Satellite power system fault location method based on abnormal simulation results

Abstract

This disclosure relates to a method for locating satellite power system faults based on abnormal simulation results. The method comprises: analyzing the abnormal impacts of various factors on the satellite power system and establishing an abnormal evolution model for the satellite power system; importing the abnormal evolution model into a normal state simulation system to obtain abnormal simulation results; and utilizing a neural network to compare the abnormal simulation results with actual satellite data to locate the fault location of the satellite power system. This method can intelligently locate abnormal states in satellite power systems, providing support for subsequent on-orbit satellite anomaly analysis and processing.

Description

Technical Field

[0001] The present disclosure relates to the field of aerospace measurement and control technology, and in particular to a method for locating faults in a satellite power system based on abnormal simulation results. Background Art

[0002] Currently, the process of analyzing satellite anomalies on-orbit is extremely complex, making satellite simulation systems increasingly important. However, in practice, simulation results are often manually compared with real satellite data. This requires extensive experience and the ability to distinguish the differences and manifestations of various anomalies, which significantly limits their application. Furthermore, due to the limited number of types and low recurrence rates of satellite anomalies on-orbit, it is difficult to provide diverse training samples, which falls short of the requirements for intelligent learning and judgment. Therefore, it is necessary to address one or more of the issues identified in the aforementioned technical solutions to improve the global estimation accuracy and fault tolerance of the navigation system.

[0003] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute prior art known to ordinary technicians in the field. Summary of the Invention

[0004] The purpose of the embodiments of the present disclosure is to provide a satellite power system fault location method based on abnormal simulation results, which can intelligently locate the fault of the satellite power system and provide support for subsequent satellite on-orbit abnormality analysis and processing.

[0005] The present disclosure provides a method for locating a fault in a satellite power system based on abnormal simulation results, the method comprising the following steps:

[0006] Analyze the abnormal impacts of various factors on the satellite power system and establish an abnormal evolution model of the satellite power system;

[0007] Importing the abnormal evolution model of the satellite power system into a normal state simulation system to obtain abnormal simulation results;

[0008] By using a neural network, the abnormal simulation result is compared with the actual satellite data to obtain the fault location of the satellite power system.

[0009] In an exemplary embodiment of the present disclosure, in the step of analyzing the abnormal impacts of various inducements on the satellite power system and establishing an abnormal evolution model of the satellite power system, the abnormal impacts of the various inducements on the satellite power system include reduced solar panel current, short circuit of battery pack cells and abnormal battery pack voltage telemetry.

[0010] In an exemplary embodiment of the present disclosure, the calculation formula of the solar panel current under normal conditions includes:

[0011]

[0012] Where, I represents the output current of the solar cell array; I' SC represents the short-circuit current of the solar cell array; C1 represents the first coefficient; V represents the output voltage of the solar cell array; C2 represents the second coefficient; V O ' C Represents the open circuit voltage of the solar cell array; I' mp Indicates the output current of the solar cell array at the optimal operating point; V m ' p Indicates the output voltage of the solar cell array at the optimal operating point;

[0013] When the inducement is a decrease in solar panel current,

[0014] I = -a × sin (0.94 × (t × 40 / 1638 + Δ) - 0.2876) + b (2)

[0015] Where, a and b are normal coefficients; t represents time; and Δ represents the decay function.

[0016] In an exemplary embodiment of the present disclosure, when the solar panel current decreases, an abnormal evolution model of the solar panel current decrease is imported into a normal state simulation system to obtain an abnormal simulation result of the solar panel current decrease, and the abnormal simulation result of the solar panel current decrease is compared with the actual satellite data, wherein the calculation formula of the nonlinear correlation coefficient includes:

[0017]

[0018] Among them, SS tot It represents the mean square error; it represents the sum of the squares of the differences between the real data and the simulated predicted data.

