Electric aircraft corona discharge electromagnetic interference prediction method

Abstract

The invention relates to an electric aircraft corona discharge electromagnetic interference prediction method, which comprises the following steps: carrying out time domain analysis on corona discharge test data in an airborne environment, and dividing a corona discharge process into three continuous stages in a time domain, the three continuous stages comprising a charge accumulation stage, a transient discharge stage and a steady discharge stage; respectively carrying out feature extraction on the three continuous stages to obtain feature parameters of each stage; respectively reconstructing the three continuous stages based on the characteristic parameters to obtain a complete corona discharge current pulse time domain signal model in the airborne environment; and constructing a corona discharge electromagnetic interference prediction model based on the complete corona discharge current pulse time domain signal model. According to the method, a modeling strategy of three-stage division is provided, special mathematical models are established for physical characteristics of different stages, and compared with a traditional overall modeling method, the prediction accuracy and reliability are improved.

Description

Technical Field

[0001] The invention belongs to the technical field of electromagnetic compatibility research in aviation electrical engineering, and in particular relates to a method for predicting corona discharge electromagnetic interference of an electric aircraft. Background Art

[0002] Electric aircraft involve many key technical features, one of which is that the rated power of the aircraft power supply system will gradually increase to the megawatt level, and the corresponding voltage level will jump significantly from the current common 115VAC and 270VDC to thousands of volts.

[0003] Corona discharge itself is a localized, self-sustained discharge phenomenon in gas. When the electric field strength around a high-voltage conductor exceeds the gas breakdown threshold, the gas molecules ionize to form a plasma, generating pulsed currents. These pulsed currents, in turn, radiate broadband electromagnetic waves ranging from tens of kilohertz (kHz) to tens of megahertz (MHz), constituting electromagnetic interference (EMI) signals. These interference signals can affect critical aircraft avionics systems such as communication, navigation, and control through various coupling pathways, including conduction and radiation. Especially as modern aircraft increasingly rely on sophisticated electronic equipment, the resulting electromagnetic compatibility (EMC) issues pose a serious threat to flight safety.

[0004] Corona discharge, a potential new failure mode for electric aircraft compared to conventional aircraft, generates electromagnetic interference (EMI) issues. The resulting corona current pulses pose urgent technical challenges for the design and assessment of electrical circuits for future electric aircraft. Traditional research on corona discharge in ground-based high-voltage transmission lines only models the steady-state phase and cannot accurately predict discharge behavior under dynamic conditions. This impacts the accuracy of electromagnetic compatibility (EMC) design and the reliability of airworthiness certification assessments for electric aircraft. Summary of the Invention

[0005] In view of the above-mentioned defects or deficiencies in the prior art, the present invention proposes a method for predicting corona discharge electromagnetic interference in electric aircraft.

[0006] In a first aspect, a method for predicting electromagnetic interference caused by corona discharge in an electric aircraft is provided, comprising: performing time domain analysis on corona discharge test data in an airborne environment, dividing the corona discharge process into three continuous stages in the time domain, the three continuous stages comprising: a charge accumulation period, a transient discharge period, and a steady-state discharge period; performing feature extraction on the three continuous stages respectively to obtain characteristic parameters of each stage; reconstructing the three continuous stages respectively based on the characteristic parameters to obtain a complete corona discharge current pulse time domain signal model in an airborne environment; and constructing a corona discharge electromagnetic interference prediction model based on the complete corona discharge current pulse time domain signal model.

[0007] In an optional embodiment, the three consecutive stages are reconstructed separately based on the characteristic parameters to obtain a complete corona discharge current pulse time domain signal model in an airborne environment, specifically including: reconstructing based on the transient discharge period and the characteristic parameters of the transient discharge period to obtain a random pulse sequence; reconstructing based on the steady-state discharge period and the characteristic parameters of the steady-state discharge period to obtain a periodic pulse sequence; obtaining the complete corona discharge current pulse time domain signal model based on the single pulse parameter distribution, the random pulse sequence and the periodic pulse sequence.

[0008] In an optional embodiment, the transient discharge period is characterized by a pulse amplitude distribution and a pulse time interval distribution, wherein the pulse amplitude obeys a Gaussian distribution and a probability density function is: Where A trans is the amplitude, μ AA is the mean of the amplitude, σ A is the standard deviation of the amplitude; the pulse time interval obeys the lognormal distribution, and the probability density function is: Where Δt is the time interval, μ Δt is the logarithmic mean of the time interval, σ Δt is the logarithmic standard deviation; and the transient discharge period is reconstructed based on the amplitude and the amplitude time interval to obtain the random pulse sequence.

