Explore the vast range of titles available.

In recent years, advanced methods and smart solutions have been investigated for the safe, secure, and environmentally friendly operation of ships. Since data acquisition capabilities have improved, data processing has become of great importance for ship operators. In this study, we introduce a novel approach to ship machinery monitoring, employing generative adversarial networks (GANs) augmented with failure mode and effect analysis (FMEA), to address a spectrum of failure modes in diesel generators. GANs are emerging unsupervised deep learning models known for their ability to generate realistic samples that are used to amplify a number of failures within training datasets. Our model specifically targets critical failure modes, such as mechanical wear and tear on turbochargers and fuel injection system failures, which can have environmental effects, providing a comprehensive framework for anomaly detection. By integrating FMEA into our GAN model, we do not stop at detecting these failures; we also enable timely interventions and improvements in operational efficiency in the maritime industry. This methodology not only boosts the reliability of diesel generators, but also sets a precedent for prescriptive maintenance approaches in the maritime industry. The model was demonstrated with real-time data, including 33 features, gathered from a diesel generator installed on a 310,000 DWT oil tanker. The developed algorithm provides high-accuracy results, achieving 83.13% accuracy. The final model demonstrates a precision score of 36.91%, a recall score of 83.47%, and an F1 score of 51.18%. The model strikes a balance between precision and recall in order to eliminate operational drift and enables potential early action in identified positive cases. This study contributes to managing operational excellence in tanker ship fleets. Furthermore, this study could be expanded to enhance the current functionalities of engine health management software products.

The paper presents a technical and economic analysis for two energy systems (conventional and renewable) with grid connection. The investigation was carried out using an experimental measurement for the desired load and weather data (solar irradiance and ambient temperature), were 5.1 kWh the daily energy consumption as measured and 4.6 kWh/m /day the annual average of the solar irradiance. The simulation process was done by using MATLAB and HOMER software at a 1 min time step resolution. The economic optimization objective presented for two energy system scenarios (i) photovoltaic/grid and (ii) diesel/grid, takes into account the economic aspects and component prices based on the Iraqi market and regulations. The diesel generator, very popular in rural areas, is designed to work during the same period as the photovoltaic system (only during day hours). The yearly operating hours were recorded at 4380 h/year, and energy generation was approx. 2349 kWh/year while fuel consumption was 1826 L/year. The results showed that the photovoltaic system in scenario (i) can generate about 7895 kWh, and for the diesel generator in scenario (ii), it can generate approximately 2346 kWh. Furthermore, for scenario (i) the levelized net present cost is $1079 and the cost of energy is about $0.035/kWh, while for scenario (ii) the levelized net present cost is $12,287 and the cost of energy is $0.598/kWh. The use of solar energy is highly recommended compared to diesel generators due to the lowest cost and delivery of energy to the grid. Furthermore, it can capture carbon dioxide by about 5295 kg/year.

Currently, the high consumption of fossil fuels is causing problems, perhaps, irreversible to the planet, investing in green energy is an essential point for the sustainable development of any country. Concomitant to this, biomass fuels, in particular natural vegetable oils, appear as an option for the total or partial replacement of these fuels in the production of energy in the current world energy matrix. Corroborating this pretext, the objective of this work is to analyze the effects of using different mixtures of Brazilian commercial diesel oil (92% diesel, 8% biodiesel) with refined soybean oil from the effective cylinder pressure of a stationary diesel generator set. To achieve this purpose, the generator set of the Branco brand, model BD-6500, was used, with the aid of the pressure acquisition system of the Austrian company AVL. Analyzes of the requested fuels were carried out in addition to the experimental tests. The results presented show that the use of mixtures with volumetric ratios of soybean oil may show a drop in the cylinder pressure.

