Automatic intelligent power supply network reconstruction method and energy supply stability control system

Abstract

The invention relates to an automatic intelligent power supply network reconstruction method and an energy supply stability control system, and the method comprises the following steps: obtaining the state data of a power supply network in an energy system, and carrying out the network topology analysis and fault diagnosis according to the state data; if the power supply network breaks down, obtaining a reconstruction scheme of the power supply network according to a preset power supply optimization strategy; and performing network topology reconstruction on the power supply network according to the reconstruction scheme of the power supply network, according to the power supply optimization strategy, a stability optimal objective function and a system operation cost optimal objective function are constructed by taking a switching variable and a power generation distribution variable of a system topology as decision variables, multi-objective optimization calculation is carried out by considering steady-state constraints and transient constraints, and a reconstruction scheme of the power supply network is obtained. Compared with the prior art, the method achieves the stable control of the power supply network, and improves the reliability and stability of the power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of energy supply network stability control, and in particular to an automated intelligent power supply network reconstruction method and an energy supply stability control system. Background Art

[0002] At present, the traditional stability control system in the power system can only provide effective protection for the energy supply network in ferroalloy production, and it is difficult to effectively respond to and protect the operation failures and fluctuations of ferroalloy loads. In the ferroalloy industry, the load of ferroalloy production has its own particularity: unlike the grounding fault of traditional loads that often causes power system stability problems, its own overload, power mutation, collapse and other operation failures may also cause voltage, frequency and phase angle stability problems.

[0003] If a multi-source power generation system with a high penetration rate of renewable energy is involved, the frequency and phase stability problem will be more prominent, and the traditional power system stability control system will find it difficult to provide effective protection in this scenario. Summary of the invention

[0004] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide an automated intelligent power supply network reconstruction method and a stable energy supply control system for improving the reliability and stability of the power grid.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] An automated intelligent power supply network reconstruction method comprises the following steps:

[0007] Obtain the status data of the power supply network in the energy system, and perform network topology analysis and fault diagnosis based on the status data; if a power supply network fails, obtain a reconstruction plan for the power supply network based on the preset power supply optimization strategy;

[0008] The power supply network is topologically reconstructed according to the power supply network reconstruction scheme.

[0009] Furthermore, the power supply optimization strategy uses the switching variables and power generation allocation variables of the system topology as decision variables, constructs the optimal objective function of stability and the optimal objective function of system operation cost, and considers steady-state constraints and transient constraints to perform multi-objective optimization calculations to obtain a reconstruction plan for the power supply network.

[0010] Furthermore, the steady-state constraints and transient constraints include: flow constraints of each node based on the node admittance matrix under the current topology, dynamic stability constraints based on the moment of inertia, upper and lower limit constraints that each node must meet, and transient stability inequality constraints based on the power angle difference between each node with rotational inertia in the system and the central unit.

[0011] Furthermore, the expression of the decision variable is:

[0012] x=[S,P G ]=[S1,…,S n ,…,P G1 ,…,P Gi ,…]

[0013] In the formula, x is the decision variable, S is the switch variable matrix, P G is the generator output allocation value matrix, S n is the switching value of the nth tie switch, P 2i Assign a value to the output of the i-th generator;

[0014] The expression of the stability optimal objective function is:

[0015]

[0016] Where F1(x) is the optimal stability objective function, is the power angle of the i-th unit at time t under the k-th fault at time t, is the power angle of the central unit of the system under the kth fault at time t;

[0017] The expression of the optimal objective function of the system operation cost is:

[0018]

[0019] Where F2(x) is the optimal objective function of system operation cost, a i , b i and c i Both are operating cost coefficients.

[0020] Furthermore, the fault diagnosis process includes:

[0021] Collect the status data of the power supply network in the energy system, build the system operation status array, cluster the system operation status array, and use the pre-set expert operation set to perform fault diagnosis of the current system operation status according to the clustering results of the system operation status array.

