David Parlevliet

The accelerating deployment of photovoltaic (PV) systems intensifies the urgency to address various challenges in their performance prediction, operation and maintenance, and long-term reliability. Digital Twin (DT) technology – leveraging advancements in Industry 4.0 – offers great potential to tackle such challenges, by serving a wide range of PV applications and use cases. Nevertheless, the adoption of Digital Twins for PV systems (PVDTs) is still in its early stages, with limited published research work in this area. This paper presents a systematic literature review (SLR) of 61 peer-reviewed PVDT studies, aiming to map recent research trends, identify gaps, and provide recommendations guided by the review results. The works presented in the reviewed articles were categorized based on predefined review criteria, and were examined against a set of proposed PVDT eligibility criteria, stemming from commonly accepted generalized DT definitions and taxonomies. The review reveals that most reported implementations lack essential features, mainly bidirectional data flows and self-adaptability, with only 3.3 % of papers meeting all the eligibility criteria. Key identified trends include a dominance of data-driven models for power prediction, and limited utilization for life cycle assessments and design optimizations. Based on the review findings, the paper further introduces a general DT taxonomy tailored to PV applications and guided by the identified trends and gaps. This study emphasizes the need for unified and standardized PVDT definitions, comprehensive multi-domain modelling approaches, and integration of sustainability metrics to guide future research and industrial adoption.

Multi-robot task allocation and coalition formation are critical challenges in robotics, essential for applications such as disaster response, search and rescue, environmental monitoring, exploration and mapping, surveillance and security, logistics, agriculture, military operations and healthcare. Therefore, it is essential to address these challenges and develop optimal solutions for implementing these concepts in real-world scenarios to effectively execute the previously mentioned applications. Hence, this paper presents a comprehensive survey and comparative analysis of different approaches for allocating tasks to multiple robots and forming coalitions to accomplish these tasks efficiently. The paper first provides a systematic categorization of the existing methods into four different groups namely behavior-based, market-based, optimization-based, and learning-based methods. Next, it analyzes the trade-off between different objectives, including minimizing task completion time, maximizing resource utilization, and balancing workload among robots. The paper also explores the impact of robot heterogeneity, task dependencies, and communication constraints on the performance of various algorithms. Furthermore, it discusses the challenges of dynamic task allocation and coalition formation in response to changes in the environment or robot failures.
Accordingly, the paper presents a comprehensive comparative study of the surveyed approaches, highlighting their substantial features including limitations and suitability for different application scenarios. As such, the paper identifies promising research directions, including the integration of machine learning techniques and the development of hybrid algorithms. Through this systematic analysis, the main aim is to provide researchers with a comprehensive understanding of the state-of-the-art in multi-robot task allocation and coalition formation, enabling them to select the most appropriate approach for their specific requirements.

In practical applications, the utilization of multi-robot systems (MRS) is extensive and spans various domains such as search and rescue operations, mining operations, agricultural tasks, and warehouse management. The surge in demand for MRS has prompted extensive exploration of Multi-Robot Task Allocation (MRTA). Researchers have devised a range of methodologies to tackle MRTA problems, aiming to achieve optimal solutions, yet there remains room for further enhancements in this field. Among the complex challenges in MRTA, the identification of an optimal coalition formation (CF) solution stands out as one of the (Nondeterministic Polynomial) NP-hard problems. CF pertains to the effective coordination and grouping of agents or robots for efficient task execution, achieved through optimal task allocation. In this context, this paper delivers a succinct overview of dynamic task allocation and CF strategies. It conducts a comprehensive examination of diverse strategies employed for MRTA. The analysis encompasses the advantages, disadvantages, and comparative assessments of these strategies with a focus on CF. Furthermore, this study introduces a novel classification system for prominent task allocation methods and compares these methods with simulation analysis. The fidelity and effectiveness of the proposed CF approach are substantiated through comparative assessments and simulation studies.

