Backup System Research Articles

Project Management Enhancement through Technology

Purpose: The main goal of this manuscript is to evaluate the impact of technology adoption and integration on project management practices, identify challenges and solutions, and explore the potential of emerging technologies for enhancing project management efficiency and effectiveness. Scope: The study takes a global approach to assess how technology impacts project management across various industries and organizations, mainly focusing on technology adoption trends in different sectors and advanced solutions. Conclusions: The manuscript details how automation and software improve project efficiency and predictability, discusses limitations, including software glitches and downtime necessitating backup systems, and identifies AI (Artificial Intelligence) as the most prominent emerging technology.

An Improved Fault Detection and Isolation Method for Airborne Inertial Navigation System/Attitude and Heading Reference System Redundant System

The integrity of airborne inertial navigation systems (INSs) is the key to ensuring the safe flight of civil aircraft. The airborne attitude and heading reference system (AHRS) is introduced into the construction of a redundant inertial navigation system. As a backup system for an airborne INS, the AHRS exhibits a different device performance. A sequential weighted generalized likelihood ratio test (SWGLT) method, based on a principal component parity vector (PPV), is proposed. The PPV method improves the adaptability of the detection threshold to the inertial sensors’ noise and improves the probability of correct detection. At the same time, the multiscale problem of a heterogeneous redundant system error is solved by sequential weighting, and the false alarm rate is reduced. Simulation experiments show that the proposed method can improve fault detection sensitivity, reduce false alarm rates, and ensure the integrity of civil aircraft navigation systems.

Optimization and intelligent power management control for an autonomous hybrid wind turbine photovoltaic diesel generator with batteries

In this paper, a critical issue related to power management control in autonomous hybrid systems is presented. Specifically, challenges in optimizing the performance of energy sources and backup systems are proposed, especially under conditions of heavy loads or low renewable energy output. The problem lies in the need for an efficient control mechanism that can enhance power availability while protecting and extending the lifespan of the various power sources in the system. Furthermore, it is necessary to adapt the system's operations to variations in climatic conditions for sustained effectiveness. To address the identified problem. It is proposed the use of an intelligent power management control (IPMC) system employing fuzzy logic control (FLC). The IPMC is designed to optimize the performance of energy sources and backup systems. It aims to predict and adjust the system's operating processes based on variations in climatic conditions, providing a dynamic and adaptive control strategy. The integration of FLC is specifically emphasized for its effectiveness in balancing multiple power sources and ensuring a steady and secure operation of the system. The proposed IPMC with FLC offers several advantages over existing strategies. Firstly, it showcases enhanced power availability, particularly under challenging conditions such as heavy loads or low renewable energy output. Secondly, the system protects and extends the lifespan of the power sources, contributing to long-term sustainability. The dynamic adaptation to climatic variations adds a layer of resilience to the system, making it well-suited for diverse geographical and climatic conditions. The use of realistic data and simulations in MATLAB/Simulink, along with real-time findings from the RT-LAB simulator, indicates the reliability and practical applicability of the proposed IPMC strategy. Efficient load supply and preserved batteries further underscore the benefits of the fuzzy logic-based control strategy in achieving a well-balanced and secure system operation.

Review on microgrids design and monitoring approaches for sustainable green energy networks

Microgrids are power distribution systems that can operate either in a grid-connected configuration or in an islanded manner, depending on the availability of decentralized power resources, such as sustainable or non-sustainable power sources, battery backup systems, and power demands. The extensive adoption of inverter-based systems poses numerous technological challenges, necessitating a centralized management system to assure the system reliability and monitoring of the energy delivery networks. Thus, this research begins by highlighting these significant obstacles and then analyzes the present-day advances in multilevel control architecture for delivering on promised functionality. This article also discusses the development of innovative control technologies, such as introducing collaborative distributed approaches and reducing conventional three-stage patriarchal administration to fewer stages of system integration and functioning.

