Establishment of a novel predictive reliability assessment strategy for ship machinery

There is no doubt that recent years, maritime industry is moving forward to novel and sophisticated inspection and maintenance practices. Nowadays maintenance is encountered as an operational method, which can be employed both as a profit generating process and a cost reduction budget centre through an enhanced Operation and Maintenance (O&M) strategy. In the first place, a flexible framework to be applicable on complex system level of machinery can be introduced towards ship maintenance scheduling of systems, subsystems and components.;This holistic inspection and maintenance notion should be implemented by integrating different strategies, methodologies, technologies and tools, suitably selected by fulfilling the requirements of the selected ship systems. In this thesis, an innovative maintenance strategy for ship machinery is proposed, namely the Probabilistic Machinery Reliability Assessment (PMRA) strategy focusing towards the reliability and safety enhancement of main systems, subsystems and maintainable units and components.;In this respect, the combination of a data mining method (k-means), the manufacturer safety aspects, the dynamic state modelling (Markov Chains), the probabilistic predictive reliability assessment (Bayesian Belief Networks) and the qualitative decision making (Failure Modes and Effects Analysis) is employed encompassing the benefits of qualitative and quantitative reliability assessment. PMRA has been clearly demonstrated in two case studies applied on offshore platform oil and gas and selected ship machinery.;The results are used to identify the most unreliability systems, subsystems and components, while advising suitable practical inspection and maintenance activities. The proposed PMRA strategy is also tested in a flexible sensitivity analysis scheme.There is no doubt that recent years, maritime industry is moving forward to novel and sophisticated inspection and maintenance practices. Nowadays maintenance is encountered as an operational method, which can be employed both as a profit generating process and a cost reduction budget centre through an enhanced Operation and Maintenance (O&M) strategy. In the first place, a flexible framework to be applicable on complex system level of machinery can be introduced towards ship maintenance scheduling of systems, subsystems and components.;This holistic inspection and maintenance notion should be implemented by integrating different strategies, methodologies, technologies and tools, suitably selected by fulfilling the requirements of the selected ship systems. In this thesis, an innovative maintenance strategy for ship machinery is proposed, namely the Probabilistic Machinery Reliability Assessment (PMRA) strategy focusing towards the reliability and safety enhancement of main systems, subsystems and maintainable units and components.;In this respect, the combination of a data mining method (k-means), the manufacturer safety aspects, the dynamic state modelling (Markov Chains), the probabilistic predictive reliability assessment (Bayesian Belief Networks) and the qualitative decision making (Failure Modes and Effects Analysis) is employed encompassing the benefits of qualitative and quantitative reliability assessment. PMRA has been clearly demonstrated in two case studies applied on offshore platform oil and gas and selected ship machinery.;The results are used to identify the most unreliability systems, subsystems and components, while advising suitable practical inspection and maintenance activities. The proposed PMRA strategy is also tested in a flexible sensitivity analysis scheme

Toward Holistic Energy Management Strategies for Fuel Cell Hybrid Electric Vehicles in Heavy-Duty Applications

The increasing need to slow down climate change for environmental protection demands further advancements toward regenerative energy and sustainable mobility. While individual mobility applications are assumed to be satisfied with improving battery electric vehicles (BEVs), the growing sector of freight transport and heavy-duty applications requires alternative solutions to meet the requirements of long ranges and high payloads. Fuel cell hybrid electric vehicles (FCHEVs) emerge as a capable technology for high-energy applications. This technology comprises a fuel cell system (FCS) for energy supply combined with buffering energy storages, such as batteries or ultracapacitors. In this article, recent successful developments regarding FCHEVs in various heavy-duty applications are presented. Subsequently, an overview of the FCHEV drivetrain, its main components, and different topologies with an emphasis on heavy-duty trucks is given. In order to enable system layout optimization and energy management strategy (EMS) design, functionality and modeling approaches for the FCS, battery, ultracapacitor, and further relevant subsystems are briefly described. Afterward, common methodologies for EMS are structured, presenting a new taxonomy for dynamic optimization-based EMS from a control engineering perspective. Finally, the findings lead to a guideline toward holistic EMS, encouraging the co-optimization of system design, and EMS development for FCHEVs. For the EMS, we propose a layered model predictive control (MPC) approach, which takes velocity planning, the mitigation of degradation effects, and the auxiliaries into account simultaneously