[0009] In an optional embodiment, the characteristic parameters of the steady-state discharge period include a pulse amplitude mean and a time interval period, and the steady-state discharge period is reconstructed based on the pulse amplitude mean and the time interval period to obtain the periodic pulse sequence.

[0010] In an optional embodiment, the single pulse waveform satisfies a double exponential function: Wherein, i(t) represents the current value at time t, τ1 and τ2 represent time constants, indicating the rise and fall processes of the control pulse respectively, τ1>τ2, and A represents the amplitude constant; the single pulse parameter distribution includes the single pulse rise time, the single pulse half-peak time and the single pulse duration, the single pulse rise time and the single pulse half-peak time conform to the Weibull distribution, and the probability density function expression is: Where t represents the rise time or half-peak time, λ represents the scale of the distribution, and k represents the shape of the distribution. The duration conforms to the normal distribution, and the probability density function is: Where, T d represents the duration, σ Td represents the logarithmic standard deviation of the duration, μ Td It represents the logarithmic mean of the duration; the time constant satisfies the relationship between the single pulse rise time, single pulse half-peak time and single pulse duration: Where, T r Indicates the single pulse rise time, T f Indicates the half-peak time of a single pulse, T d represents the duration of a single pulse, 0<α<β<1.

[0011] In an optional embodiment, a corona discharge electromagnetic interference prediction model is constructed based on the complete corona discharge current pulse time domain signal model, specifically comprising: extracting model parameters based on the test data, the model parameters including charge accumulation period duration, time, transient discharge period duration, steady-state discharge period duration, transient discharge period pulse amplitude mean, standard deviation, transient discharge period pulse time interval logarithmic mean, logarithmic standard deviation, steady-state discharge period pulse mean, steady-state discharge period pulse time interval period, single pulse rise time, single pulse half-peak time and single pulse duration; constructing a coupling relationship expression based on the model parameters and environmental parameters, the environmental parameters including: air pressure, temperature, humidity, voltage and discharge structure; constructing a corona discharge electromagnetic interference prediction model based on the coupling relationship expression and the complete corona discharge current pulse time domain signal model.

[0012] In an optional embodiment, the charge accumulation period is reconstructed by determining the duration by fitting the environmental parameters, and the fitting relationship between the charge accumulation period duration and the environmental parameters is: acc =f1(P, T, H, V, S); where Tacc represents the duration of the charge accumulation period; the fitting relationship between the duration of the transient discharge period and the environmental parameters is: T trans =f2(P,T,H,V,S); where T trans represents the duration of the transient discharge period; the fitting relationship between the steady-state discharge period and the environmental parameters is: T steady =f3(P,T,H,V,S); where T steady represents the steady-state discharge period; the fitting relationship between the mean value and standard deviation of the transient discharge pulse amplitude and the environmental parameters is: (μ AA ,σ A )=f4(P,T,H,U,S); where, (μ AA ,σ A ) represents the mean and standard deviation of the pulse amplitude during the transient discharge period; the fitting relationship between the logarithmic mean and logarithmic standard deviation of the time interval of the current pulse sequence during the transient discharge period and the environmental parameters is: (μ Δt ,σ Δt )=f5(P,T,H,U,S); where (μ Δt ,σ Δt) is expressed as the logarithmic mean and logarithmic standard deviation of the time interval of the current pulse sequence during the transient discharge period; the fitting relationship between the amplitude mean and time interval period of the steady-state discharge period and the environmental parameters is: (μ AS ,C steady )=f6(P,T,H,U,S); where (μ AS ,C steady ) represents the mean amplitude and time interval of the steady-state discharge period; the fitting relationship between the single pulse rise time and the environmental parameters is: T r =f7(P,T,H,U,S); where T r Represents the single pulse rise time; the fitting relationship between the single pulse half-peak time and the environmental parameter is: T f =f8(P,T,H,U,S); where T f represents the half-peak time of a single pulse; the fitting relationship between the single pulse duration and the environmental parameter is: T d =f9(P,T,H,U,S); where T d represents the duration of a single pulse; P represents air pressure, T represents temperature, H represents humidity, U represents voltage, and S represents discharge structure.

[0013] In an optional embodiment, the air pressure ranges from 10 to 101 kPa, the temperature ranges from -70° to +85°, the humidity ranges from 5% to 98% RH, and the discharge configuration includes a line-ground parallel structure and a line-line cross structure.

[0014] The beneficial effects of the present invention are:

[0015] 1. By proposing a refined modeling strategy with three-stage division, specialized mathematical models are established for the physical characteristics of different stages. Compared with the traditional overall modeling method, it can more accurately capture the dynamic characteristics and transformation laws of each stage in the corona discharge process.

