Antarctic research station power system maintenance and power generation combined optimization scheduling method and system

Abstract

The application discloses a kind of Antarctic research station power system maintenance and power generation combined optimization scheduling method and system, the wind turbine system of station area is divided into multiple subsystems, based on the probability that all components in each subsystem still normally operate within a given time, construct subsystem joint survival function, based on subsystem joint survival function, establish a multivariate exponential distribution model, construct maintenance model, which includes station area wind turbine maintenance personnel constraint, station area wind turbine maintenance time constraint, off-site maintenance constraint and station area wind turbine health state and maintenance decision coupling constraint, construct diesel generator set model and the power balance constraint of the station area, according to multivariate exponential distribution model, maintenance model, diesel generator set model and power balance constraint generate maintenance and power generation combined optimization model, solve maintenance and power generation combined optimization model, obtain scheduling result, to realize the safe and reliable operation of power system under Antarctic environment.

Description

Technical Field

[0001] This invention relates to the field of wind turbine maintenance technology, and in particular to a method and system for joint optimization scheduling of power system maintenance and power generation at Antarctic research stations. Background Technology

[0002] Unlike conventional wind turbines operating on the mainland, Antarctic wind turbines are exposed to extreme cold, strong winds, and frequent weather changes for extended periods. Operating conditions are more stringent, leading to significantly accelerated aging and performance degradation of components, and more sudden and random component failures. Furthermore, maintenance resources within the research station are highly limited, with long spare parts transportation cycles, a limited number of maintenance personnel and equipment, and short and highly uncertain weather windows for maintenance. In the event of component failure, inappropriate maintenance strategies often result in prolonged wind turbine downtime and threaten the safety of maintenance personnel.

[0003] Therefore, with the increasing proportion of wind power at Antarctic research stations, the health of wind turbines is no longer just an equipment-level issue, but directly relates to the power supply security and economy of the entire system. If maintenance plans and power generation scheduling are disconnected, it may lead to concentrated turbine failures during peak load periods or periods of fuel shortage, forcing diesel generator sets to frequently start and stop and operate at high loads for extended periods, thus affecting the power supply security of Antarctic research stations. Summary of the Invention

[0004] The technical problem to be solved by this invention is to provide a method and system for joint optimization scheduling of power system maintenance and power generation at Antarctic research stations, which can realize the safe and reliable operation of the power system in the Antarctic environment.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A method for joint optimization scheduling of power system maintenance and power generation at an Antarctic research station includes the following steps: The wind turbine system in the station area is divided into multiple subsystems; Construct a joint survival function for each subsystem based on the probability that all components within each subsystem will still operate normally within a given time period; A multivariate exponential distribution model is established based on the joint survival function of the subsystems; A maintenance model is constructed, which includes constraints on maintenance personnel of wind turbine units in the station area, maintenance time constraints of wind turbine units in the station area, off-site maintenance constraints, and coupling constraints between the health status of wind turbine units in the station area and maintenance decisions. Construct a diesel generator set model and power balance constraints for the station area; A joint optimization model for maintenance and power generation is generated based on the multivariate exponential distribution model, the maintenance model, the diesel generator set model, and the power balance constraints. The joint optimization model for maintenance and power generation is solved to obtain the scheduling results, which include the route arrangement of maintenance personnel, the selection of wind turbine units for maintenance, and the start-stop status and output scheduling of diesel generator units.

[0006] To solve the above-mentioned technical problems, another technical solution adopted by the present invention is as follows: A joint optimization scheduling system for power system maintenance and power generation at an Antarctic research station includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the various steps of the aforementioned joint optimization scheduling method for power system maintenance and power generation at an Antarctic research station.

[0007] The beneficial effects of this invention are as follows: Antarctic wind turbines face extreme environmental conditions such as extreme cold, strong winds, and frequent weather changes. Under these conditions, the aging and performance degradation of turbine components accelerate significantly, and failures become more sudden and random. Current methods for predicting the remaining lifespan of the turbines as a whole are not feasible for wind power systems in station areas. Therefore, this invention divides the wind turbine system in a station area into multiple subsystems. A joint survival function for each subsystem is constructed based on the probability that all components within each subsystem will still operate normally within a given time. A multivariate exponential distribution model is then established based on this subsystem joint survival function, which accurately characterizes the correlation between the survival states of components and collectively determines the overall health status of the wind turbine. A maintenance model is also constructed, including constraints on station area wind turbine maintenance personnel, station area wind turbine maintenance time, off-site maintenance, and coupling constraints between the health status of station area wind turbines and maintenance decisions. This approach ensures that in the event of component failure, reasonable and reliable maintenance strategies can prevent prolonged wind turbine downtime while ensuring the safety of maintenance personnel. Constructing a diesel generator model and power balance constraints for the station area enables the diesel generators to ensure energy supply through reasonable scheduling, and also ensures the reliability of power supply through power balance constraints. Based on the multivariate exponential distribution model, maintenance model, diesel generator model, and power balance constraints, a joint optimization model for maintenance and power generation is generated and solved. This yields scheduling results including the path arrangement of maintenance personnel, the selection of wind turbines for maintenance, and the start-up and shutdown status and output scheduling of diesel generators. This allows for timely maintenance of wind turbines when they face failure, and timely replenishment of power deficits during maintenance, thereby ensuring the safe and reliable operation of the power system in the Antarctic environment. Attached Figure Description

[0008] Figure 1 This is a flowchart illustrating a method for joint optimization scheduling of power system maintenance and power generation at an Antarctic research station, according to an embodiment of the present invention. Figure 2This is a schematic diagram of a combined optimization scheduling system for power system maintenance and power generation at an Antarctic research station, according to an embodiment of the present invention. Figure 3 This is a schematic diagram of a series subsystem in a method for joint optimization scheduling of power system maintenance and power generation at an Antarctic research station according to an embodiment of the present invention; Figure 4 This is a schematic diagram of a parallel subsystem in a method for joint optimization scheduling of power system maintenance and power generation at an Antarctic research station, according to an embodiment of the present invention. Detailed Implementation

[0009] To explain in detail the technical content, objectives, and effects of the present invention, the following description is provided in conjunction with the embodiments and accompanying drawings.

