N-1 heavy-load power transmission line risk monitoring method and device based on random model, electronic equipment and storage medium

Abstract

The invention discloses an N-1 large-load power transmission line risk monitoring method and device based on a random model, electronic equipment and a storage medium, and belongs to the technical field of power system power transmission line risk monitoring. The method comprises the steps that network topology information, historical fault data, bus load data and line operation environment data of a power transmission line are acquired, a plurality of nodes and line equipment are arranged in the power transmission line, and the network topology information comprises electrical parameter data of the nodes, operation state data of the line equipment and current environment data; modeling according to the network topology information, the historical fault data, the bus load data and the line operation environment data, introducing line fault randomness, and constructing a risk monitoring model; the risk monitoring model is solved with the purpose of minimizing a balance function, the risk assessment result of the power transmission line and the optimal configuration of the risk monitoring devices are obtained, and the balance function is constructed based on the number of the risk monitoring devices and the line risk coefficient of the power transmission line. The line risk coefficient is determined based on the line connection success probability, the confidence interval and the risk function of the power transmission line, and the line connection success probability is used for simulating the N-1 fault probability of the power transmission line. According to the method, the randomness of the line fault is considered, the configuration cost of the risk monitoring device is reduced, the influence of the N-1 fault on the observability of the power transmission line is effectively detected, and the requirement of actual operation of the power transmission line on risk monitoring can be met.

Description

Technical Field

[0001] This application belongs to the technical field of risk monitoring for transmission lines in power systems, and in particular relates to a risk monitoring method, device, electronic device, and storage medium for N-1 large-load transmission lines based on a stochastic model. Background Art

[0002] In a power system, the reliable operation of transmission lines is crucial for the stability of power supply. During the process of large-load transmission lines carrying a large amount of electrical energy, once a fault occurs, it may trigger large-scale power outages, causing serious economic losses and social impacts. Traditional transmission line monitoring methods often focus on real-time monitoring of the electrical parameters and operating status of the lines, but insufficient consideration is given to the reliability of line connections and risk assessment under N-1 faults (i.e., single-component faults). With the continuous expansion of the scale and increasing complexity of power systems, there is an urgent need for a technology that can comprehensively and accurately monitor the risks of large-load transmission lines to improve the safety and reliability of power systems.

[0003] The application of large-load transmission line risk monitoring devices provides a new means for the monitoring of transmission lines. However, due to their high cost, how to optimize the layout of large-load transmission line risk monitoring devices to achieve effective monitoring of transmission lines has become a key issue. Current research still has deficiencies in considering the randomness of line faults and the impact of N-1 faults on the observability of transmission lines, and it is difficult to meet the requirements of actual transmission line operation for risk monitoring. Summary of the Invention

[0004] This application aims to solve at least one of the technical problems existing in the prior art. For this purpose, this application proposes a risk monitoring method, device, electronic device, and storage medium for N-1 large-load transmission lines based on a stochastic model, which considers the randomness of line faults, reduces the configuration cost of risk monitoring devices, effectively detects the impact of N-1 faults on the observability of transmission lines, and can meet the requirements of actual transmission line operation for risk monitoring.

[0005] In a first aspect, this application provides a risk monitoring method for N-1 large-load transmission lines based on a stochastic model, and the method includes:

[0006] Obtain the network topology information, historical fault data, bus load data, and line operation environment data of the transmission line. Multiple nodes and line devices are provided in the transmission line, and the network topology information includes the electrical parameter data of the multiple nodes, the operation status data of the line devices, and the current environment data;

[0007] Based on the network topology information, the historical fault data, the bus load data, and the line operation environment data, a risk monitoring model is constructed by introducing the randomness of line faults.

[0008] Taking the minimization of the balance function as the objective, the risk monitoring model is solved to obtain the risk assessment results of the transmission line and the optimal configuration of the risk monitoring device. The balance function is constructed based on the number of risk monitoring devices and the line risk coefficient of the transmission line. The line risk coefficient is determined based on the line connection success probability, the confidence interval, and the risk function of the transmission line. The line connection success probability is used to simulate the N-1 fault probability of the transmission line.

[0009] According to an embodiment of the present application, before constructing the risk monitoring model by introducing the randomness of line faults based on the network topology information, the historical fault data, the bus load data, and the line operation environment data, the method further includes:

[0010] Obtain the historical fault data, the bus load data, and the line operation environment data of the transmission line;

[0011] According to the historical fault data, the bus load data, and the line operation environment data of the transmission line, determine the line connection success probability, the confidence interval, and the risk parameters;

[0012] Based on the line connection success probability, the confidence interval, and the risk parameters, determine the randomness of the line faults.

