A water landing short circuit intelligent protection system for a recoverable spacecraft battery pack

Abstract

The application relates to a water landing short circuit intelligent protection system for a recyclable spacecraft battery pack and belongs to the technical field of aerospace safety. The water landing short circuit intelligent protection system comprises a dynamic sealing and pressure management subsystem, a multi-modal information fusion early warning subsystem and a fault handling subsystem. The dynamic sealing and pressure management subsystem is used for enhancing the sealing of a battery compartment after water landing and keeping the pressure difference between the inside and outside of the compartment, and the multi-modal information fusion early warning subsystem is used for evaluating the short circuit risk of the battery pack. The fault handling subsystem is used for executing fault isolation and system power supply reconstruction. The system solves the short circuit safety problem possibly caused by seawater invasion of the battery pack after water landing, realizes risk early warning, rapid fault isolation and system power supply maintenance.

Description

Technical Field

[0001] This invention relates to an intelligent protection system for water-induced short circuits in reusable spacecraft battery packs, belonging to the field of aerospace safety technology. It is used to prevent short circuits in the battery packs of reusable spacecraft caused by seawater intrusion when the spacecraft splashes down on the sea surface. Background Technology

[0002] When a reusable spacecraft splashes down at sea, its battery packs face the risk of short circuits due to seawater intrusion. High-speed water impact can damage the battery compartment's sealing structure. After the compartment splashes down, some areas become submerged in seawater, which may seep into the compartment and cause an electrical short circuit in the battery pack. Current sealing designs are typically static structures, and their sealing performance deteriorates after an impact. Protection circuits often use centralized fuses, which, upon a short circuit, will disconnect the entire battery system, causing the spacecraft to lose all power. Current technology cannot provide early warning of short circuits or provide localized isolation after a short circuit occurs. Summary of the Invention

[0003] The technical problem solved by this invention is to overcome the shortcomings of the prior art and provide an intelligent protection system for water-induced short circuits in reusable spacecraft battery packs, which solves the short circuit safety problem that may be caused by seawater intrusion into the battery pack after water landing, and realizes risk warning, rapid fault isolation and system power supply maintenance.

[0004] The technical solution of this invention is: A smart protection system for water-induced short circuits in reusable spacecraft battery packs includes: a dynamic sealing and pressure management subsystem, a multimodal information fusion early warning subsystem, and a fault handling subsystem; The dynamic sealing and pressure management subsystem includes a shape memory polymer sealing strip and a smart pressure balancing valve. The shape memory polymer sealing strip is installed in the flange mating groove between the battery compartment cover and the battery compartment body to enhance the sealing of the battery compartment after water contact. The smart pressure balancing valve is installed on the inner side wall of the battery compartment and contains molecular sieve desiccant to balance the pressure difference between the inside and outside of the battery compartment and keep the internal environment of the battery compartment dry. The multimodal information fusion early warning subsystem is used to assess the short-circuit risk of the battery pack, including a sensor array and an AI diagnostic module. The sensor array is used to collect data on insulation resistance, battery voltage, humidity inside the battery compartment, and attitude of the reusable spacecraft. The AI ​​diagnostic module receives the data collected by the sensor array, analyzes the rate of decrease in insulation resistance and the humidity distribution under tilted attitude, and outputs the probability of short-circuit risk in each area of ​​the battery compartment to achieve early warning. The fault handling subsystem is used to perform fault isolation and system power reconfiguration, including regional autonomous units and a central coordination unit; The regional autonomous unit is integrated in each battery cell, including a smart slave node and a miniature explosive fuse. The smart slave node continuously monitors the output current of the battery cell. When the current change rate exceeds a preset threshold, a short circuit occurs. The smart slave node drives the miniature explosive fuse of the battery cell to operate, realizing the physical disconnection of the fault area, and at the same time outputting a fault isolation signal to the main controller. The central coordination unit includes a main controller and a solid-state power switch matrix. The main controller communicates with all intelligent slave nodes and the AI ​​diagnostic module via a CAN bus. The main controller stores a power supply network reconfiguration logic table. When the main controller receives a fault isolation signal from any regional autonomous unit or a high-risk warning from the AI ​​diagnostic module, the main controller issues an instruction to the solid-state power switch matrix according to the power supply network reconfiguration logic table to change the switch combination, isolate the faulty battery unit from the main power supply circuit, and ensure the continued power supply to non-faulty areas.