[0010] Before detailing the embodiments of this application, some related concepts will first be explained: Joint survival function: used to describe the joint survival probability of multiple related events (such as multiple failure times or individual survival times); Piecewise linearization method: A method for analyzing nonlinear systems by dividing the nonlinear characteristics into pieces and approximating them with straight lines. It is mainly used to simplify the calculation and analysis of nonlinear systems. Big M method: A mathematical method for handling equality or greater-than constraints in linear programming. It constructs an auxiliary problem by introducing artificial variables and a maximum penalty coefficient M to find an initial basic feasible solution to achieve the solution of the simplex method.

[0011] In existing technologies, the maintenance plan for wind turbine units at Antarctic research stations is disconnected from power generation scheduling. This may lead to concentrated unit failures during peak load periods or periods of tight fuel supply, forcing diesel generator units to frequently start and stop and operate at high loads for extended periods, thus affecting the power supply security of Antarctic research stations.

[0012] To at least solve the above problems, please refer to Figure 1 This invention provides a method for joint optimization scheduling of power system maintenance and power generation at Antarctic research stations, comprising the following steps: The wind turbine system in the station area is divided into multiple subsystems; Construct a joint survival function for each subsystem based on the probability that all components within each subsystem will still operate normally within a given time period; A multivariate exponential distribution model is established based on the joint survival function of the subsystems; A maintenance model is constructed, which includes constraints on maintenance personnel of wind turbine units in the station area, maintenance time constraints of wind turbine units in the station area, off-site maintenance constraints, and coupling constraints between the health status of wind turbine units in the station area and maintenance decisions. Construct a diesel generator set model and power balance constraints for the station area; A joint optimization model for maintenance and power generation is generated based on the multivariate exponential distribution model, the maintenance model, the diesel generator set model, and the power balance constraints. The joint optimization model for maintenance and power generation is solved to obtain the scheduling results, which include the route arrangement of maintenance personnel, the selection of wind turbine units for maintenance, and the start-stop status and output scheduling of diesel generator units.

[0013] As described above, the beneficial effects of this invention are as follows: Antarctic wind turbines face extreme environmental conditions such as extreme cold, strong winds, and frequent meteorological changes. Under these conditions, the aging and performance degradation of turbine components accelerate significantly, and failures become more sudden and random. Current methods for predicting the remaining lifespan of the turbine as a whole are not feasible in wind power systems within a station area. Therefore, this invention divides the wind turbine system in the station area into multiple subsystems. A joint survival function for each subsystem is constructed based on the probability that all components within each subsystem will still operate normally within a given time. A multivariate exponential distribution model is established based on this joint survival function, which accurately characterizes the correlation between the survival states of components and jointly determines the overall health status of the wind turbine. A maintenance model is also constructed, which includes constraints on station area wind turbine maintenance personnel, station area wind turbine maintenance time, off-site maintenance constraints, and the relationship between the health status of the station area wind turbine and maintenance... The coupled constraints of maintenance decisions ensure that, in the event of component failure, reasonable and reliable maintenance strategies can prevent prolonged downtime of wind turbines while ensuring the safety of maintenance personnel. Constructing a diesel generator model and power balance constraints for the station area enables the diesel generators to ensure energy supply to the station area through reasonable scheduling, and also ensures the reliability of power supply through power balance constraints. Based on the multivariate exponential distribution model, maintenance model, diesel generator model, and power balance constraints, a joint optimization model for maintenance and power generation is generated and solved. This yields scheduling results including the path arrangement of maintenance personnel, the selection of wind turbines for maintenance, and the start-up and shutdown status and output scheduling of diesel generators. This allows for timely maintenance of wind turbines when they face failure, and timely replenishment of power deficits during maintenance, thereby ensuring the safe and reliable operation of the power system in the Antarctic environment.

[0014] Furthermore, the subsystem includes a series subsystem and a parallel subsystem; The joint survival function of each subsystem is constructed based on the probability that all components within each subsystem will still operate normally within a given time period. Specifically: ; ; ; ; ; In the formula, For subsystem Joint survival function, representing subsystem The probability that all internal components will still be operating normally within a given time period. Let be a probability function, representing the probability of an event occurring. Representation Subsystem Inner The lifespan threshold of each component. Representation Subsystem Inner The remaining service life of each component. This represents the set of components within a subsystem. Indicates the remaining service life of a parallel or series subsystem. This represents the set of components within a series subsystem. Representation Subsystem The number of components in This represents the set of components within a parallel subsystem. and Each represents a collection of components within a subsystem, through This indicates that the components in each subsystem do not overlap. Represents a set of subsystems. This indicates that the components between subsystems do not overlap. Indicates wind turbine The remaining service life, This indicates the wind turbine index. This represents a collection of wind turbine units. Indicates the number of subsystems.

[0015] As described above, the wind turbine is divided into a series subsystem and a parallel subsystem. By constructing the joint survival function of the wind turbine subsystem, the probability that all components of each subsystem will still be working normally after a given time is defined, which characterizes the correlation between the survival states of the components and jointly determines the overall health status of the wind turbine. This is beneficial for accurately predicting the failure risk of the wind turbine during subsequent scheduling.