[0013] According to an embodiment of the present application, before solving the risk monitoring model, the method further includes:

[0014] According to the network topology information of the transmission line, determine the connection state between the buses in the transmission line, the installation state of the risk monitoring device, and the type of the bus;

[0015] According to the line connection success probability between the buses, the confidence interval between the buses, and the risk function of the bus, obtain the line risk coefficient.

[0016] According to an embodiment of the present application, the risk function is:

[0017]

[0018] f2(x i ,x j ,z i ,z j )=(H ij +1)×(L i +L j )×(1-xi )×(1 - x j )

[0019] f3(x i , x j , z i , z j ) = E ij ×(1 - x i )×(1 - x j )

[0020] where x i is the installation status of the risk monitoring device in bus i; x j is the installation status of the risk monitoring device in bus j; z i is the type of bus i; z j is the type of bus j; H ij is the historical fault times of the line between buses i and j; L i is the current load of bus i; L j is the current load of bus j; E ij is the environmental risk factor of the environment where the line between bus i and bus j is located.

[0021] According to an embodiment of the present application, taking the minimization of the balance function as the goal, solving the risk monitoring model to obtain the risk assessment result of the transmission line and the optimal configuration of the risk monitoring device, including:

[0022] Solving the risk monitoring model to obtain the line risk coefficient;

[0023] Adjusting the confidence interval through the feedback of the line risk coefficient, updating the installation position of the risk monitoring device of the bus, and re - solving the risk monitoring model until the minimum value of the balance function is obtained, so as to obtain the risk assessment result and the optimal configuration of the risk monitoring device.

[0024] According to an embodiment of the present application, updating the installation position of the risk monitoring device of the bus includes:

[0025]

[0026] where is the updated installation position of the risk monitoring device between buses i and j; is the installation position of the risk monitoring device between buses i and j before update; R ij is the line risk coefficient of the line between buses i and j.

[0027] According to an embodiment of the present application, the connection success probability is determined based on the historical fault data, the line operation environment data, and the equipment aging condition;

[0028] The confidence interval is determined according to the reliability requirements of the transmission line.

[0029] In a second aspect, the present application provides an N-1 heavy load transmission line risk monitoring device based on a stochastic model. The device includes:

[0030] An acquisition module, configured to acquire the network topology information, historical fault data, bus load data, and line operation environment data of the transmission line. A plurality of nodes and line devices are provided in the transmission line. The network topology information includes the electrical parameter data of the plurality of nodes, the operation state data of the line devices, and the current environment data;

[0031] A first processing module, configured to perform modeling based on the network topology information, the historical fault data, the bus load data, and the line operation environment data and introduce the randomness of line faults to construct a risk monitoring model;

[0032] A second processing module, configured to solve the risk monitoring model with the goal of minimizing the balance function to obtain the risk assessment result of the transmission line and the optimal configuration of the risk monitoring device. The balance function is constructed based on the number of risk monitoring devices and the line risk coefficient of the transmission line. The line risk coefficient is determined based on the line connection success probability, the confidence interval, and the risk function of the transmission line. The line connection success probability is used to simulate the N-1 fault probability of the transmission line.

[0033] In a third aspect, the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the N-1 heavy load transmission line risk monitoring method based on a stochastic model as described in the first aspect above.

[0034] In a fourth aspect, the present application provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the N-1 heavy load transmission line risk monitoring method based on a stochastic model as described in the first aspect above.

[0035] In a fifth aspect, the present application provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the N-1 heavy load transmission line risk monitoring method as described in the first aspect.

[0036] In a sixth aspect, the present application provides a computer program product, including a computer program which, when executed by a processor, implements the method for monitoring the risk of N-1 heavy-load transmission lines based on a stochastic model as described in the first aspect above.

[0037] Additional aspects and advantages of the present application will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present application.

[0038] The method, device, electronic device and storage medium for monitoring the risk of N-1 heavy-load transmission lines based on a stochastic model provided by the present application have the following beneficial effects compared with the prior art:

[0039] (1) By considering the randomness of line faults, the configuration cost of the risk monitoring device is reduced, the impact of N-1 faults on the observability of transmission lines is effectively detected, the requirements of actual transmission line operation for risk monitoring can be met, while ensuring the safety of transmission lines, resources are reasonably allocated, the operation efficiency and stability of transmission lines are improved, and the technology for comprehensively and accurately monitoring the risk of heavy-load transmission lines is improved, enhancing the safety, observability and reliability of the power system under fault conditions.

