Intelligent multi-loop power supply distribution system based on base station power environment monitoring

Abstract

The invention relates to the technical field of power supply intelligent control, in particular to an intelligent multi-loop power supply distribution system based on base station power environment monitoring. The method comprises the following steps: a power environment monitoring data acquisition unit acquires power environment parameters and power operation parameters in real time; the topology reconstruction synchronous control unit constructs a time-varying graph of a topological structure, optimizes the topological structure in real time based on a genetic algorithm in combination with dynamic programming, and keeps phase synchronization by using a global and local synchronous control strategy; the chaos control scheduling optimization unit performs phase-space reconstruction on the power operation parameters, judges and controls chaos characteristics in combination with a Lyapunov exponent and a nonlinear control strategy, and adjusts the power operation parameters in real time; and the remote monitoring management platform is a graphical interface for intensively displaying the condition of the power system. According to the intelligent multi-loop power supply distribution system based on base station dynamic environment monitoring, a genetic algorithm and dynamic planning are combined to optimize a topological structure in real time, chaos behaviors in a power system are identified and suppressed based on a chaos theory, and load distribution is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of power intelligent control, and more specifically, to an intelligent multi-loop power distribution system based on base station dynamic environment monitoring. Background Art

[0002] The intelligent multi-loop power distribution system based on base station dynamic environment monitoring aims to realize the real-time dynamic optimization of the power system topology structure and the intelligent control of load distribution. By collecting power environment parameters and operation data in real time, using genetic algorithms combined with dynamic programming for topology reconstruction, and applying chaos control strategies to identify and suppress the chaotic characteristics of the system, the phase synchronization of each node in the system and the load power distribution are controlled, so as to achieve efficient and stable power distribution of the power system under variable loads and fault conditions.

[0003] Existing multi-loop power distribution systems usually have difficulty in optimizing the power grid topology in real time under the conditions of rapid changes in power loads and sudden faults. Moreover, due to the multi-loops of the power system being prone to chaotic states and the dynamic phase mismatches between multi-loops, it will lead to phase asynchronization and electromagnetic incompatibility during the topology reconstruction process, and further cause problems such as system oscillation, unstable power distribution, and increased energy consumption. Therefore, an intelligent multi-loop power distribution system based on base station dynamic environment monitoring is designed. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent multi-loop power distribution system based on base station dynamic environment monitoring to solve the problems mentioned in the above background art, that is, due to the multi-loops of the power system being prone to chaotic states and the dynamic phase mismatches between multi-loops, it will lead to phase asynchronization and electromagnetic incompatibility during the topology reconstruction process, and further cause problems such as system oscillation, unstable power distribution, and increased energy consumption.

[0005] To achieve the above purpose, the present invention provides an intelligent multi-loop power distribution system based on base station dynamic environment monitoring, including: a dynamic environment monitoring data acquisition unit, which is used to collect power environment parameters and power operation parameters in real time and store them in a database; It further includes a topology reconstruction synchronization control unit, which obtains the real-time data and historical data of the power system from the database, dynamically constructs a time-varying graph of the topology structure through time-varying graph theory, optimizes the topology structure in real time based on genetic algorithms combined with dynamic programming, and uses a global-local synchronization control strategy to maintain the phase synchronization of topology reconstruction; It further includes a chaos control scheduling optimization unit, which performs phase space reconstruction on power operation parameters through delay coordinate embedding, combines Lyapunov exponents and nonlinear control strategies to determine and control chaotic characteristics, and adjusts power operation parameters in real time based on the chaos control results; It further includes a remote monitoring and management platform, which is a graphical interface for centrally displaying the status of the power system.

