Control Hierarchies for Critical Infrastructures in Smart Grid Using Reinforcement Learning and Metaheuristic Optimization

The objective of this work is to develop robust control framework for interdependent smart grid infrastructures comprising two critical infrastructures: 1) power distribution networks that are characterized by high penetration of distributed energy resources (DERs), and 2) DC-rail transportation systems in congested urban areas. The rising integration of DERs into the power grid is causing a paradigm shift in the power distribution network. Consequently, new control challenges for efficient and robust operation of the power grid have surfaced. For instance, the intermittency of renewable energy resources necessitates coordinated control of power flows, voltage regulators, and protection device settings of the online resources in the system. This can be achieved with the help of an active distribution network, equipped with a distribution management system that provides online solutions to control problems, in real-time, by having full or partial observability. Besides, the increasing electrification of other critical infrastructures, such as transportation and communication, necessitates controllers that can accommodate the over-arching control requirements of interdependent critical infrastructures. Present control approaches lack the amalgamation of active distribution management and the flexibility to accommodate other critical infrastructures. In this work two levels of control hierarchies, viz. 1) Primary Controller and 2) Secondary Controller, have been designed for the power distribution system. These controllers can provide active distribution management, which can be expanded for seamless integration of other critical infrastructures. Besides, a real-time simulation-in-the-loop testbed has been developed, so that both transient and steady state performance of the controllers can be evaluated simultaneously. This testbed has been developed using OPAL-RT and DSpace. The effectiveness of these controllers have been tested in three types of active distribution networks: 1) A modified IEEE 5-bus system equipped with a grid-connected microgrid that consists of two DERs, 2) A modified IEEE 13-bus system equipped with an islanded community microgrid (C_-Grid) comprising four DERs, and 3) A modified IEEE-30 bus system comprising five grid-connected distributed generations (DGs). The DERs used for this work are battery energy storage systems and photovoltaic systems. The Primary Controller has been designed for regulating voltage, frequency and current in the system, while maintaining stability of these parameters, in both grid-tied and islanded operating modes. These design approaches consider multiple points of coupling among the DERs, which is lacking in the existing literature that is primarily focused on single point of common coupling. Besides, this work shows a method of incorporating communication latency, which may exist between Primary and Secondary Controller, into the control design. This facilitates performance analysis of the primary controller, when it is subjected to communication latency, and accordingly develop mitigation techniques. The Secondary Controller has been designed using a reinforcement learning technique called Adaptive Critic Design (ACD). ACD can facilitate seamless integration of a power distribution network with other critical infrastructures. The ACD based algorithm functions as a distribution management system where its control objectives are to balance load and generation, to take preventive or corrective measures for mitigating failures and improving system resiliency, to minimize the cost of energy incurred by the loads by dispatching the DERs, and to maintain their state of health. Alongside DERs, the impact of DC-rail transportation on the power distribution network has been investigated here, with an objective of efficiently and economically reducing congestion in power substations. Hybrid energy storage systems, comprising battery, supercapacitor and flywheel, have been used as wayside energy storage technologies for this purpose. These storage technologies reduce congestion by supplying energy during acceleration and coasting of the trains, and replenish their energy by recapturing the regenerative braking energy during deceleration of the same. The trains consume/regenerate energy at a very high rate during acceleration/deceleration, thereby requiring storage technologies with both high energy and power densities. Hence, the three aforementioned storage technologies have been investigated for both standalone and hybrid operations, by considering system performance and resiliency alongside the percentage of energy recovered, without comprising the cost-recuperation over time. This has been achieved using a two-stage method, where the first stage comprises the development of detailed mathematical model of the rail system and the storage technologies. This mathematical model has been optimized using Genetic Algorithm, in order to obtain optimal combinations of type and size of the storage technologies for minimum cost, within the system constraints. In the second stage, a detailed simulation model has been developed by capturing all the dynamics of the transportation network, which could not be entirely represented in the mathematical model. The optimal sizes obtained from the first stage have been used in the second stage to evaluate their performance and accordingly adjust their values. Thus, the mathematical model provides initial values in a large search space, and these values are further tuned based on the results from simulation model