[0040] (2) By simulating the working conditions of N-1 through the risk monitoring model, effective risk monitoring of heavy-load transmission lines under N-1 operating conditions and optimal layout of the risk monitoring device for heavy-load transmission lines are achieved. A new line risk coefficient is introduced to quantify line risk, which can be used for risk monitoring of overhead transmission lines under N-1 operating conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The above and / or additional aspects and advantages of the present application will become apparent and be readily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0042] Figure 1 is a schematic flowchart of the method for monitoring the risk of N-1 heavy-load transmission lines based on a stochastic model provided by an embodiment of the present application;

[0043] Figure 2 is a schematic structural diagram of the device for monitoring the risk of N-1 heavy-load transmission lines based on a stochastic model provided by an embodiment of the present application;

[0044] Figure 3 is a schematic structural diagram of the electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] Next, the technical solutions in the embodiments of the present application will be clearly described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art belong to the scope of protection of the present application.

[0046] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, rather than to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are usually of the same type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims means at least one of the connected objects, and the character " / " generally indicates an "or" relationship between the associated objects before and after.

[0047] Next, in conjunction with the accompanying drawings, through specific embodiments and their application scenarios, the N-1 large load transmission line risk monitoring method based on a stochastic model, the N-1 large load transmission line risk monitoring device based on a stochastic model, an electronic device, and a readable storage medium provided by the embodiments of the present application will be described in detail.

[0048] Among them, the N-1 large load transmission line risk monitoring method based on a stochastic model can be applied to a terminal, and specifically can be executed by hardware or software in the terminal.

[0049] The terminal includes, but is not limited to, portable communication devices such as mobile phones or tablets with a touch-sensitive surface (for example, a touch screen display and / or a touchpad). It should also be understood that in some embodiments, the terminal may not be a portable communication device, but a desktop computer with a touch-sensitive surface (for example, a touch screen display and / or a touchpad).

[0050] In the following various embodiments, a terminal including a display and a touch-sensitive surface is described. However, it should be understood that the terminal may include one or more other physical user interface devices such as a physical keyboard, a mouse, and a joystick.

[0051] The N-1 large load transmission line risk monitoring method based on a stochastic model provided by an embodiment of the present application. The execution subject of the N-1 large load transmission line risk monitoring method based on the stochastic model can be an electronic device or a functional module or functional entity in the electronic device that can implement the N-1 large load transmission line risk monitoring method based on the stochastic model. The electronic devices mentioned in the embodiments of the present application include, but are not limited to, mobile phones, tablet computers, computers, cameras, wearable devices, etc. Hereinafter, taking the electronic device as the execution subject as an example, the N-1 large load transmission line risk monitoring method provided by the embodiments of the present application will be described.

[0052] As Figure 1 shown, the N-1 large load transmission line risk monitoring method based on the stochastic model includes:

[0053] Step 110: Obtain the network topology information, historical fault data, bus load data, and line operation environment data of the transmission line. A plurality of nodes and line devices are provided in the transmission line. The network topology information includes the electrical parameter data of the plurality of nodes, the operation state data of the line devices, and the current environment data;

[0054] Step 120: Perform modeling based on the network topology information, the historical fault data, the bus load data, and the line operation environment data and introduce the randomness of line faults to construct a risk monitoring model;

[0055] Step 130: Take minimizing the balance function as the goal, solve the risk monitoring model to obtain the risk assessment result of the transmission line and the optimal configuration of the risk monitoring device. The balance function is constructed based on the number of risk monitoring devices and the line risk coefficient of the transmission line. The line risk coefficient is determined based on the line connection success probability, confidence interval, and risk function of the transmission line. The line connection success probability is used to simulate the N-1 fault probability of the transmission line.

[0056] Among them, the transmission line is an N-1 large load transmission line, and the risk monitoring model is an improved stochastic model.

[0057] It can be understood that the network topology information of the transmission line is used to describe the state of the transmission line network structure, including the electrical parameters of nodes and lines, the operation state of line devices, etc.

[0058] Nodes represent various important devices in the transmission line such as substations, circuit breakers, buses, etc. The line is the transmission path. Electrical parameters can include voltage, current, impedance, etc.; the operation state refers to the operation status of the line device, such as whether it is in a working state and whether there is a fault.

[0059] Historical fault data can include records of fault events that occur in transmission lines and equipment, such as the location, time, type of the fault, and the corresponding recovery time, etc., which serve as the basic data for risk prediction and analysis.