[0016] 2. By deeply analyzing the time domain characteristics of single pulse waveforms and the statistical laws of pulse sequences, a complete description system from the three time parameters of single pulses to the statistical characteristics of pulse sequences was established, realizing all-round modeling from micro to macro.

[0017] 3. Comprehensively considering the influence of multiple environmental factors, including air pressure, temperature, humidity, voltage level, and discharge structure, the proposed method establishes a comprehensive coupling relationship between environmental parameters. This model covers a wide range of airborne environmental conditions, from high altitude to ground level and from extreme cold to high temperatures, and is therefore widely applicable. For the first time, a quantitative coupling relationship between environmental parameters and the characteristics of the entire corona discharge process has been established, resolving the key technical challenge of inaccurate corona EMI prediction in the electromagnetic compatibility design of electric aircraft. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Other features, objects and advantages of the present application will become more apparent upon reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0019] Figure 1 A flow chart of a method for predicting corona discharge electromagnetic interference in an electric aircraft provided in one embodiment of the present invention;

[0020] Figure 2 A schematic diagram of the charge accumulation period and transient discharge period of experimental data of a method for predicting electromagnetic interference of corona discharge in an electric aircraft provided by one embodiment of the present invention;

[0021] Figure 3 A schematic diagram of the amplitude characteristics and time intervals in the steady-state discharge phase of a method for predicting electromagnetic interference from corona discharge in an electric aircraft provided by one embodiment of the present invention;

[0022] Figure 4 A schematic diagram of single pulse parameters of a method for predicting electromagnetic interference caused by corona discharge in electric aircraft according to an embodiment of the present invention;

[0023] Figure 5 A schematic diagram of a reconstructed current pulse signal of a method for predicting electromagnetic interference caused by corona discharge in an electric aircraft according to an embodiment of the present invention;

[0024] Figure 6 A diagram of a complete corona discharge current pulse time domain signal reconstruction model in an airborne environment of a method for predicting electromagnetic interference caused by corona discharge in an electric aircraft provided by one embodiment of the present invention;

[0025] Figure 7 This is a flowchart of a method for predicting corona discharge electromagnetic interference in an electric aircraft provided by another embodiment of the present invention. DETAILED DESCRIPTION

[0026] The following is combined with Figures 1 to 7 The present invention is further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the relevant invention and are not intended to limit the invention. It should also be noted that for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0027] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0028] Electric aircraft involve technological innovations across numerous areas. One key technical feature is the gradual increase in the rated power of aircraft power systems to megawatts, translating to a corresponding jump in voltage levels from the currently common 115VAC and 270VDC to over 1,000V. Electric aircraft face unique challenges in low-pressure environments while operating at high altitudes, posing a severe challenge to the electrical safety of onboard high-voltage systems. In thin air, according to Paschen's law, the breakdown voltage of gases decreases significantly, exposing electrical systems designed for sea-level conditions to a higher risk of corona discharge. This is particularly true at battery connections, motor windings, and other confined spaces where geometric configurations create uneven electric field distribution. These areas are particularly prone to concentrated electric field strength. The enhanced field strength in these areas, combined with the reduced density of surrounding gas molecules, significantly reduces the corona inception voltage, leading to frequent or intense corona discharges. Lectromec, a leading global company in the aviation cable industry, has conducted initial demonstration testing addressing the issue of partial discharge in airborne high-voltage transmission cables.

[0029] Corona discharge is a new potential failure mode for electric aircraft compared to traditional aircraft. The electromagnetic interference caused by the corona current pulses it generates has put forward urgent technical requirements for the design of electrical circuits and electromagnetic interference assessment of future electric aircraft. Because corona discharge is essentially an irregular discharge phenomenon, its discharge characteristics are affected by multiple factors such as voltage and air pressure, and it exhibits significant random characteristics in both the time domain and the frequency domain. If we rely entirely on experimental methods to obtain corona current pulse fault data under various specific conditions, it will not only be time-consuming and labor-intensive, but also costly and difficult to meet the actual needs of the project. Therefore, it is urgent to establish a corresponding mathematical model through in-depth analysis of the statistical characteristics of the corona current pulse parameters, so as to achieve the goal of quickly generating fault current pulse data through model parameter input, so that the corona current pulse can be used as a known interference source to input into the electromagnetic characteristic analysis.