[0019] In an exemplary embodiment of the present disclosure, the calculation formula for the voltage of the battery pack under normal conditions includes:

[0020] U=N×(a+b×Q / (QQ 变化 )+c×exp(d×Q 变化 )) (4)

[0021] Among them, a, b, c and d represent normal coefficients respectively; N represents the number of batteries in series; U represents the voltage of the battery pack; Q represents the initial charge of the battery cell; Q 变化 Indicates the battery charge and discharge capacity;

[0022] The calculation formula for the charging current of the battery pack under normal circumstances includes:

[0023]

[0024] Among them, I ld (t) represents the data of the load power demand of the nth track changing with time; η BDR Represents the discharge regulator power; η line Represents the battery pack power supply line loss factor; V bt Indicates the battery pack discharge voltage; V bs Indicates the bus voltage when the battery pack is discharging;

[0025] When the inducement is a short circuit of a battery pack monomer, the calculation formula of the battery pack under abnormal conditions includes:

[0026] U=(N-1)×(e+f×Q / (QQ 变化 )+g×exp(h×Q 变化 )) (6)

[0027] Among them, e, f, g and h are abnormal coefficients respectively; N is the number of batteries in series; U is the voltage of the battery pack; Q is the initial charge of the battery cell; Q 变化 Indicates the battery charge and discharge capacity.

[0028] In an exemplary embodiment of the present disclosure, when a battery cell short-circuit occurs, an abnormal evolution model of the battery cell short-circuit is imported into a normal state simulation system to obtain an abnormal simulation result of the battery cell short-circuit, and the abnormal simulation result of the battery cell short-circuit is compared with the actual data of the satellite, and the discharge final voltage of the battery pack with the abnormal simulation result is lower than the normal discharge final voltage of the battery pack when the satellite is actually in orbit.

[0029] In an exemplary embodiment of the present disclosure, when the abnormal impact of the various inducements on the satellite power system is abnormal battery pack voltage telemetry, the battery pack voltage output value is changed to a constant value in the satellite power system abnormal evolution model.

[0030] In an exemplary embodiment of the present disclosure, when the battery pack voltage telemetry is abnormal, the abnormal evolution model of the battery pack voltage telemetry is imported into the normal state simulation system to obtain the abnormal simulation result of the battery pack voltage telemetry, and the abnormal simulation result of the battery pack voltage telemetry is compared with the actual satellite data to obtain the battery voltage displayed as a constant value.

[0031] In an exemplary embodiment of the present disclosure, in the step of using a neural network to compare the actual satellite data and the abnormal simulation results to obtain the fault location of the satellite power system, the neural network is a BP neural network, and the BP neural network includes an input layer, a hidden layer and an output layer.

[0032] In an exemplary embodiment of the present disclosure, the input layer of the BP neural network includes n nodes, the hidden layer includes q nodes, and the output layer includes m nodes;

[0033] The output formula of the nodes of the hidden layer includes:

[0034] z l =f1(V l T X) (7)

[0035] The output formula of the node of the output layer includes:

[0036]

[0037] Among them, V kl Represents the weight between the input layer and the hidden layer; w ji Represents the weight between the hidden layer and the output layer; f1() represents the transfer function of the hidden layer; f2() represents the transfer function of the output layer.

[0038] The technical solution provided by the present disclosure may include the following beneficial effects: In the embodiment of the present disclosure, the proposed satellite power system fault location method based on abnormal simulation results can analyze the abnormal impacts of various inducements on the satellite power system and establish an abnormal evolution model of the satellite power system; import the abnormal evolution model into the normal state simulation system to obtain abnormal simulation results; and compare the obtained abnormal simulation results with the actual satellite data through a neural network, so as to intelligently locate the abnormal state of the satellite power system and provide support for subsequent satellite on-orbit abnormality analysis and processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The accompanying drawings are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification, serve to explain the principles of the present disclosure. Obviously, the drawings described below are only some embodiments of the present disclosure, and those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0040] Figure 1 A schematic diagram showing the steps of a fault location method for a satellite power supply system in an exemplary embodiment of the present disclosure;

[0041] Figure 2 a-2d are schematic diagrams showing the input results of abnormal simulation results of solar panel current reduction and actual satellite on-orbit data in an exemplary embodiment of the present disclosure;

[0042] Figure 3 a-3b are schematic diagrams showing abnormal simulation results of a short circuit of a battery pack cell in an exemplary embodiment of the present disclosure;

[0043] Figure 4 A schematic diagram showing simulation results of abnormal battery pack voltage telemetry in an exemplary embodiment of the present disclosure;

[0044] Figure 5 A schematic diagram showing the structure of a BP neural network in an exemplary embodiment of the present disclosure;

[0045] Figure 6 A schematic diagram showing the convergence result of the BP neural network in an exemplary embodiment of the present disclosure;

[0046] Figure 7 A schematic diagram illustrating determination results of three abnormal states in an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0047] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0048] In addition, the accompanying drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0049] This example embodiment provides a method for locating satellite power system faults based on abnormal simulation results. Figure 1 As shown, the method may include the following steps:

[0050] Step S101: Analyze the abnormal impacts of various factors on the satellite power system and establish an abnormal evolution model of the satellite power system;

[0051] Step S102: importing the abnormal evolution model of the satellite power system into a normal state simulation system to obtain abnormal simulation results;

[0052] Step S103: using a neural network, the abnormal simulation result is compared with the actual satellite data to obtain the fault location of the satellite power system.