[0022] Furthermore, the method further includes using an adaptive learning algorithm to perform secondary correction on the input system operation state array before performing fault diagnosis, and the corresponding secondary correction expression is:

[0023] D norm ′(i,k)=(D(i,j)-min(D(:,j))) / (max(D(:,j))-min(D(:,j)))+pΔD

[0024] Where Dnorm ′(i,j) is the system operation state array after the second correction, D(i,j) is the system operation state array before the second correction, min(D(:,j)) is the minimum value in the system operation state array before the second correction, max(D(:,j)) is the maximum value in the system operation state array before the second correction, p is the normalized correction coefficient, ΔD is the correction matrix;

[0025] The adaptive learning algorithm generates a normalized correction coefficient p and a correction matrix ΔD according to feedback on whether the fault diagnosis result is correct, and corrects the system operation state array to improve the accuracy of the fault diagnosis result.

[0026] Furthermore, the method is used for stable control and protection of a multi-source power generation system with high renewable energy penetration containing ferroalloy loads.

[0027] Furthermore, the state data collected for fault diagnosis includes: the three-phase active power P of the ore-fired furnace LF_anc (t), three-phase reactive power Q of the ore-arc furnace LF_abc (t), arc resistance r A 、Resistance of the material layer of the ore-heat furnace r B , Three-dimensional operating temperature of the submerged arc furnace T LF (t), the depth of the submerged arc furnace rod h(t), the current energy storage remaining capacity ratio SOC(t), the energy storage charging and discharging power P BESS (t), photovoltaic power P PV (t), load power P Load (t), on-grid power P Grid (t), impedance matrix RLC of the line in the system line , the loop matrix A of the network topology, the three-phase current I of each link line in the system line_abc (t), three-phase voltage U of each node of the system node_abc (t), maximum energy storage capacity ratio SOC max , minimum energy storage capacity ratio SOC min , Maximum discharge power P discharge_max , Maximum charging power P charge_max 、The maximum current I that the line can carry line_max 、The maximum voltage U that each node of the system can carry node_max .

[0028] Furthermore, the output results of the fault diagnosis include: the 0-1 variable matrix F of the system fault equipment eq , integer variable Fault_type of fault type; estimated value of system power equipment life L eq .

[0029] The present invention also provides a high-proportion new energy supply stability control system for ferroalloy production loads using the above-mentioned automated intelligent power supply network reconstruction method, comprising:

[0030] A data acquisition module, used to obtain status data of the power supply network in the energy system;

[0031] A fault diagnosis module, used for performing fault diagnosis based on the status data acquired by the data acquisition module;

[0032] The power supply network reconstruction module is used to obtain a reconstruction plan of the power supply network according to a preset power supply optimization strategy when the fault diagnosis module diagnoses a network fault; and reconstruct the network topology of the power supply network according to the reconstruction plan of the power supply network.

[0033] Compared with the prior art, the present invention has the following advantages:

[0034] (1) The present invention first collects the operating status data of the power supply network, constructs the current system operating status array, and performs clustering, and then uses the expert operating set to perform fault diagnosis on the current operating status of the system. After diagnosing the fault information, the system stability control and protection will act quickly to obtain a reconstruction plan for the power supply network, realize fault recovery, effectively reduce the impact of the fault on the ferroalloy production output, and improve the reliability and stability of the power grid.

[0035] (2) Traditional system reconstruction has the disadvantage of low flexibility. The reconstructed small system still does not have good robustness and stability, which may lead to further system decoupling. The present invention first introduces the switch variables and power generation allocation variables of the decision system topology into the decision variables of power generation allocation, and sets the optimal stability objective function and the optimal system operation cost objective function to perform multi-objective optimization. The topology reconstruction method is taken into account in the optimal dynamic stability allocation of the system, and the power grid is quickly reconstructed and the power generation strategy is allocated, so that the system has higher flexibility, stability and robustness.