[0060] A busbar is a converging node in the power system, connecting multiple transmission lines or multiple load devices. Load data can reflect the working state and operating pressure of the transmission line.

[0061] The load data of the busbar is the load condition of each busbar in the transmission line, used to reflect the magnitude of the power borne by the busbar at different time periods;

[0062] Line operation environment data is data on external environmental factors that affect the operation of transmission lines, such as long-term meteorological data (temperature, humidity, wind speed, precipitation, etc.), geographical conditions (such as regional characteristics of mountainous areas, cities, etc.), and natural disasters (such as earthquakes, lightning, etc.) related data. These factors can affect the stability of the line and the probability of faults occurring.

[0063] A risk monitoring device is a device installed at the nodes of the transmission line for real-time monitoring and assessment of the state of the transmission line and equipment. Its functions include fault detection, status monitoring, data collection and analysis, etc. The device can obtain real-time data on the operation of the line through means such as sensors and monitoring systems.

[0064] A risk monitoring model is a mathematical model constructed based on historical fault data, line operation environment data, and network topology information, used to evaluate the fault risk of the transmission line and its impact on system stability. The model can simulate the probability of line faults under various conditions and evaluate the potential impact of the faults on the system.

[0065] The line risk coefficient is an index that quantifies the fault risk of a transmission line or equipment, which can be a value calculated comprehensively through factors such as fault probability, fault impact, and its impact on the entire power system. A higher line risk coefficient indicates a greater possibility of faults occurring in the line or equipment and more serious consequences.

[0066] The confidence interval is used to represent the uncertainty in risk assessment, and the risk function is used to quantify the probability of line faults and their impact on the power system.

[0067] The N-1 fault probability is used to characterize the probability that the network can still operate without faults after a node fails. The line connection success probability can simulate the N-1 fault probability and can affect redundancy and resilience.

[0068] In actual implementation, sensors installed at each node of the transmission line, such as voltage sensors, current sensors, power sensors, etc., and meteorological monitoring devices including wind speed, temperature, humidity sensors, etc. are used to collect electrical parameter data of the transmission line and current environmental data in real time. At the same time, the operating status data of line equipment, such as line insulation performance, equipment temperature and other information, will also be collected. These data together constitute the network topology information of the transmission line, providing basic information for subsequent risk monitoring and analysis;

[0069] Collect multi-dimensional data from the sensor network and historical record system, including historical fault data that has occurred on the transmission line in the past, bus load data, and line operating environment information;

[0070] According to the topology of the power system, integrate historical fault data, load data, and current environmental data to construct a dynamically updatable mathematical model. Since the occurrence of faults is random, the probability distribution of fault occurrence needs to be considered. By using methods such as Monte Carlo simulation to simulate the occurrence probability of different fault modes, the probability of line faults and their impact on the system power are quantified to obtain a risk monitoring model;

[0071] With the goal of minimizing the balance function, solve the model to reduce risk exposure by reasonably configuring monitoring devices and ensure the safety of the transmission line on the premise of effectively reducing the number of devices.

[0072] Intelligent optimization algorithms such as genetic algorithms, simulated annealing algorithms, or particle swarm optimization can be used to optimize the configuration of risk monitoring devices to ensure that all high-risk areas can be covered and the number of monitoring devices can be minimized.

[0073] According to the solution results, generate a risk assessment report, evaluate the line risk coefficient under different configurations as the risk assessment result, and give the optimal monitoring device deployment plan to achieve the optimal configuration of risk monitoring devices.

[0074] According to the monitoring device deployment plan, select suitable monitoring devices and deploy them to key nodes and line segments with higher risks in the power network to integrate them into a risk monitoring system. Different types of sensors and monitoring devices can be selected, such as temperature sensors, current sensors, weather stations, infrared monitoring, etc.

[0075] Through this risk monitoring system, the status and environmental changes of the transmission line are monitored in real time, potential risks are quickly detected and warned. The risk monitoring model and the configuration of monitoring devices can also be dynamically adjusted according to real-time data.

[0076] According to the N-1 heavy-load transmission line risk monitoring method based on a stochastic model provided by an embodiment of the present application, by considering the randomness of line faults, the configuration cost of risk monitoring devices is reduced, the impact of N-1 faults on the observability of transmission lines is effectively detected, the requirements for risk monitoring in the actual operation of transmission lines can be met, while ensuring the safety of transmission lines, resources are rationally allocated, the operation efficiency and stability of transmission lines are improved, and the technology for comprehensively and accurately monitoring the risks of heavy-load transmission lines is improved, enhancing the safety, observability, and reliability of the power system under fault conditions.