Power and Energy Student Summit 2019: 9 – 11 July 2019 Otto von Guericke University Magdeburg ; Conference Program

The book includes a short description of the conference program of the "Power and Energy Student Summit 2019". The conference, which is orgaized for students in the area of electric power systems, covers topics such as renewable energy, high voltage technology, grid control and network planning, power quality, HVDC and FACTS as well as protection technology. Besides the overview of the conference venue, activites and the time schedule, the book includes all papers presented at the conference

Frequency regulation in wind integrated power system

This Thesis has broader implications in terms of improvement in wind generation modeling which is a current requirement for prospective operational planning tools for future grid. This thesis mainly deals with various modelling issues encountered in wind integrated power system for frequency regulation. Thesis provides development of grid code compatible, frequency responsive type 4 wind turbine generator system and analysis of the wind energy systems frequency regulation capability and their integration impact on interconnected power system.<br /

Aeronautical engineering: A continuing bibliography with indexes (supplement 268)

This bibliography lists 406 reports, articles, and other documents introduced into the NASA scientific and technical information system in July, 1991. Subject coverage includes: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics

Humanoid Robots

For many years, the human being has been trying, in all ways, to recreate the complex mechanisms that form the human body. Such task is extremely complicated and the results are not totally satisfactory. However, with increasing technological advances based on theoretical and experimental researches, man gets, in a way, to copy or to imitate some systems of the human body. These researches not only intended to create humanoid robots, great part of them constituting autonomous systems, but also, in some way, to offer a higher knowledge of the systems that form the human body, objectifying possible applications in the technology of rehabilitation of human beings, gathering in a whole studies related not only to Robotics, but also to Biomechanics, Biomimmetics, Cybernetics, among other areas. This book presents a series of researches inspired by this ideal, carried through by various researchers worldwide, looking for to analyze and to discuss diverse subjects related to humanoid robots. The presented contributions explore aspects about robotic hands, learning, language, vision and locomotion

Modelling temporal diffusion of PV mircogeneration systems in a rural developing community

Development of electricity delivery infrastructures are path-dependent, meaning, each development decision and step affects subsequent steps, and the final outcome. Human actors are therefore the most important variables in any energy development plan as their decisions affect the way a system evolves. Proper policy-planning tools are therefore required to guide decision-makers on least-cost rural electrification topologies. Many factors influence choices of technologies used in rural electrification, the main ones being availability of resources, availability of necessary technical infrastructures, demand, investment costs, and local socio-political and cultural environments. Different modelling tools and techniques have been applied in planning rural electrification paths in many developing countries. However, these often view this problem as a question of expansion of grid coverage through extensions of existing transmission and distribution lines from central power generation stations and seldom address the unique and regionally-specific challenges presented by each developing nation. To the best of our knowledge, no work has captured, in one study, the unique socio-economic, cultural and political environments, and market and technical infrastructural challenges presented by different rural communities in developing nations. In this work decentralized power systems based on locally available renewable energy resources, in this case solar, are explored as cost-effective electrification alternatives to national utility grid extensions to rural developing communities. An agent-based model (ABM) is developed in Netlogo to provide decision-makers with a user-friendly tool for PV-based rural electrification policy development, planning, and implementation. The model takes into account the complexities and limitations of solar electricity microgeneration technologies, decisions by human actors, geographical factors, and interactions between the three factors in order to capture the overall macro-effects of different micro-decisions in a virtual world; ABMs seek to model individual entities within a complex system and the rules that govern the interactions of the entities within the system, to capture the overall effect of such interactions. The novelty of the model developed in this work is that it simultaneously simulates how technical, socio-economic, and political factors affect temporal diffusion of PV microgeneration systems in a typical rural developing community. The model further simulates how households with PV, driven by demand for more power and other factors, come together to form communal grids. Survey data from Kendu Bay area of Kenya are used to inform the model. Empirical data provided by the Kenyan government are used to validate the model. The model developed in this work could be used by developing nations in their rural electrification planning and implementations, and a test-implementation funded by the Kenyan government is currently underway. Results show that given various electrification options, households in rural developing communities would overwhelmingly choose small PV microgeneration systems as stepping stones to future grid electrification. This is mainly due to initial basic electricity needs, rapidly falling PV costs, and affordability of such small PV systems; these small PV microgeneration systems allow households to enjoy the benefits of electricity with modest investments while also allowing future modifications with increasing household incomes, increasing power demands, and changing technologies. Another key finding of this research is that as their power demands increase beyond what could be fulfilled by small stand-alone PV systems, most rural households opt for PV-based communal grids as opposed to connecting to the national grids due to low cost, control over a community’s own power source, increased reliability and availability, and security of power source. Results also show that increased PV installations, and correspondingly more connections to communal grids, could be realized with introduction of favourable government policies such as subsidies, introduction of favourable microcredit facilities, increased social pressure through advertisements and neighbourhood influence. Furthermore, results indicate that, based on control methods and architectures, start-up and maintenance and operations costs of communal grids could be minimized and thus become more attractive to would be consumers, compared to the national grids