[0006] As a further improvement of this technical solution, the dynamic environment monitoring data acquisition unit includes a data acquisition module and a data storage module; Among them, the data acquisition module acquires power environment parameters and power operation parameters, performs noise filtering and basic calibration preprocessing on the data, and converts the preprocessed data into digital signals for transmission to the data storage module; The power environment parameters include ambient temperature ; the power operation parameters include voltage , current , phase angle and load power ; is a time variable; represents the th node of the power system; ; is the total number of nodes in the power system; The data storage module is used to store the power environment parameters and power operation parameters in the database.

[0007] As a further improvement of this technical solution, the topology reconstruction synchronization control unit includes a topology reconstruction optimization module and a global-local synchronization control module; Among them, the topology reconstruction optimization module dynamically constructs a time-varying graph of the topology structure through time-varying graph theory, and optimizes the topology structure in real time based on the genetic algorithm and combined with dynamic programming; The global-local synchronization control module controls and adjusts the phase angle of the topology structure based on the optimized topology structure, using the global synchronization control strategy and the local synchronization control strategy.

[0008] As a further improvement of this technical solution, the topology reconstruction optimization module dynamically constructs a time-varying graph of the topology structure through time-varying graph theory, and optimizes the topology structure in real time based on the genetic algorithm and combined with dynamic programming. The specific method steps are as follows: S2.1.1. Obtain the real-time data and historical data of the power system from the database. The real-time data includes voltage , current , phase angle and load power ; the historical data includes voltage , current , phase angle and load power ; is the historical time, and ; Obtain power environment parameters, including ambient temperature ; S2.1.2. Define the time-varying graph as , is the node set, is the time and the edge set at; Define the adjacency variable : ; wherein, is the adjacency variable and can be symmetric, , ; S2.1.3. Define the comprehensive cost function , and the comprehensive cost function represents the optimization objective function of the topological structure: ; ; ; ; wherein, is the line energy consumption weight coefficient; is the phase synchronization error weight coefficient; is the node and the node the line energy consumption between; is at time and the node the set of adjacent nodes with edge connections; is the node and the node the resistance between; is the node and the node the current between; is the phase angle of the node ; is the phase angle of the node ; is the reference temperature the resistance under; node and the node the temperature coefficient between; is the reference temperature; S2.1.4. Based on the genetic algorithm and combined with dynamic programming, optimize the topological structure in real time.

[0009] As a further improvement of this technical solution, in S2.1.4, the topological structure is optimized in real time based on the genetic algorithm combined with dynamic programming. The specific method steps are as follows: S2.1.4.1. Set the population size , which represents the number of candidate topological structures in each generation; set each individual in the current generation , which represents a topological structure and is encoded as the adjacency variable of individual , ; Use binary encoding to represent the existence or non-existence of edges: ; Among them, represents the adjacency variable between node and node in the th individual ; S2.1.4.2. Evaluate the fitness of each individual by calculating the comprehensive cost function of each individual : ; ; Among them, is the set of adjacent nodes of node at time in the th individual; is the comprehensive cost function of the th individual; is the fitness of the th individual; is a small positive number used to avoid division by zero errors; S2.1.4.3. Select high-quality individuals to enter the next generation according to the fitness of each individual using the roulette wheel selection algorithm: ; ; Among them, is the sum of the fitness of all individuals; is the probability that the th individual is selected; S2.1.4.4. Set the crossover probability , randomly select two parent individuals and through single-point crossover operation, randomly select a crossover point , and generate two offspring individuals: ​ ; ; Among them, is the th individual; is the th offspring individual; is the th offspring individual; S2.1.4.5. Set the mutation probability , and randomly fine-tune the adjacent variables of each offspring individual with the mutation probability : ; S2.1.4.6. Obtain the new individual and the adjacent variables of the new individual , recalculate the comprehensive cost function of the new individual and the fitness value of the new individual ; S2.1.4.7. Combine dynamic programming to optimize the topological evolution path: Define the state transition equation: ; ; Among them, is the minimum cumulative cost from the initial state to this state when using the topological structure at time ; is the minimum cumulative cost from the initial state to this state when using the topological structure at time ; is the th individual of the previous generation; is the topological structure of the th individual at time ; is the topology at time , and the incremental cost of converting to the current topology ; is the time increment; S2.1.4.8. Combine the results of genetic algorithm and dynamic programming, and select the individual with the highest fitness and the minimum cumulative cost as the optimal topological structure ; ; Among them, is the individual with the highest fitness and the minimum cumulative cost; is the current time the optimal topological structure at.