[0077] In some embodiments, before building a risk monitoring model by modeling based on the network topology information, the historical fault data, the bus load data, and the line operating environment data and introducing the randomness of line faults, the method further includes:

[0078] Obtain the historical fault data, bus load data, and line operating environment data of the transmission line;

[0079] Determine the line connection success probability, confidence interval, and risk parameters according to the historical fault data, bus load data, and line operating environment data of the transmission line;

[0080] Determine the randomness of the line fault based on the line connection success probability, confidence interval, and risk parameters.

[0081] In some embodiments, the connection success probability is determined according to the historical fault data, the line operating environment data, and the equipment aging condition;

[0082] The confidence interval is determined according to the reliability requirements of the transmission line.

[0083] In actual implementation, the collected historical fault data, bus load data, and line operating environment data are processed, including removing outliers and noise to ensure the accuracy and reliability of the data. At the same time, according to the actual situation and user requirements, the randomness of line faults required for building the model is set. For example, the line connection success probability (η), confidence interval (α), and risk parameters related to factors affecting line risk, such as risk function weight (w), line historical fault times (H), bus load (L), environmental risk factor (E), etc., are determined according to the historical fault data and line operating environment data.

[0084] Based on the network topology information, line connection success probability, confidence interval, and risk parameters, a highly refined graphical model and mathematical model are constructed for the transmission line. Based on the constructed mathematical model, optimization is carried out for the randomness of line faults. Parameters such as line connection success probability (η) and confidence interval (α) are introduced. According to comprehensive information such as historical fault data, line operation environment data (such as whether the line passes through disaster-prone areas), and equipment aging conditions, a reasonable probability value of line connection success probability is assigned to each line connection. At the same time, a corresponding confidence interval is set according to the reliability requirements of the transmission line, and the mathematical model introducing the randomness of line faults is used as the risk monitoring model.

[0085] In some embodiments, before solving the risk monitoring model, the method further includes:

[0086] According to the network topology information of the transmission line, determine the connection status between buses in the transmission line, the installation status of risk monitoring devices, and the type of bus;

[0087] According to the line connection success probability between buses, the confidence interval between buses, and the risk function of the bus, obtain the line risk coefficient.

[0088] In actual implementation, according to the topological structure in the graphical model of the transmission line, the connection status between buses in the transmission line, the installation status of risk monitoring devices, and the type of bus, etc. can be obtained.

[0089] The connection status a between buses i and j ij is:

[0090]

[0091] The installation status x of the risk monitoring device for bus i i is:

[0092]

[0093] The type of bus z of bus i i is:

[0094]

[0095] Using big data analysis and machine learning techniques, conduct risk assessment based on the stored data; and provide a user-friendly interface to display information such as the graphical model of the transmission line, the risk monitoring device layout plan corresponding to the optimal configuration of risk monitoring devices, and the risk assessment results to the user in an intuitive graphical interface;

[0096] η ijDenote the line connection success probability between busbars \(i\) and \(j\), with a value ranging from 0 to 1. 0 represents no connection, and 1 represents a complete connection, which is used to simulate N - 1 faults.

[0097] α ij Denote the confidence interval between busbars \(i\) and \(j\), which is set by the user.

[0098] Let \(R\) ij be the line risk coefficient of the line between busbars \(i\) and \(j\), and the calculation method is:

[0099]

[0100] where \(\omega\) k is the weight of the \(k\) - th factor, and \(f\) k (\(x\) i , \(x\) j , \(z\) i , \(z\) j ) is the risk function related to the busbar.

[0101] In some sets of embodiments, the risk function is:

[0102]

[0103] \(f_2(x\) i , \(x\) j , \(z\) i , \(z\) j ) = (\(H\) ij + 1)×(\(L\) i +\(L\) j )×(1 - \(x\) i )×(1 - \(x\) j )

[0104] \(f_3(x\) i , \(x\) j , \(z\) i , \(z\) j ) = \(E\) ij ×(1 - \(x\) i )×(1 - \(x\) j )

[0105] where \(x\) i is the installation status of the risk monitoring device in busbar \(i\); \(x\) j is the installation status of the risk monitoring device in busbar \(j\); \(z\) i is the type of busbar \(i\); \(z\) j is the type of busbar \(j\); \(H\) ij is the historical failure times of the line between busbars \(i\) and \(j\); \(L\) i is the current load of busbar \(i\); \(L\) j is the current load of busbar \(j\); \(E\) ijis the environmental risk factor of the environment where the line between bus i and bus j is located.

