To address the drift of equivalent parameters in lithium-ion batteries during operation due to varying operating conditions and aging, which degrades model adaptability, and the difficulty of accurately tracking multiple coupled states simultaneously, this article proposes a digital twin-driven joint state estimation method to enable coordinated updating of electrothermal parameters and multiple states in a battery pack. First, an online-oriented digital twin-driven joint state estimation framework is established, where real-time measured voltage, current, and temperature data are used for model-parameter updating and state correction, ensuring real-time consistency between the twin model and the physical counterpart. Second, an embeddable electrothermal coupled equivalent model is developed, and an online parameter identification strategy based on an improved metaheuristic algorithm is proposed. By using a sliding window and a parameter-variation penalty term, parameter fluctuations induced by noise are suppressed, thereby improving the tracking performance of time-varying parameters. Finally, a multitime-scale joint state estimation algorithm is designed to achieve coordinated state of charge (SOC)-state of health (SOH) updating at the sampling scale and the cycle scale, enabling accurate joint estimation under multistate coupling. Experimental results show that the proposed method can track parameter variations more stably and improve the accuracy of joint state estimation, while the computational time satisfies the requirements for online operation, providing theoretical and technical support for online battery state sensing and management.

With the rapid development of aerospace power distribution systems, conventional solid-state power controller (SSPC) modeling approaches exhibit significant limitations in multiphysics coupling coordination, complex characteristic representation, and predictive capability, which is difficult to meet the design requirements of high reliability and high efficiency. To address these challenges, this article proposes a collaborative electromagnetic-thermal-mechanical co-modeling and simulation method for SSPC applications. By integrating detailed electrical circuit simulations with finite element thermal-mechanical analyses, a unified cross-domain modeling framework encompassing electrical, thermal, and mechanical fields is established. A multitime scale coupling strategy related to SSPC operating conditions is designed to efficiently and accurately capture steady-state and rapid transient behaviors, balancing computational efficiency and simulation precision. The proposed method is validated on a 270 V/200 A SSPC module, demonstrating a good match between simulated and experimental results, with errors of key electrical and temperature parameters below 5%, and thermal strain differences less than 10%, which verifies the accuracy of the proposed method. The proposed methodology provides a comprehensive and precise tool for SSPC design optimization and reliability evaluation, and effectively improves the accuracy, efficiency, and reliability of product development.

Pulsed power loads (PPLs) present significant challenges for the design of aircraft power systems. A hybrid power system (HPS) comprising batteries (BATs) and supercapacitors (SCs), integrated with the existing generators (GENs), shows promise as a solution. However, optimizing the proportions of different energy storages is critical for minimizing system weight and maximizing efficiency. To address this challenge, this study proposes a serial-nested co-optimization design method. This approach optimizes energy types, component capacities, and voltage levels, as well as power allocations considering PPL characteristics. To achieve this end, relationships between PPL parameters and energy configuration are established by analyzing the spectrum characteristics of PPL. These nonlinear relationships provide a universal configuration criterion, represented by a response surface calculated via design of experiment (DoE) data. To strike a balance between system weight and efficiency, multidisciplinary design models for each component are developed. A multilevel optimization design method is proposed, enabling simultaneous system-level and component-level co-design. Extensive simulations validate the effectiveness of the proposed co-optimization approach. Optimization results of four power distribution strategies across two architectures are compared to obtain optimal HPS solutions that meet requirements of an aircraft load profile.

Series arc faults (SAFs) are a primary cause of fire incidents in photovoltaic systems. Accurately and rapidly detecting SAF under the interference of power electronic devices remains a significant challenge. This article proposes an SAF detection method based on the time-frequency Markov permutation transition field (TFMPTF). First, variational mode decomposition is used to decompose the current signal into modes containing different frequency components to prevent interference between the information of different frequency bands. Then, the modes are transformed into two-dimensional matrices using TFMPTF. Innovatively, the concept of time-frequency permutation patterns state transition analysis in TFMPTF is proposed, from which the distinct structural information of the current signal can be effectively depicted. Afterward, singular value decomposition is employed to extract fault features from matrices. Finally, fault features are processed using a kernel extreme learning machine to obtain detection results. Offline experimental results show that the average detection accuracy of the proposed method is 98.97%, and the advancedness and adaptability of the proposed method are verified by comparing it with different methods. The proposed method and comparison methods are implemented in a microprogrammed control unit (MCU) for online experiments, further confirming that the detection speed and detection accuracy of the proposed method are reliable.