[0010] As a further improvement of this technical solution, the global-local synchronization control module controls and adjusts the phase angle of the topological structure based on the optimized topological structure, using the global synchronization control strategy and the local synchronization control strategy. The specific method steps are as follows: S2.2.1. Obtain the optimal topological structure the optimal topological structure adjacency variable of and node phase angle of ; S2.2.2. Set the global synchronization step size and update the phase angle of node : ; Among them, is the phase angle of the updated node ; is the weight parameter between node and node ; is the global synchronization step size; S2.2.2. Set the local synchronization step size and optimize the phase angle of the final node based on the phase angle of the updated node : ; Among them, is the phase angle of the final node ; is the set of adjacent nodes of node under the optimal topological structure .

[0011] As a further improvement of this technical solution, the chaotic control scheduling optimization unit includes a chaotic identification and analysis module and a non-linear control optimization module; Among them, the chaotic identification and analysis module performs phase space reconstruction on the power operation parameters through the delay coordinate embedding method, and calculates the Lyapunov exponent to identify and analyze the chaotic characteristics; The non-linear control optimization module suppresses the chaotic characteristics using the non-linear control strategy according to the results of the chaotic identification and analysis module, and adjusts the power operation parameters in real time.

[0012] As a further improvement of this technical solution, the chaotic recognition and analysis module performs phase space reconstruction on power operation parameters through the delay coordinate embedding method, calculates the Lyapunov exponent to identify and analyze chaotic characteristics, and the specific method steps are as follows: S3.1.1. Obtain the real-time data and historical data of the power system from the database. The real-time data includes voltage , current , phase angle and load power ; The historical data includes voltage , current , phase angle and load power ; is the historical time, and ; is the node ; S3.1.2. Perform phase space reconstruction on the preprocessed power operation parameters through the delay coordinate embedding method to construct a phase space vector: Select the time delay according to the autocorrelation function; Use the hypothesis method to determine the embedding dimension of the phase space model ; Construct the phase space vector of node : ; Among them, is any one of voltage , current , phase angle and load power ; S3.1.3. Select the phase space vector of node at time and the of the phase space vector of the node closest to node to calculate the Lyapunov exponent ; ; Among them, is the distance between the phase space vector of node and the of the phase space vector of the node closest to it; is the total number of iterations; is the index of the current iteration step; S3.1.4. If , the nodes of the power system are in a chaotic state.

[0013] As a further improvement of this technical solution, the non-linear control optimization module uses non-linear control strategies to suppress chaotic characteristics and adjusts power operation parameters in real time according to the results of the chaotic identification and analysis module. The specific method steps are as follows: S3.2.1. Adopt sliding mode control to suppress chaotic characteristics: Define the sliding mode surface of node based on the state variable : : ; ; where, is the sliding mode surface of node ; is the state variable of node ; is the transpose of the sliding mode surface coefficient vector ; S3.2.2. Design the control input of node according to the sliding mode surface : : ; where, is the control input of node ; is the control gain of node ; S3.2.3. Control and adjust the voltage , current , phase angle and load power according to the control gain of node ; ; where, is any one of the voltage , current , phase angle and load power .

[0014] As a further improvement of this technical solution, the remote monitoring and management platform includes a data display module and an interactive control module; Among them, the data display module is used to display the real-time operation data of the power system in real time and display the historical data of the power system; The interaction control module is used to query the system status, and based on the real-time data, the user can manually adjust the topology structure and power operation parameters of the power system.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. In the intelligent multi-loop power distribution system based on base station dynamic environment monitoring, based on the real-time topology structure optimization method combining genetic algorithm and dynamic programming, the high-efficiency and real-time optimization of the power system topology structure can be realized, ensuring the stable operation of the system during the process of dynamic load and dynamic power distribution.