[0030] Traditional studies of corona discharge in ground-based high-voltage transmission lines only model the steady-state phase. A major drawback is that they ignore critical information about transient processes, such as charge accumulation and discharge initiation. Although the unstable discharge period is brief, these phases often contain unique spectral and pulse characteristics that are crucial for electromagnetic interference prediction. In the complex flight environment of electric aircraft, voltage and pressure conditions fluctuate frequently, and the system often undergoes a complete discharge process. Traditional steady-state modeling methods are unable to accurately predict discharge behavior under these dynamic conditions, resulting in inaccurate EMI predictions, insufficient understanding of physical mechanisms, and weakened early fault detection and diagnosis capabilities, thus limiting the applicability and engineering value of traditional models in electric aircraft applications. Existing models are based on steady-state data of 10,000-volt high-voltage stranded wire discharges under ground atmospheric pressure. These models are unsuitable for the actual operating conditions of electric aircraft in terms of discharge structure, pressure environment, and voltage level. Prior art research on corona discharge has primarily focused on ground-based standard atmospheric conditions. Traditional methods focus solely on steady-state discharge characteristics, ignoring the dynamic evolution of the discharge process. This research approach has the following limitations: first, it lacks systematic research tailored to the specific conditions of the airborne environment; second, it overlooks the important role of the charge accumulation period and transient discharge period; and third, it cannot accurately predict the corona EMI characteristics under the actual operating conditions of electric aircraft. In particular, under complex environmental conditions such as low pressure, wide temperature ranges, and variable humidity, the time-domain characteristics of the corona discharge current pulse have not been fully understood, and there is a lack of effective prediction models that cover the entire discharge process.

[0031] The lack of this technology has, to a certain extent, affected the accuracy of the electromagnetic compatibility design of electric aircraft and the reliability of airworthiness certification assessment. Therefore, it is necessary to establish a reconstruction model that can accurately predict the time domain signal of the corona discharge current pulse in the airborne environment to improve the scientificity and accuracy of related design and assessment work.

[0032] The present invention reconstructs a complete current pulse model based on test data characteristics and divides it into three discharge stages: charge accumulation period, transient discharge period, and steady-state discharge period. Figure 2 Displays charge accumulation period and transient discharge, Figure 3 The steady-state discharge period is shown. It can be clearly seen that there is no pulse characteristic in the charge accumulation period, and the amplitude of the transient discharge period shows a fluctuating characteristic compared to the steady-state discharge period. Figure 4 Displays single pulse parameter definitions.

[0033] Please refer to Figure 1 , is a flowchart of a method for predicting electromagnetic interference caused by corona discharge of an electric aircraft according to an embodiment of the present invention. The method for predicting electromagnetic interference caused by corona discharge of an electric aircraft comprises the following steps:

[0034] Step S101: performing time domain analysis on the corona discharge test data in an airborne environment, and dividing the corona discharge process into three consecutive stages in the time domain. The three consecutive stages include: a charge accumulation period, a transient discharge period, and a steady-state discharge period.

[0035] Step S103: extract features from the three consecutive stages respectively to obtain feature parameters for each stage.

[0036] Step S105: reconstructing the three consecutive stages respectively based on the characteristic parameters to obtain a complete corona discharge current pulse time domain signal model in an airborne environment.

[0037] Step S107: constructing a corona discharge electromagnetic interference prediction model based on the complete corona discharge current pulse time domain signal model.

[0038] In this embodiment, when a corona discharge test is performed in an airborne environment, a detailed time domain analysis is performed on the collected test data to identify and divide the three main stages of the corona discharge process, thereby more accurately describing the dynamic characteristics of the discharge process.

[0039] The charge accumulation period starts from the moment the high-voltage side of the discharge structure is powered up until the first corona current pulse appears. During this period, no current pulses are generated, and the process mainly involves the accumulation of charge on the electrode surface.

[0040] Transient discharge phase: From the appearance of the first current pulse until the discharge stabilizes. This phase is characterized by randomness in both pulse amplitude and time interval. From the moment the first current pulse appears for a period of time, the amplitude and time interval of the current pulse sequence exhibit strong randomness. In particular, the current pulse amplitude during this phase is significantly higher than that during the steady-state discharge phase.

[0041] Steady-state discharge phase: The discharge reaches a stable state and the pulse characteristics are relatively fixed. This phase is characterized by the discharge amplitude tending to a stable value and the time interval showing periodic characteristics.

[0042] Based on the extracted characteristic parameters, the three consecutive stages are reconstructed separately. The reconstruction process requires accurate simulation of the current pulse waveform of each stage to ensure the continuity and consistency of the entire discharge process. The reconstructed stages are combined in a time sequence to form a complete time-domain signal model of the corona discharge current pulse.

