Intelligent Multi-loop Power Distribution System Based on Base Station Dynamic Environment Monitoring

Abstract

The present invention relates to the field of power intelligent control technology, and specifically, to an intelligent multi-loop power distribution system based on base station dynamic environment monitoring. It includes: a dynamic environment monitoring data acquisition unit that real-time collects power environment parameters and power operation parameters; a topology reconstruction synchronization control unit that constructs a time-varying graph of the topology structure, real-time optimizes the topology structure based on the genetic algorithm combined with dynamic programming, and uses a global-local synchronization control strategy to maintain phase synchronization; a chaos control scheduling optimization unit that performs phase space reconstruction on the power operation parameters, combines the Lyapunov exponent and a nonlinear control strategy to determine and control the chaos characteristics, and real-time adjusts the power operation parameters; a remote monitoring and management platform that is a graphical interface for centrally displaying the status of the power system. The intelligent multi-loop power distribution system based on base station dynamic environment monitoring combines the genetic algorithm and dynamic programming to real-time optimize the topology structure, identifies and suppresses chaotic behavior in the power system based on chaos theory, and optimizes the load distribution.

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, it controls the phase synchronization of each node in the system and the load power distribution, 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 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, thereby causing 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, thereby causing 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;

[0006] 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 the topology reconstruction;

[0007] It further includes a chaos control scheduling optimization unit, which reconstructs the phase space of the power operation parameters through delay coordinate embedding, determines and controls the chaotic characteristics in combination with Lyapunov exponents and nonlinear control strategies, and adjusts the power operation parameters in real time based on the chaos control results;

[0008] It also includes a remote monitoring and management platform, which is a graphical interface for centrally displaying the status of the power system.

[0009] 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;

[0010] 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 and transmits them to the data storage module;

[0011] 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;

[0012] The data storage module is used to store the power environment parameters and power operation parameters in the database.

[0013] 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;

[0014] 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;

[0015] 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.

[0016] 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:

[0017] 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 historical time, and ;

[0018] Obtain power environment parameters, including ambient temperature ;

[0019] S2.1.2. Define the time-varying graph as , is the node set, is the time the edge set at;

[0020] Define the adjacency variable :

[0021] ;

[0022] Among them, is the adjacency variable and can be symmetric, , ;

[0023] S2.1.3. Define the comprehensive cost function , the comprehensive cost function represents the optimization objective function of the topological structure:

[0024] ;

[0025] ;

[0026] ;

[0027] ;

[0028] 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 the set of adjacent nodes connected to the node under; 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 node Phase angle; is the reference temperature of the resistance; Node and the node between the temperature coefficients; is the reference temperature;

[0029] S2.1.4. Optimize the topology structure in real time based on the genetic algorithm and combined with dynamic programming.

[0030] As a further improvement of this technical solution, in the S2.1.4, the topology structure is optimized in real time based on the genetic algorithm and combined with dynamic programming. The specific method steps are as follows:

[0031] S2.1.4.1. Set the population size , which represents the number of candidate topology structures in each generation; set each individual in the current generation , which represents a topology structure, and is encoded as the adjacency variable of the individual ; , ;

[0032] Use binary encoding to represent the existence or non-existence of edges:

[0033] ;

[0034] Among them, represents the node and the node in the th individual adjacency variable;

[0035] S2.1.4.2. Evaluate the fitness of each individual by calculating the comprehensive cost function of each individual: ;

[0036] ;

[0037] ;

[0038] Among them, is the th individual, the set of adjacent nodes of the node 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;

[0039] 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:

[0040] ;

[0041] ;

[0042] where, is the sum of the fitness of all individuals; is the probability that the -th individual is selected;

[0043] S2.1.4.4. Set the crossover probability , and randomly select two parent individuals and through single-point crossover operation, and randomly select a crossover point to generate two offspring individuals:

[0044] ;

[0045] ;

[0046] where, is the -th individual; is the -th offspring individual; is the -th offspring individual;

[0047] S2.1.4.5. Set the mutation probability , and randomly fine-tune the adjacent variables of each offspring individual with the mutation probability through mutation operation:

[0048] ;