[0016] 2. In the intelligent multi-loop power distribution system based on base station dynamic environment monitoring, through the real-time chaos control and load distribution optimization method based on chaos theory, the chaotic behavior in the system can be suppressed, the load distribution can be optimized, and the power distribution stability of the power system can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is the overall flow block diagram of the present invention; The meanings of the various labels in the figure are as follows: 1. Dynamic environment monitoring data acquisition unit; 2. Topology reconstruction synchronization control unit; 3. Chaos control scheduling optimization unit; 4. Remote monitoring and management platform. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0019] Please refer to Figure 1 as shown, an intelligent multi-loop power distribution system based on base station dynamic environment monitoring is provided, including: A dynamic environment monitoring data acquisition unit 1, and the dynamic environment monitoring data acquisition unit 1 is used to collect power environment parameters and power operation parameters in real time and store them in a database; In this embodiment, the dynamic environment monitoring data acquisition unit 1 includes a data acquisition module and a data storage module; Among them, the data acquisition module collects power environment parameters and power operation parameters, performs noise filtering and basic calibration preprocessing on the data, and converts the preprocessed data into digital signals and transmits them to the data storage module; The power environment parameters include ambient temperature ; The power operation parameters include voltage , current , phase angle and load power ; is a time variable; represents the th node of the power system; ; is the total number of nodes in the power system; In this embodiment, there are multiple power circuits or nodes in the power system, and is used to represent the th node of the power system.

[0020] It also includes a topology reconstruction synchronization control unit 2. The topology reconstruction synchronization control unit 2 obtains the real-time data and historical data of the power system from the database, dynamically constructs a time-varying graph of the topology structure through time-varying graph theory, optimizes the topology structure in real time based on the genetic algorithm and combined with dynamic programming, and uses the global-local synchronization control strategy to maintain the phase synchronization of the topology reconstruction; In this embodiment, the topology reconstruction synchronization control unit 2 includes a topology reconstruction optimization module and a global-local synchronization control module; Among them, the topology reconstruction optimization module dynamically constructs a time-varying graph of the topology structure through time-varying graph theory, and optimizes the topology structure in real time based on the genetic algorithm and combined with dynamic programming; The global-local synchronization control module controls and adjusts the phase angle of the topology structure based on the optimized topology structure, using the global synchronization control strategy and the local synchronization control strategy.

[0021] The topology reconstruction optimization module dynamically constructs a time-varying graph of the topology structure through time-varying graph theory, and optimizes the topology structure in real time based on the genetic algorithm and combined with dynamic programming. The specific method steps are as follows: S2.1.1. Obtain the real-time data and historical data of the power system from the database. The real-time data includes voltage , current , phase angle and load power ; The historical data includes voltage , current , phase angle and load power ; is the historical time, and ; Obtain the power environment parameters, including the ambient temperature ; S2.1.2. Define the time-varying graph as , is the node set, is time the edge set under; Define the adjacency variable : ; Among them, is the adjacency variable and can be symmetric, , ; S2.1.3. Define the comprehensive cost function , the comprehensive cost function represents the optimization objective function of the topological structure: ; ; ; ; Among them, is the line energy consumption weight coefficient; is the phase synchronization error weight coefficient; is the node and the node the line energy consumption between; is at time under the node the set of adjacent nodes with edge connections; is the node and the node the resistance between; is the node and the node the current between; is the node the phase angle of; is the node the phase angle of; is the reference temperature the resistance under; node and the node the temperature coefficient between; is the reference temperature; S2.1.4. Based on the genetic algorithm and combined with dynamic programming, optimize the topological structure in real time.