[0036] (3) The present invention takes into account the disadvantage that the fault diagnosis method based on the expert system may have insufficient flexibility, resulting in misdiagnosis of faults. Therefore, it further proposes to use an adaptive learning algorithm to perform secondary correction on the input system operation status array, thereby achieving accurate and rapid online diagnosis of the system status. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 A schematic diagram of a flow chart of an automated intelligent power supply network reconstruction method provided in an embodiment of the present invention;

[0038] Figure 2 The present invention provides a schematic diagram of a fault diagnosis process. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0040] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0042] Example 1

[0043] like Figure 1 As shown, this embodiment provides an automated intelligent power supply network reconstruction method, comprising the following steps:

[0044] Data collection step S1: obtaining status data of the power supply network in the energy system;

[0045] Fault diagnosis step S2: performing network topology analysis and fault diagnosis based on the status data;

[0046] Network reconstruction step S3: If a power supply network fails, a reconstruction plan of the power supply network is obtained according to a preset power supply optimization strategy; and the network topology of the power supply network is reconstructed according to the reconstruction plan of the power supply network.

[0047] This embodiment applies the above method to the stable control and protection of a multi-source power generation system with a high renewable energy penetration rate and containing ferroalloy loads.

[0048] Specifically, in step S1, the operating status data, including ferroalloy load power, current, voltage, frequency and other parameters, are first collected in real time through monitoring equipment and sensors connected to each key node and key equipment of the system. The collected data is preprocessed, cleaned and feature extracted to obtain an accurate data basis. Then, the real-time operating data of all nodes and equipment in the whole network is obtained through the state estimation method to provide support for subsequent real-time rapid fault diagnosis and stable control.

[0049] In step S2, firstly, an expert database of ferroalloy production load failures and renewable energy failures and fluctuations is innovatively established through multi-time scale modeling, ferroalloy load thermoelectric coupling modeling, and analysis. This expert system is applied to fault diagnosis to achieve high-speed estimation and diagnosis of the operating status of system components.

[0050] It is further proposed to use an adaptive learning algorithm to extract, learn and iterate the system state variables at the time of failure, so as to achieve secondary correction of the expert system fault diagnosis results and obtain accurate and rapid online diagnosis results of the system status.

[0051] Specifically, the parameters input for fault diagnosis include: the three-phase active power P of the ore-fired furnace LF_abc (t), three-phase reactive power Q of the ore-arc furnace LF_abc (t), arc resistance r A 、Resistance of the material layer of the ore-heat furnace r B , Three-dimensional operating temperature of the submerged arc furnace T LF (t), the depth of the submerged arc furnace rod h(t); the current energy storage remaining capacity ratio SOC(t), the energy storage charging and discharging power P BESS (t), photovoltaic power P PV (t), load power P Load (t), on-grid power P Frid (t); impedance matrix RLC of the line in the system line , the loop matrix A of the network topology, the three-phase current I of each link line in the system line_abc (t), the three-phase voltage U of each node of the system node_abc (t); Maximum energy storage capacity ratio SOC max , minimum energy storage capacity ratio SOC min , maximum discharge power P discharge_max , maximum charging power P charge_max , the maximum current that the line can carry I line_max , the maximum voltage U that each node of the system can carry node_max .

[0052] The output parameters of fault diagnosis include: 0-1 variable matrix F of system fault equipment eq , integer variable Fault_type of fault type; estimated value of system power equipment life L eq .

[0053] The process of using the expert database for fault diagnosis includes: using the FCM algorithm to cluster the current system operation state array D, and then using the preset expert operation set to perform fault diagnosis on the current system operation state.

[0054] The FCM algorithm is defined as having the following objective function and its constraints. The objective function represents the distance between each cluster center and all parameter points. By calculating to minimize the objective function, the optimal sample classification is obtained:

[0055]

[0056] where u ij Indicates that the jth data sample belongs to v i The degree of membership of the cluster, the matrix has the following characteristics:

[0057] 1) The sum of the membership degrees of each sample belonging to each category is 1.

[0058] 2) Each type of fuzzy subset is not an empty set.

[0059] where u ij With v i The following iteration formula is satisfied:

[0060]

[0061] When calculating, first randomly generate a u that meets the conditions ij , and then use this to find v i Start iteration. During this process, the objective function J keeps changing. When J reaches a minimum value, it can be considered that the algorithm has converged to a better result.