[0005] Furthermore, the phase transition temperature of the shape memory polymer sealing strip is 70°C. When the water impact sensor signal installed on the reusable spacecraft confirms the end of the water impact process, the spacecraft's central controller powers the shape memory polymer sealing strip to heat it, causing its temperature to rise to 75°C within 60 seconds, resulting in expansion and filling the gap in the sealing surface, thus achieving secondary enhanced sealing after the impact.

[0006] Furthermore, when the pressure difference between the inside and outside of the battery compartment exceeds 500Pa, the valve diaphragm of the intelligent pressure balancing valve opens to achieve rapid pressure balance. Under normal conditions, the intelligent pressure balancing valve relies on spring force to maintain a small opening, allowing the gas inside the battery compartment to slowly discharge out at a flow rate of no more than 0.5 liters per minute, maintaining a positive pressure and dry environment inside the compartment during the floating phase.

[0007] Furthermore, the sensor array includes: an insulation resistance monitor for detecting the insulation integrity between the high-voltage circuit of the battery pack and the metal casing of the battery compartment; the three terminals of the insulation resistance monitor are respectively connected to the positive terminal P+ of the battery pack, the negative terminal N- of the battery pack, and the metal casing GND of the battery compartment; the insulation resistance to be monitored includes: the resistance between the positive terminal P+ of the battery pack and the metal casing GND of the battery compartment, and the resistance between the negative terminal N- of the battery pack and the metal casing GND of the battery compartment; The voltage acquisition module is connected to each battery cell to acquire voltage signals. Digital humidity sensors are located in the four corners of the battery compartment to collect humidity signals; Micromechanical IMUs provide attitude data for reusable spacecraft, including roll and pitch angle data.

[0008] Furthermore, the AI ​​diagnostic module is based on a microcontroller and has a built-in trained neural network algorithm. This neural network algorithm executes once per second, and its input feature vector includes: insulation resistance value and its rate of change over the past 3 seconds, readings from four digital humidity sensors, roll angle and pitch angle. The neural network algorithm outputs a risk probability value for each battery cell, which is between 0 and 1. When the risk probability value of a battery cell exceeds 0.7 for two consecutive cycles, it is determined to be high-risk, and a high-risk warning signal and the battery cell number are sent to the main controller.

[0009] Furthermore, the risk probability value is obtained through a neural network algorithm. Specifically, a neural network model is trained using historical data to identify patterns similar to historical fault precursors from the multidimensional features of the current moment, thereby obtaining the risk probability value. The historical data includes data from the normal state, the insulation degradation process, and data up to the moment before the short circuit occurs.

[0010] Furthermore, the intelligent system sets the current change rate threshold to 1×10⁻⁶ within the node. 5 When the current change rate exceeds the threshold, the intelligent slave node drives the miniature explosive fuse to operate within 50 microseconds (A / s).

[0011] Furthermore, when the main controller receives a fault isolation signal from any regional autonomous unit or a high-risk warning from the AI ​​diagnostic module, it sends an instruction to the solid-state power switch matrix to change the switching combination within 200 milliseconds.

[0012] Furthermore, the solid-state power switch matrix consists of multiple high-power MOSFET switches, and its topology supports at least one redundant power supply path.

[0013] Secondly, the present invention proposes a water-based short-circuit intelligent protection method based on the aforementioned water-based short-circuit intelligent protection system, comprising: S1: After a reusable spacecraft lands in water, the dynamic sealing and pressure management subsystem is activated to enhance the seal and maintain positive pressure and dryness inside the battery compartment; S2: The multimodal information fusion early warning subsystem operates continuously, assesses short-circuit risks, and issues early warnings when high risks are identified; S3: If a short circuit occurs in a battery cell, the corresponding regional autonomous unit of the battery cell will immediately take action to achieve local isolation; S4: After receiving an isolation or early warning signal, the central coordination unit of the fault handling subsystem performs power supply network reconstruction to ensure power supply to non-faulty areas.

[0014] The advantages of this invention compared to the prior art are: (1) This invention improves the long-term sealing reliability after water impact through dynamic sealing design. Through multimodal information fusion analysis, it can provide early warning of short-circuit risks. Through regional autonomous units, it achieves microsecond-level rapid isolation of fault points. By controlling the solid-state power switch matrix, it realizes power supply network reconfiguration and can maintain part of the system's power supply function after the fault is isolated.