[0022] In this embodiment, in S2.1.4, based on the genetic algorithm and combined with dynamic programming, optimize the topological structure in real time. The specific method steps are as follows: S2.1.4.1. Set the population size , which represents the number of candidate topological structures in each generation; Set each individual in the current generation , which represents a topological structure and is encoded as an individual Adjacent variables , ; Use binary coding to represent the existence or non - existence of edges: ; Among them, represents the adjacent variable of node and node in the th individual; S2.1.4.2. Evaluate the fitness of each individual by calculating the comprehensive cost function of each individual: ; ; Among them, is the set of adjacent nodes of node in the th individual at time ; is the comprehensive cost function of the th individual; is the fitness of the th individual; is a small positive number used to avoid division - by - zero errors; S2.1.4.3. According to the fitness of each individual, use the roulette wheel selection algorithm to select high - quality individuals into the next generation: ; ; Among them, is the sum of the fitness of all individuals; is the probability that the th individual is selected; S2.1.4.4. Set the crossover probability , randomly select two parent individuals and through single - point crossover operation, and randomly select a crossover point to generate two offspring individuals: ; ; Among them, is the th individual; is the th offspring individual; is the th offspring individual;​​ S2.1.4.5, Set the mutation probability , and randomly fine-tune the adjacent variables of each offspring individual with the mutation probability : ; S2.1.4.6, Obtain the new individual and the adjacent variables of the new individual , and recalculate the comprehensive cost function of the new individual and the fitness value of the new individual ; S2.1.4.7, Combine dynamic programming to optimize the topological evolution path Define the state transition equation ; ; where is at time , when adopting the topological structure is at time , when adopting the topological structure is the th individual of the previous generation is the th individual's topological structure at time ; is the topology at time , and the incremental cost of converting to the current topology ; is the time increment S2.1.4.8, Combine the results of genetic algorithm and dynamic programming, and select the individual with the highest fitness and the smallest cumulative cost as the optimal topological structure at the current time : ; ; where is the individual with the highest fitness and the smallest cumulative cost is the optimal topological structure at the current time .

[0023] The global-local synchronization control module controls and adjusts the phase angle of the topological structure based on the optimized topological structure, using the global synchronization control strategy and the local synchronization control strategy. The specific method steps are as follows: S2.2.1. Obtain the optimal topological structure The optimal topological structure of the adjacency variable and the node phase angle ; S2.2.2. Set the global synchronization step size and update the phase angle of the node : ; Among them, is the phase angle of the updated node ; is the node and the node weight parameter between; is the global synchronization step size; S2.2.2. Set the local synchronization step size and optimize the phase angle of the final node based on the phase angle of the updated node : ; Among them, is the phase angle of the final node ; is the node in the optimal topological structure adjacent node set below.

[0024] It also includes a chaos control scheduling optimization unit 3. The chaos control scheduling optimization unit 3 performs phase space reconstruction on the power operation parameters through delay coordinate embedding, and combines the Lyapunov exponent and the nonlinear control strategy to determine and control the chaos characteristics, and adjusts the power operation parameters in real time based on the chaos control results; In this embodiment, the chaos control scheduling optimization unit 3 includes a chaos identification and analysis module and a nonlinear control optimization module; Among them, the chaos identification and analysis module performs phase space reconstruction on the power operation parameters through the delay coordinate embedding method, and calculates the Lyapunov exponent to identify and analyze the chaos characteristics; The nonlinear control optimization module uses the nonlinear control strategy to suppress the chaos characteristics and adjusts the power operation parameters in real time according to the results of the chaos identification and analysis module.