[0016] Furthermore, establishing a multivariate exponential distribution model based on the joint survival function of the subsystems includes: An initial multivariate exponential distribution model is established based on the joint survival function of the aforementioned subsystems, specifically as follows: ; In the formula, Representation Subsystem The frequency of being impacted by extreme weather Indicates the component index of the subsystem. This indicates that for the subsystem The magnitude of the impact caused by extreme weather Representation Subsystem Inner The lifespan threshold of each component. Representation Subsystem Inner The lifespan threshold of each component. Representation Subsystem Inner The lifespan threshold of each component; The initial multivariate exponential distribution model is rewritten to obtain the final multivariate exponential distribution model, as follows: ; ; ; ; In the formula, Indicates a time index. Representation Subsystem Inner The frequency with which individual components are subjected to impacts caused by extreme weather express The time reset variable for a given period represents the remaining lifespan reset of a component after maintenance. Representation Subsystem Inner The initial remaining lifetime of each component at the start of the scheduling cycle. This indicates that for the subsystem Inner The magnitude of the impact on each component caused by extreme weather. Indicates the scheduling period. express The time reset variable for the time period. express The time reset variable for the time period. Representation Subsystem Inner The maintenance status of each component is represented by a 0-1 variable, where a value of 1 indicates that it is under maintenance, and a value of 0 indicates that it is not under maintenance. Representation Subsystem Inner Maintenance time for each component Indicates wind turbine The maintenance status is a variable ranging from 0 to 1, where a value of 1 indicates that the device is under maintenance, and a value of 0 indicates that it is not under maintenance. Indicates wind turbine Maintenance time.

[0017] As described above, in order to accurately characterize the collaborative failure behavior of multi-component systems of wind turbines under extreme environments, a multivariate exponential distribution model is used to represent its joint survival function. The multivariate exponential distribution model can effectively simulate the joint failure process between mechanical and electronic components due to stress correlation and degradation coupling. It can describe the life dependency of multiple dependent components in a complex system, thereby improving the accuracy of system reliability assessment. In addition, by rewriting the initial multivariate exponential distribution model, the remaining life of each component can be obtained more intuitively, thereby characterizing the failure characteristics of the components in detail.

[0018] Furthermore, the construction of the maintenance model includes: Establish constraints on maintenance personnel of wind turbine units in the station area based on maintenance personnel path variables and maintenance personnel access variables of wind turbine units. Establish maintenance time constraints for wind turbines in the station area based on maintenance personnel path variables, maintenance personnel access variables to wind turbines, and the travel time and component maintenance time consumed during maintenance. Off-site maintenance constraints are established based on the maximum wind speed and temperature that maintenance personnel are allowed to leave the site for maintenance, as well as the perceived temperature for maintenance personnel. Establish coupling constraints between the health status of wind turbine units and maintenance decisions in the station area based on the health status of wind turbine units and components.

[0019] As described above, when constructing the maintenance model, constraints on maintenance personnel of wind turbine units in the station area are established based on the path variables of maintenance personnel and the access variables of maintenance personnel to wind turbine units. Constraints on maintenance time of wind turbine units in the station area are established based on the path variables of maintenance personnel, the access variables of maintenance personnel to wind turbine units, the travel time consumed for maintenance, and the component maintenance time. Constraints on off-site maintenance are established based on the maximum wind speed and temperature that maintenance personnel are allowed to go out for maintenance and the perceived temperature of maintenance personnel. Constraints on the coupling between the health status of wind turbine units in the station area and maintenance decisions are established based on the health status of wind turbine units and the health status of components. This can ensure that the safety of maintenance personnel is guaranteed while achieving fast and reliable maintenance of faulty wind turbine units.

[0020] Furthermore, the constraints on the maintenance personnel of the wind turbine units in the station area are as follows: ; ; ; ; In the formula, This is the first path variable, indicating whether maintenance personnel drive a vehicle from the starting point to the wind turbine. A value of 1 indicates that maintenance personnel drive the vehicle from the starting point to the wind turbine. A value of 0 indicates that maintenance personnel did not drive the vehicle from the starting point to the wind turbine. , This is the second path variable, indicating whether maintenance personnel drive vehicles from the wind turbine. Heading towards the starting point, a value of 1 indicates that maintenance personnel are driving vehicles from the wind turbine. A value of 0 indicates that maintenance personnel did not drive the vehicle from the wind turbine. Heading towards the starting point, This is a third path variable, indicating whether maintenance personnel drive vehicles from the wind turbine. Heading back to the return point A value of 1 indicates that maintenance personnel are driving vehicles from the wind turbine. Heading back to the return point A value of 0 indicates that maintenance personnel did not drive a vehicle from the wind turbine. Heading back to the return point , This is the fourth path variable, indicating whether maintenance personnel drive the vehicle from the return point. Heading towards the wind turbine A value of 1 indicates that the maintenance personnel drove the vehicle back from the point. Heading towards the wind turbine A value of 0 indicates that the maintenance personnel did not drive the vehicle from the return point. Heading towards the wind turbine , The fifth path variable indicates whether maintenance personnel drive vehicles from the wind turbine. Heading towards the wind turbine A value of 1 indicates that maintenance personnel are driving vehicles from the wind turbine. Heading towards the wind turbine A value of 0 indicates that maintenance personnel did not drive a vehicle from the wind turbine. Heading towards the wind turbine , This is the sixth path variable, indicating whether maintenance personnel drive vehicles from the wind turbine. Heading towards the wind turbine A value of 1 indicates that maintenance personnel are driving vehicles from the wind turbine. Heading towards the wind turbine A value of 0 indicates that maintenance personnel did not drive a vehicle from the wind turbine. Heading towards the wind turbine , For maintenance personnel to inspect the wind turbine The access variable, when its value is 1, indicates the wind turbine unit. During maintenance, the value is accessed by maintenance personnel; a value of 0 indicates a wind turbine unit. It was not visited by maintenance personnel during the maintenance task.

[0021] As described above, assuming the station dispatch center can know the outdoor weather conditions and the lifespan of the wind turbines, establishing constraints on the maintenance personnel of the wind turbines in the station area can accurately depict the route scheduling and planning of the maintenance personnel.

