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The objective of this paper is to describe an energy-based approach to visualize, identify, and monitor faults that may occur in a water-to-water transcritical CO 2 heat pump system. A representation using energy attributes allows the abstraction of all physical phenomena present during operation into a compact and easily interpretable form. The use of a linear graph representation, with heat pump components represented as nodes and energy interactions as links, is investigated. Node signature matrices are used to present the energy information in a compact mathematical form. The resulting node signature matrix is referred to as an attributed graph and is populated in such a way as to retain the structural information, i.e., where the attribute points to in the physical system. To generate the energy and exergy information for the compilation of the attributed graphs, a descriptive thermal–fluid model of the heat pump system is developed. The thermal–fluid model is based on the specifications of and validated to the actual behavioral characteristics of a physical transcritical CO 2 heat pump test facility. As a first step to graph-matching, cost matrices are generated to represent a characteristic residual between a normal system node signature matrix and a faulty system node signature matrix. The variation in the eigenvalues and eigenvectors of the characteristic cost matrices from normal conditions to a fault condition was used for fault characterization. Three faults, namely refrigerant leakage, compressor failure and gas cooler fouling, were considered. The paper only aims to introduce an approach, with the scope limited to illustration at one operating point and considers only three relatively large faults. The results of the proposed method show promise and warrant further work to evaluate its sensitivity and robustness for small faults

The abundance of solar energy in Africa provides the ideal opportunity to mitigate the concerns on price, reliability and voltage quality of electricity supply to end-users. Rooftop PV and local energy storage solutions in a microgrid where energy is well managed, could unlock significant savings in energy costs and improve voltage quality and reliability. Each microgrid is a unique use-case due to the variation in type, size and costing of energy resources, and the loading and billing structure. This paper presents a methodology to identify and select microgrid energy management objectives using levelized cost of energy as the reference metric. The theoretical framework is validated by implementation in a real microgrid and concludes that Li-Ion energy storage is still too expensive for grid-tied applications in South Africa

Simulation during the design and operation of microgrids should include dynamic phenomena that would be a result of the unpredictable variation in renewable generation and loading. Detailed modelling of different components is constrained when using simplified models from suppliers. Accurate prediction of system response is needed to maintain voltage stability within a microgrid during islanded conditions and when grid-connected, between interconnected microgrids. Distributed energy resources in a microgrid are constituted by local generation (mostly renewable and standby diesel), a grid connection and increasingly, local storage (most commonly batteries). Synchronised field data is used to validate, and improve, the results obtained from a microgrid simulation model during both steady-state and transient conditions. This microgrid simulation approach makes use of MATLAB™ and DIgSILENT™, and is shown to be useful in designing for predictable performance. With field representative results, the model developed in DIgSILENT™ can be used as a development tool allowing various control strategies and component configurations to be tested

A major challenge related to the design of a hybrid renewable energy hydrogen system is which energy sources to include and at what capacity, for regionally different potentials of renewable energy and hydrogen demand. In addition, once the plant is in operation, control variables need to be optimised. The problem resorts to an area of multiple criteria decision making referred to as multi-objective optimisation. The results obtained from these type of algorithms include not only one optimal solution, but a set of optimal solutions (Pareto front) thereby offering a system designer several options. This set of solutions can be hard to interpret and a method is needed to automatically extract useful design and control strategies from this information. A methodology that is quite successful in deriving human interpretable rules from this type of information is genetic fuzzy systems. In this work a k-means clustering algorithm is used to generate membership functions and a fuzzy rule-base is trained by means of a genetic algorithm. The genetic fuzzy system obtained is reduced by determining the minimum number of rules followed by a membership function reduction process. The reduced genetic fuzzy system is deemed more interpretable. Geographic weather data from three different sites are used to generate data to be used in the genetic fuzzy method. Results show that the technique provides valuable information that can be used for the design of such hybrid renewable energy hydrogen production systems

The focus of this paper is the development of attributed graph representations of industrial processes. In this case energy attributes are used since it serves a data reduction purpose and allows for the consideration of multi-domain systems. Pattern recognition approaches towards FDI are considered advantageous due to their visual interpretation qualities. It is therefore envisaged that these attributed graphs can be used in a new innovative graph matching methodology to be able to detect and isolate faults. A two-tank thermo-fluid system is considered in this paper as a case study. An attributed graph containing exergy and energy flows is derived and from this graph node signature matrices are extracted that represent normal and fault conditions. Fault signatures are compared to the normal signature by deriving a cost matrix using a Heterogenous Eucledian-Overlap Metric (HEOM). Eigenvalues of the cost matrices are analysed in a qualitative way as a first stage of fault detection

Physics-based battery models can be very complex and require careful experimental validation, but yield greater insights into the internal processes and their interactions than other cell modelling approaches. The complexity is associated with the large number of input parameters that (a) have varying degrees of identifiability, (b) can be constant or varying, and (c) appear in complex combinations with the model variables and each other. The current work studies this complexity by proposing a unique stepwise approach and purposefully addresses the computational cost associated with estimating these parameters. An initial set of 50 input parameters is reduced to only 8 highly influential parameters that can be subjected to parameter optimization. Elementary effects analysis using the Morris method is applied and demonstrates that the electrode kinetic parameters dominate the cell’s voltage response as simulated by the model. These influential parameters are subjected to variance-based sensitivity analysis using Monte Carlo experiments and Jansen’s formulae for variance decomposition. The first-order and total sensitivities during various modes of operation indicate that the charge transfer coefficients and effective exchange current densities have the most influence on the model error and should be subjected to further optimization. The model error’s sensitivity also reveals high parameter identifiability within subsets of the experimental data and indicates that some parameter values might be valid for longer timescales rather than shorter timescales. The proposed stepwise approach can be applied to any complex physics-based cell model regardless of the cell’s chemistry, format or form factor