Aviation cables are highly susceptible to defects under the long-term influence of extreme environments. If these defects are not promptly detected and addressed, they can lead to severe safety incidents. This article presents an enhanced spread spectrum time domain reflectometry (ESSTDR) for the online identification and localization of aviation cable defects. First, a 2-D finite element model of cables with insulation and shielding layer damages is established. The severity of cable defects is quantitatively set to analyze the variation patterns of the cable's characteristic impedance. Second, an ESSTDR is developed by integrating interference wave elimination and time-frequency cross correlation with spread spectrum time domain reflectometry (SSTDR). Through a combined simulation of the finite element model and circuit model, the online identification and localization of cable defects based on ESSTDR are theoretically achieved. Finally, an experimental platform is set up to conduct online identification and localization of different defects in various types of cables, verifying the effectiveness and superiority of the proposed method.

Direct current (DC) arc faults are a leading cause of fire incidents in photovoltaic (PV) systems. Accurate modeling of DC arc faults is essential for understanding the underlying mechanisms of DC arcs and for developing effective detection strategies. In this study, we propose a novel model for DC arcs, referred to as the exponent segmented noise model. This model effectively characterizes arc noise by establishing an exponential relationship between frequency values and spectral energy. To enable precise parameter extraction from the exponent segmented noise model, we introduce a new metaheuristic algorithm called the feedback chaotic growth optimizer (FCGRO). FCGRO improves upon the traditional growth optimizer (GRO) by integrating feedback operators and chaos mechanisms. Firstly, the convergence performance of FCGRO is rigorously evaluated through comparative experiments on three well-established benchmark engineering optimization problems. Subsequently, based on data collected from an established experimental platform, the proposed FCGRO and eight state-of-the-art algorithms are employed to extract parameters of the exponent segmented noise model for DC arc faults. The FCGRO achieves an overall average root mean square error (RMSE) of 0.0418 with a standard deviation of 0.00818, representing reductions of at least 10.43 % and 26.86 %, respectively, compared to the other eight methods. These results indicate that FCGRO delivers more accurate and stable parameter estimations than the competing algorithms. Regarding computational efficiency, FCGRO has an average processing time of 9.969 s, ranking it third among the nine evaluated methods, which confirms its competitiveness in terms of speed. Finally, compared with existing DC arc models, the proposed exponent segmented noise model reduces RMSE by an average of 53.26 %, demonstrating its superior modeling capability.

Aviation cables operate for extended periods in complex environments with high-frequency vibrations, making them highly susceptible to abrasion and penetration, which may lead to critical aircraft electrical system failures. To address the challenges of ambiguous fault characteristics and difficult diagnosis in aviation cable abrasion and penetration, this paper first presents the typical failure factors and mechanisms during cable operation. Subsequently, a Maxwell electromagnetic simulation model is established for typical aviation cables, with characteristic impedance variation (∆Z₀) introduced as the key diagnostic parameter. A systematic analysis is conducted on the influence of weak fault types and severity levels on ∆Z₀, establishing a quantitative correlation between the characteristic parameter and fault severity. Finally, a MATLAB-based cable fault detection platform is implemented to simulate weak faults using distributed parameters of the fault location. The reflection coefficient spectrum is analyzed for diagnosis, revealing that the characteristic impedance variation is proportional to the reflection coefficient magnitude. Weak faults as subtle as 1% can be accurately detected. The diagnostic false-alarm rate can be reduced by adaptively adjusting the weak fault detection threshold. This approach significantly enhances the detection accuracy of reflection coefficient spectrum (RCS) for weak fault diagnosis.

Series arc faults (SAFs) pose a significant threat to the safety of photovoltaic (PV) systems. However, the complex operating conditions of PV systems make accurate SAF detection challenging. To tackle this issue, this article proposes a SAF detection method based on time–frequency composite recurrence plots (TFCRPs). Initially, variational mode decomposition (VMD) is employed to decompose the current into distinct modes. Subsequently, the proposed TFCRP transforms these modes into two-dimensional matrices, enabling the measurement of composite similarity between different phase states. Lastly, extra tree (ET) is utilized to fuse the fractional recurrence entropy (FRE) and the singular values extracted from the matrices, thereby achieving SAF detection. Experimental results indicate that the proposed method achieves a detection accuracy of 98.75% and can accurately detect SAFs under various operating conditions. Comparisons with different methods further highlight the advancement of the proposed method. Furthermore, the detection time of the proposed method (209 ms) meets the requirements of standard UL1699B.