[0062] Since the fault diagnosis method based on expert system may have the disadvantage of insufficient flexibility, which may cause misjudgment of fault diagnosis, an adaptive learning algorithm is used to perform a secondary correction on the input system operation status array D:

[0063] D norm ′(i,j)=(D(i,j)-min(D(:,j))) / (max(D(:,j))-min(D(:,j)))+pΔD

[0064] Where D norm ′(i,j) is the system operation state array after the second correction, D(i,j) is the system operation state array before the second correction, min(D(:,j)) is the minimum value in the system operation state array before the second correction, max(D(:,j)) is the maximum value in the system operation state array before the second correction, p is the normalized correction coefficient, and ΔD is the correction matrix.

[0065] The adaptive learning algorithm generates a normalized correction coefficient p and a correction matrix ΔD based on the feedback of whether the fault diagnosis result is correct, and corrects the system operation state array to improve the accuracy of the fault diagnosis result.

[0066] The adaptive learning algorithm generates the normalized correction coefficient p and the correction matrix ΔD based on the Q learning algorithm, which specifically includes the following processes:

[0067] According to the size of the Q matrix elements, the unit is selected to inject each bit of the corresponding binary code. The action selection has only 0-1 variables. After the action selection is completed, it is converted into continuous system operation state parameters through encoding and substituted into the fault diagnosis model, and converted into corresponding action rewards to update the Q matrix until the optimal strategy is obtained to maximize the reward return. When the multi-energy system optimization model converges to an infeasible solution, the action reward is 0. Among them, the action selection strategy is to select in the binary space in a roulette way:

[0068]

[0069] Where: r is a random number between [0,1]; Based on The probability matrix The status is The probability of selecting action 1; for The Q value of the state is s and the action is a.

[0070] State-Action Q Matrix Elements The update strategy is as follows:

[0071]

[0072] in: is the value of a in the k-th iteration. (k) After the action, the state changes from s (k) Transfer to state s (k+1) The reward value; ρ is the discount factor; a (k) is the learning factor of the k-th iteration, and the variable learning rate Q learning based on natural logarithmic decay is adopted, that is:

[0073]

[0074] like Figure 2 As shown in the figure, this fault diagnosis method uses an expert fault diagnosis system, FCM clustering algorithm, and adaptive learning algorithm to extract features, learn, and iterate the system state variables at the time of the fault, thereby achieving accurate and rapid online diagnosis of the system state. Based on the real-time high-speed monitoring system to obtain the operating status data of the entire system, within 100ms after the fault occurs, the multi-time scale, two-stage fault diagnosis method based on the expert system and adaptive learning algorithm can accurately identify the fault type and locate the faulty component.

[0075] In step S3, after the system protection quickly removes the fault, the stability control system quickly reconstructs the power grid and allocates the power generation strategy.

[0076] Traditional system reconstruction has the disadvantage of low flexibility. The reconstructed small system still does not have good robustness and stability, which may lead to further system decommissioning. This solution innovatively improves the system reconstruction method after a fault, taking the topology reconstruction method into account in the optimal dynamic and stable allocation of the system, making the system more flexible, stable and robust. The specific process is as follows:

[0077] Set the decision variable x for power generation allocation, including the switch variable of the decision system topology and the power generation allocation variable: the switch quantity S of the nth tie switch n (0-1 variable), the output distribution value P of the i-th generator Gi , the corresponding calculation expression is:

[0078] x=[S,P G ]=[S1,…,S n ,…,P G1 ,…,P Gi ,…]

[0079] The multi-objective optimization includes two objectives: F1 is the optimal stability objective function, which means that the sum of the relative power angle differences of the central units of each small system is minimized under the full fault set; F2 is the optimal objective function for system operation cost, which adopts the quadratic polynomial cost model:

[0080]

[0081] Where F1(x) is the optimal stability objective function, is the power angle of the i-th unit at time t under the k-th fault at time t, is the power angle of the central unit of the system under the kth fault at time t, F2(x) is the optimal objective function of the system operation cost, a i , b i and c i Both are operating cost coefficients.