Energy-based fault detection and isolation (FDI) schemes show promise in terms of applicability to petrochemical industries and the broader process industries. Model-based FDI schemes offer pronounced advantages over more easily implemented data-driven FDI techniques. However, the implementation of model-based FDI schemes require the development of analytic models, and as such, widespread adoption is lacking. Although process simulators derive analytic models internally, comprehensive exergy-based parameters are not calculated automatically. In this work Aspen Hysys is extended by means of user variables to allow the automatic calculation of physical and chemical exergy for each process stream. Initial results indicate that additional computational overhead of approximately 20% is incurred and that the calculation technique scales well with model size. This calculation method allows the development of a truly hybrid model- and data-based approach to FDI within the process industries

The dynamic reconfiguration of an FPGA has many advantages, but the overhead from the process reduces the functional density of applications. Functional density is an indication of the composite benefits a reconfigured application obtains above its generic counterpart and measures the computational throughput per unit hardware resources. Typically, only quasi-static applications obtain a functional density advantage by dynamically reconfiguring its parameters. Contributing to the functional density reduction of applications with tight time constraints is the overhead to generate a new configuration, and the time it takes to load it onto the device. Normally these applications have to reuse their hardware numerous times between configurations before obtaining a functional density advantage. The most promising reconfiguration method to improve functional density with minimal hardware reuse was one that extracts certain characteristics from the bitstream and then implements a bitstream specialiser that generates new hardware at bit-level while the device is being reconfigured. While it was shown that this method allows reconfiguration of an application in real-time, its effect on functional density was not determined. This paper will show that a significant increase in functional density can be achieved for applications where reconfiguration is required before the next execution cycle of the application

Fluidised bed granulation is a widely used wet granulation technique. The operation of a fluidised bed granulator (FBG) as well as the quality of the product is strongly influenced by multiple process variables and disturbances. Controlling this process is difficult due to long lag times between sample analysis. Inference sensors are therefore an effective control solution for this complex process. A continuous industrial FBG was used to develop multiple linear regression (MLR) models that included two-way interaction effects. Elementary artificial neural network (ANN) models were developed to qualitatively assess the MLR models. The influences of the fluidizing air, the spray liquid and the seed particle size on the product quality were investigated and modelled. The spray liquid was found to have the largest correlation with the quality variables. Both modelling techniques produced accurate models, however undertraining of some ANN models resulted in a larger deviation between the model and validation data

The complexity and multi-domain nature of petrochemical (PC) plants make the application of conventional model-based fault detection and isolation (FDI) techniques a challenging endeavour. Although hybrid FDI schemes aim to address this shortfall, many are simply a combination of data-driven techniques that exclude physical system information. In this work, a hybrid approach to FDI of a PC process is proposed that is based on an exergy-data abstraction. Data from an actual system is abstracted to system exergy, based on physical knowledge of the system and then used as a diagnostic metric for the FDI scheme. In this paper, it is shown why energy-based approaches are lacking when considering petrochemical processes. After presenting a novel method for the real-time, automatic calculation of chemical exergy in Aspen HySys® the applicability of exergy-based fault detection is investigated. Application of the exergy-based fault detection scheme shows a marked improvement over the energy-based approach with perfect detectability and isolability of the considered process faults. The exergy-based fault detection technique shows merit in comparison to the energy-based detection scheme. Additionally, and more importantly, exergy-based characterisation allows the use of more sophisticated model-based fault detection schemes to petrochemical processes

Fault detection and isolation (FDI) within the petrochemical industries (PCIs) is largely dominated by statistical techniques. Although a signal-based technique centered on exergy flows within a process plant was proposed, it has only been applied to single process units. The exergy-based scheme has not yet been applied to process plants that feature at least a single recycle stream. The Tennessee Eastman process (TEP) is commonly used as an FDI benchmark process, but due to obfuscation, the TEP cannot be directly implemented in a commercial process simulator. Thus, application of FDI techniques to proprietary processes will require significant investment into the implementation of the FDI scheme. This is a key impediment to the wide-spread comparison of various FDI techniques to non-benchmark processes. In this paper, a gas-to-liquids (GTL) process model is developed in Aspen HYSYS®, and the model’s performance is validated. The exergy-based FDI technique is applied to the GTL process while the process is subjected to carefully selected faults. The selected faults aim to affect several process units, and specifically, the resultant recycle stream of the GTL process is considered. The results indicate that even though the exergy-based technique makes use of fixed thresholds, complete detection and isolation can be achieved for a list of common process faults. This is significant since it shows, for the first time, that the exergy-based FDI scheme can successfully be deployed in processes with recycle streams

The need exists for a benchmark multi-domain practical system that could emulate typical process faults for the purposes of condition monitoring. A proposition was made to develop a heated two-tank system, due to its multi-domain nature i.e. thermal-fluid, mechanical, electrical as well as its non-linear nature. The paper presents an analysis of a helical coil heat exchanger. A comparison is made between steam and water as a heat transfer medium. The study is conducted to determine which medium will be the most feasible to implement in a laboratory scale two-tank heated system. The convection coefficients on the hot and cold side of the helical coil are calculated to determine the inlet and outlet temperatures of the system. The temperatures are compared at various mass flows

Each year, latest state of the art technologies and algorithms arise that claim and prove to-shine their predecessors. One such algorithm is the Colour-based Retina Key-point (CREAK) descriptor, which is based on the FAST Retina Key-point (FREAK) descriptor with the included functionality of considering colour information in its Key-point description. This paper explores the implementation of CREAK in a “real-time” visual odometry application by means of a comparative study of the more well-known FREAK algorithm. Although FREAK achieved more accurate odometry when key-points were abundant, this proved to be too computationally expensive. CREAK on the other hand outperformed FREAK when key-points are scarce due to its lack of false-positive matches