[0043] Through statistical analysis of a large amount of experimental data, it is found that the corona discharge process has obvious stage characteristics. Therefore, the use of a segmented modeling method can more accurately describe the dynamic characteristics of the discharge process. This division method is based on the physical mechanism of corona discharge and can effectively distinguish the discharge characteristics of different stages. The present invention proposes a refined modeling strategy of three-interval division, and establishes special mathematical models for the physical characteristics of different stages. Compared with the traditional overall modeling method, it can more accurately capture the dynamic characteristics and conversion laws of each stage in the corona discharge process. Based on the complete corona discharge current pulse time domain signal model, a corona discharge electromagnetic interference (EMI) prediction model is constructed, which can improve the reliability and accuracy of the prediction model.

[0044] Furthermore, step S105, based on the characteristic parameters, the three consecutive stages are reconstructed respectively to obtain a complete corona discharge current pulse time domain signal model in an airborne environment, which specifically includes the following steps:

[0045] Step S1051, reconstructing based on the transient discharge period and the characteristic parameters of the transient discharge period to obtain a random pulse sequence;

[0046] Step S1053, reconstructing based on the steady-state discharge period and characteristic parameters of the steady-state discharge period to obtain a periodic pulse sequence;

[0047] Step S1055 , obtaining a complete corona discharge current pulse time domain signal model based on the single pulse parameter distribution, the random pulse sequence and the periodic pulse sequence.

[0048] Characteristic parameters are extracted from the experimental data of the transient discharge period. A probability distribution fit is performed based on the extracted characteristic parameters to obtain a probability distribution model. Using this fitted probability distribution model, a random pulse sequence is generated to simulate the random characteristics of the transient discharge period. When reconstructing the steady-state discharge period, characteristic parameters are extracted from the experimental data of the steady-state discharge period. Based on the extracted characteristic parameters, a periodic pulse sequence is generated to simulate the stable characteristics of the steady-state discharge period. Based on the single-pulse parameter distribution, a single-pulse waveform is reconstructed. The pulse sequences of the transient and steady-state discharge periods are combined with the reconstructed single-pulse waveform to generate a complete corona discharge current pulse time-domain signal. Through these steps, a current pulse time-domain signal model capable of accurately simulating the corona discharge process in an airborne environment can be constructed.

[0049] The characteristic parameters of the charge accumulation period include the duration of the charge accumulation period, the characteristic parameters of the transient discharge period include the amplitude distribution and the single pulse parameter distribution, the amplitude distribution includes the amplitude and the amplitude time interval, the single pulse parameter distribution includes the rise time, the half-peak time and the duration, and the characteristic parameters of the steady-state discharge period include the pulse amplitude mean and the time interval period.

[0050] Furthermore, based on step S1051 , the characteristics of the transient discharge period are reflected in the pulse amplitude distribution and the pulse time interval distribution, and the pulse amplitude obeys the Gaussian distribution.

[0051] The probability density function is:

[0052]

[0053] Where A trans is the pulse amplitude, μ AA is the mean of the amplitude, σ A is the standard deviation of the amplitude.

[0054] The pulse time interval obeys the log-normal distribution, and the probability density function is:

[0055]

[0056] Where Δt is the time interval, μ Δt is the logarithmic mean of the time interval, σ Δt is the logarithmic standard deviation;

[0057] The transient discharge period is reconstructed based on the amplitude and the amplitude time interval to obtain a random pulse sequence.

[0058] The reconstruction of pulse sequences in different intervals shows that the discharge amplitude in the stable discharge period tends to a stable value and the time interval is periodic. The amplitude and period of the current pulse sequence are based on the pulse mean and period in the stable discharge period of the test data.

[0059] Furthermore, based on step S1053, the characteristic parameters of the steady-state discharge period include the pulse amplitude mean and the time interval period, and the steady-state discharge period is reconstructed based on the pulse amplitude mean and the time interval period to obtain a periodic pulse sequence.

[0060] When establishing an accurate reconstruction model of a single pulse waveform, the probability distribution law of the characteristic parameters of the single pulse is statistically analyzed, where the pulse amplitude obeys a Gaussian distribution, the rise time and half-peak time obey a Weibull distribution, and the duration obeys a lognormal distribution. A double exponential function is used to describe the single pulse waveform.

[0061] Combined with attachment Figure 5 As shown, further, based on step S1055, the single pulse waveform satisfies the double exponential function:

[0062]

[0063] Where i(t) represents the current value at time t, τ1 and τ2 are both time constants, indicating the rise and fall processes of the control pulse respectively, τ1>τ2, and A represents the amplitude constant.

