Power electronic systems are prone to instability. The problem, generally attributed to the constant power load (CPL) behavior of their power electronic controlled loads, can become more acute when the systems are subject to parametric uncertainties. The structured singular value (SSV) based μ method has proven to be a reliable approach for assessing the stability robustness of such uncertain systems. Despite its numerous benefits, the μ method is not often applied to electrical power systems (EPS) with multiple uncertainties. This may be due to the mathematical complexity underlying the μ theory. This paper aims to make the μ approach more application-friendly by providing clearer insights into the meaning and usefulness of the robust stability measure μ for EPS with multiple parametric uncertainties. This is achieved by presenting a methodology for translating μ analysis results from the frequency domain to the more perceivable uncertain parameters domain. The method directly demonstrates dependences of system stability on uncertain system parameters. Further, it clearly identifies robust stability domains as subsets of the much wider stability domains. The work is based on a representative EPS connected to an ideal CPL. μ analysis predictions are evaluated and validated against analytical results, for example, CPL system.

Stability studies are a crucial part of the design of power electronic (PE) systems, especially for safety critical applications. Standard methods can guarantee stability under nominal conditions but do not take into account the multiple uncertainties that are inherent in the physical system or in the system model. These uncertainties, if unaccounted for, may lead to highly optimistic or even erroneous stability margins. The structured singular value-based μ method justifiably takes into account all possible uncertainties in the system. However, the application of the μ method to PE systems with multiple uncertainties is not widely discussed in the literature. This work presents practical approaches to applying the μ method in the robust stability analysis of such uncertain systems. Further, it reveals the significant impact of various types of parametric uncertainties on the reliability of stability assessments of PE systems. This is achieved by examining the robust stability margin of the dc/dc buck converter system, when it is subject to variations in system load, line resistance, operating temperature and uncertainties in the system model. The μ predictions are supported by time domain simulation and experimental results.

A centralised smart supervisor (CSS) controller with enhanced electrical energy management (E2-EM) capability has been developed for an Iron Bird Electrical Power Generation and Distribution System (EPGDS) within the Clean Sky 2 ENhanced electrical energy MAnagement (ENIGMA) project. The E2-EM strategy considers the potential for eliminating the 5 min overload capability of the generators to achieve a substantial reduction in the mass of the EPGDS. It ensures optimal power and energy sharing within the EPGDS by interfacing the CSS with the smart grid network (SGN), the energy storage and regeneration system (ESRS), and the programmable load bank 1 secondary distribution board (PLB1 SDU) during power overloads and failure conditions. The CSS has been developed by formalizing E2-EM logic as an algorithm operating in real time and by following safety and reliability rules. The CSS undergoes initial verification using model-in-the-loop (MIL) testing. This paper describes the EPGDS simulated for the MIL testing and details the E2-EM strategy, the algorithms, and logic developed for the ENIGMA CSS design. The CSS was subjected to two test cases using MIL demonstration, and based on the test results, the performance of the ENIGMA CSS is verified and validated.

Transport accounts for nearly two-thirds of the global crude oil consumption and about a quarter of carbon dioxide (CO₂) emissions (International Energy Agency 2009, Intergovernmental Panel on Climate Change 2014). The energy use and CO₂ emissions in this sector are predicted to increase 80% by 2050 (International Energy Agency 2009). The major contributors of greenhouse effects are expected to be light-duty vehicles (43%), trucks (21%), aviation (20%), and shipping (8%) by 2050 (International Energy Agency 2009). Buses and railways are already sustainable modes of transport. To mitigate the impact of the emissions on climate change, the Intergovernmental Panel on Climate Change, which is the leading international body assessing climate change, recommends a reduction of at least 50% in global CO₂ emissions by 2050 (International Energy Agency 2009). This target cannot be met unless there is a deep cut in CO₂ emissions from the transportation sector. On the other hand, independent of climate policy actions, the projections are that fossil fuel reserves will become exhausted within the next 50 years. If a more sustainable future is to be achieved, the issues of greenhouse emissions and energy security must be addressed. One long-term solution may well lie in both the adoption of current best technologies and in the development of more advanced technologies, in all sectors of transportation (International Energy Agency 2009). A shift toward more efficient modes of transport, including the more electric aircraft (MEA), are not merely needed, but are required.

This paper develops a method to analyse robust stability of a generic electrical power system for safe-critical applications over all operating conditions. Standard methods can guaranty stability under nominal conditions but do not take into account any uncertainties of the model. In this work, stability is assessed by using a Structural Singular Value concept that can provide a measure of stability robustness of a Linear Fractional Transformation (LFT)-based linear system with structured parametric uncertainties. In line with this, the first step was to develop a parameter-dependent linear time-invariant state-space model of the system that is valid for all operating conditions. The model was obtained by symbolic linearisation of the system non-linear model and was further extended to include structured parametric uncertainties of the system. The developed approach was successfully applied to determine the critical destabilising torque of a 4 kW permanent magnet motor drive over the defined range of operating conditions. Matlab robust stability toolbox was used for this analysis. The results were validated against simulation and experimental data.

Sustainable aviation requires electrical power systems that can deliver both high energy and high power. Hybrid fuel cell–battery architectures offer a promising solution to meet these demands, and their overall performance can be significantly enhanced through the application of energy management strategies (EMSs). This paper develops several EMSs approaches, including rule-based state machine, equivalent consumption minimisation strategy (ECMS), and dynamic programming (DP) for a hybrid fuel cell–battery aircraft targeting specific objectives, such as improving system efficiency, reducing hydrogen consumption and extending battery lifetime. The EMS approaches are then evaluated across both nominal missions and a fuel cell-failure scenario to assess their effectiveness in meeting their defined objectives. Results show that while ECMS achieves the lowest cost per mission, it does not maximise system efficiency. DP provides the highest overall energy efficiency and longest battery lifetime but is limited to offline implementation. To bridge this gap, a hybrid DP–ECMS strategy is introduced and evaluated. The results show that the approach delivers globally optimal performance under nominal conditions, accounting for the trade-offs between cost, efficiency, and battery ageing, while also preserving real-time responsiveness during unforeseen events. This demonstrates the benefits of combining offline optimisation with real-time control for hybrid electric aircraft. Software-in-the-loop (SIL) further validates the real-time applicability and robustness of the proposed strategy.