[0106] Among them, L i The value of is normalized between 0 and 1.

[0107] In some embodiments, taking minimizing the balance function as the goal, solving the risk monitoring model to obtain the risk assessment result of the transmission line and the optimal configuration of the risk monitoring device includes:

[0108] Solving the risk monitoring model to obtain the line risk coefficient;

[0109] Adjust the confidence interval through the feedback of the line risk coefficient, update the installation position of the risk monitoring device of the bus, and solve the risk monitoring model again until the minimum value of the balance function is obtained, so as to obtain the risk assessment result and the optimal configuration of the risk monitoring device.

[0110] It should be noted that the balance function:

[0111]

[0112] Among them, μ1 and μ2 are used to balance the number of risk monitoring devices and the line risk coefficient of the transmission line, and the values need to be determined according to the emphasis of the system on cost and reliability; x i is the installation status of the risk monitoring device in bus i; n is the total number of buses in the transmission line; R ij is the line risk coefficient.

[0113] In addition, during the process of solving the risk monitoring model, constraint conditions need to be satisfied, including that different types of buses need to satisfy the constraints of different numbers of risk monitoring devices and the constraints of the line risk coefficient.

[0114] For general buses:

[0115]

[0116] Among them, y ij is an intermediate variable.

[0117] For zero-injection buses:

[0118]

[0119] Based on the constraint of the line risk coefficient:

[0120]

[0121] Among them, β is an adjustment coefficient used to control the influence degree of risk on the bus observability constraint.

[0122] During the solution process, establish R ij For α ij A feedback mechanism is established so that α ij Changes dynamically during the solution process, reflecting the reliability changes of the line under N - 1 operating conditions.

[0123] In addition, it is also necessary to update the balance function and constraint conditions;

[0124] In some embodiments, update the installation location of the risk monitoring device for the bus, including:

[0125]

[0126] Wherein, Is the installation location of the updated risk monitoring device between bus i and bus j; Is the installation location of the risk monitoring device before update between bus i and bus j; R ij Is the line risk coefficient of the line between bus i and j.

[0127] After the update, the line risk coefficient R of each line can be obtained ij And the installation location x of the risk monitoring device i , thereby reflecting the risk situation of the line.

[0128] In this embodiment, by simulating the N - 1 working conditions through the risk monitoring model, effective risk monitoring of large - load transmission lines under N - 1 operating conditions and optimal layout of risk monitoring devices for large - load transmission lines are realized. Introducing a new line risk coefficient to quantify the line risk, which can be used for risk monitoring of overhead transmission lines under N - 1 operating conditions.

[0129] The N - 1 large - load transmission line risk monitoring method based on a stochastic model provided by the embodiments of the present application, the execution subject can be an N - 1 large - load transmission line risk monitoring device based on a stochastic model. In the embodiments of the present application, taking the N - 1 large - load transmission line risk monitoring device based on a stochastic model to execute the N - 1 large - load transmission line risk monitoring method based on a stochastic model as an example, the N - 1 large - load transmission line risk monitoring device provided by the embodiments of the present application is described.

[0130] The embodiments of the present application also provide an N - 1 large - load transmission line risk monitoring device based on a stochastic model.

[0131] As Figure 2 shown, the N - 1 large - load transmission line risk monitoring device based on a stochastic model includes:

[0132] An acquisition module 210 is configured to acquire network topology information, historical fault data, bus load data, and line operation environment data of a transmission line. A plurality of nodes and line devices are provided in the transmission line. The network topology information includes electrical parameter data of the plurality of nodes, operation status data of the line devices, and current environment data;

[0133] A first processing module 220 is configured to perform modeling based on the network topology information, the historical fault data, the bus load data, and the line operation environment data, and introduce line fault randomness to construct a risk monitoring model;

[0134] A second processing module 230 is configured to solve the risk monitoring model with the goal of minimizing a balance function to obtain a risk assessment result of the transmission line and an optimal configuration of the risk monitoring device. The balance function is constructed based on the number of risk monitoring devices and the line risk coefficient of the transmission line. The line risk coefficient is determined based on the line connection success probability, confidence interval, and risk function of the transmission line. The line connection success probability is used to simulate the N-1 fault probability of the transmission line.

[0135] According to the risk monitoring device for an N-1 heavy-load transmission line based on a stochastic model provided by an embodiment of the present application, by considering the randomness of line faults, the configuration cost of the risk monitoring device is reduced, the influence of N-1 faults on the observability of the transmission line is effectively detected, the requirements of actual transmission line operation for risk monitoring can be met, while ensuring the safety of the transmission line, resources are reasonably configured, the operation efficiency and stability of the transmission line are improved, the technology for comprehensively and accurately monitoring the risks of heavy-load transmission lines is improved, and the safety, observability, and reliability of the power system under fault conditions are improved.