An accurate thermal model will commonly require empirical parameter identification, specifically for the convection coefficients and interface resistances. A high-speed permanent magnet synchronous machine test platform, equipped with various temperature and power measuring equipment, is used to determine these parameters. Specifically, two tests, a dc injection test and rotational test with no load connected, were performed. The results were compared with a lumped thermal model and the parameters updated until an acceptable match was achieved. There were significant differences in the temperature rise when activating forced air cooling, thus significantly influencing the convection coefficients. Also, a significant difference in the interface resistances showed that in these high-speed machines, doing only the dc injection test will not give accurate interface resistance values. The work is novel through combining systematic empirical parameter identification to determine the convection coefficients and interface resistances for a machine where the end windings are cooled by forced tangential air flow

This paper presents the experimental validation of BBSim: a non-linear model for simulation of rotor delevitation in an active magnetic bearing system with backup bearings (BBs). The rotor delevitation model’s purposes are to understand the dynamics involved during delevitation and to quantify the severity of the delevitation event. The subsequent challenge consists of validating both the delevitation dynamics and the impact of the rotor on the backup bearing as a quantitative measure of delevitation severity. Severity indicators are derived from the relationship between the rotor’s impact and the average lateral rotor speed after delevitation. Model validation employs both the defined severity indicators and the rotor delevitation orbit characteristics as basis for comparing experimental and simulated results. The severity indicator validation confirms the rotor lateral kinetic energy during delevitation as a satisfactory predictor of the BB impact forces. Furthermore, calibration of the simulation model together with the subsequent validation process affirms the model’s validity

Backup bearings (BBs) are used to protect active magnetic bearings in the event of rotor delevitation. To gain insight into the design, selection and operation of BBs, a model that can accurately simulate the behaviour of a rotor and BB system during a rotor delevitation event is required. This paper presents a model that can accurately simulate rotor delevitation and predict the occurrence of forward and backward whirl. The model, called BBSim, is based on the integration of a number of simpler models for each of the important mechanisms that govern the behaviour of a BB system. Estimation of parameters that are critical in determining the rotor’s behaviour inside the BB clearance is achieved using empirical methods. Rotor orbit plots confirm the model’s capability of simulating the characteristic motions of rotor delevitation

Condition monitoring has traditionally been deployed for the monitoring of a single component, such as a rotating machine. The application of condition monitoring techniques to entire petrochemical process plants, however, remains lacking. This can be ascribed to the significant complexity involved in applying existing techniques to such plants. In this work a novel energy-based fault detection scheme is applied to an autothermal reformer of a gas-to-liquids process. The performance of the technique is evaluated, against a set of representative faults, and it is shown to provide adequate fault detection performance. The use of the energy-based detection scheme shows promise in terms of reduced modelling efforts and increased computational efficiency

Sensor fault detection and isolation (FDI) is an important element in modern nuclear power plant (NPP) diagnostic systems. In this respect, sensor FDI of generation II and III water-cooled nuclear energy systems has become an active research topic to continually improve levels of reliability, safety, and operation. However, evolutionary advances in reactor and component technology together with different energy conversion methodologies support the investigation of alternative approaches to sensor FDI. Within this context, the basic aim of this two part series is to propose, implement and evaluate an integrated approach for sensor FDI and signal reconstruction in generation IV nuclear high temperature gas-cooled reactors (HTGRs). In part I of this two part series, the methodology and theoretical background of the integrated sensor FDI and signal reconstruction approach are given. This approach combines techniques such as non-temporal parity space analysis (PSA), principal component analysis (PCA), sensor fusion and fuzzy decision systems to form a more powerful sensor FDI methodology that exploits the strengths of the individual techniques. An illustrative example of the PCA algorithm is given making use of actual data retrieved from a pilot plant called the pebble bed micro model (PBMM). This is a prototype gas turbine power plant based on the first design configuration of the pebble bed modular reactor (PBMR). In part II, the described integrated sensor fault detection approach will be evaluated by means of two case studies. In the first case study the approach will be evaluated on real PBMM data and in the second case study the approach will be evaluated on a highly detailed Flownex® model of the new generation PBMR

The hydrocyclone is widely used throughout the mineral processing industry when working with slurries. It is either used for classifying, desliming or dewatering. Hydrocyclones are inexpensive, application-efficient and relatively small to employ. In order to quantify its separation efficiency, models are utilised to estimate the cut-size and sharpness of classification coefficient, usually in the form of a partition curve. Most models are based on experimentally obtained data and are therefore not always universally applicable. Over the last decade researchers have started employing Artificial Neural Networks (ANNs) in order to obtain a dynamic model. This study endeavoured to use experimentally acquired data to develop models that predict the cut-size. The models are discussed and evaluated in detail and the best predicting model was compared to a conventional model from literature

This paper considers an integrated procedure to verify and validate (V&V) the rotordynamic analyses of a magnetic bearing supported rotor delevitation system (RDS). Rotor-AMB (active magnetic bearing) systems in general have complex geometries which make accurate modelling thereof a difficult task. For this purpose, simple rotordynamic analyses methods, practical measurements, and finite element method (FEM) models are applied in an integrated framework to characterize the natural vibration frequencies of the system. First, a numerical model (Rayleigh’s energy method) and an experimental modal analysis of the rotor-only are utilized to V&V the bending natural frequency estimates of a rotor FEM simulation model. Thereafter, an analytical model (two degree of freedom method) and a modal analysis of the integrated rotor-AMB system are employed to V&V the rigid body natural frequency estimates of a rotor-AMB FEM simulation model. Adjustments of the FEM models are realized using feedback to fine-tune the geometric, material, and operating properties of the discs, the rotor, and the AMBs. A practical case study of a RDS successfully demonstrates application of the integrated procedure, which ultimately facilitates improved correlation between FEM simulation and experimental results