[0053] A satellite power system fault location method based on abnormal simulation results proposed in this embodiment can intelligently locate the abnormal state of the satellite power system and provide support for subsequent satellite on-orbit abnormality analysis and processing.

[0054] Below, each step of the above method in this exemplary embodiment is described in more detail.

[0055] The primary method used in the simulation system for analyzing on-orbit satellite anomalies is to configure the simulation system accordingly or add an anomaly model based on the analysis results of on-orbit satellite anomaly parameters. After the system outputs the anomaly results, the simulation results are compared with the actual satellite data to determine whether the satellite anomaly is the intended one. This disclosure incorporates various hypothetical or actual satellite power anomalies into the simulation system, deriving and labeling simulation results for each parameter. The resulting satellite anomaly parameters are then input into the model for classification and comparison to determine the cause of the failure.

[0056] In the first embodiment of this example, the abnormal impact on the satellite power system caused by the reduction of solar panel current is proposed, and an abnormal evolution model of the satellite power system is analyzed and established.

[0057] In this embodiment, the causes of reduced solar panel current are analyzed. During satellite in-orbit operation, abnormal reductions in solar panel current can occur due to factors such as the space environment, aging, and orbital attenuation. In actual on-orbit operation, failure of a satellite's entire solar array is rare; such failures can result in a complete loss of output power for a single solar panel. The most common failure mode for solar panels is reduced power output capability, including failure of individual solar cells or cell circuits, increased solar incidence angles, and degradation of cell performance. Solar cell circuit failures can be caused by a variety of factors, including micrometeoroid damage, high-voltage arcing, and failure of power supply components in the power controller. In a properly designed solar panel, the cells are equipped with bypass diodes and string isolation diodes. Failure of individual cells or strings of cells has minimal impact on mission safety and successful completion.

[0058] After analysis, a model for the abnormal evolution of solar panel current reduction was established. For example, if the entire solar array loses power on a satellite, the solar panel current is assumed to be zero. However, after a satellite has been in orbit for a period of time, the solar panels age, some battery strings fail, and the solar panel output current decreases. The formula for calculating the solar panel current under normal conditions includes:

[0059]

[0060] Where, I represents the output current of the solar cell array; I' SC represents the short-circuit current of the solar cell array; C1 represents the first coefficient; V represents the output voltage of the solar cell array; C2 represents the second coefficient; V O ' C Represents the open circuit voltage of the solar cell array; I' mp Indicates the output current of the solar cell array at the optimal operating point; V m ' p Indicates the output voltage of the solar cell array at the optimal operating point.

[0061] When the solar panel current decreases, the solar panel loses power partially, and the output current, operating voltage, short-circuit current, etc. of the optimal working point of the solar array change. This change is difficult to grasp on the ground and difficult to calculate using the original formula (1). When the satellite state is stable, the solar panel current simulation result at this time can be obtained by fitting the formula with the on-orbit parameter data.

[0062] When the inducement is that the solar panel current decreases, the

[0063] I=-a×sin(0.94×(t×40 / 1638+Δ)-0.2876)+b(2)

[0064] Where, a and b are normal coefficients; t represents time; and Δ represents the decay function.

[0065] The calculation formula of the nonlinear correlation coefficient includes:

[0066]

[0067] Among them, SS tot It represents the mean square error; it represents the sum of the squares of the differences between the real data and the simulated predicted data.

[0068] Then, the abnormal evolution model of the solar panel current reduction is added to the normal state simulation system to obtain the abnormal simulation result, which is used as the basis for subsequent comparison.

[0069] like Figure 2As shown in the figure, due to a severe loss of solar panel current on a satellite, the solar panel current was severely reduced, and the sunlit area could not meet the payload startup load. The sunlit area refers to the area of ​​the satellite that is exposed to sunlight during its orbit. However, when the payload is not powered on, the battery pack can meet the energy needs of the satellite in orbit. The abnormal evolution model of the solar panel current reduction was added to the normal state simulation system, replacing the solar panel current model under normal conditions, and the abnormal simulation results of the solar panel current reduction were obtained.