[0136] The risk monitoring device for an N-1 heavy-load transmission line based on a stochastic model in an embodiment of the present application may be an electronic device or a component in an electronic device, such as an integrated circuit or a chip. The electronic device may be a terminal or other devices other than a terminal. Exemplarily, the electronic device may be a mobile phone, a tablet computer, a notebook computer, a handheld computer, a Mobile Internet Device (MID), an Ultra-Mobile Personal Computer (UMPC), a netbook, or a Personal Digital Assistant (PDA), etc. The embodiment of the present application does not make a specific limitation.

[0137] The N-1 large load transmission line risk monitoring device based on a stochastic model in the embodiments of the present application can be a device with an operating system. The operating system can be the Android operating system, can be the iOS operating system, or can also be other possible operating systems, which are not specifically limited in the embodiments of the present application.

[0138] The N-1 large load transmission line risk monitoring device based on a stochastic model provided in the embodiments of the present application can implement each process implemented by the embodiment of the N-1 large load transmission line risk monitoring method based on a stochastic model in the above embodiment. To avoid repetition, it will not be elaborated here.

[0139] In some embodiments, as Figure 3 shown, the embodiments of the present application also provide an electronic device 300, including a processor 301, a memory 302, and a computer program stored on the memory 302 and executable on the processor 301. When the program is executed by the processor 301, it implements each process of the embodiment of the above N-1 large load transmission line risk monitoring method based on a stochastic model and can achieve the same technical effects. To avoid repetition, it will not be elaborated here.

[0140] It should be noted that the electronic device in the embodiments of the present application includes the above-mentioned mobile electronic devices and non-mobile electronic devices.

[0141] The embodiments of the present application also provide a non-transitory computer-readable storage medium. A computer program is stored on the non-transitory computer-readable storage medium. When the computer program is executed by a processor, it implements each process of the embodiment of the above N-1 large load transmission line risk monitoring method based on a stochastic model and can achieve the same technical effects. To avoid repetition, it will not be elaborated here.

[0142] Among them, the processor is the processor in the electronic device in the above embodiment. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs, etc.

[0143] The embodiments of the present application also provide a computer program product, including a computer program. When the computer program is executed by a processor, it implements the above N-1 large load transmission line risk monitoring method based on a stochastic model.

[0144] Among them, the processor is the processor in the electronic device in the above embodiment. The readable storage medium includes computer-readable storage media, such as computer read-only memory ROM, random access memory RAM, magnetic disks, or optical discs, etc.

[0145] Another embodiment of the present application further provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement each process of the above-mentioned embodiment of the N-1 heavy-load transmission line risk monitoring method based on a random model, and can achieve the same technical effects. To avoid repetition, it will not be elaborated here.

[0146] It should be understood that the chip mentioned in the embodiments of the present application may also be referred to as a system-on-chip, system chip, chip system, or system-on-a-chip, etc.

[0147] It should be noted that in this article, the term "including", "comprising", or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article, or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such process, method, article, or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, method, article, or device including that element. In addition, it should be pointed out that the methods and devices in the embodiments of the present application are not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in the reverse order according to the functions involved. For example, the described methods may be performed in an order different from that described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0148] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases, the former is a better implementation method. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disc), and includes several instructions for causing a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the N-1 heavy-load transmission line risk monitoring method based on a random model in each embodiment of the present application.

[0149] In the description of the present application, "the first feature", "the second feature" may include one or more of such features.

[0150] In the description of the present application, the meaning of "a plurality of" is two or more.

[0151] The embodiments of the present application have been described above in conjunction with the accompanying drawings. However, the present application is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present application, those of ordinary skill in the art can also make many forms without departing from the spirit of the present application and the scope protected by the claims, and all of them fall within the protection scope of the present application.

[0152] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0153] Although the embodiments of the present application have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present application. The scope of the present application is defined by the claims and their equivalents.