Background PV/diesel microgrids are getting more popular in rural areas of sub-Saharan Africa, where the national grid is often unavailable. Most of the time, for economic purposes, these hybrid PV/diesel power plants in rural areas do not include any storage system. This is the case in the Bilgo village in Burkina Faso, where a PV/diesel microgrid without any battery storage system has been set up. This power plant is composed of three diesel generators operating in parallel (two of 16 kW and one of 24 kW), coupled with a photovoltaic field of 30 kWp. It was observed that for such power plants, the grid management is not always efficient due to constantly fluctuating solar output and loads. This inconsistency in energy output raises the question if integrating battery energy storage systems could improve the grid's performance. While many studies in the literature focus on hybrid energy systems, only a few of them have tackled the optimization of existing and operational systems. Methods This study investigated three scenarios based on the existing microgrid's characteristics: conventional standalone diesel generators, PV/diesel without battery storage and PV/diesel with a battery storage system which are the main technologies used for off-grid rural electrification in Burkina Faso. The levelized cost of electricity (LCOE) was used to assess the economic performance of each scenario, and the calculations were made using the HOMER software. Results It was found that the best among the scenarios considered is the PV/diesel/battery configuration which has the lowest LCOE of US$ 0.524/kWh. The battery storage system for the optimal configuration has a capacity of 182 kWh with about 8 h of autonomy. Conclusions It can be inferred from this study that a storage unit is necessary for an optimal management of a PV/diesel microgrid. Indeed, the storage unit significantly reduces the operating and maintenance costs associated with running diesel generators, as well as the excess electricity. The storage system also allows for a greater reduction in CO 2 emissions compared to systems without storage.

This paper proposes an energy management strategy (EMS) for a hybrid stand-alone plant destined to supply controllable loads. The plant is composed of photovoltaic panels (PV), a wind turbine, a diesel generator, and a battery bank. The set of the power sources supplies controllable electrical loads. The proposed EMS aims to ensure the power supply of the loads by providing the required electrical power. Moreover, the EMS ensures the maximum use of the power generated by the renewable sources and therefore minimizes the use of the genset, and it ensures that the batteries bank operates into the prefixed values of state of charge to ensure their safe operation. The EMS provides the switching control of the switches that link the plant components and decides on the loads’ operation. The simulation of the system using measured climatic data of Mostoles (Madrid, Spain) shows that the proposed EMS fulfills the designed objectives.

Electromagnetic and energy processes in a synchronous salient-pole generator (SG) with electromagnetic excitation operating as part of an autonomous diesel generator unit are analyzed. In contrast to the classical theory of synchronous machines, the mathematical model and calculation of electromagnetic, electromechanical and energy SG characteristics take into account the interdependence of the current, its load angle and EMF, physical dependences, and limitations associated with the generator design and the structure of the entire diesel generator unit.

Islanded microgrids with low-inertia distributed energy resources (DERs) are prone to frequency fluctuations. With the increasing integration of DERs in microgrids, the complexity of control and stability has also increased. Moreover, the integration of DERs into microgrids may result in a power imbalance between energy supply and demand during sudden changes in load or energy generation. This can cause frequency variations in the microgrid, which could have disastrous consequences such as equipment damage or even blackouts. This paper proposes a control strategy to ensure the efficient operation of an islanded hybrid microgrid consisting of a PV generator, battery energy storage system (BESS), and emergency diesel generator. According to Energy Exchange Model proposed in this paper, the hybrid system presented operates independently without being connected to the electrical grid, where the PV system and BESS act as the main energy sources, while the emergency diesel generator provides active power backup with voltage and frequency regulation. The novel in this paper is also that DER aids in frequency regulation during active power transients by delivering and absorbing active power in accordance with the inverter's suggested P droop control strategy. In this way inverter droop control decreases system frequency nadir emulating so called “synthetic inertia”. To design both the islanded hybrid system and the proposed control strategy, the MATLAB/Simulink environment is utilized. Based on the results, it can be concluded that the analyzed microgrid system is capable of maintaining stability and operating efficiently in a range of operating conditions.