A stochastic multi-period energy hubs through backup and storage systems: enhancing cost efficiency, and sustainability

A stochastic multi-period energy hubs through backup and storage systems: enhancing cost efficiency, and sustainability

754. Utilization of Internal Special Program Review to Institute Change in an Academic ID Fellowship

Abstract Background Fellowship accreditation is crucial to ensure adequate clinical education for physicians in training, yet there is limited data on use of accreditation survey results for program quality improvement. The Accreditation Council for Graduate Medical Education (ACGME) administers annual surveys to trainees and core faculty to monitor education and identify potential non-compliance with ACGME accreditation standards. Our purpose is to describe the creation of an internal program review process for program improvement based on results of nationally implemented accreditation surveys. Methods We collected data from standardized ACGME survey responses administered to core faculty and fellows at our academic Infectious Diseases fellowship program. The identification of areas scoring lower than 20% in comparison with national average prompted creation of a Special Program Review (SPR) committee comprised of a program director, a program coordinator, and a trainee from programs outside of the program under review. This committee met separately with core faculty, trainees, and program leadership to compile an assessment of each survey component and opportunities for improvement. This review process of survey results was completed annually from 2020-2022. Results Between 2020-2022, surveys were completed by 100% of fellows (7/7 or 8/8) and up to 92% of core faculty (11/12). Key findings raised concerns involving clinical education, faculty supervision, patient safety, and teamwork. Actions taken in response to the SPR included the creation of: an updated backup and weekend coverage system, new didactic curricula, a morbidity and mortality conference, documentation limits, informal fellow/faculty dinners, opt out counseling, and a division culture committee. Conclusion A non-punitive internal review process mimicking accreditation standards is valuable for program assessment and improvement as means to identify areas of risk for accreditation issues at a national level. This review process is generalizable, as all accredited programs complete the survey annually. The quality improvement interventions taken will differ depending on survey findings and areas to be addressed. Disclosures All Authors: No reported disclosures

Intelligent charging and discharging of electric vehicles in a vehicle-to-grid system using a reinforcement learning-based approach

Recent advances in electric vehicle (EV) technology have increased the importance of vehicle-to-grid (V2G) systems in the smart grid domain. These systems allow bidirectional energy and information flow between consumers and suppliers, enabling the EV to act as an energy storage system that can provide surplus energy to the grid. V2G is particularly useful for reducing the peak demand and load shifting for utilities, acting as a backup system for renewable energy. To optimize the benefits of these systems, the intelligent management of charging and discharging is essential, while considering the electricity prices and user requirements. However, uncertainties such as commuting behavior, charging preferences, and energy requirements, pose challenges in determining the optimal charging/discharging strategy. In this study, individual EV charging/discharging is formulated as a sequential decision-making problem and a model-free reinforcement learning (RL) approach is utilized to learn the optimal sequential charging/discharging decisions until the EV battery reaches its end-of-life. The goal is to minimize the charging cost for the individual user and maximize the use of the EV battery as the vehicle proceeds through various charging and discharging cycles, while also considering the distance traveled by the vehicle. The proposed algorithm is evaluated using real-world data, and the learned charging and discharging strategies are examined to investigate the effectiveness of the proposed method. The experimental scenarios demonstrated that utilizing the RL approach is advantageous compared to the other approaches for reducing the overall cost and maximizing the use of EV batteries.

Towards a sustainable environment and carbon neutrality: Optimal sizing of standalone, green, reliable, and affordable water-power cogeneration systems

