High-power pulsed load (HPPL) such as airborne laser weapon and radar poses a severe challenge to aircraft electrical power system. Since peak power cannot be satisfied only by generator, hybrid energy storage system (HESS) should be well sized to match their energetic performances versus instantaneous power requirement and improve reliability of electrical power system. To deal with these challenges, a sizing optimization for HESS with considering energy management strategy (EMS) based on virtual-droop control in aircraft is proposed in this paper. By analyzing the spectrum characteristics of HPPL profile, splitting frequency between different types of energy storage devices is specially analyzed and designed to give full play to HESS. HESS weight, loss and parameters in EMS are taken into account in this multi-objective optimization based on Non-dominated Sorting Genetic Algorithms II (NSGA-II). Series of Pareto fronts can be obtained through the optimization process, and the most suitable solutions of different HESS combinations are selected by a decision-making means which combines subjective and objective weighting methods. Finally, optimization results of four different combinations are compared. Simulation results are obtained in MATLAB/Simulink environment to prove the rationality and applicability of the optimal sizing tool. The research has resulted in a solution of HESS and EMS optimization for HPPL used in aircraft.

The increasement in electrical power consumption for pulsed power loads such as beam weapon has led to a severe power supply challenge to airborne electrical power system due to insufficient generator output power capability. Therefore, the application of energy storage equipment for compensating peak power has become a research hotspot. In order to reasonably distributed load power to electrical sources for ensuring pulsed power loads operation and system stability, this paper proposed an improved virtual impedance droop control which takes generator, battery and supercapacitor into consideration while state of charge (SoC) recovery and DC bus voltage compensation are also studied. As a type of decentralized control, virtual impedance droop control has the ability to satisfy the reliability requirement of airborne platforms since single point failure can be avoided. This article analyzes the operation of system in fault mode and plug-and-play mode theoretically to verify the high reliability of the proposed method. The calculation results show that the proposed method can achieve power allocation in both normal and fault modes while the SoC can also be restored. Finally, the correctness of the proposed method is validated in both MATLAB/Simulink and hardware-in-the-loop (HIL) environments.

Owing to peak power demands of pulsed power load like radar and beam weapon is much larger than capability of generator, researches about energy storage equipment sizing optimization have been extensively carried out. However, these researches are mainly considered from static perspective without taking dynamic characteristic into consideration. To cover this gap and meet strict weight requirements of airborne platform, this paper proposed a hybrid energy storage system sizing process to satisfy peak power demand while dynamic standard set up by MIL-STD-704F is also studied by means of spectrum and impedance analysis. The overall filter capacitor on DC BUS is considered as a part of energy storage system for improving system dynamic characteristics. The research content is divided into four parts. First, the power waveform is discussed through spectrum analysis to give full play to hybrid energy storage system. Second, the influence of energy storage equipment on system dynamic characteristics is analyzed and the results are taken as constraints for optimization. Then, combined with dynamic and static constraints, a hybrid energy storage system sizing process depend on Non-dominated Sorting Genetic Algorithm II and decision-making method are proposed to obtain the most suitable result. Finally, simulation model is established in MATLAB/Simulink environment for verification.

Turbo-electric hybrid power architecture, including generator driven by gas turbine and energy storage system (ESS), is considered as one of the attractive topologies for meeting the power demands of distributed electric propulsion (DEP). To achieve autonomous decentralized power sharing and stable DC bus voltage regulation of the turbogenerator (TG) and ESS hybrid power supply system (HPSS), a novel control strategy that combines virtual impedance droop (VID) with load-side series virtual moment of inertia (LSVI) is proposed in this article. First, the VID control strategy is designed to allow the TG to provide the low-frequency portion of the load fluctuations, while the ESS to buffer the high-frequency fluctuations. Second, as an electromech-anically coupled system, it suffers from the instability caused by the mismatch between the gas turbine’s mechanical characteristics and the load’s electrical characteristics. The proposed LSVI is introduced to reshape the load-side input mechanical impedance, which prevents inducing and propagation of gas turbine self-oscillations and further improves the stability. The operational principle of the proposed control strategy and the system design are elaborated. Finally, a 3.5- kW hybrid-electric experiment setup is fabricated to verify the feasibility and effectiveness of the theoretical analysis.

In recent years, preventive maintenance has emerged as a focal point of research in the aerospace field. The concept of equipment maintenance, exemplified by prognosis and health management (PHM), has permeated every aspect of development and design. Extracting degradation features presents a fundamental and challenging task for health assessment and remaining useful life prediction. To facilitate the efficient operation of the incipient fault diagnosis model, this paper proposes a data-driven feature extraction process for converters, which consists of two main stages. First, feature extraction and comparison are conducted in the time domain, frequency domain, and time–frequency domain. By employing wavelet decomposition and the Hilbert transform method, a highly correlated time–frequency domain feature is obtained. Second, an improved feature selection approach that combines the ReliefF algorithm with the correlation coefficient is proposed to effectively minimize redundancy within the feature subset. Furthermore, an incipient fault diagnosis model is established using neural networks, which verifies the effectiveness of the data-driven feature extraction process presented herein. Experimental results indicate that this method not only maintains fault diagnosis accuracy but also significantly reduces training time.

This paper studies the extended dissipative stabilization problem for discrete-time switched linear systems (DSLSs) via hybrid-triggered intermittent control. By using the hybrid-triggered intermittent control scheme and a non-increasing switching-path-dependent Lyapunov functions (SPDLFs) approach, sufficient conditions ensuring the stability and extended dissipative performance of the closed-loop DSLSs are obtained. The research results show that hybrid-triggered mechanism can save control resources while ensuring the extended dissipative performance of the system. All the controller gains can be gotten by solving a set of strict linear matrix inequalities (LMIs). Finally, a numerical example is provided to illustrate the effectiveness of the results.