[0070] The abnormal simulation results of the solar panel current reduction are compared with the actual data of the satellite.

[0071] Figure 2 a is the result input diagram of the simulation result of solar array current and actual on-orbit data after a satellite solar panel fails; Figure 2 b is the result input diagram of the simulation result of the battery pack charging current input into the actual on-orbit data after the solar panel fails; Figure 2 c is the result input diagram of the actual on-track data of the load current (in the simulation model, the load current is the input parameter); Figure 2 d is the result input diagram of the simulation results of the battery pack voltage and the actual on-orbit data after the solar panel fails.

[0072] In the second embodiment of this example, the abnormal impact on the satellite power system caused by the short circuit of the battery pack is proposed, and an abnormal evolution model of the satellite power system is analyzed and established.

[0073] In this example, the causes of battery cell short circuits were analyzed. Sudden on-orbit failures are rare for battery packs. For most satellites, battery performance degrades with increasing on-orbit cycles. However, the risk of battery failure increases with the satellite's on-orbit lifespan. A case study of a satellite on-orbit failure revealed that after a short circuit in a battery pack, the six cells connected in series caused the highest cell voltage to exceed the preset maximum value, resulting in overcharging. This reduced the battery pack's output voltage, increased discharge current, and increased depth of discharge, shortening the battery pack's lifespan.

[0074] After analysis, an abnormal evolution model of battery cell short circuit is established.

[0075] The calculation formula for the battery pack voltage under normal conditions includes:

[0076] U=N×(a+b×Q / (QQ 变化 )+c×exp(d×Q 变化 )) (4)

[0077] Among them, a, b, c and d represent normal coefficients respectively; N represents the number of batteries in series; U represents the voltage of the battery pack; Q represents the initial charge of the battery cell; Q 变化 Indicates the battery charge and discharge capacity.

[0078] Here, due to a short circuit in one battery pack, formula (4) is transformed into

[0079] U=(N-1)×(e+f×Q / (QQ 变化 )+g×exp(h×Q 变化 )) (6)

[0080] Among them, e, f, g and h are abnormal coefficients respectively; N is the number of batteries in series; U is the voltage of the battery pack; Q is the initial charge of the battery cell; Q 变化 Indicates the battery charge and discharge capacity.

[0081] The calculation formula for the battery pack's charging current under normal circumstances includes:

[0082]

[0083] Among them, I ld (t) represents the data of the load power demand of the nth track changing with time; η BDR Represents the discharge regulator power; η line Represents the battery pack power supply line loss factor; V bt Indicates the battery pack discharge voltage; V bs Indicates the bus voltage when the battery pack is discharging.

[0084] Next, the abnormal evolution model of battery cell short circuit is imported into the normal state simulation system, replacing the battery pack voltage and discharge current calculation model in the simulation system of the battery pack in the normal state, to obtain the abnormal simulation results of battery cell short circuit, and compare the abnormal simulation results of battery cell short circuit with the actual satellite data.

[0085] like Figure 3 As shown, without a satellite in orbit, only simulation results are available. The simulation results show that the battery pack's final discharge voltage is significantly lower than the normal discharge voltage of a satellite in orbit, indicating a decrease in battery pack performance. Under the same load conditions, the battery pack's discharge current increases significantly, indicating a decrease in battery pack performance.

[0086] Figure 3 a is the battery pack voltage simulation result diagram after a satellite battery pack has an abnormality; Figure 3 b is the simulation result of the battery pack discharge current after an abnormality occurs in a satellite battery pack.

[0087] In the third embodiment of this example, the abnormal impact on the satellite power system caused by the abnormality of battery pack voltage telemetry is proposed, and an abnormal evolution model of the satellite power system is analyzed and established.

[0088] In this example, the cause of abnormal battery voltage telemetry is analyzed. Based on a satellite on-orbit failure, a circuit failure in the power controller used to measure battery voltage caused the battery voltage to display a constant value. The power system displays normal bus voltage, but abnormal battery voltage telemetry.

[0089] After analysis, a battery pack voltage telemetry abnormal evolution model is established.