Today, the limited sources of freshwater supply are a significant concern. Exploiting alternative sources, especially seawater, has been the focus, but purifying it is energy-intensive. Integrating desalination with renewable energy is a proposed solution, but it comes with high costs and environmental risks during construction. Hence, this study presents a framework to enhance the modeling, optimization, and evaluation of green water-power cogeneration systems to achieve the sustainability goals of cities and societies. An improved division algorithm (DA) determines the optimal component sizes based on criteria like minimal energy demand, reduced environmental and resource damage, low total life cycle cost (TLCC), and high reliability. Optimization considers varying loss of power supply probability (LPSP) levels (0 %, 2 %, 5 %, and 10 %). The environmental assessment utilizes a life cycle assessment (LCA) approach with IMPACT 2002+ and cumulative energy demand (CED) calculations. The study models the green cogeneration systems based on weather conditions, water demand, and power requirements of Al Lulu Island, Abu Dhabi, UAE. The system comprises photovoltaic panels, wind turbines, tidal generators, and backup systems (fuel cells). Results reveal that TLCC ranges from $186,263 to $486,876 for the highest LPSP. The solar-tidal-based configuration offers the lowest TLCC ($186,263) while substituting solar with wind energy increases TLCC by 160 %. The wind-tidal-based configuration has the lowest specific environmental impact (1020 mPt/yr) and cumulative energy demand (39.06 GJ/yr) for the highest LPSP. In contrast, the solar-tidal-wind-based configuration inflicts the most damage, with 62.63 GJ/yr and 1794 mPt/yr for the highest LPSP. The finding indicates that the DA is faster (100 iterations) than the genetic algorithm (1000 iterations), particle swarm optimization (400 iterations), and artificial bee swarm optimization (300 iterations). The study underscores the solar-tidal-based configuration as the optimal choice across multiple criteria, offering a promising solution for freshwater supply and environmental sustainability on Al Lulu Island.

An Industry 5.0 Perspective on Feeding Production Lines

The emerging concept of Industry 5.0 is fostering companies to consider the three pillars of human-centricity, sustainability, and resilience. How such a new perspective can be effectively declined and practically guide the introduction of new technologies is a challenge to be addressed. This study proposes a framework to support companies when introducing new solutions to feed production lines by adopting an Industry 5.0 perspective. For each fundamental pillar, critical points to focus on have been highlighted and operational checklists have been developed to effectively support the analysis and implementation of new solutions. The application of the framework and related operational checklists to a case study regarding the integration of an Autonomous Mobile Robot system has proved its validity. Following the human-centricity checklist, full acceptance of the new technology by workers has been gained, together with a safer workplace. Energy savings for material handling and recycling have been supported about the sustainability pillar, while redundancy and backup systems have increased the resilience of the feeding system.

Design of Monitoring Tools for Data Centre Downtime Reduction

This paper presents a new monitoring tool and event management method for data centre compute, network and storage infrastructure based on node event processing. The uptime of highly classified data centres are not only to be maintained at the highest level of reliability and availability of the operation, but also fast, specific event identification and rectification, which altogether improves availability of the resources is important. The new method, using a tree node for each element of the data centre resources provides information about the compute, network and storage file system configuration in a specific node. Its major advantage is that in our case where a large number of heterogeneous computers are present, it helps us in monitoring all the elements of the computer resources and gives information for alerting the associated work centres before any of the identified events that might occur. By monitoring and informing apriori to the concerned work centres the state of the systems, it lowers errors in data centre physical infrastructure operating costs, improving at the same time the level of operations efficiency. This method resulted that the use of tree nodes significantly reduces the number of unexpected events, the time needed for the main event identification, and the maintenance response time to events. By using event entities processing, multilayer nodes have a significant impact on the efficient operation of data centre physical infrastructure. In this paper, the design and development of two customised dashboards to monitor the compute, storage and network elements of the heterogeneous data centre for uptime maintenance and optimal performance is presented. The dashboards are designed, keeping in view the nature of tasks carried out and the resource requirements of various work centres in the data centre. One dashboard displays dynamically created icons for each of the compute resources in the data centre. On clicking any of the icon, complete details of the corresponding server is fetched showing the status, usage, configuration and available resources. Furthermore, a unique colouring scheme is followed wherein the icon is displayed green if the server is healthy and orange if the server is facing a resource crunch (disk, memory, etc.) and red if the server is not reachable. The dashboard GUI refreshes every 5 min (is configurable), displaying the latest status details of the servers in the data centre. The second Dashboard is developed with the capability to monitor the storage, cloud and network infrastructure components. The dashboard collects data from different elements of the storage i.e. Meta Data Servers, Storage, Core and Edge switches etc. and processes the collected data to a customized format for display. It delivers details like availability of Storage Meta Data Servers, switches and file systems, disk space capacity monitoring, file system backup status, Monitoring of the hierarchical Storage including Tape Library and the availability of Production ESXi hosts cluster. The GUI is updated with new requirements to further fine-tune and reduce manual intervention for monitoring operations.