This second paper in a two part series presents the application and validation of an integrated approach for sensor fault detection and isolation (FDI) in high temperature gas-cooled reactors (HTGRs) in the form of two case studies. In the first case study the integrated approach is applied to actual data retrieved from the prototype pebble bed micro model (PBMM) plant. This case study is particularly important to gain valuable insight regarding the statistical parameters used for the statistical and fuzzy-rule based algorithms. In the second case study, the integrated approach is applied to a complete and detailed Flownex® model of the new generation single-shaft, Brayton cycle-based pebble bed modular reactor (PBMR). This case study provides further insight on the actual performance of the integrated approach applied to a more realistic representation of an actual HTGR plant. In part I of this two part series, the theoretical concepts and methodology of the integrated approach to sensor FDI and signal reconstruction are given. This paper attempts to investigate if the proposed approach can effectively detect and isolate sensor malfunctions such as sensor bias or offset, dead sensors, excessive noise, sensor drift and random spikes. In addition, the ability of the method to diagnose multiple simultaneous sensor failures is also evaluated. It is illustrated that this method is especially suited for safety critical systems such as nuclear power plants where sensors can only be removed or tested once the plant is open for maintenance. This is usually only once every fuel cycle. Therefore, by implementing the proposed method on-line, sensor readings can be validated in real-time. Another advantage of the method is the ability to reconstruct faulty sensors. This will enable the control of safety critical components despite sensor failure

As the complexity of industrial systems increases, the necessity also increases for more reliable, robust and accurate Fault Detection and Diagnosis (FDD) techniques. This article proposes a methodology for the design of an FDD system, based on an energy visualisation of an axial-flow compressor system. This method investigates the steady state and transients of the system for residual generation. A nonlinear model is derived for this compressor system that are capable of modelling compressor instabilities such as rotating stall, surge and leaks. A power-energy analysis is done using the state variables of the system under normal and fault conditions and are visually presented. Two fault conditions are analysed in terms of power and energy. The first test case introduces a leak in the system. The second focuses on the more complex situation when the system enters an unstable operation, namely rotating stall. The results indicate that the power and energy of the system may be useful for the purposes of FDD

Condition monitoring has traditionally been deployed for the monitoring of a single component, such as a rotating electrical machine. The application of condition monitoring to an entire process plant has not yet received much attention. In this work we outline the challenges associated with plant-wide condition monitoring, and explore the possibility of using energy as the parameter of interest. The use of energy mitigates the multi-domain monitoring problem, and could also provide a hierarchical view of the process plant's condition. A simple chemical reactor is considered and it is shown that the energy parameter can be used to perform condition monitoring of such a reactor. The identified parameter is also sensitive to time domain variations. This indicates that the concept of using energy flows as the parameter of interest for process-wide condition monitoring applications has merit

A fair amount of research has been done on the modeling of the oculomotor system. Models of the oculomotor neural circuitry that include the effect of alcohol are less plentiful in literature. We investigated the correlation between bloodalcohol levels and the response characteristics of the opticaloculomotor system. Video recordings of eye movements were regarded as the main sensor, with a breathalyzer for verification. System identification was done based on the transfer function of a proposed delay-based model. The correlation of coefficients of the model to increasing blood-alcohol concentration was seen to display behavior that was unique to each test

Despite the advantages dynamic reconfiguration adds to a system, it only improves system performance if the execution time exceeds the configuration time. As a result, dynamic reconfiguration is only capable of improving the performance of quasi-static applications. In order to improve the performance of dynamic applications, researchers focus on improving the reconfiguration throughput. These approaches are mostly limited by the bus commonly used to connect the configuration controller to the memory, which contributes to the configuration time. A method proposed to ameliorate this overhead is an architecture utilizing localised block RAM (BRAM) connected to the configuration controller to store the configuration bitstream. The aim of this paper is to illustrate the advantages of the proposed architecture, especially for reconfiguring real-time applications. This is done by validating the throughput of the architecture and comparing this to the maximum theoretical throughput of the internal configuration access port (ICAP). It was found that the proposed architecture is capable of reconfiguring an application within a time-frame suitable for real-time reconfiguration. The drawback of this method is that the BRAM is extremely limited and only a discrete set of configurations can be stored. This paper also proposes a method on how this can be mitigated without affecting the throughput

Permanent-magnet synchronous machines (PMSMs) are well suited for high-speed (HS) applications due to their high efficiency, power density, and dynamic response capability. The heat extraction area decreases as the speed increases, making thermal effects more dominant at high speed. The temperature-dependent properties of permanent magnets necessitate high-detail thermal models. This paper presents a 2-D analytical model for a HS PMSM magnet. The diffusion equation is solved where three of the PM boundaries experience convection heat flow; as the case is in radial flux machines. The heat generated on the rotor surface due to eddy currents is also taken into account. The model is verified using numerical techniques and shows good correlation (within 1.5%). The model is also validated through experiments performed on a 4 kW, 30 000 r/min PMSM

Flywheel Energy Storage (FES) is rapidly becoming an attractive enabling technology in power systems requiring energy storage. This is mainly due to the rapid advances made in Active Magnetic Bearing (AMB) technology. The use of AMBs in FES systems results in a drastic increase in their efficiency. Another key component of a flywheel system is the control strategy. In the past, decentralised control strategies implementing PID control, proved very effective and robust. In this paper, the performance of an advanced centralised control strategy namely, Model Predictive Control (MPC) is investigated. It is an optimal Multiple-Input and Multiple-Output (MIMO) control strategy that utilises a system model and an optimisation algorithm to determine the optimal control law. A first principle state space model is derived for the purpose of the MPC control strategy. The designed MPC controller is evaluated both in simulation and experimentally at a low operating speed as a proof of concept. The experimental and simulated results are compared by means of a sensitivity analysis. The controller showed good performance, however further improvements need to be made in order to sustain good performance and stability at higher speeds. In this paper advantages of incorporating a system model in a model-based strategy such as MPC are illustrated. MPC also allows for incorporating system and control constraints into the control methodology allowing for better efficiency and reliability capabilities