[0064] The single pulse parameter distribution includes single pulse rise time, single pulse half-peak time and single pulse duration.

[0065] Rise time T r :Pulse signal from rising edge 10%i p To the rising edge 90%i p Time required.

[0066] Half-peak time T f :Pulse signal from rising edge 10%i p To the falling edge 50%i p Time required.

[0067] Duration T d :Pulse signal from rising edge 10%i p To the falling edge of the pulse 10%i p Time required.

[0068] The single pulse rise time and single pulse half-peak time conform to the Weibull distribution, and the probability density function expression is:

[0069]

[0070] Where t represents the rise time or half-peak time, λ represents the scale of the distribution, and k represents the shape of the distribution. λ is a scale parameter that affects the average level of the pulse rise time or half-peak time; a larger λ indicates a longer rise time or half-peak time. k is a shape parameter that describes the shape of the distribution. When k = 1, the Weibull distribution becomes an exponential distribution. When k < 1, it indicates rapid growth, and when k > 1, it indicates a gradual increase.

[0071] The duration conforms to the normal distribution, and the probability density function is:

[0072]

[0073] Where, T d represents the duration, σ Td represents the logarithmic standard deviation of the duration, μ Td represents the logarithmic mean of duration.

[0074] The time constant satisfies the relationship between the single pulse rise time, single pulse half-peak time and single pulse duration:

[0075]

[0076] Where, T r Indicates the single pulse rise time, T f Indicates the half-peak time of a single pulse, T d represents the duration of a single pulse, 0<α<β<1.

[0077] like Figure 6 and Figure 7 As shown, further, step S107, constructing a corona discharge electromagnetic interference prediction model based on the complete corona discharge current pulse time domain signal model, specifically includes the following steps:

[0078] Step S1071: Extract model parameters based on the experimental data, which include charge accumulation period duration, time, transient discharge period duration, steady-state discharge period duration, transient discharge period pulse amplitude mean, standard deviation, transient discharge period pulse time interval logarithmic mean, logarithmic standard deviation, steady-state discharge period pulse mean, steady-state discharge period pulse time interval period, single pulse rise time, single pulse half-peak time and single pulse duration.

[0079] Step S1073: constructing a coupling relationship expression based on the model parameters and environmental parameters, where the environmental parameters include: air pressure, temperature, humidity, voltage, and discharge structure.

[0080] Step S1075: Construct a corona discharge electromagnetic interference prediction model based on the coupling relationship expression and the complete corona discharge current pulse time domain signal model, such as Figure 6 shown.

[0081] The charge accumulation period is reconstructed by fitting the environmental parameters to determine the duration. The fitting relationship between the charge accumulation period and the environmental parameters is:

[0082] T acc =f1(P,T,H,V,S);

[0083] Where Tacc is the charge accumulation period.

[0084] The fitting relationship between the transient discharge period and environmental parameters is:

[0085] T trans =f2(P,T,H,V,S);

[0086] Where, T trans Indicates the duration of the transient discharge period.

[0087] The fitting relationship between the steady-state discharge period and the environmental parameters is:

[0088] T steady =f3(P,T,H,V,S);

[0089] Where, T steady Indicates the steady-state discharge period.

[0090] The fitting relationship between the mean value and standard deviation of the pulse amplitude during transient discharge and the environmental parameters is:

[0091] (μ AA ,σ A )=f4(P,T,H,U,S);

[0092] Where, (μ AA ,σ A ) represents the mean and standard deviation of the pulse amplitude during transient discharge period.

[0093] The fitting relationship between the logarithmic mean and logarithmic standard deviation of the time interval of the current pulse sequence during the transient discharge period and the environmental parameters is:

[0094] (μ Δt ,σ Δt )=f5(P,T,H,U,S);

[0095] Where, (μ Δt ,σ Δt ) is expressed as the logarithmic mean and logarithmic standard deviation of the time interval of the current pulse sequence during the transient discharge period.

[0096] The fitting relationship between the mean amplitude and time interval of the steady-state discharge period and the environmental parameters is:

[0097] (μ AS ,C steady )=f6(P,T,H,U,S);

[0098] Where, (μ AS ,C steady ) represents the mean amplitude and time interval of the steady-state discharge period.

[0099] The fitting relationship between the single pulse rise time and the environmental parameters is: T r =f7(P,T,H,U,S);

[0100] Where, T r Indicates the rise time of a single pulse.

[0101] The fitting relationship between the single pulse half-peak time and environmental parameters is:

[0102] T f =f8(P,T,H,U,S);

[0103] Where, T f Indicates the half-peak time of a single pulse.