The growth of utility-scale solar power plants, which can now have more than a million PV modules, has created the need for automated monitoring and inspection technologies. Unmanned aerial vehicle (UAVs, or “drones”) have become widely used, especially for thermal infrared imaging, due to their falling cost, increasing technical capability, and ability to survey large areas quickly. However drones have certain limitations: they do not see the underside of PV arrays or balance-of-system equipment, it is difficult to hold a steady position for long- or repeated-exposure imaging, and operating permits can be onerous. In such cases unmanned ground vehicles (UGVs, or “robots”) can be advantageous for PV plant inspection. This paper reviews robot movement mechanisms (wheels, tracks and legs), types of PV faults for which they are suited, and their current status of use in commercial solar farms. Further, it examines typical obstacles to robot navigation in Australian solar farms. Key conclusions are that robots are likely to complement rather than replace drones for PV inspection, and are especially valuable for reducing fire risk by detecting hot-spots in electrical components.

This study introduces a novel approach to wind energy by investigating a novel Active Axis Wind Turbine design. The turbine is neither a horizontal nor vertical axis wind turbine but has an axis of operation that can actively change during operation. The design features a rotor with a single blade capable of dynamic pitch and tilt control during a single rotor rotation. This study examines the potential to balance the centrifugal and aerodynamic lift forces acting on the rotor blade assembly, significantly reducing blade, tower, foundation and infrastructure costs in larger-scale devices and decreasing the levelised cost of energy for wind energy. The design of a laboratory prototype rotor assembly is optimised by varying the masses and lengths in a lumped mass model to achieve equilibrium between centrifugal and lift forces acting on the turbine’s rotor assembly. The method involves an investigation of the variation of blade pitch angle to provide a balance between centrifugal and aerodynamic forces, thereby facilitating the cost advantages and opening the opportunity to improve the turbine efficiency across a range of operation conditions. The implication of this study extends to different applications of wind turbines, both onshore and offshore, introducing insight into innovation for sustainable energy and cost-effective solutions.

This study investigated the possibility of enhancing the biomass productivity and nutrient removal of microalgae grown in raceway ponds through temperature regulation. Scenedesmus sp. grown in anaerobically digested abattoir effluent (ADAE) was cultivated outdoor in eight raceway ponds. Seven ponds were subjected to different temperature regulation schemes (from 15 °C to 25 °C) and varying control windows (from 24 h to daytime-only). From the results of this study, an approximate 60 % increase in biomass productivity was observed for ponds operated at a minimum temperature of 15 °C coupled with a 24 h (T15P24) and 12.5 h (T15P12.5) control period in relation to the uncontrolled temperature pond (UTP). Generally, Scenedesmus ponds operated at 15 °C used 60 % less energy for heating, were more efficient in nutrient removal, and showed significantly higher biomass productivities with respect to the uncontrolled/higher temperature regulated ponds. In other words, significant productivity improvements were observed for the systems with lower energy demands.

This study investigated the use of an innovative Proportional Integral (PI) + dead-zone control strategy to improve pH control and to overcome the negative environmental impact associated with the currently employed on/off pH control schemes for microalgae cultivation. Scenedesmus sp. grown in anaerobically digested abattoir effluent (ADAE) was used in this study to evaluate the effectiveness of the alternative/advanced control strategy with respect to the on/off control strategy. Results obtained showed that the PI+dead-zone control strategy significantly improved the regulation of the pH level in the pond as evident by noteworthy reductions of key controller performance indicators with respect to the on/off control strategy. Additionally, the advanced control scheme was able to reduce the CO2 usage by a remarkable 59.21% (highest recorded in related literature), resulting in a reduction in CO2 associated costs (approximately 60%) and most importantly, reduction in the amount of greenhouse gas lost to the atmosphere.

