Robust model-based fault estimation and fault-tolerant control : towards an integration

To maintain robustly acceptable system performance, fault estimation (FE) is adopted to reconstruct fault signals and a fault-tolerant control (FTC) controller is employed to compensate for the fault effects. The inevitably existing system and estimation uncertainties result in the so-called bi-directional robustness interactions defined in this work between the FE and FTC functions, which gives rise to an important and challenging yet open integrated FE/FTC design problem concerned in this thesis. An example of fault-tolerant wind turbine pitch control is provided as a practical motivation for integrated FE/FTC design.To achieve the integrated FE/FTC design for linear systems, two strategies are proposed. A H∞ optimization based approach is first proposed for linear systems with differentiable matched faults, using augmented state unknown input observer FE and adaptive sliding mode FTC. The integrated design is converted into an observer-based robust control problem solved via a single-step linear matrix inequality formulation.With the purpose of an integrated design with more freedom and also applicable for a range of general fault scenarios, a decoupling approach is further proposed. This approach can estimate and compensate unmatched non-differentiable faults and perturbations by combined adaptive sliding mode augmented state unknown input observer and backstepping FTC controller. The observer structure renders a recovery of the Separation Principle and allows great freedom for the FE/FTC designs.Integrated FE/FTC design strategies are also developed for Takagi-Sugeno fuzzy modelling nonlinear systems, Lipschitz nonlinear systems, and large-scale interconnected systems, based on extensions of the H∞ optimization approach for linear systems.Tutorial examples are used to illustrate the design strategies for each approach. Physical systems, a 3-DOF (degree-of-freedom) helicopter and a 3-machine power system, are used to provide further evaluation of the proposed integrated FE/FTC strategies. Future research on this subject is also outlined

Optics for AI and AI for Optics

Artificial intelligence is deeply involved in our daily lives via reinforcing the digital transformation of modern economies and infrastructure. It relies on powerful computing clusters, which face bottlenecks of power consumption for both data transmission and intensive computing. Meanwhile, optics (especially optical communications, which underpin today’s telecommunications) is penetrating short-reach connections down to the chip level, thus meeting with AI technology and creating numerous opportunities. This book is about the marriage of optics and AI and how each part can benefit from the other. Optics facilitates on-chip neural networks based on fast optical computing and energy-efficient interconnects and communications. On the other hand, AI enables efficient tools to address the challenges of today’s optical communication networks, which behave in an increasingly complex manner. The book collects contributions from pioneering researchers from both academy and industry to discuss the challenges and solutions in each of the respective fields

An Overview of Kinematic and Calibration Models Using Internal/External Sensors or Constraints to Improve the Behavior of Spatial Parallel Mechanisms

This paper presents an overview of the literature on kinematic and calibration models of parallel mechanisms, the influence of sensors in the mechanism accuracy and parallel mechanisms used as sensors. The most relevant classifications to obtain and solve kinematic models and to identify geometric and non-geometric parameters in the calibration of parallel robots are discussed, examining the advantages and disadvantages of each method, presenting new trends and identifying unsolved problems. This overview tries to answer and show the solutions developed by the most up-to-date research to some of the most frequent questions that appear in the modelling of a parallel mechanism, such as how to measure, the number of sensors and necessary configurations, the type and influence of errors or the number of necessary parameters

A Wide Area Hierarchical Voltage Control for Systems with High Wind Penetration and an HVDC Overlay

The modern power grid is undergoing a dramatic revolution. On the generation side, renewable resources are replacing fossil fuel in powering the system. On the transmission side, an AC-DC hybrid network has become increasingly popular to help reduce the transportation cost of electricity. Wind power, as one of the environmental friendly renewable resources, has taken a larger and larger share of the generation market. Due to the remote locations of wind plants, an HVDC overlay turns out to be attractive for transporting wind energy due to its superiority in long distance transmission of electricity. While reducing environmental concern, the increasing utilization of wind energy forces the power system to operate under a tighter operating margin. The limited reactive capability of wind turbines is insufficient to provide adequate voltage support under stressed system conditions. Moreover, the volatility of wind further aggravates the problem as it brings uncertainty to the available reactive resources and can cause undesirable voltage behavior in the system. The power electronics of the HVDC overlay may also destabilize the gird under abnormal voltage conditions. Such limitations of wind generation have undermined system security and made the power grid more vulnerable to disturbances. This dissertation proposes a Hierarchical Voltage Control (HVC) methodology to optimize the reactive reserve of a power system with high levels of wind penetration. The proposed control architecture consists of three layers. A tertiary Optimal Power Flow computes references for pilot bus voltages. Secondary voltage scheduling adjusts primary control variables to achieve the desired set points. The three levels of the proposed HVC scheme coordinate to optimize the voltage profile of the system and enhance system security. The proposed HVC is tested on an equivalent Western Electricity Coordinated Council (WECC) system modified by a multi-terminal HVDC overlay. The effectiveness of the proposed HVC is validated under a wide range of operating conditions. The capability to manage a future AC/DC hybrid network is studied to allow even higher levels of wind

Improved coordinated automatic voltage control in power grids through complex network analysis