An economic and environmental optimization model for sizing a hybrid renewable energy and battery storage system in off-grid farms

This paper presents a model for optimizing the life cycle economic and environmental impacts of a hybrid renewable energy and battery storage system – as energy supply technologies (EST) for off-grid farms. Micro-hydropower and photovoltaics represent the primary renewable energy (RE) sources, with batteries and a diesel generator considered as possible back-up systems. A modified Non-dominated Sorting Genetic Algorithm II (NSGA-II) was developed and applied to a case study farm in Southern Spain and provided different EST solutions to optimize a design based on economic (life cycle costing or LCC) and environmental (life cycle assessment or LCA) constraints. The results highlighted that cases with a strong dependence on batteries could achieve minimum costs and lower impacts for climate change and energy resources (non-renewable fuels). In contrast, the optimal system for minimizing the consumption of material resources (metals/minerals) resulted in higher fuel use, which was up to 25% more expensive. The optimal EST solutions used up to 37% of the total energy generated; however, the results were sensitive to energy production-demand distributions. This methodology can help identify the most cost-effective solutions with the lower environmental impacts for decarbonizing the agricultural sector.

Hybrid optimized-ANFIS based MPPT for hybrid microgrid using zebra optimization algorithm and artificial gorilla troops optimizer

This paper presents an effective hybrid renewable energy system. This system utilizes primary energy sources represented by both photovoltaic (PV) and wind energy conversion system (WECS). In addition, integrating the battery energy storage system (BESS), the hydrogen energy storage system (HESS), and the supercapacitor energy storage system (SESS), as backup sources which providing reliable simultaneous power supply and loading. A new design for a wind energy conversion system based on a hybrid excitation permanent magnet synchronous generator (HEPMSG) is proposed, and then artificial intelligence strategies are utilized to determine the optimal flow of energy when the system is operating immediately. Moreover, new hybrid maximum power point tracking (MPPT) techniques have been proposed including zebra optimization algorithm (ZOA)-ANFIS-based MPPT and gorilla troops optimizer (GTO)-ANFIS-based MPPT to obtain the maximum power of solar panels and wind turbines, which leads to enhancing the performance of these energy sources. The implemented power flow management (PFM) model is designed by MATLAB/Simulink, considering three various operating cases to evaluate the performance and effectiveness of various backup system configurations under different operating scenarios. According to the results, power can be generated, and load requirements can be efficiently met by applying the proposed PFM strategy, and this leads to the system operating at optimal performance. When comparing two optimization techniques, it will become clear that both give robust performance, but ZOA technique significantly outperforms the GTO technique in the computation time (26.17 % reduction) for the photovoltaic system. On the other hand, in the case of the wind energy conversion system, the ZOA technique achieves a significant reduction in calculation time by 35.5 %. The ZOA technique achieves computation times of 1616.80 s and 672.43 s for the photovoltaic and wind energy conversion system, respectively.

Robust Control Algorithm for Virtual Synchronous Generator in Intermittent Generation

This work presents a comparison of two control algorithms for the model of a nonlinear converter system used as a virtual synchronous generator (VSG). The converter connects between the electrical grid and an intermittent renewable system, such as photovoltaic or wind, with backup for sudden disturbances in electricity generation. The proposal of the VSG arises from the fact that, on one hand, the conventional generation system consists of large power plants such as combined-cycle power plants that employ generators and turbines with significant physical inertia. On the other hand, when connecting renewable systems such as wind and solar, which use converters to integrate with the grid, they lack physical inertia. It is necessary to couple this inertia to the system in order to compensate for the disturbances of intermittent systems and thereby maintain the frequency of the electrical power network. It should be clarified that physical inertia is not available when renewable systems such as wind or photovoltaic are connected. Recent studies have shown that it is possible to compensate for the disturbance caused by this lack of inertia through a backup system and the virtual synchronous generator (power converter) using control algorithms. In this work, linearization and super twisting algorithms are employed for handling disturbances, and the main differences between the algorithms are demonstrated when facing sudden changes in active or reactive power.