[0090] The abnormal evolution model of battery pack voltage telemetry is imported into the normal state simulation system to obtain the abnormal simulation results of battery pack voltage telemetry. The abnormal simulation results of battery pack voltage telemetry are compared with the actual satellite data to obtain that the battery voltage is displayed as a constant value.

[0091] like Figure 4 As shown, since there is no actual satellite in orbit, only simulation results can be obtained.

[0092] In the above three embodiments, a neural network is used to compare the abnormal simulation results in the three embodiments with the actual satellite data to obtain the fault location of the satellite power system.

[0093] The neural network here is BP (Back Propagation) neural network. BP neural network is a multi-layer feedforward neural network that uses feedback principle to modify weights. It has the advantages of clear feedback process, rigorous derivation process, high accuracy, and good versatility. Figure 5 As shown, the BP neural network includes an input layer, a hidden layer, and an output layer. In the BP neural network, the input layer contains n nodes, the hidden layer contains q nodes, and the output layer contains m nodes.

[0094] The output formula of the nodes in the hidden layer includes:

[0095] z l =f1(V l T X) (7)

[0096] The output formula of the nodes in the output layer includes:

[0097]

[0098] Among them, V kl Represents the weight between the input layer and the hidden layer; w jiRepresents the weight between the hidden layer and the output layer; f1() represents the transfer function of the hidden layer; f2() represents the transfer function of the output layer.

[0099] Finally, the abnormal simulation results are compared with the actual satellite data through the BP neural network to obtain the fault location of the satellite power system.

[0100] Simulation software was used to simulate 21,000 seconds of data for five telemetry parameters, including bus voltage, battery voltage, and solar panel current, under three abnormal conditions described in the three examples. State 1 was characterized by reduced solar panel current, state 2 by a battery cell short circuit, and state 3 by a battery voltage telemetry anomaly. The simulated data for each state served as training data, while the satellite anomaly and reduced solar panel current data served as test data, which were fed into a BP neural network for state verification.

[0101] From the calculation results, the BP neural network algorithm converged after learning 496 times, as shown in Figure 6 and Figure 7 As shown, the training result Chen efficiency is 100%.

[0102] This disclosure provides three types of faults: battery cell short circuit (labeled as State 1), battery telemetry display error (labeled as State 2), and solar array current reduction (labeled as State 3). The simulation results of these three types of faults are used as training data to train a neural network classification model. Real-world data showing solar array current reduction is input into the model as test data, and the state with which the real-world data state best matches is calculated. The calculated results show the highest degree of agreement with State 1, thus proving the effectiveness of the algorithm.

[0103] The five telemetry parameters here refer to the solar cell array current, bus voltage, battery voltage, charging current, and discharging current.

[0104] In summary, the fault location method for satellite power systems proposed in the embodiments of this disclosure addresses the difficulty of intelligently locating faults using actual satellite data. It achieves a 100% recognition rate for known faults, meeting engineering requirements. This method utilizes high-precision simulation data to train on-orbit anomaly states, targeting small sample sizes, such as the low reproducibility of satellite anomalies. The method uses the actual satellite anomaly data as test data for validation. Validation results demonstrate that this method can effectively identify the causes of on-orbit faults.

[0105] It should be noted that although the steps of the method disclosed herein are depicted in a particular order in the accompanying drawings, this does not require or imply that the steps must be performed in that particular order, or that all steps must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into a single step, and / or a single step may be decomposed into multiple steps. Furthermore, it is readily understood that these steps may be executed synchronously or asynchronously, for example, in multiple modules / processes / threads.

[0106] It should be noted that although several units of the system for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiment of the present disclosure, the features and functions of two or more units described above can be concretized in one unit. Conversely, the features and functions of a unit described above can be further divided into multiple units for concretization. Some or all of the units can be selected according to actual needs to achieve the purpose of the disclosed solution. Those of ordinary skill in the art can understand and implement it without paying creative work.

[0107] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the appended claims.

Claims

1. A satellite power system fault location method based on abnormal simulation results, characterized in that: The following steps are involved: Analyze the abnormal impact of various factors on the satellite power system and establish an abnormal evolution model of the satellite power system; The abnormal effects of various inducements on the satellite power system include reduced solar panel current, short circuit of battery cells and abnormal battery voltage telemetry; Importing the abnormal evolution model of the satellite power system into a normal state simulation system to obtain abnormal simulation results; By using a neural network, the abnormal simulation result is compared with the actual satellite data to obtain the fault location of the satellite power system.