PhD ThesisAutomatic and Co-ordinated Voltage Regulation (CVR) contributes significantly to economy and security of transmission grids. The role of CVR will become more critical as systems are operated closer to their capacity limits due to technical, economic and environmental reasons. CVR has 1 min resolution and owing to the inherent complexity of the task, CVR is enabled through zoning-based Reduced Control Models (RCM) i.e. simplified models of the network suitable for Voltage Control. RCM not only enables CVR bus also affects its performance and robustness. This thesis contributes towards improved CVR through thorough investigation of the RCM. As a starting point, with current power systems structure in mind, this work investigates static RCM schemes (i.e. fixed Reduced Control Model for all network configurations). To that end this thesis develops: (1) a novel generic framework for CVR modelling and evaluation and (2) new zoning-based RCM approaches using Complex Network Analysis. The evaluation of CVR in conjunction with both static and adaptive RCM schemes are based on a novel framework for CVR modelling and evaluation. This framework is generic and can be used to facilitate the selection and design of any of the CVR components. As a next step, due to the fact that a single RCM cannot be optimal for all network configurations, adaptive RCM (i.e. RCM determined in an online event driven fashion) is investigated using the proposed framework. This concerns future transmission grids where RCM is driven by the need for reliability rather than economy of measurement points at a planning phase. Lastly, this thesis examines zone division in an interconnected system ranging from EHV down to MV, and assesses the required degree of co-ordination for the voltage control of these zones. Essentially, this last item extends the scope of this work’s contributions beyond a single transmission-level Independent System Operator (ISO).EPSRC for funding my Research and the Consortium of the “Autonomic Power System” project

Robot Manipulators

Robot manipulators are developing more in the direction of industrial robots than of human workers. Recently, the applications of robot manipulators are spreading their focus, for example Da Vinci as a medical robot, ASIMO as a humanoid robot and so on. There are many research topics within the field of robot manipulators, e.g. motion planning, cooperation with a human, and fusion with external sensors like vision, haptic and force, etc. Moreover, these include both technical problems in the industry and theoretical problems in the academic fields. This book is a collection of papers presenting the latest research issues from around the world

Mobile Robots Navigation

Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described

Advanced Solutions for Renewable Energy Integration into the Grid Addressing Intermittencies, Harmonics and Inertial Response

Numerous countries are trying to reach almost 100\% renewable penetration. Variable renewable energy (VRE), for instance wind and PV, will be the main provider of the future grid. The efforts to decrease the greenhouse gasses are promising on the current remarkable growth of grid connected photovoltaic (PV) capacity. This thesis provides an overview of the presented techniques, standards and grid interface of the PV systems in distribution and transmission level. This thesis reviews the most-adopted grid codes which required by system operators on large-scale grid connected Photovoltaic systems. The adopted topologies of the converters, the control methodologies for active - reactive power, maximum power point tracking (MPPT), as well as their arrangement in solar farms are studied. The unique L(LCL)2 filter is designed, developed and introduced in this thesis. This study will help researchers and industry users to establish their research based on connection requirements and compare between different existing technologies. Another, major aspect of the work is the development of Virtual Inertia Emulator (VIE) in the combination of hybrid energy storage system addressing major challenges with VRE implementations. Operation of a photovoltaic (PV) generating system under intermittent solar radiation is a challenging task. Furthermore, with high-penetration levels of photovoltaic energy sources being integrated into the current electric power grid, the performance of the conventional synchronous generators is being changed and grid inertial response is deteriorating. From an engineering standpoint, additional technical measures by the grid operators will be done to confirm the increasingly strict supply criteria in the new inverter dominated grid conditions. This dissertation proposes a combined virtual inertia emulator (VIE) and a hybrid battery-supercapacitor-based energy storage system . VIE provides a method which is based on power devices (like inverters), which makes a compatible weak grid for integration of renewable generators of electricity. This method makes the power inverters behave more similar to synchronous machines. Consequently, the synchronous machine properties, which have described the attributes of the grid up to now, will remain active, although after integration of renewable energies. Examples of some of these properties are grid and generator interactions in the function of a remote power dispatch, transients reactions, and the electrical outcomes of a rotating bulk mass. The hybrid energy storage system (HESS) is implemented to smooth the short-term power fluctuations and main reserve that allows renewable electricity generators such as PV to be considered very closely like regular rotating power generators. The objective of utilizing the HESS is to add/subtract power to/from the PV output in order to smooth out the high frequency fluctuations of the PV power, which may occur due to shadows of passing cloud on the PV panels. A control system designed and challenged by providing a solution to reduce short-term PV output variability, stabilizing the DC link voltage and avoiding short term shocks to the battery in terms of capacity and ramp rate capability. Not only could the suggested system overcome the slow response of battery system (including dynamics of battery, controller, and converter operation) by redirecting the power surges to the supercapacitor system, but also enhance the inertial response by emulating the kinetic inertia of synchronous generator

Multivariate sensor data analysis for oil refineries and multi-mode identification of system behavior in real-time

Large-scale oil refineries are equipped with mission-critical heavy machinery (boilers, engines, turbines, and so on) and are continuously monitored by thousands of sensors for process efficiency, environmental safety, and predictive maintenance purposes. However, sensors themselves are also prone to errors and failure. The quality of data received from these sensors should be verified before being used in system modeling. There is a need for reliable methods and systems that can provide data validation and reconciliation in real-time with high accuracy. In this paper, we develop a novel method for real-time data validation, gross error detection and classification over multivariate sensor data streams. The validated and high-quality data obtained from these processes is used for pattern analysis and modeling of industrial plants. We obtain sensor data from the power and petrochemical plants of an oil refinery and analyze them using various time-series modeling and data mining techniques that we integrate into a complex event processing engine. Next, we study the computational performance implications of the proposed methods and uncover regimes where they are sustainable over fast streams of sensor data. Finally, we detect shifts among steady-states of data, which represent systems' multiple operating modes and identify the time when a model reconstruction is required using DBSCAN clustering algorithm.Turkish Petroleum Refineries Inc. (TUPRAS) RD CenterPublisher versio