Energy Assessment of Solar Power Plant On-Grid Bi-direction 3 KW 1 Phase

The potential for generating electricity from solar energy as one of the renewable energies is suitable for rural areas, small populations, or communal systems. However, this type of power plant requires a high investment budget. Photovoltaic power systems must be designed to meet the demand for electrical energy but require a large supply. To build a more efficient system, it is necessary to build a communal system. For a small population, a 3000-watt power generation system is made. It will be distributed directly to users; a 5000-watt system is made for a larger area. It will be distributed directly to users and interconnected between generators with the principle of load control. In this study, a solar power plant (PLTS) was designed with an on-grid bidirectional system. Solar panels' conversion of electrical energy is channeled to users and stored in a battery backup system. This PLTS system produces a maximum power of 3000 watts by using 8 units of 450wp solar panels, an inverter with a 48-volt on or off-grid mode system, and 4 12V 200 Ah backup batteries. From the measurement results, the PLTS system will start converting DC current into AC with a radiation value of 356 to 1258 W/m2. Produce on-grid AC current energy, a maximum of 2297 watts, and charge backup batteries with DC current, a maximum of 14.3 A. This on-grid (bidirectional) backup PLTS system can convert DC current into AC, supply it directly to the network electricity (grid), and perform energy backups when solar energy is not energized.

Digital Repository as a Service (D-RaaS): Enhancing Access and Preservation of Cultural Heritage Artifacts

The employment of technology and digitization is crucial for cultural organizations to establish and sustain digital repositories for their cultural heritage artifacts. This exploitation is also essential in facilitating the presentation of cultural works and exhibits to a broader audience. Consequently, in this work, we propose a custom-developed digital repository that functions as software-as-a-service (SaaS), primarily promoting the safe storage, display, and sharing of cultural materials; enhancing accessibility; and fostering a deeper understanding and appreciation of cultural heritage. The proposed digital repository service is designed as a multitenant architecture, which enables organizations to expand their reach, enhance accessibility, foster collaboration, and ensure the preservation of their content. Moreover, our technology stack incorporates robust and reliable backend technologies, such as Django, to ensure data security and efficient management. Meanwhile, the frontend is powered by Angular, which guarantees a user-friendly and engaging interface for exploring and interacting with cultural materials. Specifically, this project aims to assist each cultural institution in organizing its digital cultural assets into collections and feeding other digital platforms, including educational, museum, pedagogical, and games, through appropriate interfaces. The creation of this digital repository offers a cutting-edge and effective open-access laboratory solution. It allows organizations to have a significant influence on their audiences by fostering cultural understanding and appreciation. Additionally, it facilitates the connection between different digital repositories and national/European aggregators, promoting collaboration and information sharing. By embracing this innovative solution, cultural institutions can benefit from shared resources and features, such as system updates, backup and recovery services, and data analytics tools, attributes that are currently provided by the platform.

ELoran signal message prediction algorithm based on Alexnet‐ECA

AbstractEnhanced Loran (eLoran) is the most important backup system for Global Navigation Satellite System (GNSS). However, most current eLoran demodulation methods are based on digital signal processing, which may produce large errors under the influence of strong interference or noise. The authors propose an eLoran signal message prediction algorithm based on deep learning. Using time‐frequency (TF) analysis convert one‐dimensional signal into two‐dimensional high‐resolution image. It improves Alexnet by adjusting the convolutional layer structure, adding an attention module, and reducing the pooling layer window to construct the Alexnet‐ECA network. The network is used to identify TF image features and predict signal message. The experimental results show that novel method for eLoran signal message prediction based on Alexnet‐ECA can achieve high accuracy and robustness in various SNR scenarios. The accuracy is more than 90% at SNR greater than 10db and not less than 80% even in the complex environments. Compared with Resnet34 and VGG16 networks, Alexnet‐ECA demonstrates its efficiency and effectiveness using real measurement data. This method can enhance the performance and reliability of eLoran receivers and applications.