[0049] S2.1.4.6. Obtain the adjacent variables of the new individual and the new individual , recalculate the comprehensive cost function of the new individual and the fitness value of the new individual ;

[0050] S2.1.4.7. Combine dynamic programming to optimize the topological evolution path:

[0051] Define the state transition equation:

[0052] ;

[0053] ;

[0054] wherein, is the minimum cumulative cost from the initial state to this state at time when adopting the topological structure is the minimum cumulative cost from the initial state to this state 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;

[0055] S2.1.4.8, Combining the results of genetic algorithm and dynamic programming, select the individual with the highest fitness and the minimum cumulative cost as the optimal topological structure at the current time :

[0056] ;

[0057] ;

[0058] wherein, is the individual with the highest fitness and the minimum cumulative cost; is the optimal topological structure at the current time .

[0059] As a further improvement of the present 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:

[0060] S2.2.1, Obtain the optimal topological structure , the adjacency variable of the optimal topological structure and the phase angle of node ;

[0061] S2.2.2, Set the global synchronization step size and update the phase angle of node :

[0062] ;

[0063] Wherein, is the phase angle of the updated node ; is the node and the node is the weight parameter between them; is the global synchronization step size;

[0064] 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 :

[0065] ;

[0066] Wherein, is the phase angle of the final node ; is the node in the optimal topological structure under the adjacent node set.

[0067] As a further improvement of the present technical solution, the chaos control scheduling optimization unit includes a chaos identification and analysis module and a non-linear control optimization module;

[0068] Wherein, the chaos identification and analysis module performs phase space reconstruction on the power operation parameters by the delay coordinate embedding method, and calculates the Lyapunov exponent to identify and analyze the chaos characteristics;

[0069] The non-linear control optimization module uses a non-linear control strategy to suppress the chaos characteristics according to the results of the chaos identification and analysis module, and adjusts the power operation parameters in real time.

[0070] As a further improvement of the present technical solution, the chaos identification and analysis module performs phase space reconstruction on the power operation parameters by the delay coordinate embedding method, and calculates the Lyapunov exponent to identify and analyze the chaos characteristics. The specific method steps are as follows:

[0071] 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 ; For the node ;

[0072] S3.1.2. Reconstruct the phase space of the pre - processed power operation parameters through the delay coordinate embedding method to construct a phase - space vector:

[0073] Select the time delay according to the autocorrelation function ; Determine the embedding dimension of the phase - space model using the hypothesis method ;

[0074] Construct the phase - space vector of the node : :

[0075] ;

[0076] Among them, is the voltage , current , phase angle and load power any one of them;

[0077] S3.1.3. Select the phase - space vector of the node at time and the phase - space vector of the node closest to it , and calculate the Lyapunov exponent :

[0078] ;

[0079] ;

[0080] Among them, is the distance between the phase - space vector of the node and the phase - space vector of the node closest to it ; is the total number of iterations; is the index of the current iteration step;

[0081] S3.1.4. If , then the node of the power system is in a chaotic state.

[0082] As a further improvement of this technical solution, the non - linear control optimization module uses a non - linear control strategy to suppress chaotic characteristics and adjusts the power operation parameters in real time according to the results of the chaotic recognition and analysis module. The specific method steps are as follows:

[0083] S3.2.1. Suppress chaotic characteristics using sliding mode control:

[0084] Based on the state variables of the node define the sliding mode surface of the node as follows: :

[0085] ;

[0086] ;

[0087] 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 ;

[0088] S3.2.2. Design the control input of the node according to the sliding mode surface of the node as follows: :

[0089] ;

[0090] where is the control input of the node ; is the control gain of the node ;

[0091] S3.2.3. Control and adjust the voltage , current , phase angle and load power according to the control gain of the node ;

[0092] ;

[0093] where is any one of the voltage , current , phase angle and load power .

[0094] As a further improvement of this technical solution, the remote monitoring and management platform includes a data display module and an interactive control module;

[0095] wherein, 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;

[0096] 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.

[0097] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0098] 1. In the intelligent multi-loop power distribution system based on base station dynamic environment monitoring, based on the real-time topology optimization method combining genetic algorithm and dynamic programming, the efficient and real-time optimization of the power system topology can be realized, ensuring the stable operation of the system during the process of dynamic load and dynamic power distribution.