The world’s energy systems are transforming rapidly and switching from fossil fuels to renewables to address the current emission reduction targets. With the decrease in the cost of solar PV module globally by about 55% since 2013, the uptake of solar PV has increased dramatically. As these modules are exposed to ambient conditions in the field, they can develop defects or faults. These defects can affect the output power of the PV module and overall system output. To mitigate this, early and easy detection of defects is considered critical for operation and maintenance. Some defects can be easily identified through the infrared (IR) imaging and the presence of hot-spots on the PV module. This study highlights the best operational and environmental conditions for conducting IR imaging of PV module to detect defects. This study reveals that hot-spots with a minor temperature difference of 1.3–1.4 °C compared to the adjacent healthy cells likely indicate the presence of internal defects such as shunt. These hot-spots only appeared at lower irradiance conditions irrespective of the cloud condition. The IR imaging on partially and cloudy days showed that the presence of intermittent clouds, high ambient temperature and low wind speed helps the detection of these internal defects in the PV module.

The choice of components in industrial design involves setting design parameters that typically must reside inside permissible ranges called “design margins”. This paper proposes a novel automated method called the Margin-Based General Regression Neural Network (MB-GRNN) that classifies design errors for design parameters that are outside of permissible ranges as outliers, directly from industrial design data, using an unsupervised machine learning approach. The method is based on a modified GRNN that estimates extremal margin boundaries of design parameters by self-learning the features from datasets. These extremal permissible margin boundaries are determined by “stretching out” the upper and lower GRNN surfaces using an iterative application of stretch factors (a second kernel weighting factor). The method creates a variable insensitive band surrounding the data cloud, interlinked with the normal regression function, providing upper and lower margin boundaries. These boundaries can then be used to determine outliers and to predict a range of permissible values of design parameters during design. Pushing out extremal margin boundaries reduce the false identification of outliers. This classification technique could be used by industrial engineers to detect likely outliers and to predict a range of permissible output limits for chosen design parameters. The efficacy of this method has been validated against the widespread Parzen window method by comparing experimental results from three multivariate datasets. It was found that the two methods have different but complementary capabilities. The MB-GRNN also uses a modified algorithm for estimating the smoothing parameter using a combination of clustering, k-nearest neighbor, and localized covariance matrix.

Solar cultivation of microalgae in photobioreactors is a valuable bioprocess for the sustainable production of commercially useful metabolites. However, the conventional culture temperature control method in solar closed photobioreactors of evaporative cooling is neither economical nor sustainable. In this study, a novel spectrally selective, insulated glazed flat plate (IGP) photobioreactor employing an infrared reflecting system embedded in the illumination surface was used for cultivation of Nannochloropsis sp. The impact of the temperature control technology on protein, lipid, carbohydrate content and fatty acid profile of Nannochloropsis sp. was investigated and compared to closed photobioreactors using passive evaporative cooling (PEC) and an infrared reflecting film (IRF) on the surface as well as an open raceway pond (ORP). Among all cultivation systems tested, the biochemical composition of biomass (mg g−1 organic biomass) showed a general trend of lipid > protein > carbohydrate, with no large variation of each across treatments. However, the areal and volumetric productivities of these constituents were significantly higher in the photobioreactors than in the ORP; results consistent with biomass productivity data. Of the major saturated and monounsaturated fatty acids present, only the proportion of C16:0, which is 24% higher in the photobioreactors than in the ORP, changed significantly among cultivation systems. The highest content of high-value dietary fatty acids, eicosapentaenoic acid (EPA, C20:5n-3; 15.5%) and ϒ-linolenic acid (C18:3n-6; 8%) were found in the ORP but were similar to that produced in the IGP (15.9 and 3.4%, respectively). Among all photobioreactors, the IGP had the least diel temperature changes and an EPA content that was 21% higher than PEC. Photobioreactors constructed with spectrally selective materials effectively allow management of internal reactor temperature with no significant negative impact on biochemical and fatty acid profiles of microalgae.