Accurate analytical models are fundamental to design and optimise high performance magnetic bearings. Understanding the bearing’s dynamic behaviour over its operational envelope is essential to this task and is performed through frequency response analyses. This work formulates an integrated frequency dependent model of a radial heteropolar active magnetic bearing (AMB) in which terms for the stray capacitance, winding resistance, and winding leakage inductance are included. The model is based on a lumped parameter model frequently used for high frequency laminated inductor applications. Two magnetic core models are evaluated. The first comprises a reluctance network approach and incorporates eddy-current, fringing, and leakage correction terms, while the second constitutes a single gapless magnetic core of equivalent length and effective permeability. The accuracy of the latter is improved by intuitively adjusting two parameters in the analytical model. The magnetic bearing parameters calculated as functions of frequency are compared with practical measurements. The integrated single core model demonstrates superior correlation with experimental results for frequencies up to 1 MHz

This paper is concerned with the control of an under-actuated, uncertain, delayed non-linear system through the implementation of arti cial neural networks(ANNs). The aim is the development of a series-parallel training scheme for the on-line observer to ensure faster convergence and more accurate estimations. Reinforcement learning is used to improve future performance and maintain stability while an estimated tracking error is minimised. Lyapunov stability measures are employed to guarantee the uniform ultimate boundedness of the closedloop tracking error. Real-time learning algorithms are derived for the individual components (observer, actor, critic). Final performance is tested on a mathematical helicopter model and real-world helicopter ight data

In a renewable energy system, lead-acid batteries are required to last as long as possible whilst providing deep discharges and are usually charged in an intermittent manner. This type of operation is severely detrimental to the electrodes and optimal performance cannot be guaranteed. Correct operation can be achieved if a charge controller receives accurate information about the state of the battery and its cells. Accurate approximation of the available active surface area is essential to optimal performance since it determines the overall capacity and reaction rates of various secondary processes. This paper improves on the current understanding of the active surface area in a lead-acid cell during discharge by comparing three approximation methods. Two methods use the state-of-charge based on porosity whilst the other method uses the charge per unit volume to determine the remaining capacity. These three methods are used in a comprehensive macro-homogeneous electrochemical model which accounts for initial temperature differences. Reported experimental behaviour is used to evaluate each method and recommendations are made regarding the selection of associated parameter values. The approximation method by Cugnet et al. (2009) is the most suitable because it always results in a concave morphology. The current work can aid in the development of a health-conscious battery management system

This paper presents the cooperative control between the active magnetic bearing system and the permanent magnet synchronous drive. More specifically, the coupling between the vibration control and sensorless drive control is considered since both systems have the ability to estimate the angular speed. The coupling of the estimated speed in each controller is considered. Also, a modified start-up procedure for the sensorless vector control had to be developed when the drive is combined with the AMB system to accommodate the low friction which does not yield damping of the speed perturbation at start-up. Simulation results are presented for the developed control using detailed models for both systems

Reconfigurable computing is a paradigm in computing architecture that refers to the practice of using interchangeable hardware modules to enhance the performance of conventional Von-Neumann style computing. Despite the numerous advantages of reconfiguration, it is only suitable for quasi-static applications with slowly changing reconfiguration criteria. In general, it is only advantageous to reconfigure if the execution time exceeds recon guration time. Since the execution time of real-time systems are quite limited, the control of dynamic non-linear systems are typically not reconfigured. Instead, adaptivity is mostly gained from reading coefficients or gains from memory. Where different controller architectures are required, the route of parallel implementation could be taken, switching between architectures. The drawback of this approach is an increase in area required to implement the controllers. Reconfiguration on the other hand could allow the different control architectures to be swapped on the y without interrupting operation of the controller, while minimizing the area required. Unfortunately, this process is limited by the overhead introduced by the reconfiguration. Even though various survey papers exist on the topic of reconfiguration, none really focus on methods to reduce the cost of reconfiguration. This survey summarizes different means of reducing configuration overhead in an attempt to allow reconfiguration of applications with limited execution time. A block RAM- based (BRAM) architecture is proposed as the optimal architecture for reconfiguring dynamic applications. As an example, this architecture is used to discuss the methodology used to design a reconfigurable PID controller

Energy is a universal concept that can be used across physical domains to describe complex large-scale industrial systems. This brief survey and framework gives a perspective on energy as a unifying domain for system modelling, supervision, and control. Traditionally, modelling and control problems have been approached by adopting a signal-processing paradigm. However, this approach becomes problematic when considering non-linear systems. A behavioural viewpoint, which incorporates energy as basis for modelling and control, is considered a viable solution. Since energy is seen as a unifying concept, its relationship to Euler-Lagrange equations, state space representation, and Lyapunov functions is discussed. The connection between control and process supervision using passivity theory coupled with a system energy balance is also established. To show that complex industrial systems comprising multiple energy domains can be modelled by means of a single electric circuit, its application to a large-scale thermo-hydraulic system is presented. Next, a simple non-linear transmission impedance electric circuit is used to illustrate how energy can be used to not only describe a system, but also serve as basis for system optimisation. An energy-based framework is proposed whereby energy is used as a unifying domain to work in, to analyse, and to optimise large-scale industrial systems

A method is proposed to model the electrochemical characteristics of a Proton Exchange Membrane (PEM) electrolyser. The electrochemical characteristics, which include the Ohmic, activation and concentration losses, are modelled by means of an equivalent electric circuit impedance. The equivalent electric circuit impedance under consideration is the Randles–Warburg (RW) cell and the parameters are obtained through the current interrupt (CI) method. The CI method consists of two parts, 1) the natural voltage response (NVR) method to model the Ohmic losses, and 2) the current switching (CS) method to model the activation and concentration losses. A simulation model of the RW cell is used to verify and validate the CI method. Thereafter, the CI method is practically implemented and the results presented. Results show that the Ohmic losses correlates well with existing literature. The paper furthermore illustrates the capability of the CI method to visually illustrate the RW impedance through Nyquist diagrams. Nyquist diagrams are used to illustrate the concentration losses and indicate the degradation state of the PEM at specific operating conditions. Results show that the concentration losses decrease with an increase in the operating current density and temperature