Biomethane Microturbines as a Storage-Free Dispatchable Solution for Resilient Critical Buildings

Climate-change-related events are increasing the costs of power outages, including losses of product, revenue, and productivity. Given the increase in meteorological disasters in recent years related to climate change effects, the number of costly blackouts, from an economic perspective, has increased in a directly proportional manner. As a result, there is increasing interest in the use of alternators to supply dependable, instantaneous, and uninterruptible electricity. Traditional research has focused on the installation of diesel backup systems to ensure power requirements without deeply considering the resilience capabilities of systems, which is the ability of a system to recover or survive adversity, such as a power outage. This research presents a novel approach focusing on the resiliency impact of backup systems’ storage-free dispatchable solutions on buildings and compares the advantages and disadvantages of biomethane microturbines, natural gas engines, and diesel engines backup systems, discussing the revenue resulting from the resilience provided by emergency generators. The results show that, for several diesel fuel and natural gas safety assumptions, natural gas alternators have a lower probability of failure at the time of a blackout than diesel generators, and therefore, resilience increases.

지능정보 시대의 사이버 테러 대응 전략과 과제

In the era of intelligent information, the technological development of the 4th industrial revolution is transforming all functions of the state, such as administration, economy, and industry, into cyberspace, which increases cyber threats such as hacking and crime in virtual space as well as positive functions. In recent years, cyber terrorism has been increasing in various types of threats, such as industrial property theft, system paralysis, cyber warfare, and virtual currency theft, due to the expansion of the attack surface due to the expansion of the digital domain. The forms of attack are becoming more sophisticated. Now, the response to cyber terrorism should not be limited to the technological domain of information and communication, such as vaccine and defense program development, recovery system construction, and cyber forensic have to build Under this recognition, this paper aims to respond to cyber terrorism with strategies and tasks. First, unification of cyber terrorism response system and strengthening information sharing among response organizations through the enactment of the 'Framework Act on Cyber terrorism' in terms of laws and institutions. Second, cyber security experts Third, raising cyber security awareness and strengthening practice Fourth, establishing a cyber security inspection system and backup system Fifth, suggesting an AI-dedicated department and enactment of AI-related laws to actively respond to new vulnerable factors caused by the development of artificial intelligence technology.

Electrolyte Additives for Reversible Dissolution/Deposition of Mn2+/Mn4+ in a Zinc-Manganese Flow Battery

Due to its adaptability in scaling up, a redox flow battery (RFB) is seen to be one of the finest options for large-scale electrical backup systems. As a result, it is feasible to create RFB systems that are both cost and performance effective. Recently, a zinc-manganese RFB that relies on Zn(s)/Zn2+(aq) and Mn2+(aq)/MnO2 redox couples has gained attention since both zinc and manganese are cheap, abundant, and eco-friendly. However, the reversibility of Mn2+(aq)/MnO2 at the positive electrode is limited by the formation of Mn3+ species upon charge/discharge (CD) cycling, resulting in severe capacity fading. Herein, this study examines the use of reducing agents as electrolyte additives to enhance the reversibility of the Mn2+(aq)/MnO2 reaction. Experimental results indicate that sulfuric acid and oxalic acid as additives can significantly improve the reversibility of the Mn2+(aq)/MnO2 reaction and the cycling stability of zinc-manganese RFBs. The acetate-based system demonstrates better reversible reaction than the sulfate-based system having more than 100 CD cycles at a current density of 10 mA/cm2. Coulombic efficiency (CE) is also seen to be higher than 90%. Overall, results lead to increased efficiency and cycling stability for zinc-manganese RFBs.

Multi-Type Reserve Collaborative Optimization for Gas-Power System Constrained Unit Commitment to Enhance Operational Flexibility

With the wide application of the gas-power system, gas-power coupling equipment such as gas turbines are gradually becoming widely used, and the problem of insufficient system reserve capacity needs to be solved. In order to improve the operational flexibility of the gas-power system, this paper combines the source side, the load side, and the energy storage side to propose a multi-type backup system, and constructs the source-load-storage multiple reserve capacity system of the gas-power system. Through the participation of gas turbine, steam turbine, interruptible load, and energy storage battery to provide reserve capacity, it can fully cope with the output fluctuation of the load side and source side and realize the coordinated operation of multiple resources to provide reserve capacity. In addition, the coordination of gas turbines and steam turbines can further improve the operation flexibility of the gas-power system. Through the example analysis, it was found that the proposed method reduced the total operating cost of the system by 10.6%.