[0022] Furthermore, the maintenance time constraints for the wind turbine units in the station area are specifically as follows: ; ; ; ; ; ; In the formula, Indicates the start time of a single maintenance task. This indicates that maintenance personnel have arrived at the wind turbine. At that moment, This indicates that maintenance personnel are working on the wind turbine. Drive the vehicle to the wind turbine Time, This indicates that maintenance personnel have arrived at the wind turbine. At that moment, This indicates the single maintenance scheduling cycle. Indicates wind turbine The variable indicating the end of maintenance; The off-site maintenance constraints are specifically as follows: ; ; In the formula, express Wind speed during the period, express Temperature during the period This indicates the maximum wind speed at which maintenance personnel are permitted to conduct maintenance outside the premises. This indicates the maximum temperature at which maintenance personnel are permitted to conduct maintenance outside the premises. This indicates the maximum permitted time for maintenance personnel to leave the premises. This indicates the perceived temperature for maintenance personnel. An empirical coefficient representing perceived temperature. express Temperature during the period express Humidity during the period express Wind speed during the period; The coupling constraints between the health status of the wind turbine units in the station area and maintenance decisions are as follows: ; ; In the formula, This indicates the health status of the wind turbine generator. A value of 1 indicates that the wind turbine generator is in a healthy state, while a value of 0 indicates that the wind turbine generator is in an unhealthy state. This indicates the health status of the component. A value of 1 indicates that the component is in a healthy state, and a value of 0 indicates that the component is in an unhealthy state. express Time period subsystem m Middle components n The remaining lifespan, Representation Subsystem m Middle components n The remaining lifetime threshold.

[0023] As described above, the maintenance time constraint for wind turbines in the station area further incorporates travel time and component maintenance time. In the extreme Antarctic environment, maintenance personnel leaving the station to perform maintenance tasks need to comprehensively assess meteorological conditions, which directly determine the feasibility of going out and working on-site. For example, strong winds and extreme cold may hinder personnel from approaching the turbines or performing outdoor operations. It is also necessary to assess the potential personal risks brought about by prolonged exposure to harsh environments during operations. Therefore, a departure maintenance constraint based on meteorological thresholds is established to dynamically assess and determine the time window when maintenance personnel can be safely dispatched. In addition, the coupling constraint between the health status of wind turbines in the station area and maintenance decisions also introduces wind turbine and component health status variables to determine maintenance decisions in the optimization problem. Through the above constraints, the effectiveness and reliability of the generated maintenance strategy are effectively guaranteed.

[0024] Furthermore, the construction of the diesel generator set model includes: Establish operating constraints, combination variable constraints, minimum start-up time constraints, and minimum downtime constraints for diesel generator sets; A diesel generator set model is generated based on the diesel generator set operation constraints, the diesel generator set combination variable constraints, the diesel generator set minimum start-up time constraints, and the diesel generator set minimum downtime constraints.

[0025] As described above, in the extreme environment of Antarctica, diesel generators are a key guarantee for the station's energy supply system. They not only provide stable and continuous power output when renewable energy sources such as wind and solar power are insufficient due to weather fluctuations, but also serve as backup power in emergencies, ensuring the uninterrupted operation of the station's life support systems, scientific research equipment, and communication facilities. Especially during long polar nights or severe storms, diesel generators, with their reliable operation and high output power, become the energy cornerstone for maintaining the basic operation of the research station and the safety of personnel, thus supporting the basic needs of Antarctic scientific research and long-term station work. Therefore, it is necessary to establish operating constraints, combination variable constraints, minimum start-up time constraints, and minimum downtime constraints for diesel generator sets to ensure that diesel generator sets provide a reliable power source during maintenance and power generation, and during wind turbine maintenance.

[0026] Furthermore, the power balance constraints of the station area are constructed as follows: ; In the formula, This represents a collection of diesel generators. express Dispatch output of diesel generators during +1 time period Indicates the output of the wind turbine unit. This indicates the electrical load demand of the wind turbine generator.

[0027] As described above, power balance constraints are constructed for the substation area to ensure the reliability of power supply.

[0028] Furthermore, the joint optimization model for maintenance and power generation is solved to obtain the scheduling results. These results include the route arrangement for maintenance personnel, the selection of wind turbine units for maintenance, and the start-stop status and output scheduling of diesel generator units. The nonlinear terms of the maintenance model in the joint optimization model of maintenance and power generation are transformed into linear terms using the piecewise linearization method and the big M method, resulting in the transformed joint optimization model of maintenance and power generation. The transformed joint optimization model of maintenance and power generation is solved using a solver to obtain the scheduling results.

[0029] As described above, the nonlinear terms of the maintenance model in the joint optimization model of maintenance and power generation are transformed into linear terms by using the piecewise linearization method and the Big M method, so that they can be solved quickly by the solver.

[0030] Please refer to Figure 2Another embodiment of the present invention provides a joint optimization scheduling system for power system maintenance and power generation at an Antarctic research station, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the various steps of the aforementioned joint optimization scheduling method for power system maintenance and power generation at an Antarctic research station.

[0031] The above-described method and system for joint optimization scheduling of power system maintenance and power generation at Antarctic research stations are applicable to the maintenance and power generation scenarios of power systems at Antarctic research stations. The specific implementation methods are described below: Please refer to Figure 1 One embodiment of the present invention is as follows: A method for joint optimization scheduling of power system maintenance and power generation at an Antarctic research station includes the following steps: S1. Divide the wind turbine system in the station area into multiple subsystems; the subsystems include series subsystems and parallel subsystems.

[0032] The station area mentioned above refers to the Antarctic research station area.

[0033] For example, the wind turbine system in the station area can be divided into multiple subsystems according to the working principle of the wind turbine components, such as... Figure 3 and Figure 4 As shown.

[0034] S2. Construct a joint survival function for each subsystem based on the probability that all components within each subsystem will still operate normally within a given time period. Specifically: ; ; ; ; ; In the formula, For subsystem Joint survival function, representing subsystem The probability that all internal components will still be operating normally within a given time period. Let be a probability function, representing the probability of an event occurring. Representation Subsystem Inner The lifespan threshold of each component. Representation Subsystem Inner The remaining service life of each component. This represents the set of components within a subsystem. Indicates the remaining service life of a parallel or series subsystem. This represents the set of components within a series subsystem. Representation Subsystem The number of components in This represents the set of components within a parallel subsystem. and Each represents a collection of components within a subsystem, through This indicates that the components in each subsystem do not overlap. Represents a set of subsystems. This indicates that the components between subsystems do not overlap. Indicates wind turbine The remaining service life, This indicates the wind turbine index. This represents a collection of wind turbine units. Indicates the number of subsystems.