Condition monitoring of tool performance using a machine learning-based on-machine vision system during face milling of Inconel 718

The superior properties of Inconel 718 necessitate its use in manufacturing more than 50% of aircraft engine structural components, including high-pressure compressor blades, casings, and discs. However, literature attributed the synergistic impact of these properties and process parameters as the primary cause of wear complexity, notably affecting the performance of PVD-coated carbide inserts during CNC milling of Inconel 718. Features stemming from the wear complexity include uncontrolled wear mechanisms, failure modes, and a rapid flank wear rate, serving as significant indicators of sub-optimal cutting conditions. In trying to diagnose tool wear, previous Tool Condition Monitoring (TCM) techniques could not decipher, explore, and synthesise the diverse features essential for the predictive control of tool performance in challenging CNC machining conditions. Therefore, the successful implementation of advanced feature engineering and Machine Learning (ML) models in Machine Vision-based TCM (MVTCM) offers a proactive approach in predicting and controlling the performance of PVD-coated carbide tools in challenging CNC machining domains. The hypothesis of this study encompassed three aspects. The first aspect focused on the study of tool wear complexity by characterizing the dominant wear mechanisms, failure modes, and flank wear depth (VB) during face milling of Inconel 718. These features were correlated with the process parameters to establish a coherent tool wear dataset for training the feature engineering and ML models. The second aspect involved the development of feature engineering and ML models, including the multi-sectional singular value decomposition (SVD), a YOLOv3 Tool Wear Detection Model (YOLOv3-TWDM), a multi-layer perceptron neural network (MLPNN), and an inductive-reasoning algorithm. The final aspect pertained to the development of a volatile MV-TCM system’s design, which was integrated with feature engineering and ML techniques to create an enhanced ML-based MV-TCM system. The system was vigorously validated by conducting an online experiment, where the predicted were compared with the actual wear measurements. Furthermore, the inductive reasoning algorithm was devised to regulate process parameters for in-process control of flank wear evolution. The findings demonstrate that the Diverse Feature Synthesis Vector devised in this research was superior in representing the complex flank wear morphology as compared to some data reported by relevant literature, where geometric and fractal features were used to predict VB progression online. In addition, the ML-based MV-TCM system successfully utilized the DFSV to predict and control VB rate during face milling of Inconel 718. The system achieved higher predictive efficiency than image processing-based MV-TCM systems applied in the previous studies, with an offline validation RMSE of 45.5µm, R2 of 96.52%, and MAPE of 2.36%, as well as an online validation RMSE of 29.09µm, R2 of 97%, and MAPE of 3.52%. Additionally, the system employed a multi-stage optimization strategy that regulated process parameters at different VB levels to minimize the magnitudes of flank wear and chipping. This strategy extended tool life by 63.63% (relative to the conventional method) and 56.52% (relative to the GKRR soft-computing technique). Therefore, this research demonstrates the significance of applying ML-based MV-TCM system for predictive control of tool wear evolution during CNC milling of Inconel 718

