This paper proposes an improved strategy for the optimization of dynamic photovoltaic arrays (DPVA) utilizing the 'irradiance equalization' reconfiguration strategy. This type of reconfigurable array is already very robust as it amalgamates the flexibility of dynamic reconfiguration with the averaging ability of Total Cross Tied (TCT) array architecture. This paper identifies four areas to further increase the power yield and significantly reduce the time for a return on investment. Results indicate potential efficiency improvements of more than 10% in some cases, and between 4-10% across a number of random and abrupt shading conditions. As in any DPVA system the proposed approaches require additional hardware and advanced control algorithms compared to a static PV array, but anyone implementing a dynamic array has already committed themselves to including the majority of this infrastructure. This investigation supports the idea of a fully dynamic IEq-DPVA with the ability to resize its array dimensions while implementing a rapid sorting algorithm based on information gathered using a novel precision irradiance profiling technique.

This paper presents a novel system for producing the optimum power output from photovoltaic arrays using dynamic cell reconfiguration. The proposed approach is the first in the literature that creates multi-cell sub-strings using individual cells that have been characterized and categorized ensuring maximum power extraction for a given irradiance profile. This optimized and decentralized PV architecture can produce significantly more power than a static equivalent (by an average of 22.6%) and also compared to an alternative irradiance equalized dynamic photovoltaic array (IEq-DPVA) (by an average of 13.7%)). The paper identifies the hardware requirements to produce such a system and it describes an algorithm that performs the optimized string reconfiguration strategy. Finally, a simulator programmed in MATLAB is used to compare the performance of the optimized string DPVA (OS-DPVA) against an Irradiance equalized DPVA in a series of flexibility tests.

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This paper presents a new simulator platform with findings from experiments aiming to identify the electrical characteristics of a marine vessel covered in photovoltaic modules, operating in various sea conditions. More specifically, we show that by giving a solar array the ability to reconfigure the arrangement of its modules in real time, that significant improvements (up to 50%) in power yield can be achieved compared to typical static arrays. A bespoke MATLAB simulator has been developed in order to model the complex interplay between the electrical arrangement of photovoltaic modules, the position of the photovoltaic modules on the vessel, the vessel's tilting motion on the surface of the sea and the resultant irradiance based on the position of the Sun in the sky. Our approach allows the user to define these factors using a simple and intuitive graphical user interface so that a range of scenarios can be quickly simulated. We have used a basic test strategy that allows us to measure the effectiveness of different arrays and quantify performance in terms of mean output power and power stability over a range of sea conditions. A key factor in the effectiveness of the use of marine survey vessels is their ability to remain at sea for extended periods, preferably avoiding the use of high-carbon fuel sources such as diesel generators. This is of particular importance when observing marine life as the platform needs to operate as quietly as possible. The ASV Global C-Enduro autonomous, self-righting platform is the initial application for this new energy harvesting system, with the aim to extend mission endurance. A second case study has also been performed in parallel with this, using a much more divergent orientation of onboard photovoltaic modules in order to asses the ability for a dynamic photovoltaic array to increase and stabilise power output.

A persistent problem for Aircraft Manufacturers has been the difficulty in carrying out accurate and robust simulations of the complete aircraft power network, while including numerous models from a variety of individual equipment suppliers. Often the models are of variable or low quality, with ill-defined parameters or behavior, and in many cases of the wrong level of abstraction to be appropriate for large scale network simulations. In addition, individual equipment suppliers often provide poor models for network integration, with a common issue being low robustness of models leading to lack of convergence, excessive simulation times and delays in development due to the need for rework and extensive testing of these models. In order to address this specific issue a complete library of power electronic system models for Aerospace applications has been developed that encompasses the range of functions from elementary components (passives, devices, switches and magnetic components), intermediate building blocks (rectifiers, inverters, motors, protection devices) and finally complete system models (variable frequency starter generators, power converters, battery and storage elements, transformers). These models have been developed in partnership with several key aircraft equipment suppliers and in partnership with Airbus to ensure that the resulting models are complete and robust. Specific equipment models were also developed in this library including permanent magnet generators, synchronous machines, environmental control systems, wing ice protection systems, power electronic modules and advanced power protection systems. The specific models have been validated against reference and measured data to ensure that they are consistent and accurate. This paper will describe the techniques used to achieve more robust models, using model based engineering, the integration of specific equipment models into the complete aircraft network and the validation of the behavior against measured results. The paper will provide the results of a complete aircraft power network highlighting how the individual models are integrated into the overall network model and the inherent robustness ensure effective, accurate and robust simulations.