[0035] S3. Establish a multivariate exponential distribution model based on the joint survival function of the subsystems, specifically including S31-S32: S31. Based on the joint survival function of the subsystem, establish an initial multivariate exponential distribution model, specifically as follows: ; In the formula, Representation Subsystem The frequency of being impacted by extreme weather Indicates the component index of the subsystem. This indicates that for the subsystem The magnitude of the impact caused by extreme weather, which combines the normal aging of components over time with the impact of weather, is a constant with a range of values. , Representation Subsystem Inner The lifespan threshold of each component. Representation Subsystem Inner The lifespan threshold of each component. Representation Subsystem Inner The lifespan threshold of each component.

[0036] S32. The initial multivariate exponential distribution model is rewritten to obtain the final multivariate exponential distribution model, specifically as follows: ; ; ; ; In the formula, Indicates a time index. Representation Subsystem Inner The frequency with which individual components are subjected to impacts caused by extreme weather express The time reset variable for a given period represents the remaining lifespan reset of a component after maintenance. Representation Subsystem Inner The initial remaining lifetime of each component at the start of the scheduling cycle. This indicates that for the subsystem Inner The magnitude of the impact on each component caused by extreme weather. Indicates the scheduling period. express The time reset variable for the time period. express The time reset variable for the time period. and This reflects the changes in the time reset variable between adjacent time periods. If they are the same, it indicates that the maintenance action has not been completed. When the component is in If no maintenance action is triggered during a given time period, and the maintenance status is 0, then the time reset variable retains the value from the previous time period, and the remaining lifespan of the component continues to decrease over time according to the predetermined degradation pattern (normal aging and weather shock). However, when the component is in... The maintenance action is triggered during the specified time period and the maintenance time continues. Afterwards, the time reset variable remains unchanged during the maintenance period, and is reset to the current time at the end of the maintenance (i.e., ...). This restores the lifespan of the component. Representation Subsystem Inner The maintenance status of each component (including the start and ongoing maintenance stages) is a 0-1 variable, where a value of 1 indicates that it is under maintenance, and a value of 0 indicates that it is not under maintenance. Representation Subsystem Inner Maintenance time for each component Indicates wind turbine The maintenance status is a variable ranging from 0 to 1, where a value of 1 indicates that the device is under maintenance, and a value of 0 indicates that it is not under maintenance. Indicates wind turbine Maintenance time.

[0037] Specifically, for each component in the subsystem, Set as a constant value. This can be characterized as a degradation effect that evolves over time and a meteorological shock effect. Some meteorological shocks can act on multiple components simultaneously, inducing cascade failure of components.

[0038] The station area has extremely limited operation and maintenance resources, specifically reflected in long spare parts transportation cycles, insufficient maintenance personnel and equipment, and short and highly uncertain weather windows suitable for maintenance. Under these conditions, if a component fails, improper maintenance strategies can easily lead to prolonged unit downtime and may endanger personnel safety. Therefore, the following maintenance model is constructed.

[0039] S4. Construct a maintenance model, which includes constraints on maintenance personnel for wind turbine units in the station area, maintenance time constraints for wind turbine units in the station area, off-site maintenance constraints, and coupling constraints between the health status of wind turbine units in the station area and maintenance decisions. Specifically, it includes S41-S44: S41. Establish maintenance personnel constraints for wind turbine units in the station area based on maintenance personnel path variables and maintenance personnel access variables to wind turbine units, specifically as follows: ; ; ; ; In the formula, This is the first path variable, indicating whether maintenance personnel drive a vehicle from the starting point to the wind turbine. A value of 1 indicates that maintenance personnel drive the vehicle from the starting point to the wind turbine. A value of 0 indicates that maintenance personnel did not drive the vehicle from the starting point to the wind turbine. , This is the second path variable, indicating whether maintenance personnel drive vehicles from the wind turbine. Heading towards the starting point, a value of 1 indicates that maintenance personnel are driving vehicles from the wind turbine. A value of 0 indicates that maintenance personnel did not drive the vehicle from the wind turbine. Heading towards the starting point, This is a third path variable, indicating whether maintenance personnel drive vehicles from the wind turbine. Heading back to the return point A value of 1 indicates that maintenance personnel are driving vehicles from the wind turbine. Heading back to the return point A value of 0 indicates that maintenance personnel did not drive a vehicle from the wind turbine. Heading back to the return point , This is the fourth path variable, indicating whether maintenance personnel drive the vehicle from the return point. Heading towards the wind turbine A value of 1 indicates that the maintenance personnel drove the vehicle back from the point. Heading towards the wind turbine A value of 0 indicates that the maintenance personnel did not drive the vehicle from the return point. Heading towards the wind turbine , The fifth path variable indicates whether maintenance personnel drive vehicles from the wind turbine. Heading towards the wind turbine A value of 1 indicates that maintenance personnel are driving vehicles from the wind turbine. Heading towards the wind turbine A value of 0 indicates that maintenance personnel did not drive a vehicle from the wind turbine. Heading towards the wind turbine , This is the sixth path variable, indicating whether maintenance personnel drive vehicles from the wind turbine. Heading towards the wind turbine A value of 1 indicates that maintenance personnel are driving vehicles from the wind turbine. Heading towards the wind turbine A value of 0 indicates that maintenance personnel did not drive a vehicle from the wind turbine. Heading towards the wind turbine , For maintenance personnel to inspect the wind turbine The access variable, when its value is 1, indicates the wind turbine unit. During maintenance, the value is accessed by maintenance personnel; a value of 0 indicates a wind turbine unit. It was not visited by maintenance personnel during the maintenance task.

