Hybrid power systems energy management based on Artificial Intelligence

This thesis presents a novel adaptive scheme for energy management in stand-alone hybrid power systems. The proposed management system is designed to manage the power flow between the hybrid power system and energy storage elements in order to satisfy the load requirements based on artificial neural network (ANN) and fuzzy logic controllers. The neural network controller is employed to achieve the maximum power point (MPP) for different types of photovoltaic (PV) panels, based on Levenberg Marquardt learning algorithm. The statistical analysis of the results indicates that the R2 value for the testing set was 0.99. The advance fuzzy logic controller is developed to distribute the power among the hybrid system and to manage the charge and discharge current flow for performance optimization. The developed management system performance was assessed using a hybrid system comprises PV panels, wind turbine, battery storage, and proton exchange membrane fuel cell (PEMFC). To improve the generating performance of the PEMFC and prolong its life, stack temperature is controlled by a fuzzy logic controller. Moreover, perturb and observe (P&O) algorithm with two different controller techniques - the linear PI and the non-linear passivity-based controller (PBC) - are provided for a comparison with the proposed MPPT controller system. The comparison revealed the robustness of the proposed PV control system for solar irradiance and load resistance changes. Real-time measured parameters and practical load profiles are used as inputs for the developed management system. The proposed model and its control strategy offer a proper tool for optimizing the hybrid power system performance, such as the one used in smart-house applications. The research work also led to a new approach in monitoring PV power stations. The monitoring system enables system degradation early detection by calculating the residual difference between the model predicted and the actual measured power parameters. Measurements were taken over 21 month’s period; using hourly average irradiance and cell temperature. Good agreement was achieved between the theoretical simulation and the real time measurement taken the online grid connected solar power plant

An intelligent controlling method for battery lifetime increment using state of charge estimation in PV-battery hybrid system

In a photovoltaic (PV)-battery integrated system, the battery undergoes frequent charging and discharging cycles that reduces its operational life and affects its performance considerably. As such, an intelligent power control approach for a PV-battery standalone system is proposed in this paper to improve the reliability of the battery along its operational life. The proposed control strategy works in two regulatory modes: maximum power point tracking (MPPT) mode and battery management system (BMS) mode. The novel controller tracks and harvests the maximum available power from the solar cells under different atmospheric conditions via MPPT scheme. On the other hand, the state of charge (SOC) estimation technique is developed using backpropagation neural network (BPNN) algorithm under BMS mode to manage the operation of the battery storage during charging, discharging, and islanding approaches to prolong the battery lifetime. A case study is demonstrated to confirm the effectiveness of the proposed scheme which shows only 0.082% error for real-world applications. The study discloses that the projected BMS control strategy satisfies the battery-lifetime objective for off-grid PV-battery hybrid systems by avoiding the over-charging and deep-discharging disturbances significantly

The Road Ahead for Networking: A Survey on ICN-IP Coexistence Solutions

In recent years, the current Internet has experienced an unexpected paradigm shift in the usage model, which has pushed researchers towards the design of the Information-Centric Networking (ICN) paradigm as a possible replacement of the existing architecture. Even though both Academia and Industry have investigated the feasibility and effectiveness of ICN, achieving the complete replacement of the Internet Protocol (IP) is a challenging task. Some research groups have already addressed the coexistence by designing their own architectures, but none of those is the final solution to move towards the future Internet considering the unaltered state of the networking. To design such architecture, the research community needs now a comprehensive overview of the existing solutions that have so far addressed the coexistence. The purpose of this paper is to reach this goal by providing the first comprehensive survey and classification of the coexistence architectures according to their features (i.e., deployment approach, deployment scenarios, addressed coexistence requirements and architecture or technology used) and evaluation parameters (i.e., challenges emerging during the deployment and the runtime behaviour of an architecture). We believe that this paper will finally fill the gap required for moving towards the design of the final coexistence architecture.Comment: 23 pages, 16 figures, 3 table

