Model predictive control for power system frequency control taking into account imbalance uncertainty

© IFAC.Model predictive control (MPC) is investigated as a control method for frequency control of power systems which are exposed to increasing wind power penetration. For such power systems, the unpredicted power imbalance can be assumed to be dominated by the fluctuations in produced wind power. An MPC is designed for controlling the frequency of wind-penetrated power systems, which uses the knowledge of the estimated worst-case power imbalance to make the MPC more robust. This is done by considering three different disturbances in the MPC: one towards the positive worst-case, one towards the negative worst-case, and one neutral in the middle. The robustified MPC is designed so that it finds an input which makes sure that the constraints of the system are fulfilled in case of all three disturbances. Through simulations on a network with concentrated wind power, it is shown that in certain cases where the state-of-the-art frequency control (PI control) and nominal MPC violate the system constraints, the robustified MPC fulfills them due to the inclusion of the worst-case estimates of the power imbalance

On output feedback nonlinear model predictive control using high gain observers for a class of systems

In recent years, nonlinear model predictive control schemes have been derived that guarantee stability of the closed loop under the assumption of full state information. However, only limited advances have been made with respect to output feedback in connection to nonlinear predictive control. Most of the existing approaches for output feedback nonlinear model predictive control do only guarantee local stability. Here we consider the combination of stabilizing instantaneous NMPC schemes with high gain observers. For a special MIMO system class we show that the closed loop is asymptotically stable, and that the output feedback NMPC scheme recovers the performance of the state feedback in the sense that the region of attraction and the trajectories of the state feedback scheme are recovered for a high gain observer with large enough gain and thus leading to semi-global/non-local results

Applying model predictive control to power system frequency control

Model predictive control (MPC) is investigated as a control method which may offer advantages in frequency control of power systems than the control methods applied today, especially in presence of increased renewable energy penetration. The MPC includes constraints on both generation amount and generation rate of change, and it is tested on a one-area system. The proposed MPC is tested against a conventional proportional-integral (PI) controller, and simulations show that the MPC improves frequency deviation and control performance. © 2013 IEEE

‘Negotiating Knowledge’: The Case of a Russian–Norwegian Software Outsourcing Project

This paper presents an empirical analysis of a global software development relationship between a Norwegian client and Russian contractor for the redesign of a payroll system called SalarySystem. The empirical analysis, which involved multiple visits to both the Russian and Norwegian sites and meetings and interviews with people from different levels involved with the system, revealed some interesting insights into how the project was initiated, how it nearly degenerated into a breakdown situation, and how learning took place and the project was first salvaged and then was grown. The theoretical notion of embedded knowledge (Nicholson and Sahay 2004) is drawn upon, and extended to describe features of embedding arising from domain, language, and project management issues. Four mechanisms through which this embedding was negotiated are discussed: use of TestTool; use of test cases; use of ICQ; and increased face to face interaction

Identifiability and physical interpretability of hybrid, gray-box models -- a case study

Model identifiability concerns the uniqueness of uncertain model parameters to be estimated from available process data and is often thought of as a prerequisite for the physical interpretability of a model. Nevertheless, model identifiability may be challenging to obtain in practice due to both stochastic and deterministic uncertainties, e.g. low data variability, noisy measurements, erroneous model structure, and stochasticity and locality of the optimization algorithm. For gray-box, hybrid models, model identifiability is rarely obtainable due to a high number of parameters. We illustrate through an industrial case study - modeling of a production choke valve in a petroleum well - that physical interpretability may be preserved even for non-identifiable models with adequate parameter regularization in the estimation problem. To this end, in a real industrial scenario, it may be beneficial for the model's predictive performance to develop hybrid over mechanistic models, as the model flexibility is higher. Modeling of six petroleum wells on the asset Edvard Grieg using historical production data show a 35\% reduction in the median prediction error across the wells comparing a hybrid to a mechanistic model. On the other hand, both the predictive performance and physical interpretability of the developed models are influenced by the available data. The findings encourage research into online learning and other hybrid model variants to improve the results.Comment: 6 pages, 4 figure

The influence of anaerobic muscle activity, maturation and season on the flesh quality of farmed turbot

In order to test seasonal, rearing, maturing and anaerobic muscle activity effect on the flesh quality of turbot (Scophthalmus maximus) a total of 80 farmed turbot from three different strains from reared under natural or continuous light were killed by a percussive blow to the head in November (winter, Icelandic strain), March (spring, Portuguese strain) and June (summer, domesticated strain (France turbot)). To test the effect of anaerobic muscle activity, 10 fish were on each occasion pre rigor filleted, where one fillet was used as a control, while the other fillet was electrically stimulated using a squared 5 Hz, 10 V pulsed DC for 3 min. All pre rigor fillets were measured for pH, weighed, wrapped in aluminum foil and stored in polystyrene boxes with ice. After 7 days of storage the fillets were measured instrumentally for pH, drip loss, colour (CIE L* a* b*) and texture properties such as hardness and shear force, while fillet shrinkage and colour (RBG) were evaluated with computer imaging on photographs from a standard lightbox. Results showed that softness of the flesh was mainly influenced by factors associated with growth, such as season, photoperiod and maturation. Anaerobic muscle activity simulated with electrical stimulation caused an increase in drip loss (<1%) and loss of shear force (<4%), but had no effect on hardness or fillet shrinkage. Computer imaging revealed that muscle contractions related to the electrical stimulus forced out blood from the fillet causing less reddishness for the entire storage period. We conclude that a pH drop upon slaughter associated with anaerobic muscle activity has a minor effect on the flesh quality in the short run, while seasonal/alternatively genetic effects are predominant

The role of trust in global software outsourcing relationships

Over the last decades, a trend towards the globalization of business, and of software-intensive sectors in particular, has emerged. These circumstances, in addition to new technical solutions, have had a large impact on software development. Because of advantages like cost savings, access to world-class IT professionals, and shortened time-to-market, several companies explore these new opportunities. One of these opportunities is Global Software Outsourcing, or GSO, meaning software development taking place outside the national border of the customer country. However, GSO also brings along questions about how to successfully operate across national and cultural boundaries. In this thesis, a better understanding of trust is suggested as a way to deal with some of these problems. In order to make trust identifiable in the GSO context, seven attributes of trust are suggested. Furthermore, the seven attributes of trust are evaluated by being applied to the findings from a GSO project between a Norwegian customer and a Russian software supplier. Based on this evaluation, the attributes of trust form the basis for a suggested model to identify and describe trust in GSO relationships

Fault diagnosis of downhole drilling incidents using adaptive observers and statistical change detection

Downhole abnormal incidents during oil and gas drilling cause costly delays, and may also potentially lead to dangerous scenarios. Different incidents will cause changes to different parts of the physics of the process. Estimating the changes in physical parameters, and correlating these with changes expected from various defects, can be used to diagnose faults while in development. This paper shows how estimated friction parameters and flow rates can be used to detect and isolate the type of incident, as well as isolating the position of a defect. Estimates are shown to be subjected to non-Gaussian, $t$-distributed noise, and a dedicated multivariate statistical change detection approach is used that detects and isolates faults by detecting simultaneous changes in estimated parameters and flow rates. The properties of the multivariate diagnosis method are analyzed, and it is shown how detection and false alarm probabilities are assessed and optimized using data-based learning to obtain thresholds for hypothesis testing. Data from a 1400 m horizontal flow loop is used to test the method, and successful diagnosis of the incidents drillstring washout (pipe leakage), lost circulation, gas influx, and drill bit nozzle plugging are demonstrated