[0040] S42. Based on the maintenance personnel's path variables, the maintenance personnel's access variables to the wind turbine units, the travel time consumed during maintenance, and the component maintenance time, establish the maintenance time constraints for the wind turbine units in the station area, specifically as follows: ; ; ; ; ; ; In the formula, Indicates the start time of a single maintenance task. This indicates that maintenance personnel have arrived at the wind turbine. At that moment, This indicates that maintenance personnel are working on the wind turbine. Drive the vehicle to the wind turbine Time, This indicates that maintenance personnel have arrived at the wind turbine. At that moment, This indicates the single maintenance scheduling cycle. Indicates wind turbine The maintenance completion variable, whose value is determined by the maintenance completion variable of the wind turbine components. The decision is that a value of 1 indicates the maintenance is complete, while a value of 0 indicates the maintenance is not complete.

[0041] S43. Establish off-site maintenance constraints based on the maximum wind speed and temperature allowed for maintenance personnel to leave the site for maintenance, as well as the perceived temperature for maintenance personnel, specifically: ; ; In the formula, express Wind speed during the period, express Temperature during the period This indicates the maximum wind speed at which maintenance personnel are permitted to conduct maintenance outside the premises. This indicates the maximum temperature at which maintenance personnel are permitted to conduct maintenance outside the premises. This indicates the maximum permitted time for maintenance personnel to leave the premises. This indicates the perceived temperature for maintenance personnel. Empirical coefficients representing perceived temperature (all are constants). express Temperature during the period express Humidity during the period express Wind speed during the period.

[0042] S44. Establish coupling constraints between the health status of wind turbine units and maintenance decisions in the station area based on the health status of wind turbine units and components, specifically: ; ; In the formula, This indicates the health status of the wind turbine generator. A value of 1 indicates that the wind turbine generator is in a healthy state, while a value of 0 indicates that the wind turbine generator is in an unhealthy state. This indicates the health status of the component. A value of 1 indicates that the component is in a healthy state, and a value of 0 indicates that the component is in an unhealthy state. express Time period subsystem m Middle components n The remaining lifespan, Representation Subsystem m Middle components n The remaining lifetime threshold.

[0043] S5. Construct the diesel generator set model and the power balance constraints of the station area, specifically including S51-S53: S51. Establish operating constraints, combination variable constraints, minimum start-up time constraints, and minimum downtime constraints for diesel generator sets.

[0044] The operating constraints of the diesel generator set are as follows: ; ; ; In the formula, This variable represents the operating status of the diesel generator. A value of 1 indicates that the diesel generator is running, and a value of 0 indicates that the diesel generator is offline. This indicates the minimum output limit of the diesel generator set. This indicates the maximum output limit of the diesel generator set. express Dispatch output of diesel generators during +1 time period Rated start-up ramp rate for diesel generators indicates the maximum output increment of the unit when it is put into operation from a stopped state. For the unit's upward rotation and ramp-up, it represents the maximum increase in output between two adjacent time periods during continuous operation. Rated shutdown ramp rate for diesel generators indicates the maximum output reduction when the unit is shut down. The term "downhill ramp" represents the maximum output decrease between two adjacent time periods during continuous operation. This represents a collection of diesel generators. This indicates the index of the diesel generator.

[0045] The constraints on the combined variables of the diesel generator set are as follows: ; ; In the formula, Indicates that the diesel generator set is in t The start-up status variable for a given time period, a value of 1 indicates that the diesel generator set is operating normally. t The generator set is in an on / off state during the specified time period. A value of 0 indicates that the diesel generator set is in operation. t The device was not powered on during that period. Indicates that the diesel generator set is in t The shutdown status variable for a given period, a value of 1 indicates that the diesel generator set is in operation. t The period is in a shutdown state; a value of 0 indicates that the diesel generator set is in operation. t The system is not in a downtime state during this period. Indicates that the diesel generator set is in t +1 time period power-on status variable Indicates that the diesel generator set is in t The shutdown status variable during the +1 time period Indicates that the diesel generator set is in t +1 indicates that the diesel generator set is in an operating or offline state during the +1 period. A value of 1 indicates that the diesel generator set is in operation. t +1 indicates that the diesel generator set is in operation; a value of 0 indicates that the diesel generator set is not in operation. t +1 is offline during the period.

[0046] When the diesel generator set is t +1 is offline during the period ( ), t The period is in operation ( ), indicating that in t Time-based unit startup ( , When the diesel generator set is t +1 is in operation ( ), t The period was offline ( ),but t Periodic unit shutdown ( , When the diesel generator set is t +1 time period and t The system is in operation during all time periods. , () or offline status () , ), indicating that the unit is t The machine will not start or stop during certain periods. , ).

[0047] Frequent start-ups and shutdowns of diesel generator sets can affect their service life. The minimum start-up time constraint for the diesel generator set is: ; ; In the formula, This indicates the minimum start-up time for the diesel generator set. Indicates that the diesel generator set is in The time period is either in running or offline status.

[0048] The minimum downtime constraint for the diesel generator set is: ; ; In the formula, This indicates the minimum downtime of the diesel generator set.

[0049] S52. Generate a diesel generator set model based on the diesel generator set operation constraints, the diesel generator set combination variable constraints, the diesel generator set minimum start-up time constraints, and the diesel generator set minimum downtime constraints.

[0050] S53. Construct the power balance constraints for the station area, specifically as follows: ; In the formula, express Dispatch output of diesel generators during +1 time period Indicates the output of the wind turbine unit. This indicates the electrical load demand of the wind turbine generator.

[0051] S6. Generate a joint optimization model for maintenance and power generation based on the multivariate exponential distribution model, the maintenance model, the diesel generator set model, and the power balance constraints.

[0052] S7. Solve the joint optimization model of maintenance and power generation to obtain the scheduling results. The scheduling results include the route arrangement of maintenance personnel, the selection of wind turbine units for maintenance, and the start-stop status and output scheduling of diesel generator units, specifically including S71-S72: S71. Use the piecewise linearization method and the Big M method to transform the nonlinear terms of the maintenance model in the joint optimization model of maintenance and power generation into linear terms, and obtain the transformed joint optimization model of maintenance and power generation.

[0053] S72. Use a solver to solve the transformed joint optimization model of maintenance and power generation to obtain the scheduling results.

[0054] Specifically, the scheduling cycle was set to 720 hours, and the Qinling Station in Antarctica was selected as the case scenario. The GUROBI solver was called in the Yalmip environment of MATLAB software to solve the transformed joint optimization model of maintenance and power generation, and the scheduling results were obtained.