[0015] (2) The present invention is equipped with a shape memory polymer sealing strip and a heating trigger circuit at the key gaps of the battery compartment. When the sensor detects that the water impact overload has ended, the main controller starts heating to cause the sealing strip to undergo a preset deformation, thereby achieving a secondary enhanced seal after the impact. At the same time, the system integrates an intelligent pressure balancing valve with a desiccant inside. This valve quickly balances the pressure difference between the inside and outside of the compartment at the moment of water contact and maintains positive pressure inside the compartment during the floating stage, keeping the internal environment dry.

[0016] (3) The multimodal information fusion early warning subsystem of the present invention is used to assess the short-circuit risk of the battery pack, including a sensor array and an AI diagnostic module. The sensor array is used to collect data on insulation resistance, battery voltage, cabin humidity and aircraft attitude. The AI ​​diagnostic module receives the sensor data and, by analyzing the rate of decrease in insulation resistance and the humidity distribution under tilted attitude, outputs the short-circuit risk probability of each area in the battery pack, thereby achieving early warning. Attached Figure Description

[0017] Figure 1 This invention provides an overall architecture block diagram of a smart protection system for water-induced short circuits in reusable spacecraft battery packs.

[0018] Figure 2 This invention presents a schematic diagram of a dynamic sealing and pressure management subsystem for an intelligent protection system against water short circuits in reusable spacecraft battery packs.

[0019] Figure 3 This invention discloses a multimodal information fusion early warning logic flowchart for a smart protection system for water-induced short circuits in reusable spacecraft battery packs.

[0020] Figure 4 This invention discloses a fault handling subsystem logic flowchart for a smart protection system for water-induced short circuits in reusable spacecraft battery packs. Detailed Implementation

[0021] The specific embodiments of the present invention will now be described in further detail with reference to the accompanying drawings.

[0022] This invention provides an intelligent protection system for water-induced short circuits in reusable spacecraft battery packs, comprising three subsystems: a dynamic sealing and pressure management subsystem 100, a multimodal information fusion early warning subsystem 200, and a fault handling subsystem 300, such as... Figure 1 As shown.

[0023] This invention incorporates shape memory polymer sealing strips and a heating trigger circuit at key gaps in the battery compartment. When the sensor detects a water impact overload, the main controller initiates heating, causing the sealing strip to undergo a preset deformation, achieving a secondary enhanced seal after the impact. Simultaneously, the system integrates an intelligent pressure balancing valve with a desiccant inside. This valve quickly balances the pressure difference between the inside and outside of the compartment upon water contact and maintains positive pressure inside the compartment during the floating phase, keeping the internal environment dry.

[0024] Specifically, the dynamic sealing and pressure management subsystem 100 includes a shape memory polymer sealing strip 101 and a smart pressure balancing valve 102, such as... Figure 2 As shown; A shape memory polymer sealing strip 101 is installed in the flange mating groove between the battery compartment cover and the compartment body. Its phase change temperature is 70°C, used to enhance the battery compartment seal after water immersion. When the water immersion impact sensor signal on the reusable spacecraft confirms the end of the impact process, the central controller of the reusable spacecraft controls the sealing strip 101 to be electrically heated, raising its temperature to 75°C within 60 seconds, causing it to expand and fill the gap in the sealing surface, achieving a secondary enhanced seal after the impact.

[0025] Pressure sensors are installed both inside and outside the battery compartment, and the collected pressure signals are provided to the central controller. An intelligent pressure balancing valve 102 is installed on the side wall of the battery compartment. The valve contains a molecular sieve desiccant. When the pressure difference between the inside and outside of the battery compartment exceeds 500 Pa, the valve diaphragm opens to achieve rapid pressure balance. Under normal conditions, the valve maintains a small opening due to spring force, allowing gas inside the compartment to slowly escape at a flow rate not exceeding 0.5 liters per minute, maintaining a positive pressure and dry environment inside the compartment during the floating phase.