This paper presents a sizing and control optimisation architecture for the design and evaluation of a small-scale stand-alone hybrid PV-wind-battery system for the production of hydrogen (H2) using proton exchange membrane (PEM) technology. Three objectives are considered simultaneously namely cost, efficiency and reliability. For this task an optimisation approach is developed combining a single objective genetic algorithm (GA) with a multiobjective GA (MOGA) to optimise nine system sizing variables and six power management system control set-point variables. The nine sizing and six control variables are combined to form a solution vector. The optimisation algorithm searches the search space, with user defined boundaries, for non-dominated solution vectors. The result is a set of solution vectors which are useful in the selection of components for the design and evaluation of these systems. The optimisation approach developed sufficiently searches the bounded search space and provides results in the form of a set of non-dominated solution vectors. These results are useful in understanding how the different components of such a non-linear complex system affect each other as well as the three objectives considered in this study

A discontinuity seems to exist in present aided inertial navigation systems – the operational availability of high cost inertial navigation currently depends on the availability of a valid GPS lock. In general GPS solutions can be two or more orders cheaper than their inertial sensors counterparts. This paper addresses the north alignment by gyrocompassing in the context of more modern smoothing techniques. Incremental smoothing is becoming a serious contender for in-situ state estimation. The work here presents a probabilistic model for north alignment with an analysis towards the achievable accuracy of such a system. A pose graph formulation is used to estimate inertial sensor calibration terms over a long spanning trajectory, from which a true north alignment can be extracted. The preintegral method is used to condense high rate inertial data, but allow for post integration bias correction. Results show that the proposed alignment scheme is feasible, and would for gyrocompassing alignment relaxed sensor performance constraints

In this paper, optimal linear control techniques are utilised to control a radio-controlled helicopter (30 size) in the AeroSIMRC simulation environment. A grey-box time-domain system identification method is used to estimate a linear state space model that operates in hover mode. Identifying the unknown parameters in the model is highly dependent on the initial values and the input data. The model is divided into sub-systems to make estimation possible. The identified state space model shows a good measure of fit compared to the simulator’s flight data. A linear quadratic controller forms the inner-loop, and an optimised PID outer-loop generates attitude commands from a given inertial position trajectory. An observer estimates the unmeasured states such as blade flapping. The controller is developed in Simulink® with a plug-in written for the AeroSIMRC flight simulator. The plug-in enables Simulink® to control the simulator through a User Datagram Protocol interface for the purpose of model and controller validation. The proposed methodology facilitates a low cost test environment for new flight control algorithms. The simulation results show that the controller keeps the helicopter stable in the presence of considerable environmental disturbances and plant uncertainties

Electrochemical impedance spectroscopy (EIS), current interrupt (CI) and current mapping (CM) were investigated as in-situ characterisation tools for PEM electrolysers. A 25 cm2 cell with titanium anode and carbon cathode plates were utilised in this study. A commercial MEA consisting of 1 mg IrO2/cm2 on the anode and 0.3 mg Pt/cm2 on the cathode was used. The electrocatalyst was deposited on Nafion® membranes. The electrochemical losses in a PEM electrolyser namely: activation, ohmic and mass transfer losses were identified using EIS and CI and both the advantages and disadvantages of the methods were discussed. The current distribution over the membrane electrode assembly (MEA) at different current densities was measured using the current mapping method. It is also shown that under the given experimental conditions the current density decreases along the serpentine flow field

A Proton Exchange Membrane (PEM) electrolyser is characterised and modelled to identify important electrochemical effects. These electrochemical effects include the ohmic, activation, and concentration losses within the PEM electrolyser during hydrogen production. The electrochemical effects of the PEM electrolyser are modelled by means of equivalent electrical circuits. The equivalent electrical circuit components of importance are the membrane resistance, the charge transfer resistance, the double layer capacitance and the Warburg impedance. The current interrupt method is proposed by using, (i) the natural voltage response method and (ii) system identification, for solving the parameters of the equivalent electrical circuits. In this paper both simulation and experimental results are provided

This second paper in a two part series presents the application of a developed state space model extraction methodology applied to a Brayton cycle-based PCU (power conversion unit) of a PBMR (pebble bed modular reactor). The goal is to investigate if the state space extraction methodology can cope with larger and more complex thermohydraulic systems. In Part I the state space model extraction methodology for the purpose of control was described in detail and a state space representation was extracted for a U-tube system to illustrate the concept. In this paper a 25th order nonlinear state space representation in terms of the different energy domains is extracted. This state space representation is solved and the responses of a number of important states are compared with results obtained from a PBMR PCU Flownex model. Flownex is a validated thermo fluid simulation software package. The results show that the state space model closely resembles the dynamics of the PBMR PCU. This kind of model may be used for nonlinear MIMO (multi-input, multi-output) type of control strategies. However, there is still a need for linear state space models since many control system design and analysis techniques require a linear state space model. This issue is also addressed in this paper by showing how a linear state space model can be derived from the extracted nonlinear state space model. The linearised state space model is also validated by comparing the state space model to an existing linear Simulink model of the PBMR PCU system.