[0099] 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

[0100] Figure 1 It is the overall flow block diagram of the present invention;

[0101] The meanings of each label in the figure are as follows:

[0102] 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

[0103] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with 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.

[0104] Please refer to Figure 1 As shown, an intelligent multi-loop power distribution system based on base station dynamic environment monitoring is provided, including:

[0105] 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;

[0106] In this embodiment, the dynamic environment monitoring data acquisition unit 1 includes a data acquisition module and a data storage module;

[0107] 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;

[0108] The power environment parameters include the 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;

[0109] 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.

[0110] 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;

[0111] In this embodiment, the topology reconstruction synchronization control unit 2 includes a topology reconstruction optimization module and a global-local synchronization control module;

[0112] 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;

[0113] 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.

[0114] 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:

[0115] 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 ;

[0116] Obtain power environment parameters, including ambient temperature ;

[0117] S2.1.2. Define the time-varying graph as , is the node set, is the time the edge set at;

[0118] Define the adjacency variable :

[0119] ;

[0120] wherein, is the adjacency variable and can be symmetric, , ;

[0121] S2.1.3. Define the comprehensive cost function , the comprehensive cost function represents the optimization objective function of the topological structure:

[0122] ;

[0123] ;

[0124] ;

[0125] ;

[0126] 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 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 node Temperature coefficient between; is the reference temperature;

[0127] S2.1.4. Optimize the topology structure in real time based on the genetic algorithm combined with dynamic programming.

[0128] In this embodiment, 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:

[0129] S2.1.4.1. Set the population size , which represents the number of candidate topology structures in each generation; set each individual in the current generation , which represents a topology structure, and is encoded as the adjacency variable of individual ; , ;

[0130] Use binary encoding to represent the existence or non-existence of edges:

[0131] ;

[0132] Among them, represents the adjacency variable between node and node in the th individual ;

[0133] S2.1.4.2. Evaluate the fitness of each individual by calculating the comprehensive cost function of each individual :

[0134] ;

[0135] ;

[0136] 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;

[0137] 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:

[0138] ;

[0139] ;

[0140] Among them, is the sum of the fitness values of all individuals; is the probability that the -th individual is selected;

[0141] S2.1.4.4. Set the crossover probability , and randomly select two parent individuals and through single-point crossover operation, and randomly select a crossover point to generate two offspring individuals:

[0142] ;

[0143] ;

[0144] Among them, is the -th individual; is the -th offspring individual; is the -th offspring individual;

[0145] S2.1.4.5. Set the mutation probability , and randomly fine-tune the adjacent variables of each offspring individual with the mutation probability through mutation operation:

[0146] ;

[0147] S2.1.4.6. Obtain the new individuals 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 ;

[0148] S2.1.4.7. Combine dynamic programming to optimize the topological evolution path:

[0149] Define the state transition equation:

[0150] ;

[0151] ;

[0152] Among them, is At a certain time, adopt a topological structure The minimum cumulative cost to reach this state from the initial state when is At a certain time, adopt a topological structure The minimum cumulative cost to reach this state from the initial state when is the th individual of the previous generation; is the time of the th individual's topological structure; is the time topology , the incremental cost of converting to the current topology ; is the time increment;

[0153] 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 at the current time : :

[0154] ;

[0155] ;

[0156] Among them, is the individual with the highest fitness and the minimum cumulative cost; is the current time under the optimal topological structure.

[0157] The global-local synchronization control module, based on the optimized topological structure, uses the global synchronization control strategy and the local synchronization control strategy to control and adjust the phase angle of the topological structure. The specific method steps are as follows:

[0158] S2.2.1. Obtain the optimal topological structure , the optimal topological structure adjacency variable of and the phase angle of node ; ;

[0159] S2.2.2. Set the global synchronization step size and update the phase angle of node :

[0160] ;

[0161] Among them, is the updated phase angle of node ; is the node The weight parameter between nodes ; is the global synchronization step size;

[0162] 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 :

[0163] ;

[0164] wherein, is the phase angle of the final node ; is the node under the optimal topological structure adjacent node set.