[0026] The multimodal information fusion early warning subsystem 200 includes a sensor array 201 and an AI diagnostic module 202, used to assess the short-circuit risk of the battery pack in the battery compartment. The sensor array 201 collects data on insulation resistance, battery voltage, humidity inside the compartment, and spacecraft attitude. The AI ​​diagnostic module receives the sensor data and, by analyzing the rate of decrease in insulation resistance and the humidity distribution under tilted attitude, outputs the short-circuit risk probability for each area within the battery pack, achieving early warning. Figure 3 As shown.

[0027] Specifically, the sensor array 201 includes: an insulation resistance monitor, a voltage acquisition module, a digital humidity sensor, and a micro-mechanical IMU.

[0028] An insulation resistance monitor is used to detect the insulation integrity between the high-voltage circuit of the battery pack and the metal shell of the battery compartment. The three terminals of the insulation resistance monitor are connected to the positive terminal P+ of the battery pack, the negative terminal N- of the battery pack, and the metal shell GND of the battery compartment, respectively. The insulation resistance to be monitored includes the resistance between the positive terminal P+ of the battery pack and the metal shell GND of the battery compartment, and the resistance between the negative terminal N- of the battery pack and the metal shell GND of the battery compartment. The voltage acquisition module is connected to each battery cell to acquire voltage signals. Digital humidity sensors are located in the four corners of the battery compartment to collect humidity signals; Micromechanical IMUs provide attitude data for reusable spacecraft, including roll and pitch angle data.

[0029] The AI ​​diagnostic module 202 uses a microcontroller as its core and has a built-in trained neural network algorithm. This neural network algorithm executes once per second, and its input feature vector includes: insulation resistance value and its rate of change over the past 3 seconds, readings from four digital humidity sensors, roll angle and pitch angle. The neural network algorithm outputs a risk probability value for each battery cell, which is between 0 and 1. When the risk probability value of a battery cell exceeds 0.7 for two consecutive cycles, it is determined to be high-risk, and a high-risk warning signal and the battery cell number are sent to the main controller.

[0030] Pattern recognition and risk assessment based on multi-feature input utilizes historical data (including data from normal conditions, insulation degradation processes, and up to the moment before a short circuit) to train a neural network model. This model identifies patterns similar to historical fault precursors from the multi-dimensional features of the current moment. The decision-making process is as follows: (1) Collect raw data; (2) Construct the feature vector X(t) for the current time step according to the above rules; (3) Input X(t) into the deployed, lightweight neural network model; (4) The model performs forward propagation calculations and outputs the current risk probability value; (5) Apply decision rules: when the risk probability value of a certain battery module exceeds 0.7 for two consecutive cycles, it is judged as high risk. This avoids false alarms caused by a single fluctuation and improves the reliability of early warning.

[0031] The input vector needs to include the current value and statistical features within the historical window, using data from the past 3 seconds (times t, t-1, t-2) to construct the input feature vector. Insulation resistance features (3D) include the current insulation resistance, the average rate of decrease of insulation resistance over the past 3 seconds, and the standard deviation of insulation resistance. Humidity-attitude correlation features (8D) include the average humidity on the front side, the product of the average humidity on the front side and the pitch angle, the average humidity on the rear side, the product of the average humidity on the rear side and the pitch angle, the average humidity on the left side, the product of the average humidity on the left side and the roll angle, the average humidity on the right side, and the product of the average humidity on the right side and the roll angle. Battery voltage equalization features (2D) include the maximum value of the battery module voltage and the standard deviation of the battery module voltage. Time features (1D) include the time elapsed since contact with water.

[0032] The neural network model can be a lightweight multilayer perceptron (MLP), which trains a 3-layer fully connected neural network (input layer - hidden layer - output layer). The input layer receives the input feature vector; each neuron in the hidden layer performs linear transformations and non-linear activations to learn the complex interaction relationships between features; the output layer calculates and outputs a risk probability value (between 0 and 1). The closer the risk probability value is to 1, the more similar the model believes the current state is to the state before the short circuit occurred in historical data.

[0033] The fault handling subsystem 300 is used to perform fault isolation and system power reconfiguration, including regional autonomous unit 301 and central coordination unit 302.

[0034] Regional Autonomous Unit 301 is integrated into each battery module, including a smart slave node and a miniature explosive fuse. The smart slave node continuously monitors the output current of the battery module. The current change rate threshold is set to 1×10⁻⁶ within the node. 5 A / s. When the current change rate is detected to exceed this threshold, a short circuit occurs. The intelligent slave node drives the miniature explosive fuse of this module to operate within 50 microseconds, physically disconnecting the fault area and simultaneously outputting a fault isolation signal to the main controller.