[0025] The chaos recognition and analysis module performs phase space reconstruction on power operation parameters through the delay coordinate embedding method, and calculates the Lyapunov exponent to identify and analyze chaos characteristics. The specific method steps are as follows: S3.1.1. Obtain the real-time data and historical data of the power system from the database. The real-time data includes voltage , current , phase angle , and load power ; The historical data includes voltage , current , phase angle , and load power ; is the historical time, and ; is the node ; S3.1.2. Perform phase space reconstruction on the preprocessed power operation parameters through the delay coordinate embedding method to construct a phase space vector: Select the time delay according to the autocorrelation function; Use the hypothesis method to determine the embedding dimension of the phase space model ; Construct the phase space vector of node : ; where is any one of voltage , current , phase angle , and load power ; S3.1.3. Select the phase space vector of node at time and the of the phase space vector of the node closest to node to calculate the Lyapunov exponent ; ; where is the distance between the phase space vector of node and the of the phase space vector of the node closest to it; is the total number of iterations; is the index of the current iteration step; S3.1.4. If , then the nodes of the power system are in a chaotic state.

[0026] According to the results of the chaos recognition and analysis module, the non-linear control optimization module uses non-linear control strategies to suppress chaotic characteristics and adjusts power operation parameters in real time. The specific method steps are as follows: S3.2.1. Adopt sliding mode control to suppress chaotic characteristics: Based on the state variables of the node , define the sliding mode surface of the node : ; ; where is the sliding mode surface of the node ; is the state variable of the node ; is the transpose of the sliding mode surface coefficient vector ; S3.2.2. Design the control input of the node according to the sliding mode surface of the node : ; where is the control input of the node ; is the control gain of the node ; S3.2.3. Control and adjust the voltage , current , phase angle and load power according to the control gain of the node ; ; where is any one of the voltage , current , phase angle and load power .

[0027] It also includes a remote monitoring and management platform 4, and the remote monitoring and management platform 4 is a graphical interface for centrally displaying the status of the power system; In this embodiment, the remote monitoring and management platform 4 includes a data display module and an interactive control module; Among them, the data display module is used to display the real-time operation data of the power system in real time and display the historical data of the power system; The interaction control module is used to query the system status, and based on the real-time data, the user can manually adjust the topology structure of the power system and the power operation parameters.

[0028] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and the descriptions in the specification are only preferred examples of the present invention and are not used to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed.

Claims

1. Intelligent multi-circuit power distribution system based on base station dynamic environment monitoring, characterized in that: include: A power environment monitoring data acquisition unit (1), the power environment monitoring data acquisition unit (1) is used to collect power environment parameters and power operation parameters in real time, and store them in a database; A topology reconstruction synchronization control unit (2), wherein the topology reconstruction synchronization control unit (2) obtains real-time data and historical data of the power system from a database, dynamically constructs a time-varying graph of the topology structure through time-varying graph theory, optimizes the topology structure in real time based on a genetic algorithm combined with dynamic programming, and uses a global-local synchronization control strategy to maintain phase synchronization of the topology reconstruction; A chaos control scheduling optimization unit (3), wherein the chaos control scheduling optimization unit (3) reconstructs the phase space of the power operation parameters by delayed coordinate embedding, determines and controls the chaos characteristics in combination with the Lyapunov exponent and the nonlinear control strategy, and adjusts the power operation parameters in real time based on the chaos control results; A remote monitoring and management platform (4), wherein the remote monitoring and management platform (4) is a graphical interface that centrally displays the status of the power system.

2. The intelligent multi-circuit power distribution system based on base station dynamic environment monitoring according to claim 1 is characterized in that: The dynamic environment monitoring data acquisition unit (1) comprises a data acquisition module and a data storage module; The data acquisition module collects power environment parameters and power operation parameters, performs noise filtering and basic calibration preprocessing on the data, and converts the preprocessed data into digital signals and transmits them to the data storage module; Power environment parameters include ambient temperature ;Electricity operation parameters include voltage , Current , Phase Angle and load power ; is the time variable; The power system nodes; ; is the total number of power system nodes; The data storage module is used to store power environment parameters and power operation parameters in a database.