Fractional Calculus and the Future of Science

Newton foresaw the limitations of geometry’s description of planetary behavior and developed fluxions (differentials) as the new language for celestial mechanics and as the way to implement his laws of mechanics. Two hundred years later Mandelbrot introduced the notion of fractals into the scientific lexicon of geometry, dynamics, and statistics and in so doing suggested ways to see beyond the limitations of Newton’s laws. Mandelbrot’s mathematical essays suggest how fractals may lead to the understanding of turbulence, viscoelasticity, and ultimately to end of dominance of the Newton’s macroscopic world view.Fractional Calculus and the Future of Science examines the nexus of these two game-changing contributions to our scientific understanding of the world. It addresses how non-integer differential equations replace Newton’s laws to describe the many guises of complexity, most of which lay beyond Newton’s experience, and many had even eluded Mandelbrot’s powerful intuition. The book’s authors look behind the mathematics and examine what must be true about a phenomenon’s behavior to justify the replacement of an integer-order with a noninteger-order (fractional) derivative. This window into the future of specific science disciplines using the fractional calculus lens suggests how what is seen entails a difference in scientific thinking and understanding

Condition monitoring of tool performance using a machine learning-based on-machine vision system during face milling of Inconel 718

The superior properties of Inconel 718 necessitate its use in manufacturing more than 50% of aircraft engine structural components, including high-pressure compressor blades, casings, and discs. However, literature attributed the synergistic impact of these properties and process parameters as the primary cause of wear complexity, notably affecting the performance of PVD-coated carbide inserts during CNC milling of Inconel 718. Features stemming from the wear complexity include uncontrolled wear mechanisms, failure modes, and a rapid flank wear rate, serving as significant indicators of sub-optimal cutting conditions. In trying to diagnose tool wear, previous Tool Condition Monitoring (TCM) techniques could not decipher, explore, and synthesise the diverse features essential for the predictive control of tool performance in challenging CNC machining conditions. Therefore, the successful implementation of advanced feature engineering and Machine Learning (ML) models in Machine Vision-based TCM (MVTCM) offers a proactive approach in predicting and controlling the performance of PVD-coated carbide tools in challenging CNC machining domains. The hypothesis of this study encompassed three aspects. The first aspect focused on the study of tool wear complexity by characterizing the dominant wear mechanisms, failure modes, and flank wear depth (VB) during face milling of Inconel 718. These features were correlated with the process parameters to establish a coherent tool wear dataset for training the feature engineering and ML models. The second aspect involved the development of feature engineering and ML models, including the multi-sectional singular value decomposition (SVD), a YOLOv3 Tool Wear Detection Model (YOLOv3-TWDM), a multi-layer perceptron neural network (MLPNN), and an inductive-reasoning algorithm. The final aspect pertained to the development of a volatile MV-TCM system’s design, which was integrated with feature engineering and ML techniques to create an enhanced ML-based MV-TCM system. The system was vigorously validated by conducting an online experiment, where the predicted were compared with the actual wear measurements. Furthermore, the inductive reasoning algorithm was devised to regulate process parameters for in-process control of flank wear evolution. The findings demonstrate that the Diverse Feature Synthesis Vector devised in this research was superior in representing the complex flank wear morphology as compared to some data reported by relevant literature, where geometric and fractal features were used to predict VB progression online. In addition, the ML-based MV-TCM system successfully utilized the DFSV to predict and control VB rate during face milling of Inconel 718. The system achieved higher predictive efficiency than image processing-based MV-TCM systems applied in the previous studies, with an offline validation RMSE of 45.5µm, R2 of 96.52%, and MAPE of 2.36%, as well as an online validation RMSE of 29.09µm, R2 of 97%, and MAPE of 3.52%. Additionally, the system employed a multi-stage optimization strategy that regulated process parameters at different VB levels to minimize the magnitudes of flank wear and chipping. This strategy extended tool life by 63.63% (relative to the conventional method) and 56.52% (relative to the GKRR soft-computing technique). Therefore, this research demonstrates the significance of applying ML-based MV-TCM system for predictive control of tool wear evolution during CNC milling of Inconel 718