[0082] Constraints can be divided into steady-state constraints (i.e., power flow constraints) and transient constraints of the system:

[0083] 1. Flow Constraints

[0084] First, the node admittance matrix Y of the network is calculated through the variable switch quantity S: From the following formula, the value of each element in the node admittance matrix under different topologies can be obtained by traversing the m-tree by computer.

[0085] Y -1 (S 1×n )=X(S 1×n )

[0086] Y(S 1×n)U=I

[0087]

[0088] Where ΔY(S 1×n )=det(Y(S 1×n )) is the determinant of the node admittance matrix Y with the switch quantity S as the variable; ΔY ij (S 1×n ) corresponds to the element Y in Y ij The algebraic cofactor of .

[0089] Based on the node admittance matrix Y under this topology, each node in the system needs to satisfy the following power flow constraints:

[0090] Y ij =G ij +jB ij

[0091]

[0092] 2. Dynamic stability constraints

[0093] When the system is disconnected, the stability support of the system is mainly provided by the grid-building new energy equipment and conventional units. These devices have virtual or actual rotational inertia and can be analyzed by second-order dynamic modeling:

[0094]

[0095] 3. Power flow inequality constraints

[0096] That is, operation constraints refer to the upper and lower limit constraints that each component in the system needs to meet, including the upper and lower limit constraints of active and reactive power of power generation equipment, the upper and lower limit constraints of line voltage, and the line transmission power constraints:

[0097] P Gi_min ≤P Gi ≤P Gi_max

[0098] Q Gi_min ≤Q Gi ≤Q Gi_max

[0099] U i_min ≤U i ≤U i_max

[0100] P ij_min ≤P ij ≤P ij_max

[0101] 4. Transient stability inequality constraints

[0102] Define the power angle of the central unit of the system under the kth fault for:

[0103]

[0104] The transient stability inequality constraint is that the power angle difference between each component with rotational inertia in the system and the central unit shall not exceed a certain range:

[0105]

[0106] By solving this multi-objective optimization problem, we can obtain the optimal dynamic stable power generation allocation strategy for the reconstructed system taking into account the topological approach. While ensuring the safe and stable operation of the reconstructed system, the operation of important system loads and the insulation of ferroalloy production loads can be guaranteed, ensuring the operational stability, economy and robustness after the system fault is removed.

[0107] This embodiment also provides a high proportion of new energy supply stability control system for ferroalloy production load using the above-mentioned automated intelligent power supply network reconstruction method, including:

[0108] A data acquisition module, used to obtain status data of the power supply network in the energy system;

[0109] A fault diagnosis module, used for performing fault diagnosis based on the status data acquired by the data acquisition module;

[0110] The power supply network reconstruction module is used to obtain a reconstruction plan of the power supply network according to a preset power supply optimization strategy when the fault diagnosis module diagnoses a network fault; and reconstruct the network topology of the power supply network according to the reconstruction plan of the power supply network.

[0111] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. An automated intelligent power supply network reconstruction method, characterized in that: The following steps are involved: Obtain the status data of the power supply network in the energy system, and perform network topology analysis and fault diagnosis based on the status data; if a power supply network fails, obtain a reconstruction plan for the power supply network based on the preset power supply optimization strategy; The power supply network is topologically reconstructed according to the power supply network reconstruction scheme.

2. The method for reconfiguring an automated intelligent power supply network according to claim 1, characterized in that: The power supply optimization strategy uses the switching variables and power generation allocation variables of the system topology as decision variables, constructs the optimal stability objective function and the optimal system operation cost objective function, and considers steady-state constraints and transient constraints to perform multi-objective optimization calculations to obtain a reconstruction plan for the power supply network.

3. The method for reconfiguring an automated intelligent power supply network according to claim 2, characterized in that: The steady-state constraints and transient constraints include: flow constraints of each node based on the node admittance matrix under the current topology, dynamic stability constraints based on rotational inertia, upper and lower limit constraints that each node must meet, and transient stability inequality constraints based on the power angle difference between each node with rotational inertia in the system and the central unit.