3. The intelligent multi-circuit power distribution system based on base station dynamic environment monitoring according to claim 1 is characterized in that: The topology reconstruction synchronization control unit (2) comprises a topology reconstruction optimization module and a global-local synchronization control module; The topology reconstruction optimization module dynamically constructs a time-varying graph of the topology structure through time-varying graph theory, and optimizes the topology structure in real time based on a genetic algorithm combined with dynamic programming; The global and local synchronization control module controls and adjusts the phase angle of the topological structure based on the optimized topological structure by using the global synchronization control strategy and the local synchronization control strategy.

4. The intelligent multi-circuit power distribution system based on base station dynamic environment monitoring according to claim 3 is characterized in that: The topology reconstruction optimization module dynamically constructs a time-varying graph of the topology structure through time-varying graph theory, and optimizes the topology structure in real time based on a genetic algorithm combined with dynamic programming. The specific method steps are as follows: S2.1.

1. Obtain real-time and historical data of the power system from the database. Real-time data includes voltage , Current , Phase Angle and load power ; Historical data includes voltage , Current , Phase Angle and load power ; is the historical time, and ; Get power environment parameters, including ambient temperature ; S2.1.2, define the time-varying graph as , is a node set, For time The edge set below; Defining adjacency variables : ； in, are adjacent variables and can be symmetric. , ; S2.1.

3. Define the comprehensive cost function , comprehensive cost function The optimization objective function representing the topology structure is: ； ； ； ； in, is the line energy consumption weight coefficient; is the phase synchronization error weight coefficient; For Node With Node Energy consumption of the line between For in time Next and Node A set of adjacent nodes connected by edges; For Node With Node The resistance between For Node With Node The current between For Node The phase angle of For Node The phase angle of is the reference temperature The resistance under node With Node The temperature coefficient between is the reference temperature; S2.1.

4. Real-time optimization of topology structure based on genetic algorithm combined with dynamic programming.

5. The intelligent multi-circuit power distribution system based on base station dynamic environment monitoring according to claim 4 is characterized in that: In S2.1.4, the topology structure is optimized in real time based on the genetic algorithm combined with dynamic programming. The specific method steps are as follows: S2.1.4.

1. Setting the population size , expressed as the number of candidate topological structures in each generation; set each individual in the current generation , represented as a topological structure, encoded as individuals Adjacent variables , ; Use binary encoding to indicate the existence of an edge: ； in, Representation Node With Node In the Individual Adjacent variables in ; S2.1.4.

2. By calculating each individual The comprehensive cost function , evaluate the fitness of each individual: ； ； in, For the In each individual, the node In time The set of adjacent nodes under ; For the The comprehensive cost function of each individual; For the The fitness of each individual; is a small positive number used to avoid division by zero errors; S2.1.4.

3. Based on the fitness of each individual, use the roulette wheel selection algorithm to select high-quality individuals to enter the next generation: ； ； in, is the sum of fitness of all individuals; For the The probability of an individual being selected; S2.1.4.

4. Setting the crossover probability , two parent individuals are randomly selected through a single-point crossover operation and , randomly select an intersection point , generating two offspring individuals: ； ； in, For the individual; For the offspring individuals; For the offspring individuals; S2.1.4.

5. Setting the mutation probability , through mutation operation with mutation probability Make a random tweak to the neighboring variables for each offspring individual: ； S2.1.4.

6. Obtaining new individuals and new individuals Adjacent variables , recalculate the new individual The comprehensive cost function and new individuals The fitness value of ; S2.1.4.

7. Combine dynamic programming to optimize the topology evolution path: Define the state transition equation: ； ； in, for Time, using topology When , the minimum cumulative cost to reach this state from the initial state; for Time, using topology When , the minimum cumulative cost to reach this state from the initial state; For the previous generation individual; For time No. The topological structure of each individual; For time Topology Switch to current topology The incremental cost of is the time increment; S2.1.4.