Applications for the use of microwaves reach far and wide in various industries. Commonly known is the use of microwaves to heat foodstuffs. Research and development conducted in the development of a microwave combination washer dryer required the development of a system model, allowing for the development of a control system. Considered in this work is the propagation of microwaves within the system along with the heat transfer throughout the system. This paper presents a FEM model based on the geometry of the prototype combination washer dryer. Model results are validated by practical measurements using the combination washer dryer laboratory prototype. The model is at the time of writing undergoing final validation, with preliminary results indicating that the model fairly accurately represents the system. The simulation and practical system were compared by calculating the average of the absolute value of the difference between the data sets. Three of the five temperature points logged have an average absolute temperature difference of less then 0.7°C and the final two points logged have an average absolute difference of 1.2°C and 2.5°C respectively

The thermal behaviour of permanent magnet synchronous machines (PMSMs) is important since overheating of the permanent magnets can lead to demagnetization. This article gives an overview of a hybrid thermal model for a high speed PMSM which combines lumped parameter and analytical models. The losses found in a slotless PMSM are also discussed since they are the heat sources. An overview of the experiments performed to determine some of the thermal parameters and verify the models are also given. It is shown that bearing loss can make a significant contribution to the thermal behavior of a machine

Active magnetic bearings (AMBs) have become a key technology in various industrial applications. Self-sensing AMBs provide an integrated sensorless solution for position estimation, consolidating the sensing and actuating functions into a single electromagnetic transducer. The approach aims to reduce possible hardware failure points, production costs, and system complexity. Despite these advantages, self-sensing methods must address various technical challenges to maximize the performance thereof. This paper presents the direct current measurement (DCM) approach for self-sensing AMBs, denoting the direct measurement of the current ripple component. In AMB systems, switching power amplifiers (PAs) modulate the rotor position information onto the current waveform. Demodulation self-sensing techniques then use bandpass and lowpass filters to estimate the rotor position from the voltage and current signals. However, the additional phase-shift introduced by these filters results in lower stability margins. The DCM approach utilizes a novel PA switching method that directly measures the current ripple to obtain duty-cycle invariant position estimates. Demodulation filters are largely excluded to minimize additional phase-shift in the position estimates. Basic functionality and performance of the proposed self-sensing approach are demonstrated via a transient simulation model as well as a high current (10 A) experimental system. A digital implementation of amplitude modulation self-sensing serves as a comparative estimator

This second paper in a two part series presents the application of a developed state space model extraction methodology applied to a Brayton cycle-based PCU (power conversion unit) of a PBMR (pebble bed modular reactor). The goal is to investigate if the state space extraction methodology can cope with larger and more complex thermohydraulic systems. In Part I the state space model extraction methodology for the purpose of control was described in detail and a state space representation was extracted for a U-tube system to illustrate the concept. In this paper a 25th order nonlinear state space representation in terms of the different energy domains is extracted. This state space representation is solved and the responses of a number of important states are compared with results obtained from a PBMR PCU Flownex model. Flownex is a validated thermo fluid simulation software package. The results show that the state space model closely resembles the dynamics of the PBMR PCU. This kind of model may be used for nonlinear MIMO (multi-input, multi-output) type of control strategies. However, there is still a need for linear state space models since many control system design and analysis techniques require a linear state space model. This issue is also addressed in this paper by showing how a linear state space model can be derived from the extracted nonlinear state space model. The linearised state space model is also validated by comparing the state space model to an existing linear Simulink model of the PBMR PCU system.

This paper presents an integrated fuzzy logic controller (FLC) to address challenges associated with complex split-range control of a two-valves-in-parallel hybrid actuator. Split-range hybrid valve (HV) control aims to provide a more effective strategy to overcome flow problems associated with nonlinear valve flow coefficients and discontinuities while extending rangeability. The main issue is the trade-off between system performance, control scheme complexity, and the cost of extra equipment (multiple actuators). The hybrid configuration facilitates a large and small valve in an overlapping operating regime, providing the controller with some freedom to minimize the feedback error. The control challenge lies at the control valve boundaries where nonlinear transitions in mass flow rate occur. The aim of the complex split-range control is therefore to minimize the disturbance in the mass flow rate due to the mentioned valve non-idealities during critical valve transitions. A novel split-range control scheme is devised that comprises an integrated FLC, upper and lower boundary neural network (NN) signal predictors, a fuzzy logic inference system (FLIS), and a crisp controller to quantify all the control decisions that cannot be fuzzified. The NN predictors envisage the necessity for a large valve nonlinear transition in order to minimize its effect on the flow rate of the HV. Expert knowledge is used as basis for parameter definitions in the FLIS, thereby facilitating the implementation of control structures to address phenomena such as nonlinear mass flow transitions, valve stiction, and poor valve resolution. Simulation results indicate that the integrated FLC effectively coordinates and switches actuators for complex input requests, significantly reducing nonlinear transitions in the total mass flow rate compared to PID control

Despite the numerous advantages reconfigurable computing adds to a system, it is only advantageous if the execution time exceeds the configuration time. As a result of the long configuration time, reconfiguration is only suitable for quasi-static applications. Due to the additional overhead required for communication, the bus-based architectures most commonly used to connect the configuration controller to the memory contribute to the configuration time. A method proposed to ameliorate this overhead is an architecture utilizing localized block RAM (BRAM), directly connected to the configuration controller to store the configuration data. The drawback of this method is that the BRAM is extremely limited and only a discrete set of configurations can be stored. This paper is a work in progress and proposes a hardware reconfiguration architecture that addresses the size limitation of the localized BRAM-architecture by using parameterizable configuration. This will allow a single bitstream stored in the BRAM to be specialized according to certain parameters, which will be used to reconfigure the device. This will migrate reconfigurable computing to more dynamic applications. The architecture proposed in this paper will be validated using real-time PID control of a five degree of freedom active magnetic bearing system