Transitioning to clean energy in off-grid remote locations is essential to reducing fossil-fuel-generated greenhouse gas emissions and supporting renewable energy growth. While hybrid renewable energy systems (HRES), including multiple renewable energy (RE) sources and energy storage systems are instrumental, it requires technical reliability with economic efficiency. This study examines the feasibility of an HRES incorporating solar, wind, hydrogen, and biofuel energy at a remote location in Australia. An electric vehicle charging load alongside a residential load is considered to lower transportation-based emissions. Additionally, the input data (load profile and solar data) is validated through statistical analysis, ensuring data reliability. HOMER Pro software is used to assess the techno-economic and environmental performance of the hybrid systems. Results indicate that the optimal HRES comprising of photovoltaic, wind turbines, fuel cell, battery, and biodiesel generators provides a net present cost of AUD 9.46 million and a cost of energy of AUD 0.183, outperforming diesel generator-inclusive systems. Hydrogen energy-based FC offered the major backup supply, indicating the potential role of hydrogen energy in maintaining reliability in off-grid hybrid systems. Sensitivity analysis observes the effect of variations in biodiesel price and electric load on the system performance. Environmentally, the proposed system is highly beneficial, offering zero carbon dioxide and sulfur dioxide emissions, contributing to the global net-zero target. The implications of this research highlight the necessity of a regional clean energy policy facilitating energy planning and implementation, skill development to nurture technology-intensive energy projects, and active community engagement for a smooth energy transition. Potentially, the research outcome advances the understanding of HRES feasibility for remote locations and offers a practical roadmap for sustainable energy solutions.

This paper demonstrates the optimum design and analysis of hybrid renewable energy system (HRES) for village electrification in Korkadu, Puducherry, India. Renewable energy sources (RES) comprises of photovoltaic, wind turbine and bio-diesel generators. The main target of this work is to design an optimal HRES system that can generate and provide cost-effective electricity to satisfy the electricity need. In the pre-hybrid optimization model for the electric renewable (HOMER), the paper evaluates the load forecasting for the selected district. For reliable electrification, the desired HRES needs to meet the forecasted load demand. HOMER software is used to estimate the different feasible hybrid configurations. The configurations are hybrid conventional (bio-gasifiers) and renewable energy system, standalone renewable energy system with high renewable fractions and standalone conventional (bio-gasifiers) system. From the investigations, it indicates that the Korkadu zone is highly potential area for implementing standalone hybrid electrification system. Furthermore, the proposed work result demonstrates that the HRES-based power generation at off-grid location can be a cost-effective. Additionally, our proposed strategy can conquer the uncertainty found in RES and the over-sizing issues in installed capacity.

Fuel cell technologies are increasingly investigated as alternatives to conventional auxiliary diesel generators in order to enhance shipboard energy efficiency and reduce greenhouse gas emissions. This study presents a unified and uncertainty-driven system-level assessment of solid oxide fuel cell (SOFC) and proton exchange membrane fuel cell (PEMFC) systems operating as auxiliary power sources on a 200 m bulk carrier. Both technologies are evaluated under identical vessel characteristics, operating profiles, auxiliary load levels (360–600 kW), and cost assumptions, and are benchmarked directly against a conventional three–diesel-generator configuration. A modular numerical framework is developed to model propulsion–auxiliary interactions for ship speeds between 10 and 14 knots. SOFC systems are assessed using grey, bio-derived, and green natural gas pathways, while PEMFC systems are examined under grey, blue, and green hydrogen supply routes. Performance indicators include annual fuel consumption, carbon dioxide (CO2) emission reduction, net present value (NPV), internal rate of return (IRR), payback period (PBP), and marginal abatement cost (MAC). Economic uncertainty is explicitly embedded in the framework through Monte Carlo simulation, where fuel prices (±20%) and capital costs are sampled across defined ranges, generating probabilistic distributions rather than single deterministic estimates. This uncertainty-centred approach enables assessment of robustness, downside risk, and probability of profitability. Results show that replacing a single operating 600 kW diesel generator with fuel cell systems reduces auxiliary fuel energy demand by 25–35% for SOFC and approximately 15–25% for PEMFC relative to the diesel benchmark. Annual CO2 reductions range from 1.1 to 1.3 kt for SOFC systems and 1.8–2.8 kt for PEMFC configurations. Under grey fuel pathways, median NPVs reach approximately 2–4.5 M $ for SOFC and 9–17 M$for PEMFC as load increases, with IRRs exceeding 15% and 30%, respectively. Transitional pathways exhibit narrower margins, while renewable pathways remain more sensitive to fuel price variability. The findings demonstrate that fuel pathway cost dominates lifecycle outcomes under uncertainty and that hydrogen-based PEMFC systems exhibit the strongest economic resilience within the examined market ranges. The framework provides structured, uncertainty-aware decision support and establishes a foundation for integration into model-based systems engineering (MBSE) environments for early stage ship energy system design.