[0104] The fitting relationship between the single pulse duration and the environmental parameters is:

[0105] T d =f9(P,T,H,U,S);

[0106] Where, T d Indicates the duration of a single pulse.

[0107] P represents air pressure, T represents temperature, H represents humidity, U represents voltage, and S represents discharge structure.

[0108] Specifically, the air pressure ranges from 10 to 101 kPa, the temperature ranges from -70° to +85°, the humidity ranges from 5% to 98% RH, and the discharge configurations include a line-ground parallel structure and a line-line cross structure.

[0109] By thoroughly analyzing the time-domain characteristics of single pulses, a precise reconstruction method based on three key time parameters—single pulse rise time, single pulse half-peak time, and single pulse duration—was established. Under varying environmental parameters, by determining these three core variables, combined with the mathematical expression of a biexponential function, high-precision reconstruction of the single pulse waveform was achieved. This method uses a biexponential function as the fundamental mathematical model for a single pulse, where the time constant is determined through a mathematical relationship with the three time characteristic parameters, thus establishing a complete mapping from statistical characteristic parameters to specific waveforms.

[0110] The method of the present invention adopts a segmented reconstruction strategy, dividing the corona discharge process into three stages: charge accumulation period, transient discharge period and steady-state discharge period, and reconstructing them using different algorithms: the charge accumulation period is determined by fitting the environmental parameters; the transient discharge period is based on the statistical characteristics of Gaussian distribution and lognormal distribution, combined with the Monte Carlo method to reconstruct the random pulse sequence; the steady-state discharge period is reconstructed into a periodic pulse sequence. The single pulse waveform adopts a double exponential function model, and the parameters obey the Weibull distribution and lognormal distribution. A coupled prediction model including five environmental parameters: air pressure, temperature, humidity, voltage level and discharge structure is established, and the mathematical relationship between the characteristic parameters and the environmental conditions is obtained through multivariate nonlinear fitting. This parameterized modeling method has good flexibility and adaptability. According to the different parameter combinations obtained under different environments, it can flexibly establish and accurately predict the behavioral characteristics and evolution laws of a single pulse in the entire pulse sequence. This model provides a precise theoretical basis and technical support for electromagnetic interference assessment and corona discharge prediction in aircraft airworthiness certification, and has important engineering application value.

[0111] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of the invention herein is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also encompasses other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the inventive concept. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features having similar functions disclosed in this application.

Claims

1. A method for predicting electromagnetic interference caused by corona discharge in electric aircraft, characterized in that: include: The corona discharge test data in an airborne environment were analyzed in the time domain, and the corona discharge process was divided into three consecutive stages in the time domain: the charge accumulation period, the transient discharge period, and the steady-state discharge period. Performing feature extraction on the three consecutive stages respectively to obtain feature parameters of each stage; Reconstructing the three consecutive stages based on the characteristic parameters to obtain a complete corona discharge current pulse time domain signal model in an airborne environment; A corona discharge electromagnetic interference prediction model is constructed based on the complete corona discharge current pulse time domain signal model.

2. The method for predicting corona discharge electromagnetic interference of an electric aircraft according to claim 1, characterized in that: The three consecutive stages are reconstructed based on the characteristic parameters to obtain a complete corona discharge current pulse time domain signal model in an airborne environment, specifically including: Reconstructing based on the transient discharge period and characteristic parameters of the transient discharge period to obtain a random pulse sequence; Reconstructing based on the steady-state discharge period and characteristic parameters of the steady-state discharge period to obtain a periodic pulse sequence; The complete corona discharge current pulse time domain signal model is obtained based on the single pulse parameter distribution, the random pulse sequence and the periodic pulse sequence.

3. The method for predicting electromagnetic interference caused by corona discharge in electric aircraft according to claim 2, characterized in that: The characteristics of the transient discharge period are reflected in the distribution of pulse amplitude and pulse time interval. The pulse amplitude obeys Gaussian distribution, and the probability density function is: Where A trans is the amplitude, μ AA is the mean of the amplitude, σ A is the standard deviation of the amplitude; The pulse time interval obeys the lognormal distribution, and the probability density function is: Where Δt is the time interval, μ Δt is the logarithmic mean of the time interval, σ Δt is the logarithmic standard deviation; The transient discharge period is reconstructed based on the amplitude and the amplitude time interval to obtain the random pulse sequence.

4. The method for predicting electromagnetic interference caused by corona discharge in electric aircraft according to claim 2, characterized in that: The characteristic parameters of the steady-state discharge period include a pulse amplitude mean and a time interval period. The steady-state discharge period is reconstructed based on the pulse amplitude mean and the time interval period to obtain the periodic pulse sequence.