[0035] The main controller of the central coordination unit 302 communicates with all intelligent slave nodes and the AI ​​diagnostic module 202 via a CAN bus. The main controller stores a power supply network reconfiguration logic table. When the main controller receives a fault isolation signal from any regional autonomous unit 301, or a high-risk warning from the AI ​​diagnostic module 202, it issues an instruction to the solid-state power switch matrix within 200 milliseconds according to the reconfiguration logic table to change the switch combination, isolating the faulty module from the main power supply circuit and ensuring the power supply safety of non-faulty areas. Figure 4 As shown.

[0036] The solid-state power switch matrix consists of multiple high-power MOSFET switches, and its topology supports at least one redundant power supply path.

[0037] The workflow of this invention is as follows: S1: After the reusable spacecraft lands in water, the dynamic sealing and pressure management subsystem 100 is activated to enhance the seal and maintain positive pressure and dryness inside the chamber; S2: The multimodal information fusion early warning subsystem 200 operates continuously, assesses short-circuit risks, and issues early warnings when high risks are identified; S3: If a severe short circuit occurs in the battery module, its regional autonomous unit 301 will immediately activate to achieve local isolation; S4: After receiving an isolation or early warning signal, the central coordination unit 302 of the fault handling subsystem 300 performs power supply network reconstruction to ensure power supply to non-faulty areas.

[0038] The parts of this invention not described in detail are common knowledge to those skilled in the art.

Claims

1. A smart protection system for water-induced short circuits in reusable spacecraft battery packs, characterized in that, include: Dynamic sealing and pressure management subsystem (100), multimodal information fusion early warning subsystem (200) and fault handling subsystem (300); The dynamic sealing and pressure management subsystem (100) includes a shape memory polymer sealing strip (101) and a smart pressure balancing valve (102). The shape memory polymer sealing strip (101) is installed in the flange joint groove between the battery compartment cover and the battery compartment body to enhance the sealing of the battery compartment after water contact. The smart pressure balancing valve (102) is installed on the inner side wall of the battery compartment. The smart pressure balancing valve (102) is filled with molecular sieve desiccant to balance the pressure difference between the inside and outside of the battery compartment and keep the internal environment of the battery compartment dry. The multimodal information fusion early warning subsystem (200) is used to assess the short-circuit risk of the battery pack, including a sensor array (201) and an AI diagnostic module (202); the sensor array (201) is used to collect data on insulation resistance, battery voltage, humidity inside the battery compartment and attitude of the reusable spacecraft; the AI ​​diagnostic module (202) receives the data collected by the sensor array, analyzes the rate of decrease in insulation resistance and the humidity distribution under tilted attitude, outputs the probability of short-circuit risk in each area of ​​the battery compartment, and realizes early warning; The fault handling subsystem (300) is used to perform fault isolation and system power reconfiguration, including regional autonomous units (301) and a central coordination unit (302). The regional autonomous unit (301) is integrated in each battery cell, including a smart slave node and a miniature explosive fuse; the smart slave node continuously monitors the output current of the battery cell, and when the current change rate exceeds a preset threshold, a short circuit occurs. The smart slave node drives the miniature explosive fuse of the battery cell to operate, realizing the physical disconnection of the fault area, and at the same time outputting a fault isolation signal to the main controller. The central coordination unit (302) includes a main controller and a solid-state power switch matrix. The main controller communicates with all intelligent slave nodes and AI diagnostic modules via a CAN bus. The main controller stores a power supply network reconfiguration logic table. When the main controller receives a fault isolation signal from any regional autonomous unit (301) or a high-risk warning from the AI ​​diagnostic module (202), the main controller issues an instruction to the solid-state power switch matrix according to the power supply network reconfiguration logic table to change the switch combination, isolate the faulty battery unit from the main power supply circuit, and ensure the continued power supply to the non-faulty area.

2. The intelligent protection system for water-induced short circuits in reusable spacecraft battery packs according to claim 1, characterized in that: The shape memory polymer sealing strip (101) has a phase change temperature of 70°C. When the water impact sensor signal installed on the reusable spacecraft confirms the end of the water impact process, the spacecraft central controller powers the shape memory polymer sealing strip (101) to heat it up to 75°C within 60 seconds, causing it to expand and fill the gap in the sealing surface, thus achieving a secondary enhanced seal after the impact.