With the advancement of aircraft electrification, the number and complexity of aviation cables continue to increase, and thei r health status plays a crucial role in ensuring the safe and stable operation of aircraft electrical systems. However, existin g simulation studies on AC arc in multiphysics fields often overlook the unique characteristics of the aviation environment, especially the impact of the open and specialized voltage conditions. In this research, we focus on the fundamental physical properties of AC arc and develop a magnetohydrodynamic (MHD) model specifically tailored for aviation electrical systems, taking into account their distinct operating conditions (Open space, AC voltage amplitude 230V, AC voltage frequency 360Hz~800Hz). AC arc, as a form of plasma, is analyzed using COMSOL Multiphysics to simulate multiphysics fields, enabling a better understanding of the complex physical processes involved. The simulations investigate the distribution of thermal, flow, and electric fields under aviation voltage conditions, as well as the relationship between arc current and vol tage. By observing the initial arc ignition times at different voltage frequencies, it is found that there is a delay in the onset of arcing as frequency increases. These findings provide valuable insights for further analysis of the mechanisms behind AC arcs in aviation systems.

With the development of aircraft power supply and distribution system to high power density, it is urgent for its core component, solid state power controller (SSPC), to realize current measurement integration to ensure reliable current monitoring and fault protection of high-power DC SSPC integrated modules. However, a large amount of losses and heat caused by the traditional current detection method based on shunts will reduce efficiency and reliability of SSPC with high current. To address the issue, this paper proposes a high-precision, high-linearity, fast-response, wide-range, and compact anisotropic magnetoresistance (AMR) current detection component integrated in the SSPC module. In order to eliminate the electromagnetic interference caused by other current paths of SSPC integrated module, a magnetic shield is proposed, and its feasibility and effectiveness are verified by electromagnetic simulation. The performance of the detection component was verified on a SSPC module prototype with a direct current rating of 540V /200A, showing detection accuracy of less than 1

This paper proposes a two-stage current limiting control strategy for DC Solid-State Power Controllers (SSPCs), aimed at addressing the challenges posed by inrush current and thermal stress during the rapid switching of large capacitive loads. In the resistor-based current limiting stage, a current-limiting resistor is utilized to suppress the inrush current, thereby protecting the system from instantaneous high current damage. In the gate voltage clamping current limiting stage, the gate voltage of the main-branch MOSFET is clamped to a given value, enabling rapid charging of the load capacitor. Throughout the limiting process, natural commutation occurs between the main branch and the current-limiting branch, resulting in a continuous variation of the SSPC's equivalent resistance. This effectively reduces secondary inrush current and fully utilizes the thermal capacity of the power devices. Experimental results show that the SSPC employing this strategy can switch on a 3300 mu F capacitive load within 10.1ms, with an initial inrush current of 191A and a secondary inrush current of less than 76A, verifying the effectiveness and reliability of the proposed strategy.

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SiC mosfets are the key ideal device for the dc solid-state power controller (dc-SSPC). The reliability of dc-SSPCs can be improved by online predicting of SiC mosfets aging degree. Among current prediction methods, numerous training data or precise physical parameters are required to establish prediction models, which will consume lots of computing resources or implement abundant aging experiments. GM(1,1)-based gray prediction methods have low requirements for data volume and system evolution law, making it more suitable for online prediction. However, SiC mosfets' aging data show the strong nonlinearity, leading to the low prediction accuracy of the traditional GM(1,1). In this article, addressing authors' past work shortcoming in terms of the aging feature parameter with the high nonlinearity, the improved low-nonlinearity aging feature parameter is proposed. Furthermore, considering that the entire life cycle of SiC mosfets can be divided into different aging zones using the proposed aging feature, a segmented variable time-step metabolic GM(1,1) prediction method is proposed to further reduce the data series nonlinearity. Finally, the effectiveness of the proposed prediction method is verified based on the aging data of SiC mosfets for dc-SSPCs, compared to the traditional GM(1,1), its prediction accuracy is improved by more than 8%.

New energy power system possesses characteristics such as nonlinearity, high complexity, multiple parameters, and high dimensionality, which pose obstacles to the efficient and accurate comprehensive diagnosis of system faults. A Fault Diagnosis Method was proposed for multi-parameter new energy power system based on. First, a full digital simulation model of the new energy power system was established by using Simulink simulation. Then, a feature scaling approach based on Multidimensional scaling (MDS) was introduced to reduce system dimensionality. We propose the use of Logistic chaotic mapping to enhance the diversity of Beluga Whale Optimizer (BWO) populations and suggest an elite learning method to improve the optimization precision and efficiency of BWO. Error Correcting Output Coding (ECOC) is used to address the limitations of Support Vector Machine (SVM) multi-classification model, ultimately constructing a fault diagnosis model and conducting instance simulations. Simulation results indicate that the proposed model demonstrates high accuracy, fast speed, and strong overall performance.