5. The method for predicting electromagnetic interference caused by corona discharge in electric aircraft according to claim 2, characterized in that: The single pulse waveform satisfies the double exponential function: Where i(t) represents the current value at time t, τ1 and τ2 are both time constants, indicating the rise and fall processes of the control pulse respectively, τ1>τ2, and A represents the amplitude constant; The single pulse parameter distribution includes the single pulse rise time, the single pulse half-peak time and the single pulse duration. The single pulse rise time and the single pulse half-peak time conform to the Weibull distribution, and the probability density function expression is: Where t represents the rise time or half-peak time, λ represents the scale of the distribution, and k represents the shape of the distribution; The duration conforms to the normal distribution, and the probability density function is: Where, T d represents the duration, σ Td represents the logarithmic standard deviation of the duration, μ Td represents the logarithmic mean of duration; The time constant satisfies the relationship between the single pulse rise time, single pulse half-peak time and single pulse duration: Where, T r Indicates the single pulse rise time, T f Indicates the half-peak time of a single pulse, T d represents the duration of a single pulse, 6. The method for predicting corona discharge electromagnetic interference of an electric aircraft according to any one of claims 1 to 5, characterized in that: A corona discharge electromagnetic interference prediction model is constructed based on the complete corona discharge current pulse time domain signal model, specifically including: Extracting model parameters based on the test data, the model parameters include charge accumulation period duration, time, transient discharge period duration, steady-state discharge period duration, transient discharge period pulse amplitude mean, standard deviation, transient discharge period pulse time interval logarithmic mean, logarithmic standard deviation, steady-state discharge period pulse mean, steady-state discharge period pulse time interval period, single pulse rise time, single pulse half-peak time and single pulse duration; Constructing a coupling relationship expression based on the model parameters and environmental parameters, wherein the environmental parameters include: air pressure, temperature, humidity, voltage and discharge structure; A corona discharge electromagnetic interference prediction model is constructed based on the coupling relationship expression and the complete corona discharge current pulse time domain signal model.

7. The method for predicting electromagnetic interference caused by corona discharge in electric aircraft according to claim 6, characterized in that: The charge accumulation period is reconstructed by determining the duration by fitting the environmental parameters. The fitting relationship between the charge accumulation period duration and the environmental parameters is: T acc =f1(P,T,H,V,S); Where Tacc represents the duration of the charge accumulation period; The fitting relationship between the duration of the transient discharge period and the environmental parameters is: T trans =f2(P,T,H,V,S); Where, T trans Indicates the duration of transient discharge period; The fitting relationship between the steady-state discharge period and the environmental parameters is: T steady =f3(P,T,H,V,S); Where, T steady represents the steady-state discharge period; The fitting relationship between the mean value and standard deviation of the transient discharge pulse amplitude and the environmental parameters is: (μ AA ,σ A )=f4(P,T,H,U,S); Where, (μ AA ,σ A ) represents the mean and standard deviation of the pulse amplitude during transient discharge period; The fitting relationship between the logarithmic mean and logarithmic standard deviation of the time interval of the transient discharge current pulse sequence and the environmental parameters is: (μ Δt ,σ Δt )=f5(P,T,H,U,S); Where, (μ Δt ,σ Δt ) represents the logarithmic mean and logarithmic standard deviation of the time interval of the current pulse train during the transient discharge period; The fitting relationship between the mean value of the steady-state discharge amplitude and the time interval and the environmental parameters is: (μ AS ,C steady )=f6(P,T,H,U,S); Where, (μ AS ,C steady ) represents the mean amplitude and time interval of the steady-state discharge period; The fitting relationship between the single pulse rise time and the environmental parameter is: T r =f7(P,T,H,U,S); Where, T r Indicates the rise time of a single pulse; The fitting relationship between the single pulse half-peak time and the environmental parameters is: T f =f8(P,T,H,U,S); Where, T f Indicates the half-peak time of a single pulse; The fitting relationship between the single pulse duration and the environmental parameter is: T d =f9(P,T,H,U,S); Where, T d Indicates the duration of a single pulse; P represents air pressure, T represents temperature, H represents humidity, U represents voltage, and S represents discharge structure.

8. The method for predicting electromagnetic interference caused by corona discharge in electric aircraft according to claim 7, characterized in that: The air pressure ranges from 10 to 101 kPa, the temperature ranges from -70° to +85°, the humidity ranges from 5% to 98% RH, and the discharge configurations include a line-ground parallel structure and a line-line cross structure.

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