3. The intelligent protection system for water-induced short circuits in reusable spacecraft battery packs according to claim 1, characterized in that: When the pressure difference between the inside and outside of the battery compartment exceeds 500Pa, the valve diaphragm of the intelligent pressure balancing valve (102) opens to achieve rapid pressure balance. Under normal conditions, the intelligent pressure balancing valve (102) relies on spring force to maintain a small opening, allowing the gas inside the battery compartment to slowly discharge out at a flow rate of no more than 0.5 liters per minute, maintaining a positive pressure dry environment inside the compartment during the floating phase.

4. The intelligent protection system for water-induced short circuits in reusable spacecraft battery packs according to claim 1, characterized in that: The sensor array (201) includes: an insulation resistance monitor for detecting the insulation integrity between the high-voltage circuit of the battery pack and the metal shell of the battery compartment; the three terminals of the insulation resistance monitor are respectively connected to the positive terminal P+ of the battery pack, the negative terminal N- of the battery pack, and the metal shell GND of the battery compartment; the insulation resistance to be monitored includes: the resistance between the positive terminal P+ of the battery pack and the metal shell GND of the battery compartment, and the resistance between the negative terminal N- of the battery pack and the metal shell GND of the battery compartment; The voltage acquisition module is connected to each battery cell to acquire voltage signals. Digital humidity sensors are located in the four corners of the battery compartment to collect humidity signals; Micromechanical IMUs provide attitude data for reusable spacecraft, including roll and pitch angle data.

5. The intelligent protection system for water-induced short circuits in reusable spacecraft battery packs according to claim 4, characterized in that: The AI ​​diagnostic module (202) is based on a microcontroller and has a built-in trained neural network algorithm. The neural network algorithm is executed once per second, and its input feature vector includes: insulation resistance value and its rate of change over the past 3 seconds, readings of four digital humidity sensors, roll angle and pitch angle. The neural network algorithm outputs a risk probability value for each battery cell, which is between 0 and 1. When the risk probability value of a battery cell exceeds 0.7 for two consecutive cycles, it is determined to be high-risk, and a high-risk warning signal and the battery cell number are sent to the main controller.

6. The intelligent protection system for water-induced short circuits in reusable spacecraft battery packs according to claim 5, characterized in that: The risk probability value is obtained through a neural network algorithm. Specifically, a neural network model is trained using historical data to identify patterns similar to historical fault precursors from the multidimensional features of the current moment, thereby obtaining the risk probability value. The historical data includes data from the normal state, the insulation degradation process, and data up to the moment before the short circuit occurs.

7. The intelligent protection system for water-induced short circuits in reusable spacecraft battery packs according to claim 1, characterized in that: The intelligent node sets the current change rate threshold to 1×10. 5 When the current change rate exceeds the threshold, the intelligent slave node drives the miniature explosive fuse to operate within 50 microseconds (A / s).

8. The intelligent protection system for water-induced short circuits in reusable spacecraft battery packs according to claim 1, characterized in that: When the main controller receives a fault isolation signal from any regional autonomous unit (301) or a high-risk warning from the AI ​​diagnostic module, it sends an instruction to the solid-state power switch matrix to change the switch combination within 200 milliseconds.

9. A smart protection system for water-induced short circuits in reusable spacecraft battery packs according to claim 8, characterized in that: The solid-state power switch matrix consists of multiple high-power MOSFET switches, and its topology supports at least one redundant power supply path.

10. A method for intelligent protection against water-borne short circuits based on the intelligent water-borne short circuit protection system according to any one of claims 1-9, characterized in that, include: S1: After the reusable spacecraft lands in water, the dynamic sealing and pressure management subsystem (100) is activated to enhance the seal and maintain positive pressure and dryness inside the battery compartment; S2: The multimodal information fusion early warning subsystem (200) operates continuously, assesses short-circuit risks, and issues an early warning when a high risk is identified; S3: If a short circuit occurs in a battery cell, the corresponding regional autonomous unit (301) of the battery cell will immediately take action to achieve local isolation; S4: After receiving the isolation or early warning signal, the central coordination unit (302) of the fault handling subsystem (300) performs power supply network reconstruction to ensure power supply to non-faulty areas.

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