This paper considers the development of a PID control strategy to optimally control the power output of a High Temperature Gas-cooled Reactor (HTGR) power plant. A specific type of HTGR called the Pebble Bed Modular Reactor (PBMR) that utilises a closed recuperative Brayton cycle with helium as working fluid is considered. The power control of this kind of plant is significantly different from conventional steam cycle nuclear power plants. A distinguishing feature that complicates the control is the use of three separate shafts for different compressor/turbine or turbine/generator pairs. In addition the power output cannot be directly controlled by means of an upstream valve that regulates the flow through the power turbine, as is the case with conventional steam cycles. This paper addresses these challenges by means of a control strategy consisting of four PID control loops. The controller gains are optimised by means of a Genetic Algorithm (GA) that uses real-valued genes and the ITAE performance measure as a cost function. The control strategy is implemented and evaluated on a linear Simulink® model of the PBMR Power Conversion Unit (PCU). Results are presented illustrating the performance of the GA optimised PID control strategy

Fault diagnosis (FD) is an important element in modern nuclear power plant (NPP) diagnostic systems. In this respect, FD of generation II and III water-cooled nuclear energy systems has become an active research topic to continually improve levels of reliability, safety, and operation. However, evolutionary advances in reactor and component technology together with different energy conversion methodologies support the investigation of alternative approaches to FD. Within this context, the basic aim of this two part series is to adopt the application of the enthalpy–entropy (h–s) graph approach for FD of generation IV nuclear high temperature gas-cooled reactor (HTGR) components. In Part I, the error method is utilized to derive fault patterns from the h–s graph in order to classify malfunctions via the fault classification index (FCI) in the nuclear reactor, turbo-machinery (gas turbine and compressors), heat exchangers (pre-cooler, intercooler, and recuperator) and the primary transporting medium of the working fluid. The study is conducted on a 165 MW model of the main power system (MPS) of the Pebble Bed Modular Reactor (PBMR) that is based on a single-shaft, closed-loop, direct Brayton thermodynamic cycle. Illustrative signatures that correspond to 24 single fault transients, categorized in three fault classes by means of a sensitivity analysis of a simplified HTGR, are presented. FD is demonstrated for steady state operation as well as load following of the MPS during normal power operation of the plant. In Part II of the series, a second classifier named the area error method is devised for NPP supervision to ultimately address the FD problem using a multiple classifier system. The application of the proposed h–s graph approach (both methods) is specifically illustrated for classification of an emulated fault transient in data from the real prototype Pebble Bed Micro Model (PBMM) plant.

The second paper in a two part series presents the area error method for generation of representative enthalpy–entropy (h–s) fault signatures to classify malfunctions in generation IV nuclear high temperature gas-cooled reactor (HTGR) components. The second classifier is devised to ultimately address the fault diagnosis (FD) problem via the proposed methods in a multiple classifier (MC) ensemble. FD is realized by way of different input feature sets to the classification algorithm based on the area and trajectory of the residual shift between the fault-free and the actual operating h–s graph models. The application of the proposed technique is specifically demonstrated for 24 single fault transients considered in the main power system (MPS) of the Pebble Bed Modular Reactor (PBMR). The results show that the area error technique produces different fault signatures with low correlation for all the examined component faults. A brief evaluation of the two fault signature generation techniques is presented and the performance of the area error method is documented using the fault classification index (FCI) presented in Part I of the series. The final part of this work reports the application of the proposed approach for classification of an emulated fault transient in data from the prototype Pebble Bed Micro Model (PBMM) plant. Reference data values are calculated for the plant via a thermo-hydraulic simulation model of the MPS. The results show that the correspondence between the fault signatures, generated via experimental plant data and simulated reference values, are generally good. The work presented in the two part series, related to the classification of component faults in the MPS of different HTGRs, suggest that the proposed h–s graph approach provides a suitable FD framework for generation IV nuclear energy systems.

Der Betrieb magnetgelagerter Maschinen er-fordert grundsätzlich den Einsatz von Fangla-gern. Diese nehmen den Rotor bei Maschinen-stillstand, bei abgeschalteter Magnetlagerung oder bei Magnetlagerversagen auf. Es wird zwischen Wälz- und Gleitfanglagerungen un-terschieden. Der Rotor-Fanglagerkontakt bei hohen Dreh-zahlen und großen mechanischen Belastungen ist durch enorme Kontaktkräfte und Relativge-schwindigkeiten zwischen Rotor und Stator gekennzeichnet. Das Fanglager muss den Ro-tor bis zum Stillstand sicher aufnehmen. Gegenwärtig werden bei schweren Rotoren, wie sie z. B. in Verdichtern, Pumpen und Tur-binen zu finden sind, vorwiegend Wälzfangla-ger eingesetzt. Der komplexe Aufbau von Wälzlagern (Innenring, Außenring sowie-Wälzelemente) gegenüber Gleitlagern (rotor- und statorseitige Laufbuchsen) führt, vor dem Hintergrund hoher mechanischer und thermi-scher Belastungen während des Eingriffes, zu erhöhten Aufwendungen bei der Konstruktion und Fertigung und damit zu hohen Kosten. Gleitfanglagerungen können eine sichere und kostengünstigere Alternative darstellen. Eine wichtige Voraussetzung dafür ist es, Gleitfanglager für solche extremen mechani-schen und thermischen Belastungen an Tur-bomaschinen konstruktiv und werkstofftech-nisch zu ertüchtigen. Für den erfolgreichen Einsatz von Gleitfangla-gerlösungen ist die Beherrschung aller Ab-wurfszenarien durch das Fanglager nachzu-weisen. Besonderes Augenmerk gilt hierbei den Steifigkeits- und Dämpfungswerten der Lagerung. Diese sind so zu wählen, dass der Rotor beim Durchfahren kritischer Drehzahlen im Fanglager keine unzulässig hohen Schwin-gungen auf die Maschine überträgt. Zur Sicherstellung dieser Eigenschaften sind im Vorfeld der Konstruktion für alle relevan-ten Fälle Simulationsrechnungen durchzufüh-ren. Dazu wurde das Gleitlager modelliert und Simulationsrechnungen unter Berücksichti-gung der Rotordynamik durchgeführt. Die Validierung der Modelle erfolgte auf der Grundlage von Experimenten an einem Klein-vers