Claims

1. A risk monitoring method for N-1 large load transmission lines based on a stochastic model, characterized in that, Including: Obtain the network topology information, historical fault data, bus load data, and line operation environment data of the transmission line. A plurality of nodes and line devices are provided in the transmission line. The network topology information includes the electrical parameter data of the plurality of nodes, the operation status data of the line devices, and the current environment data; Based on the network topology information, the historical fault data, the bus load data, and the line operation environment data, perform modeling and introduce the randomness of line faults to construct a risk monitoring model; Taking the minimization of the balance function as the goal, solve the risk monitoring model to obtain the risk assessment result of the transmission line and the optimal configuration of the risk monitoring device. The balance function is constructed based on the number of risk monitoring devices and the line risk coefficient of the transmission line. The line risk coefficient is determined based on the line connection success probability, confidence interval, and risk function of the transmission line. The line connection success probability is used to simulate the N-1 fault probability of the transmission line.

2. The risk monitoring method for N-1 large load transmission lines based on a stochastic model according to claim 1, wherein Before constructing the risk monitoring model by performing modeling based on the network topology information, the historical fault data, the bus load data, and the line operation environment data and introducing the randomness of line faults, the method further includes: Obtain the historical fault data, bus load data, and line operation environment data of the transmission line; Based on the historical fault data, bus load data, and line operation environment data of the transmission line, determine the line connection success probability, confidence interval, and risk parameters; Based on the line connection success probability, confidence interval, and risk parameters, determine the randomness of the line faults.

3. The risk monitoring method for N-1 large load transmission lines based on a random model according to claim 1, characterized in that, Before solving the risk monitoring model, the method further includes: Based on the network topology information of the transmission line, determine the connection status between the buses in the transmission line, the installation status of the risk monitoring device, and the type of bus; Based on the line connection success probability between the buses, the confidence interval between the buses, and the risk function of the bus, obtain the line risk coefficient.

4. The risk monitoring method for N-1 large-load transmission lines based on a random model according to claim 1, characterized in that The risk function is: f2(x i ,x j ,z i ,z j )=(H ij +1)×(L i +L j )×(1-x i )×(1-x j ) f3(x i ,x j ,z i ,z j )=E ij ×(1-x i )×(1-x j ) where x i is the installation status of the risk monitoring device in busbar i; x j is the installation status of the risk monitoring device in busbar j; z i is the type of busbar i; z j is the type of busbar j; H ij is the historical fault times of the line between busbars i and j; L i is the current load of busbar i; L j is the current load of busbar j; E ij is the environmental risk factor of the environment where the line between busbar i and busbar j is located.

5. The risk monitoring method for N-1 large load transmission lines based on a stochastic model according to claim 1, characterized in that Taking the minimization of the balance function as the goal, solving the risk monitoring model to obtain the risk assessment result of the transmission line and the optimal configuration of the risk monitoring device includes: Solve the risk monitoring model to obtain the line risk coefficient; Adjust the confidence interval through the feedback of the line risk coefficient, update the installation position of the risk monitoring device of the bus, and re-solve the risk monitoring model until the minimum value of the balance function is obtained to obtain the risk assessment result and the optimal configuration of the risk monitoring device.

6. The risk monitoring method for N-1 large load transmission lines based on a stochastic model according to claim 5, wherein Updating the installation position of the risk monitoring device of the bus includes: Among them, is the installation position of the updated risk monitoring device between bus i and bus j; is the installation position of the risk monitoring device between bus i and bus j before update; R ij is the line risk coefficient of the line between bus i and j.

7. The risk monitoring method for N-1 large-load transmission lines based on a stochastic model according to claim 6, characterized in that The connection success probability is determined based on the historical fault data, the line operation environment data, and the equipment aging condition; The confidence interval is determined based on the reliability requirements of the transmission line.

8. A risk monitoring device for N-1 large load transmission lines based on a random model, characterized in that, Including: An acquisition module, configured to acquire network topology information, historical fault data, bus load data, and line operation environment data of a transmission line. A plurality of nodes and line devices are provided in the transmission line. The network topology information includes electrical parameter data of the plurality of nodes, operation state data of the line devices, and current environment data; A first processing module, configured to perform modeling based on the network topology information, the historical fault data, the bus load data, and the line operation environment data and introduce line fault randomness to construct a risk monitoring model; A second processing module, configured to solve the risk monitoring model with the goal of minimizing a balance function to obtain a risk assessment result of the transmission line and an optimal configuration of the risk monitoring device. The balance function is constructed based on the number of risk monitoring devices and the line risk coefficient of the transmission line. The line risk coefficient is determined based on the line connection success probability, confidence interval, and risk function of the transmission line. The line connection success probability is used to simulate the N-1 fault probability of the transmission line.

9. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the N-1 large load transmission line risk monitoring method based on a random model according to any one of claims 1-7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the N-1 large load transmission line risk monitoring method based on a random model according to any one of claims 1-7.

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