[0165] It also includes a chaos control scheduling optimization unit 3. The chaos control scheduling optimization unit 3 performs phase space reconstruction on power operation parameters through delay coordinate embedding, and combines Lyapunov exponents and nonlinear control strategies to determine and control chaos characteristics, and adjusts power operation parameters in real time based on the chaos control results;

[0166] In this embodiment, the chaos control scheduling optimization unit 3 includes a chaos identification and analysis module and a nonlinear control optimization module;

[0167] Among them, the chaos identification and analysis module performs phase space reconstruction on power operation parameters through the delay coordinate embedding method, and calculates Lyapunov exponents to identify and analyze chaos characteristics;

[0168] The nonlinear control optimization module uses nonlinear control strategies to suppress chaos characteristics and adjusts power operation parameters in real time according to the results of the chaos identification and analysis module.

[0169] The chaos identification and analysis module performs phase space reconstruction on power operation parameters through the delay coordinate embedding method, and calculates Lyapunov exponents to identify and analyze chaos characteristics. The specific method steps are as follows:

[0170] 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 ; For the node ;

[0171] S3.1.2. Reconstruct the phase space of the preprocessed power operation parameters through the delay coordinate embedding method to construct a phase space vector:

[0172] Select the time delay according to the autocorrelation function ; Use the hypothesis method to determine the embedding dimension of the phase space model ;

[0173] Construct the phase space vector of the node : :

[0174] ;

[0175] Among them, is the voltage , current , phase angle and load power any one of;

[0176] S3.1.3. Select the phase space vector of the node at time and the phase space vector closest to the node to calculate the Lyapunov exponent : :

[0177] ;

[0178] ;

[0179] Among them, is the distance between the phase space vector of the node and the phase space vector closest to the node ; is the total number of iterations; is the current iteration step index;

[0180] S3.1.4. If , then the node of the power system is in a chaotic state.

[0181] 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 recognition and analysis module. The specific method steps are as follows:

[0182] ​S3.2.1. Suppress chaotic characteristics using sliding mode control:

[0183] Based on the state variables of the node define the sliding mode surface of the node

[0184] ;

[0185] ;

[0186] wherein, 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 ;

[0187] S3.2.2. Design the control input of the node according to the sliding mode surface of the node :

[0188] ;

[0189] wherein, is the control input of the node ; is the control gain of the node ;

[0190] S3.2.3. Control and adjust the voltage , current , phase angle , and load power according to the control gain of the node ;

[0191] ;

[0192] wherein, is any one of the voltage , current , phase angle , and load power .

[0193] 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;

[0194] In this embodiment, the remote monitoring and management platform 4 includes a data display module and an interactive control module;

[0195] 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;

[0196] The interaction control module is used to query the system status, and based on the real-time data, the user can manually adjust the topological structure of the power system and the power operation parameters.

[0197] 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. An intelligent multi-loop power distribution system based on base station dynamic environment monitoring, characterized in that Including: A dynamic loop monitoring data acquisition unit (1), which 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), which obtains real-time data and historical data of power environment parameters and power operation parameters 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 a global-local synchronization control strategy to maintain the phase synchronization of the topology reconstruction; A chaos control scheduling optimization unit (3), which reconstructs the phase space of the power operation parameters through delay coordinate embedding, determines and controls the chaos characteristics by combining the Lyapunov exponent and the nonlinear control strategy of sliding mode control, and adjusts the power operation parameters in real time based on the chaos control results; A remote monitoring and management platform (4), which is a graphical interface for centrally displaying the status of the power system; The dynamic loop 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, preprocesses the data by noise filtering and basic calibration, and converts the preprocessed data into digital signals and transmits them to the data storage module; The power environment parameters include the ambient temperature ; The power operation parameters include voltage , current , phase angle and load power ; is the 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.

2. The intelligent multi-loop power distribution system based on base station dynamic environment monitoring according to claim 1, wherein: 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. The specific method steps are as follows:

3. The intelligent multi-loop power distribution system based on the base station dynamic environment monitoring according to claim 2, wherein: 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 under the edge set; Define the adjacency variable : ； Among them, is an 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 them; is at time the set of adjacent nodes connected to the node ; is the node and the node the resistance between them; is the node and the node the current between them; is the phase angle of the node ; is the phase angle of the node ; is the resistance at the reference temperature ; The node and the node the temperature coefficient between them; is the reference temperature; S2.1.

