovery. In addition to recovery efficiency, the system also needs to monitor and record various key operational data, including the load, voltage, and frequency after recovery. All collected data will be integrated into a feedback report, which will detail the following information:

[0091] (1) The causes of each failure and the recovery measures;

[0092] (2) Operational data after each fault recovery, including the changing trends of parameters such as load, voltage, and frequency;

[0093] (3) The results of the recovery efficiency assessment and its comparison with historical data;

[0094] At the same time, the system will compare the efficiency of different fault recovery schemes and find the best practices. For example, if a certain recovery scheme performs well in multiple faults, the system will recommend that the scheme be given priority in the future. This data-based decision-making mechanism can continuously promote the optimization of the system and ensure the stable operation of the power system.

[0095] Please see the appendix Figure 2 A fault diagnosis system for a power dispatch automation system, comprising the following modules:

[0096] The data acquisition module is used to monitor the operating status of power equipment in real time and collect operating data such as current value, voltage value, frequency and equipment temperature;

[0097] The state recognition module is used to identify state variables and decision variables in the power system based on the collected operational data, and integrate them into a structured state vector.

[0098] The topology modeling module is used to perform topology modeling of power networks based on state vectors and using graph theory methods.

[0099] The state analysis module is used to analyze system state changes using a topology model, formulate state transition rules, and construct a state transition model.

[0100] The objective function definition module is used to define the overall objective function based on the state transition model, integrating the state variables and decision variables of each device.

[0101] The recovery decision module is used to determine fault recovery schemes and evaluate their effectiveness based on the defined state variables, decision variables, topology model, and objective function.

[0102] Specifically, the data acquisition module is responsible for monitoring the operating status of power equipment in real time, collecting operating data such as current, voltage, frequency, and equipment temperature. Through real-time data acquisition, it can continuously track the equipment status and promptly detect potential anomalies, which provides basic data for subsequent data analysis and decision-making, effectively reducing the risk of failure.

[0103] Based on the collected operational data, the status recognition module identifies the status variables and decision variables in the power system and integrates them into a structured status vector. Through status recognition, the system can accurately understand the health status, load conditions and fault prediction indicators of the equipment, improve the efficiency and accuracy of data processing, and make the decision-making basis more scientific and comprehensive.

[0104] The topology modeling module uses state vectors and graph theory methods to perform topology modeling of power networks, providing a clear view of power equipment and their connections, enabling effective analysis of the network structure, identification of key equipment and their interactions, and thus optimization of resource allocation and fault recovery strategies.

[0105] The state analysis module utilizes a topology model to analyze changes in system state, formulate state transition rules, and construct a state transition model. This module can clearly define the changes in equipment state under different fault modes, providing theoretical support for formulating reasonable recovery strategies and ensuring the accuracy and timeliness of fault diagnosis.

[0106] The objective function definition module integrates the state variables and decision variables of each device and defines the overall objective function based on the state transition model. This sets specific optimization objectives for the operation of the system, ensuring efficient use of resources, reducing operating costs, and improving fault response efficiency. Through the optimization of the objective function, the system can more flexibly cope with various fault situations in complex environments.

[0107] The recovery decision module, based on the defined state variables, decision variables, topology model, and objective function, is responsible for deciding on fault recovery schemes and evaluating their effectiveness. It provides targeted recovery schemes to ensure that the system can quickly and effectively return to normal operation. At the same time, through effectiveness evaluation, the system can continuously learn and adapt, improving its future fault response capabilities and overall operating efficiency.

[0108] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A fault diagnosis method for a power dispatch automation system, characterized in that, Includes the following steps: Real-time monitoring of power equipment operation and collection of operational data; Based on the data from the operation, state variables and decision variables in the power system are identified, and the state variables and decision variables are integrated into a structured state vector. Based on the state vector, graph theory methods are used to perform topology modeling of the power network; Using the aforementioned topology model, we analyze system state changes, formulate state transition rules, and construct a state transition model. Based on the state transition model, the overall objective function is defined by integrating the state variables and decision variables of each device. Based on the state variables, decision variables, topology model, and objective function, a fault recovery scheme is determined and its effectiveness is evaluated.

2. The fault troubleshooting method for a power dispatch automation system according to claim 1, characterized in that, The collected data includes the operating status, load, historical fault records, and maintenance logs of each device at different time periods.

3. The fault troubleshooting method for a power dispatch automation system according to claim 1, characterized in that, When identifying the state variables in a power system, the health status, load capacity, and fault prediction indicators of the equipment are determined by analyzing historical operating modes and fault records.

4. The fault troubleshooting method for a power dispatch automation system according to claim 1, characterized in that, The structured state vector consists of the identifier of the state variable, the device type, the current operating mode, and the load demand information.

5. A fault-solving method for a power dispatch automation system according to claim 1, characterized in that, The topology modeling uses an adjacency matrix from graph theory to represent power equipment and the connections between them.

6. A fault-solving method for a power dispatch automation system according to claim 1, characterized in that, When formulating state transition rules, a state transition probability model is constructed by combining the changes in equipment state under different fault modes.

7. A fault-solving method for a power dispatch automation system according to claim 1, characterized in that, The fault recovery plan includes resource allocation, load adjustment, and equipment scheduling. Specific operational steps include equipment restart, load redistribution, and system status monitoring.

8. A fault-solving method for a power dispatch automation system according to claim 1, characterized in that, While implementing the fault recovery scheme, the system status changes are monitored in real time, including load, voltage and frequency indicators, and the recovery strategy is automatically adjusted according to the preset thresholds.

9. A fault-solving method for a power dispatch automation system according to claim 1, characterized in that, During the evaluation, the system operation data and recovery efficiency after each fault recovery are analyzed, and a feedback report is generated.

10. A fault diagnosis system for a power dispatch automation system, used in the fault diagnosis method for a power dispatch automation system as described in claims 1-9, characterized in that, The system includes the following modules: The data acquisition module is used to monitor the operating status of power equipment in real time and collect operating data such as current value, voltage value, frequency and equipment temperature; The state recognition module is used to identify state variables and decision variables in the power system based on the collected operating data, and integrate them into a structured state vector. The topology modeling module is used to perform topology modeling of the power network based on the state vector using graph theory methods. The state analysis module is used to analyze system state changes using the topology model, formulate state transition rules, and construct a state transition model. The objective function definition module is used to define the overall objective function based on the state transition model and by integrating the state variables and decision variables of each device. The recovery decision module is used to determine fault recovery schemes and evaluate their effectiveness based on the defined state variables, decision variables, topology model, and objective function.

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