Improving availability and safety in complex offshore facilities considering the interactions between humans, assets and environment

The transition to renewable energy is a pressing global challenge, and offshore renewable energy (ORE) systems are increasingly seen as a promising source of clean energy. Offshore renewable energy systems have significant growth potential in Australia, with its vast coastline and strong winds making it an ideal location for wind and wave energy production. The offshore wind farm development is underway, with projects such as the Star of the South wind farm in Victoria and the Flinders Bay offshore wind farm in Western Australia. The Tasmanian government is also exploring the potential for offshore wind energy around the island state, and a feasibility study is currently underway to identify potential sites for future projects. While ORE systems offer benefits such as reduced greenhouse gas emissions, energy security, and job creation, they face several challenges. The complexity of the system, the uncertainty in energy production and the high variability and intermittency of ORE are the main challenges in developing and operating these systems during their lifespan. The challenge intensifies as ORE systems become, more extensive and more interconnected.
In a complex, dynamic and uncertain situation, human interaction with offshore renewable energy can take many forms, from developing and constructing the wind turbines and associated infrastructure to ongoing operation and maintenance activities. A minor error can create an error chain affecting the system during its lifespan. Increasing interdependence and coupling between system components create complexity and are ground for the fast propagation of uncertainties. Thus, to address the challenges associated with complexities and uncertainties humans add through site selection, development, and ongoing maintenance, this thesis aims to develop models, simulations, and analytical methods to shed light on human impacts on a complex engineering system. The research outcomes of the thesis provide insights into the mutual effects of humans and systems, and the findings have practical applications for the reliability, decision-making, and human operation management of offshore renewable energy systems. This research aims to contribute to the sustainable development and operation of offshore renewable energy systems in Australia and beyond by addressing some of the fundamental challenges of human-system interactions.
In the first stage, this thesis focuses on developing a methodology for quantifying the effect of harsh environmental conditions on human performance reliability during complex physical operations. A Hierarchical Bayesian Network (HBN) is adopted in modelling complex causal dependencies. Results demonstrate probabilistic interdependencies among environmental factors using a four-level HBN. A human model is developed with three reference points AAA (Awareness, Access, and Action) that drive human error modes from physiological failure mechanisms. This model will assist analysts and decision-makers in identifying the root causes of human errors and developing strategies for their minimisation.
Decision-making bias caused by human judgement is a central part of human cognition. Owners, reliability engineers, operators and regulators often benefit from more than a simple single-objective cost-benefit analysis. Many production and maintenance employees have experienced conflict between the two functions when a serious failure happens while production is under pressure. Therefore, to reach the optimal solution in risk and reliability management, it is vital to consider this conflict in decision-making, which is often ignored by classical methods. Typically, perfect cooperation is assumed among the decision-makers to reach the system's optimal solutions. These decision-makers optimise the objective function without any consideration of their own objectives. To address this issue, a game theory-based decision-making framework for site selection of offshore wind farms in Australia suggests non-cooperative behaviour. The proposed method can be used when a group of decision-makers with conflicting objectives are to solve a complex multi?dimensional problem. Furthermore, this work recommends countering the adverse effects of wind farm deployment in marine environments and examines the costs of adopting such marine protection strategies. The results highlight the importance of government commitment and incentives in encouraging decision-makers to use technologies or operations with less environmental impact.
To better understand the intertemporal dependencies between main variables in an offshore wind energy system, a System Dynamics (SD) approach is adopted in the third part of this thesis to optimise o planning and asset management, taking into account the effects of weather-related delays. The model predicts the health states of wind turbines, repairs, downtime expenses, failures, and production losses. Simulation results show the optimal scenario for effective intervention and the applicability of the proposed model in controlling wind farm assets. By considering various maintenance planning approaches, the model identifies the factors which influence maintenance and minimises the impact of human performance on O&M activities in OWFs.
In the final section, SD has been used to reveal the interaction and demonstrate the causality of the machine failure and human error in the O&M of offshore wind energy systems highlighting the reinforcing loops between all system variables to model their interdependencies. The model shows that the interaction between humans and machines can significantly impact the outcome of maintenance plans. The proposed framework aids organisations in concentrating on the true causes and factors behind occurring errors rather than just dealing with the consequences. It is also helpful in understanding why a maintenance plan has failed to achieve its intended outcome and why maintenance crews repeatedly make the same error in their tasks. The outcome of this research is presented as a dynamic model applicable to offshore renewable energy systems subjected to performance degradation.
This thesis focuses on the complicated dynamics of human interactions within complex engineering systems. Recognising that humans, from engineers to policymakers, are integral components of these systems, the study investigates their role, beliefs, and values in influencing and being influenced by the overarching system. A focus on offshore platforms and wind turbines, especially under varied environmental conditions, reveals that both machine operations and human performances are significantly impacted by external factors such as weather and organisational policies. Through an extensive review of existing literature, the research underscores the importance of HRA in understanding human errors and their probabilities. It brings to light the significance of both cognitive and physical factors that influence human performance in extreme conditions and under organisational pressure. The study also delves into the consequences of system failures, emphasising that the implications of such failures often have broader ramifications than the failures themselves. The research highlights the interdependencies between social, technical, and environmental facets in complex systems, providing insights into their combined influence on the reliability and efficacy of engineering operations. This holistic approach underscores the need for a comprehensive understanding of the intertwined dynamics of human interactions, machine operations, and environmental factors to ensure optimal system performance.