4. Optimize the topology structure in real time based on the genetic algorithm and combined with dynamic programming.

4. The intelligent multi-loop power distribution system based on the base station dynamic environment monitoring according to claim 3, wherein: In the above S2.1.4, optimize the topology structure in real time based on the genetic algorithm and combined with dynamic programming. The specific method steps are as follows: S2.1.4.

1. Set the population size , representing the number of candidate topological structures in each generation; Set each individual in the current generation , representing a topological structure, encoded as the individual 's adjacency variable , ; Use binary coding to represent the existence or non-existence of edges: ； Among them, represents the node and the node in the th individual adjacency variable; S2.1.4.

2. Evaluate the fitness of each individual by calculating the comprehensive cost function of each individual of the comprehensive cost function , and evaluate the fitness of each individual: ； ； Among them, is the th individual, and the set of adjacent nodes of node 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 to enter 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 through single-point crossover operation and , randomly select a crossover point , 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 adjacency variables of each offspring individual with a mutation probability through the mutation operation: ； S2.1.4.

6. Obtain a 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 topology evolution path: Define the state transition equation: ； ； wherein, is the minimum cumulative cost to reach this state from the initial state at time when adopting the topological structure is the minimum cumulative cost to reach this state from the initial state at time when adopting the topological structure is the th individual of the previous generation; is the topological structure of the th individual at time ; is the incremental cost for the topology at time to be converted to the current topology ; is the time increment; S2.1.4.

8. Select the individual with the highest fitness and the smallest cumulative cost by combining the results of the genetic algorithm and dynamic programming as the current time for the optimal topological structure : ； ； Among them, is the individual with the highest fitness and the smallest cumulative cost; is the current time is the optimal topological structure at this time.

5. The intelligent multi-loop power distribution system based on the base station dynamic environment monitoring according to claim 4, wherein: 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. The specific method steps are as follows: S2.2.

1. Obtain the optimal topological structure The optimal topological structure of the adjacency variables and the nodes of the phase angles ; S2.2.

2. Set the global synchronization step and update the phase angle of the node : ； Among them, is the phase angle of the updated node ; is the node and the node is the weight parameter between them; 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 .

6. The intelligent multi-loop power distribution system based on the base station dynamic environment monitoring according to claim 1, wherein: 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 reconstructs the phase space of the power operation parameters through the delay coordinate embedding method, and calculates the Lyapunov exponent to identify and analyze the chaos characteristics; The non-linear control optimization module suppresses the chaotic characteristics using a non-linear control strategy and adjusts the power operation parameters in real time according to the results of the chaotic identification and analysis module.

7. The intelligent multi-loop power distribution system based on base station dynamic environment monitoring according to claim 6, characterized in that: 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 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 ; Determine the embedding dimension of the phase space model using the hypothesis method ; Construct a node phase space vector of : ； Among them, is voltage , current , phase angle and load power any one of; S3.1.

3. Select at time the phase space vector of the selected node at and the phase space vector of the node closest to to calculate the Lyapunov exponent : ​ ； ； Among them, is the node of the phase space vector and the phase space vector of the closest node of the distance between; is the total number of iterations; is the index of the current iteration step; S3.1.

4. If , the node of the power system is in a chaotic state.

8. The intelligent multi-loop power distribution system based on base station dynamic environment monitoring according to claim 7, characterized in that: The non-linear control optimization module suppresses the chaotic characteristics using a non-linear control strategy and adjusts the 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 the chaotic characteristics: According to the state variable of the node define the sliding mode surface of the node as follows : ​ ； ； Among them, 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 of node : of the sliding mode surface Design node of the control input : ； Among them, is the control input of the node ; is the control gain of the node ; S3.2.

3. Control the regulated voltage , current , phase angle and load power according to the control gain of the node ; ； Among them, is voltage , current , phase angle and load power any one of them.

9. The intelligent multi-loop power distribution system based on base station dynamic environment monitoring according to claim 1, characterized in that: 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 interactive 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.

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