The insulation performance of cables plays an important role in the safe and stable operation of aircraft. Time–frequency domain reflectometry (TFDR) is an effective method to detect the local insulation faults of cables. Usually, Wigner–Ville distribution (WVD) is used to analyze the energy distribution of reflected signals in TFDR. However, WVD can easily introduce cross-term interference to the detection results. In addition, most of the current studies have used TFDR to perform offline detection of cable insulation fault, and the performance of TFDR for online operation is unclear. In order to solve the above problems, first, this paper eliminates the interference of cross-term based on smoothed pseudo-Wigner–Ville distribution. Then, an online cable insulation fault detection platform based on non-contact signal injection is built. According to the requirements of the standard GJB181A, the interference degree of the incident signal of TFDR to the original signal of cable is analyzed, and the feasibility of using TFDR method to perform online detection is proved. Finally, the correlation between different features of reflected signals under different fault degrees of cable is analyzed. It is proved that time-domain correlation has a better correlation with the fault degree of cable compared to other four features.

Arcs occur frequently in aviation electrical systems, and can lead to fires. Therefore, it is important to diagnose and eliminate arc in a short time. Since most arc models cannot reflect arc’s random noise, this paper principally derives the random conductance AC arc model (RCAAM) based on the simplified Schavemaker model (S-SM), along with the even-order harmonics and high-frequency harmonics data of arc noise. The derived random-conductance AC arc model (RCAAM) is compared with the simplified Schavemaker model (S-SM) and experimental data in frequency domain. The result shows that the RCAAM can reflect an arc’s harmonics accurately.

The cable fault on-line diagnosis method based on SSTDR may exhibit insufficient noise suppression capability in low signal-to-noise ratio scenarios, thereby increasing the likelihood of misjudgments and constraining its practical utility. In response to this challenge, this study addresses the issue by leveraging noise characteristics, proposing a diagnostic approach that integrates the one-dimensional slice of the fourth-order cumulant with SSTDR, and elucidating the implementation procedure. Through the processing of signals using higher-order cumulants, this method effectively mitigates the impact of noise. Experimental simulations conducted under conditions of high background noise convincingly demonstrate that this methodology enables accurate cable fault diagnosis even in low signal-to-noise ratio scenarios.

In the DC distribution system, the propagation of arc noise can interfere with normal lines, and accurate and timely diagnosis of the location of series arc fault (SAF) is a challenging problem. In this article, a SAF diagnosis method is proposed from a system perspective, which can accurately identify the fault line. First, multiple wavelet transform is used to decompose the currents of different lines, and the fractional wavelet energy entropy is extracted to construct the feature vector. Then, random forest is employed to analyze the importance of features and to select the optimal features. Finally, a kernel extreme learning machine can fuse the features and output the diagnosis results. The offline experimental results indicate that the proposed method has a diagnosis accuracy of 99.82%, which is higher than those of nine comparison methods, and the effectiveness and advancement of the proposed method are verified. The online experimental results show that the proposed method can diagnose SAF within 110 ms, and the diagnosis speed is able to satisfy the requirements of UL1699B. Moreover, under transient conditions, the proposed method can effectively avoid false alarms and maintain stability.

With the current increase in the number of aviation cables and the complexity of wiring, cable safety issues are becoming increasingly prominent. This paper addresses the problems of low diagnostic accuracy and high false alarm rates under high signal-to-noise ratio (SNR) environments that existing cable detection methods struggle with. It analyzes the impact of noise interference on traditional Spread Spectrum Time Domain Reflectometry (SSTDR), summarizes the required characteristics of signals used for cable diagnostics in high SNR environments, and utilizes the Zero Correlation Zone (ZCZ) sequence's feature of having a low correlation coefficient with noise signals within its zero zone. A spread spectrum cable online fault diagnosis method based on the ZCZ sequence is proposed to improve diagnostic accuracy in high SNR environments. This paper verifies the superiority of the ZCZ sequence in cable fault diagnosis through simulations and experiments. The experimental results show that with an SNR not less than -10dB, the method achieves a fault detection accuracy higher than 95% and a false alarm rate lower than 2%.

Addressing the challenge of dealing effectively with multiple variables and complex nonlinear relationships in aircraft power generation systems, which are typically difficult to handle using traditional linear methods, this paper presents an enhanced fault diagnosis method. The method combines the squirrel search algorithm and support vector machine (ISSA-SVM) to improve the accuracy of fault classification. The ISSA is enhanced by incorporating the periodic variational perturbation formula, optimizing the performance of the fault classification model. To validate the effectiveness of the proposed method, MATLAB/Simulink simulation experiments are performed using a fault dataset of the power generation system. The experimental results demonstrate that the diagnostic accuracy of the proposed method is significantly high.