4. The method for reconfiguring an automated intelligent power supply network according to claim 2, characterized in that: The expression of the decision variable is: x=[S,P G ]=[S1,…,S n ,…,P G1 ,…,P Gi ,…] In the formula, x is the decision variable, S is the switch variable matrix, P G is the generator output allocation value matrix, S n is the switching value of the nth tie switch, P Gi Assign a value to the output of the i-th generator; The expression of the stability optimal objective function is: Where F1(x) is the optimal stability objective function, is the power angle of the i-th unit at time t under the k-th fault at time t, is the power angle of the central unit of the system under the kth fault at time t; The expression of the optimal objective function of the system operation cost is: Where F2(x) is the optimal objective function of system operation cost, a i , b i and c i Both are operating cost coefficients.

5. The method for reconfiguring an automated intelligent power supply network according to claim 1, characterized in that: The fault diagnosis process includes: Collect the status data of the power supply network in the energy system, build the system operation status array, cluster the system operation status array, and use the pre-set expert operation set to perform fault diagnosis of the current system operation status according to the clustering results of the system operation status array.

6. The method for automatic intelligent power supply network reconstruction according to claim 5, characterized in that: The method further includes using an adaptive learning algorithm to perform secondary correction on the input system operation state array before performing fault diagnosis, and the corresponding secondary correction expression is: D norm ′(i,j)=(D(i,j)-min(D(:,j))) / (max(D(:,j))-min(D(:,j)))+pΔD Where D norm ′(i,j) is the system operation state array after the second correction, D(i,j) is the system operation state array before the second correction, min(D(:,j)) is the minimum value in the system operation state array before the second correction, max(D(:,j)) is the maximum value in the system operation state array before the second correction, p is the normalized correction coefficient, ΔD is the correction matrix; The adaptive learning algorithm generates a normalized correction coefficient p and a correction matrix ΔD according to feedback on whether the fault diagnosis result is correct, and corrects the system operation state array to improve the accuracy of the fault diagnosis result.

7. The method for reconfiguring an automated intelligent power supply network according to claim 1, characterized in that: The method is used for stable control and protection of a multi-source power generation system with high renewable energy penetration containing ferroalloy loads.

8. The method for automatic intelligent power supply network reconstruction according to claim 7, characterized in that: The status data collected for fault diagnosis include: the three-phase active power P of the submerged arc furnace LF_abc (t), three-phase reactive power Q of the ore-arc furnace LF_abc (t), arc resistance of electric arc furnace r A 、Resistance of the material layer of the ore-heat furnace r B , Three-dimensional operating temperature of the submerged arc furnace T LF (t), the depth of the submerged arc furnace rod h(t), the current energy storage remaining capacity ratio SOC(t), the energy storage charging and discharging power P BESS (t), photovoltaic power P PV (t), load power P Load (t), on-grid power P Grid (t), impedance matrix RLC of the line in the system line , the loop matrix A of the network topology, the three-phase current I of each link line in the system line_abc (t), three-phase voltage U of each node of the system node_abc (t), maximum energy storage capacity ratio SOC max , minimum energy storage capacity ratio SOC min , Maximum discharge power P discharge_max , Maximum charging power P charge_max 、The maximum current I that the line can carry line_max 、The maximum voltage U that each node of the system can carry node_max .

9. The method for reconfiguring an automated intelligent power supply network according to claim 7, characterized in that: The output results of the fault diagnosis include: eq , integer variable Fault_type of fault type; estimated value of system power equipment life L eq .

10. A high proportion of new energy supply stability control system for ferroalloy production load using an automated intelligent power supply network reconstruction method as described in any one of claims 1 to 9, characterized in that: include: A data acquisition module, used to obtain status data of the power supply network in the energy system; A fault diagnosis module, used for performing fault diagnosis based on the status data acquired by the data acquisition module; The power supply network reconstruction module is used to obtain a reconstruction plan of the power supply network according to a preset power supply optimization strategy when the fault diagnosis module diagnoses a network fault; and reconstruct the network topology of the power supply network according to the reconstruction plan of the power supply network.

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