Next Generation Cloud Computing: New Trends and Research Directions

The landscape of cloud computing has significantly changed over the last decade. Not only have more providers and service offerings crowded the space, but also cloud infrastructure that was traditionally limited to single provider data centers is now evolving. In this paper, we firstly discuss the changing cloud infrastructure and consider the use of infrastructure from multiple providers and the benefit of decentralising computing away from data centers. These trends have resulted in the need for a variety of new computing architectures that will be offered by future cloud infrastructure. These architectures are anticipated to impact areas, such as connecting people and devices, data-intensive computing, the service space and self-learning systems. Finally, we lay out a roadmap of challenges that will need to be addressed for realising the potential of next generation cloud systems.Comment: Accepted to Future Generation Computer Systems, 07 September 201

A survey of intrusion detection system technologies

This paper provides an overview of IDS types and how they work as well as configuration considerations and issues that affect them. Advanced methods of increasing the performance of an IDS are explored such as specification based IDS for protecting Supervisory Control And Data Acquisition (SCADA) and Cloud networks. Also by providing a review of varied studies ranging from issues in configuration and specific problems to custom techniques and cutting edge studies a reference can be provided to others interested in learning about and developing IDS solutions. Intrusion Detection is an area of much required study to provide solutions to satisfy evolving services and networks and systems that support them. This paper aims to be a reference for IDS technologies other researchers and developers interested in the field of intrusion detection

Integration of Electric Energy Storage into Power Systems with Renewable Energy Resources

This dissertation investigates the distribution and transmission systems reliability and economic impact of energy storage and renewable energy integration. The reliability and economy evaluation framework is presented. Novel operation strategies of energy storage and renewable energy are proposed. The method for optimizing the energy storage sizing and operation strategy in order to achieve optimal reliability and economy level is developed. The objectives of the movement towards the smart grid include making the power systems more reliable and economically efficient. The rapid development of the large scale energy storage technology makes it an excellent candidate in achieving these goals. A novel Model Predictive Control (MPC)-based operation strategy is proposed to optimally manage the charging and discharging operation of energy storage in order to minimize the energy purchasing cost for a distribution system load aggregator in power markets. Different operation strategies of energy storage have different reliability and economic impact on power systems. Simulation results illustrate the importance of the energy storage operation strategies. A hybrid operation strategy which combines the MPC-based operation strategy and the standby backup operation strategy is proposed to flexibly adjust the reliability and economic improvement brought by energy storage. A particle swarm optimization approach is developed to determine the optimal energy storage sizing and operation strategy while maximizing reliability and economic improvement. A reliability and economy assessment framework based on sequential Monte Carlo method integrated with the operation strategies is proposed. The impact on the transmission systems reliability brought by energy storage and renewable energy with the proposed operation strategies is investigated. Case studies are conducted to demonstrate the effectiveness of the proposed operation strategies, optimization approach, and the reliability and economy evaluation framework. Insights into how energy storage and renewable energy affect power system reliability and economy are obtained

Emulating and evaluating hybrid memory for managed languages on NUMA hardware

Non-volatile memory (NVM) has the potential to become a mainstream memory technology and challenge DRAM. Researchers evaluating the speed, endurance, and abstractions of hybrid memories with DRAM and NVM typically use simulation, making it easy to evaluate the impact of different hardware technologies and parameters. Simulation is, however, extremely slow, limiting the applications and datasets in the evaluation. Simulation also precludes critical workloads, especially those written in managed languages such as Java and C#. Good methodology embraces a variety of techniques for evaluating new ideas, expanding the experimental scope, and uncovering new insights. This paper introduces a platform to emulate hybrid memory for managed languages using commodity NUMA servers. Emulation complements simulation but offers richer software experimentation. We use a thread-local socket to emulate DRAM and a remote socket to emulate NVM. We use standard C library routines to allocate heap memory on the DRAM and NVM sockets for use with explicit memory management or garbage collection. We evaluate the emulator using various configurations of write-rationing garbage collectors that improve NVM lifetimes by limiting writes to NVM, using 15 applications and various datasets and workload configurations. We show emulation and simulation confirm each other's trends in terms of writes to NVM for different software configurations, increasing our confidence in predicting future system effects. Emulation brings novel insights, such as the non-linear effects of multi-programmed workloads on NVM writes, and that Java applications write significantly more than their C++ equivalents. We make our software infrastructure publicly available to advance the evaluation of novel memory management schemes on hybrid memories