8. Combine the results of genetic algorithm and dynamic programming to select the individual with the highest fitness and the lowest cumulative cost. As current time The optimal topology under : ； ； in, It is the individual with the highest fitness and the smallest cumulative cost; For current time The optimal topology structure.

6. The intelligent multi-circuit power distribution system based on base station dynamic environment monitoring according to claim 5 is characterized in that: The global and local synchronization control module controls and adjusts the phase angle of the topological structure based on the optimized topological structure by using the global synchronization control strategy and the local synchronization control strategy. The specific method steps are as follows: S2.2.

1. Obtaining the optimal topology , optimal topology Adjacent variables and nodes Phase angle ; S2.2.

2. Set the global synchronization step and update the node The phase angle of: ； in, For the updated node The phase angle of For Node With Node The weight parameter between ; is the global synchronization step; S2.2.

2. Set the local synchronization step size based on the updated node Phase angle , optimize the final node The phase angle of: ； in, The final node The phase angle of For Node In the optimal topology The set of adjacent nodes below.

7. The intelligent multi-circuit power distribution system based on base station dynamic environment monitoring according to claim 1 is characterized in that: The chaos control scheduling optimization unit (3) includes a chaos identification analysis module and a nonlinear control optimization module; The chaos identification and analysis module reconstructs the phase space of the power operation parameters through the delayed coordinate embedding method, calculates the Lyapunov index to identify and analyze the chaos characteristics; The nonlinear control optimization module uses a nonlinear control strategy to suppress chaotic characteristics and adjust power operation parameters in real time according to the results of the chaos identification and analysis module.

8. The intelligent multi-circuit power distribution system based on base station dynamic environment monitoring according to claim 7 is characterized in that: The chaos identification and analysis module reconstructs the phase space of the power operation parameters through the delayed coordinate embedding method, calculates the Lyapunov index to identify and analyze the chaos characteristics, and the specific method steps are as follows: S3.1.

1. Obtain real-time and historical data of the power system from the database. Real-time data includes voltage , Current , Phase Angle and load power ; Historical data includes voltage , Current , Phase Angle and load power ; is the historical time, and ; For Node ; S3.1.

2. Reconstruct the phase space of the preprocessed power operation parameters by the delayed coordinate embedding method to construct the phase space vector: Select the time delay based on the autocorrelation function ; Determine the embedding dimension of the phase space model using the hypothesis method ; Build Node The phase space vector : ； in, For voltage , Current , Phase Angle and load power Any one of; S3.1.3, Select at time Select the node The phase space vector The closest node The phase space vector of , calculate the Lyapunov exponent : ； ； in, For Node The phase space vector The closest node The phase space vector of The distance between is the total number of iterations; Index of the current iteration step; S3.1.4 If , then the nodes of the power system In a state of chaos.

9. The intelligent multi-circuit power distribution system based on base station dynamic environment monitoring according to claim 8 is characterized in that: The nonlinear control optimization module uses a nonlinear control strategy to suppress chaotic characteristics and adjust power operation parameters in real time according to the results of the chaos identification and analysis module. The specific method steps are as follows: S3.2.

1. Use sliding mode control to suppress chaotic characteristics: Based on the node State variables Defining Nodes The sliding surface : ； ； in, For Node The sliding surface; For Node state variables; is the sliding surface coefficient vector The transpose of S3.2.

2. According to the node The sliding surface Design Node Control input : ； in, For Node Control input of For Node The control gain of S3.2.3, according to the node The control gain, control adjustment voltage , Current , Phase Angle and load power ; ； in, For voltage , Current , Phase Angle and load power Any one of .

10. The intelligent multi-circuit power distribution system based on base station dynamic environment monitoring according to claim 1 is characterized in that: The remote monitoring management platform (4) comprises a data display module and an interactive control module; The data display module is used to display the real-time operation data of the power system and the historical data of the power system; The interactive control module is used to query the system status, and based on real-time data, the user can manually adjust the topology of the power system and the power operation parameters.

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