[0055] According to another aspect of the invention, Figure 2 This is a schematic diagram illustrating a combined optimization scheduling system for power system maintenance and power generation at an Antarctic research station according to an embodiment of the present invention. The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the various steps of the combined optimization scheduling method for power system maintenance and power generation at an Antarctic research station as described above.

[0056] In summary, the present invention provides a method and system for joint optimization scheduling of power system maintenance and generation at an Antarctic research station. This method divides the wind turbine system in the station area into multiple subsystems. A joint survival function for each subsystem is constructed based on the probability that all components within each subsystem will still operate normally within a given time period. A multivariate exponential distribution model is established based on the subsystem joint survival function. A maintenance model, a diesel generator set model, and power balance constraints for the station area are constructed. A joint optimization model for maintenance and generation is generated based on the multivariate exponential distribution model, the maintenance model, the diesel generator set model, and the power balance constraints. The joint optimization model is solved to obtain the scheduling results. Under the premise of meeting the safety constraints of maintenance personnel (such as wind and cold conditions, workable time windows, travel and work accessibility, etc.), the output of the diesel generators can be adjusted in advance or dynamically according to the output reduction caused by wind turbine shutdown for maintenance, timely compensating for the power deficit caused by maintenance, and avoiding power outages of critical loads and system frequency / voltage exceeding limits. Meanwhile, this method can comprehensively consider factors such as maintenance time, unit availability, reserve requirements and fuel consumption, and output feasible maintenance arrangements and power generation dispatch schemes, thereby improving the safety, reliability and economy of system operation under extreme environment and resource-constrained conditions, and ensuring long-term stable power supply to Antarctic research stations.

[0057] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for joint optimization scheduling of power system maintenance and power generation at an Antarctic research station, characterized in that, Including the following steps: The wind turbine system in the station area is divided into multiple subsystems; Construct a joint survival function for each subsystem based on the probability that all components within each subsystem will still operate normally within a given time period; A multivariate exponential distribution model is established based on the joint survival function of the subsystems; A maintenance model is constructed, which includes constraints on maintenance personnel of wind turbine units in the station area, maintenance time constraints of wind turbine units in the station area, off-site maintenance constraints, and coupling constraints between the health status of wind turbine units in the station area and maintenance decisions. Construct a diesel generator set model and power balance constraints for the station area; A joint optimization model for maintenance and power generation is generated based on the multivariate exponential distribution model, the maintenance model, the diesel generator set model, and the power balance constraints. The joint optimization model for maintenance and power generation is solved to obtain the scheduling results, which include the route arrangement of maintenance personnel, the selection of wind turbine units for maintenance, and the start-stop status and output scheduling of diesel generator units.

2. The method for joint optimization scheduling of power system maintenance and power generation at an Antarctic research station according to claim 1, characterized in that, The subsystem includes a series subsystem and a parallel subsystem; The joint survival function of each subsystem is constructed based on the probability that all components within each subsystem will still operate normally within a given time period. Specifically: ； ； ； ； ； In the formula, For subsystem Joint survival function, representing subsystem The probability that all internal components will still be operating normally within a given time period. Let be a probability function, representing the probability of an event occurring. Representation Subsystem Inner The lifespan threshold of each component. Representation Subsystem Inner The remaining service life of each component. This represents the set of components within a subsystem. Indicates the remaining service life of a parallel or series subsystem. This represents the set of components within a series subsystem. Representation Subsystem The number of components in This represents the set of components within a parallel subsystem. and Each represents a collection of components within a subsystem, through This indicates that the components in each subsystem do not overlap. Represents a set of subsystems. This indicates that the components between subsystems do not overlap. Indicates wind turbine The remaining service life, This indicates the wind turbine index. This represents a collection of wind turbine units. Indicates the number of subsystems.

3. The method for joint optimization scheduling of power system maintenance and power generation at an Antarctic research station according to claim 2, characterized in that, The establishment of a multivariate exponential distribution model based on the joint survival function of the subsystems includes: An initial multivariate exponential distribution model is established based on the joint survival function of the aforementioned subsystems, specifically as follows: ； In the formula, Representation Subsystem The frequency of being impacted by extreme weather Indicates the component index of the subsystem. This indicates that for the subsystem The magnitude of the impact caused by extreme weather Representation Subsystem Inner The lifespan threshold of each component. Representation Subsystem Inner The lifespan threshold of each component. Representation Subsystem Inner The lifespan threshold of each component; The initial multivariate exponential distribution model is rewritten to obtain the final multivariate exponential distribution model, as follows: ； ； ； ； In the formula, Indicates a time index. Representation Subsystem Inner The frequency with which individual components are subjected to impacts caused by extreme weather express The time reset variable for a given period represents the remaining lifespan reset of a component after maintenance. Representation Subsystem Inner The initial remaining lifetime of each component at the start of the scheduling cycle. This indicates that for the subsystem Inner The magnitude of the impact on each component caused by extreme weather. Indicates the scheduling period. express The time reset variable for the time period. express The time reset variable for the time period. Representation Subsystem Inner The maintenance status of each component is represented by a 0-1 variable, where a value of 1 indicates that it is under maintenance, and a value of 0 indicates that it is not under maintenance. Representation Subsystem Inner Maintenance time for each component Indicates wind turbine The maintenance status is a variable ranging from 0 to 1, where a value of 1 indicates that the device is under maintenance, and a value of 0 indicates that it is not under maintenance. Indicates wind turbine Maintenance time.

4. The method for joint optimization scheduling of power system maintenance and power generation at an Antarctic research station according to claim 3, characterized in that, The construction of the maintenance model includes: Establish constraints on maintenance personnel of wind turbine units in the station area based on maintenance personnel path variables and maintenance personnel access variables of wind turbine units. Establish maintenance time constraints for wind turbines in the station area based on maintenance personnel path variables, maintenance personnel access variables to wind turbines, and the travel time and component maintenance time consumed during maintenance. Off-site maintenance constraints are established based on the maximum wind speed and temperature that maintenance personnel are allowed to leave the site for maintenance, as well as the perceived temperature for maintenance personnel. Establish coupling constraints between the health status of wind turbine units and maintenance decisions in the station area based on the health status of wind turbine units and components.