POWER CONDITIONING UNIT FOR SMALL SCALE HYBRID PV-WIND GENERATION SYSTEM

Small-scale renewable energy systems are becoming increasingly popular due to soaring fuel prices and due to technological advancements which reduce the cost of manufacturing. Solar and wind energies, among other renewable energy sources, are the most available ones globally. The hybrid photovoltaic (PV) and wind power system has a higher capability to deliver continuous power with reduced energy storage requirements and therefore results in better utilization of power conversion and control equipment than either of the individual sources. Power conditioning units (p.c.u.) for such small-scale hybrid PV-wind generation systems have been proposed in this study. The system was connected to the grid, but it could also operate in standalone mode if the grid was unavailable. The system contains a local controller for every energy source and the grid inverter. Besides, it contains the supervisory controller. For the wind generator side, small-scale vertical axis wind turbines (VAWTs) are attractive due to their ability to capture wind from different directions without using a yaw. One difficulty with VAWTs is to prevent over-speeding and component over-loading at excessive wind velocities. The proposed local controller for the wind generator is based on the current and voltage measured on the dc side of the rectifier connected to the permanent magnet synchronous generator (PMSG). Maximum power point tracking (MPPT) control is provided in normal operation under the rated speed using a dc/dc boost converter. For high wind velocities, the suggested local controller controls the electric power in order to operate the turbine in the stall region. This high wind velocity control strategy attenuates the stress in the system while it smoothes the power generated. It is shown that the controller is able to stabilize the nonlinear system using an adaptive current feedback loop. Simulation and experimental results are presented. The PV generator side controller is designed to work in systems with multiple energy sources, such as those studied in this thesis. One of the most widely used methods to maximize the output PV power is the hill climbing technique. This study gives guidelines for designing both the perturbation magnitude and the time interval between consecutive perturbations for such a technique. These guidelines would improve the maximum power point tracking efficiency. According to these guidelines, a variable step MPPT algorithm with reduced power mode is designed and applied to the system. The algorithm is validated by simulation and experimental results. A single phase H-bridge inverter is proposed to supply the load and to connect the grid. Generally, a current controller injects active power with a controlled power factor and constant dc link voltage in the grid connected mode. However, in the standalone mode, it injects active power with constant ac output voltage and a power factor which depends on the load. The current controller for both modes is based on a newly developed peak current control (p.c.c.) with selective harmonic elimination. A design procedure has been proposed for the controller. Then, the method was demonstrated by simulation. The problem of the dc current injection to the grid has been investigated for such inverters. The causes of dc current injection are analyzed, and a measurement circuit is then proposed to control the inverter for dc current injection elimination. Characteristics of the proposed method are demonstrated, using simulation and experimental results. At the final stage of the study, a supervisory controller is demonstrated, which manages the different operating states of the system during starting, grid-connected and standalone modes. The operating states, designed for every mode, have been defined in such a hybrid model to allow stability and smooth transition between these states. The supervisory controller switches the system between the different modes and states according to the availability of the utility grid, renewable energy generators, the state of charge (SOC) of energy storage batteries, and the load. The p.c.u. including the supervisory controller has been verified in the different modes and states by simulation

Reinforcement Learning based Adaptive Model Predictive Power Pinch Analysis Systems Level Energy Management Approach to Uncertainty in Isolated Hybrid Energy Storage Systems

Ph. D. ThesisHybrid energy storage systems (HESS) involves the integration of multiple energy storage technologies with different complementary characteristics which are significantly advantageous compared to a single energy storage system, and can greatly improve the reliability of intermittent renewable energy sources (RES). Aside from the advantages HESS offer, the control and coordination of the multiple energy storages and the vital elements of the system via an optimised energy management strategy (EMS) involves increased computational time. Nevertheless, a systems-level graphical EMS based on Power Pinch Analysis (PoPA) which is a low burden computational tool was recently proposed for HESS. In this respect, the EMS which effectively resolved deficit and excess energy objectives was effected via the graphical PoPA tool, the power grand composite curve (PGCC). PGCC is basically a plot of integrated energy demands and sources in the system as a function of time. Although of proven success, accounting for uncertainty with PoPA is a cogent research question due to the assumption of an ideal day ahead (DA) generation and load profiles forecast. Therefore, the proposition of several graphical and reinforcement learning based ‘adaptive’ PoPA EMSs in order to address the issue of uncertainty with PoPA, has been the major contribution of this thesis. Firstly, to counteract the combined effect of uncertainty with PoPA, an Adaptive PoPA EMS for a standalone HESS has been proposed. In the Adaptive PoPA, the PGCC was implemented within a receding horizon model predictive framework with the current output state of the energy storage (in this case the battery) used as control feedback to derive an updated sequence of EMS, inferred via PGCC shaping. Additionally, during the control and operation of the HESS, re-computation of the PGCC only occurs if a forecast uncertainty occurs such that the error between the real and estimated battery’s state of charge becomes greater than an arbitrarily chosen threshold value of 5%. Secondly a Kalman filter for the optimal estimation of uncertainty distributed as a normal Gaussian is integrated into the Adaptive PoPA in order to recursively predict the State of Charge of the battery based on the likelihood of uncertainty. Thus, the Kalman filter Adaptive PoPA by anticipating the effect of uncertainty offers an improved approach to the Adaptive PoPA particularly when the uncertainty is of a Gaussian distribution. The algorithm is therefore more sophisticated than the Adaptive PoPA but nevertheless computationally efficient and offers a preventive measure as an improvement. Furthermore, Tabular Dyna Q-learning algorithm, a subset of reinforcement learning which employs a learning agent to solve a discrete Markov Decision Process by maximising an expected reward in accordance with the Bellman optimality, is integrated within the Power Pinch Analysis. Thereafter, a deep neural network is used to approximate the Q-Learning Table. These aforementioned methods which have been highlighted in order of computational time can be deployed with only a minimal level of historical data requirements such as the average load profile or base load data and solar irradiance forecast to produce a deterministic solution. Nevertheless, this thesis proposed a probabilistic adaptive PoPA strategy based on a (recursive least square) Monte Carlo simulation chance constrained framework, in the event where there is sufficient amount of historical data such as the probability distribution of the uncertain model parameters. The probabilistic approach is no doubt more computationally intensive than the deterministic methods presented though it proffers a much more realistic solution to the problem of uncertainty. In order to enhance the probabilistic adaptive PoPA, an actor-critic deep neural network reinforcement learning agent is incorporated. The six methods are evaluated against the DA PoPA on an actual isolated HESS microgrid built in Greece with respect to the violation of the energy storage operating constraints and plummeting carbon emission footprint.Petroleum Technology Development Funds (PTDF

Advances in Theoretical and Computational Energy Optimization Processes

The paradigm in the design of all human activity that requires energy for its development must change from the past. We must change the processes of product manufacturing and functional services. This is necessary in order to mitigate the ecological footprint of man on the Earth, which cannot be considered as a resource with infinite capacities. To do this, every single process must be analyzed and modified, with the aim of decarbonising each production sector. This collection of articles has been assembled to provide ideas and new broad-spectrum contributions for these purposes