2. The satellite power system fault location method according to claim 1, characterized in that: The calculation formula of the solar panel current under normal circumstances includes: Where, I represents the output current of the solar cell array; I' SC represents the short-circuit current of the solar cell array; C1 represents the first coefficient; V represents the output voltage of the solar cell array; C2 represents the second coefficient; V′ OC Represents the open circuit voltage of the solar cell array; I' mp Indicates the output current of the solar cell array at the optimal operating point; V′ mp Indicates the output voltage of the solar cell array at the optimal operating point; When the inducement is that the solar panel current decreases, the I=-a×sin(0.94×(t×40 / 1638+Δ)-0.2876)+b (2) Where a and b are normal coefficients; t represents time; and Δ represents the decay function.

3. The satellite power system fault locating method according to claim 2, characterized in that: When the solar panel current decreases, the abnormal evolution model of the solar panel current decrease is imported into the normal state simulation system to obtain the abnormal simulation result of the solar panel current decrease, and the abnormal simulation result of the solar panel current decrease is compared with the actual satellite data, wherein the calculation formula of the nonlinear correlation coefficient includes: Among them, SS tot It represents the mean square error; it represents the sum of the squares of the differences between the real data and the simulated predicted data.

4. The satellite power system fault locating method according to claim 1, characterized in that: The calculation formula for the voltage of the battery pack under normal conditions includes: U=N×(a+b×Q / (Q-Q 变化 )+c×exp(d×Q 变化 )) (4) Among them, a, b, c and d represent normal coefficients respectively; N represents the number of batteries in series; U represents the voltage of the battery pack; Q represents the initial charge of the battery cell; Q 变化 Indicates the battery charge and discharge capacity; The calculation formula for the charging current of the battery pack under normal circumstances includes: Among them, I ld (t) represents the data of the load power demand of the nth track changing with time; η BDR Represents the discharge regulator power; η line Represents the battery pack power supply line loss factor; V bt Indicates the battery pack discharge voltage; V bs Indicates the bus voltage when the battery pack is discharging; When the inducement is a short circuit of a battery pack monomer, the calculation formula of the battery pack under abnormal conditions includes: U=(N-1)×(e+f×Q / (Q-Q 变化 )+g×exp(h×Q 变化 )) (6) Among them, e, f, g and h are abnormal coefficients respectively; N is the number of batteries in series; U is the voltage of the battery pack; Q is the initial charge of the battery cell; Q 变化 Indicates the battery charge and discharge capacity.

5. The satellite power system fault locating method according to claim 4, characterized in that: When a battery cell short-circuit occurs, an abnormal evolution model of the battery cell short-circuit is imported into a normal state simulation system to obtain an abnormal simulation result of the battery cell short-circuit. The abnormal simulation result of the battery cell short-circuit is then compared with actual satellite data to obtain a final discharge voltage of the battery pack with the abnormal simulation result that is lower than the normal final discharge voltage of the battery pack when the satellite is actually in orbit.

6. The satellite power system fault locating method according to claim 1, characterized in that: When the abnormal impact of the various inducements on the satellite power supply system is abnormal battery pack voltage telemetry, the battery pack voltage output value is changed to a constant value in the satellite power supply system abnormal evolution model.

7. The satellite power system fault location method according to claim 6, characterized in that: When the battery pack voltage telemetry is abnormal, the abnormal evolution model of the battery pack voltage telemetry is imported into the normal state simulation system to obtain the abnormal simulation result of the battery pack voltage telemetry, and the abnormal simulation result of the battery pack voltage telemetry is compared with the actual satellite data to obtain the battery voltage displayed as a constant value.

8. The satellite power system fault location method according to claim 1, characterized in that: In the step of using a neural network to compare the actual satellite data and the abnormal simulation results to obtain the fault location of the satellite power system, the neural network is a BP neural network, and the BP neural network includes an input layer, a hidden layer and an output layer.

9. The satellite power system fault location method according to claim 8, characterized in that: In the BP neural network, the input layer includes n nodes, the hidden layer includes q nodes, and the output layer includes m nodes; The output formula of the nodes in the hidden layer is include: z l =f1(V l T X) (7) The output formula of the node of the output layer includes: Among them, V kl Represents the weight between the input layer and the hidden layer; w ji Represents the weight between the hidden layer and the output layer; f1() represents the transfer function of the hidden layer; f2() represents the transfer function of the output layer.

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