5. The method for joint optimization scheduling of power system maintenance and power generation at an Antarctic research station according to claim 4, characterized in that, The constraints on the maintenance personnel of the wind turbine units in the station area are as follows: ； ； ； ； In the formula, This is the first path variable, indicating whether maintenance personnel drive a vehicle from the starting point to the wind turbine. A value of 1 indicates that maintenance personnel drive the vehicle from the starting point to the wind turbine. A value of 0 indicates that maintenance personnel did not drive the vehicle from the starting point to the wind turbine. , This is the second path variable, indicating whether maintenance personnel drive vehicles from the wind turbine. Heading towards the starting point, a value of 1 indicates that maintenance personnel are driving vehicles from the wind turbine. A value of 0 indicates that maintenance personnel did not drive the vehicle from the wind turbine. Heading towards the starting point, This is a third path variable, indicating whether maintenance personnel drive vehicles from the wind turbine. Heading back to the return point A value of 1 indicates that maintenance personnel are driving vehicles from the wind turbine. Heading back to the return point A value of 0 indicates that maintenance personnel did not drive a vehicle from the wind turbine. Heading back to the return point , This is the fourth path variable, indicating whether maintenance personnel drive the vehicle from the return point. Heading towards the wind turbine A value of 1 indicates that the maintenance personnel drove the vehicle back from the point. Heading towards the wind turbine A value of 0 indicates that the maintenance personnel did not drive the vehicle from the return point. Heading towards the wind turbine , The fifth path variable indicates whether maintenance personnel drive vehicles from the wind turbine. Heading towards the wind turbine A value of 1 indicates that maintenance personnel are driving vehicles from the wind turbine. Heading towards the wind turbine A value of 0 indicates that maintenance personnel did not drive a vehicle from the wind turbine. Heading towards the wind turbine , This is the sixth path variable, indicating whether maintenance personnel drive vehicles from the wind turbine. Heading towards the wind turbine A value of 1 indicates that maintenance personnel are driving vehicles from the wind turbine. Heading towards the wind turbine A value of 0 indicates that maintenance personnel did not drive a vehicle from the wind turbine. Heading towards the wind turbine , For maintenance personnel to inspect the wind turbine The access variable, when its value is 1, indicates the wind turbine unit. During maintenance, the value is accessed by maintenance personnel; a value of 0 indicates a wind turbine unit. It was not visited by maintenance personnel during the maintenance task.

6. The method for joint optimization scheduling of power system maintenance and power generation at an Antarctic research station according to claim 5, characterized in that, The maintenance time constraints for the wind turbine units in the station area are as follows: ； ； ； ； ； ； In the formula, Indicates the start time of a single maintenance task. This indicates that maintenance personnel have arrived at the wind turbine. At that moment, This indicates that maintenance personnel are working on the wind turbine. Drive the vehicle to the wind turbine Time, This indicates that maintenance personnel have arrived at the wind turbine. At that moment, This indicates the single maintenance scheduling cycle. Indicates wind turbine The variable indicating the end of maintenance; The off-site maintenance constraints are specifically as follows: ； ； In the formula, express Wind speed during the period, express Temperature during the period This indicates the maximum wind speed at which maintenance personnel are permitted to conduct maintenance outside the premises. This indicates the maximum temperature at which maintenance personnel are permitted to conduct maintenance outside the premises. This indicates the maximum permitted time for maintenance personnel to leave the premises. This indicates the perceived temperature for maintenance personnel. An empirical coefficient representing perceived temperature. express Temperature during the period express Humidity during the period express Wind speed during the period; The coupling constraints between the health status of the wind turbine units in the station area and maintenance decisions are as follows: ； ； In the formula, This indicates the health status of the wind turbine generator. A value of 1 indicates that the wind turbine generator is in a healthy state, while a value of 0 indicates that the wind turbine generator is in an unhealthy state. This indicates the health status of the component. A value of 1 indicates that the component is in a healthy state, and a value of 0 indicates that the component is in an unhealthy state. express Time period subsystem m Middle components n The remaining lifespan, Representation Subsystem m Middle components n The remaining lifetime threshold.

7. The method for joint optimization scheduling of power system maintenance and power generation at an Antarctic research station according to claim 1, characterized in that, The construction of the diesel generator set model includes: Establish operating constraints, combination variable constraints, minimum start-up time constraints, and minimum downtime constraints for diesel generator sets; A diesel generator set model is generated based on the diesel generator set operation constraints, the diesel generator set combination variable constraints, the diesel generator set minimum start-up time constraints, and the diesel generator set minimum downtime constraints.

8. A method for joint optimization scheduling of power system maintenance and power generation at an Antarctic research station according to claim 6, characterized in that, The power balance constraints for the station area are constructed as follows: ； In the formula, This represents a collection of diesel generators. express Dispatch output of diesel generators during +1 time period Indicates the output of the wind turbine unit. This indicates the electrical load demand of the wind turbine generator.

9. The method for joint optimization scheduling of power system maintenance and power generation at an Antarctic research station according to claim 1, characterized in that, Solving the joint optimization model for maintenance and power generation yields scheduling results, which include the route arrangement for maintenance personnel, the selection of wind turbine units for maintenance, and the start-stop status and output scheduling of diesel generator units. The nonlinear terms of the maintenance model in the joint optimization model of maintenance and power generation are transformed into linear terms using the piecewise linearization method and the big M method, resulting in the transformed joint optimization model of maintenance and power generation. The transformed joint optimization model of maintenance and power generation is solved using a solver to obtain the scheduling results.

10. A joint optimization scheduling system for power system maintenance and power generation at an Antarctic research station, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements each step of the method for joint optimization scheduling of power system maintenance and power generation at an Antarctic research station as described in any one of claims 1 to 9.

Images