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

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

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

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

Drillstring Washout Diagnosis Using Friction Estimation and Statistical Change Detection

In oil and gas drilling, corrosion or tensile stress can give small holes in the drillstring, which can cause leakage and prevent sufficient flow of drilling fluid. If such \emph{washout} remains undetected and develops, the consequence can be a complete twist-off of the drillstring. Aiming at early washout diagnosis, this paper employs an adaptive observer to estimate friction parameters in the nonlinear process. Non-Gaussian noise is a nuisance in the parameter estimates, and dedicated generalized likelihood tests are developed to make efficient washout detection with the multivariate $t$-distribution encountered in data. Change detection methods are developed using logged sensor data from a horizontal 1400 m managed pressure drilling test rig. Detection scheme design is conducted using probabilities for false alarm and detection to determine thresholds in hypothesis tests. A multivariate approach is demonstrated to have superior diagnostic properties and is able to diagnose a washout at very low levels. The paper demonstrates the feasibility of fault diagnosis technology in oil and gas drilling

Gastric cancer in Iceland

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Use of lumpfish for sea-lice control in salmon farming: challenges and opportunities

Efficient sea-lice control remains one of the most important challenges for the salmon farming industry. The use of wrasse (Labridae) as cleaner fish offers an alternative to medicines for sea-lice control, but wrasse tend to become inactive in winter. Lumpfish (Cyclopterus lumpus) continue to feed on sea-lice at low temperatures, and commercial production has escalated from thousands of fish in 2010 to well over 30 million juveniles deployed in 2016. However, production still relies on the capture of wild broodstock, which may not be sustainable. To meet global industry needs, lumpfish production needs to increase to reach c. 50 million fish annually and this can only come from aquaculture. We review current production methods and the use of lumpfish in sea cages and identify some of the main challenges and bottlenecks facing lumpfish intensification. Our gap analysis indicates that the areas in most need of research include better control of maturation for year-round production; formulation of appropriate diets; artificial selection of elite lines with desirable traits; and development of vaccines for certified, disease-free juvenile production. The welfare of farmed lumpfish also needs to be better quantified, and more information is needed on optimal densities and tank design. Finally, the risk of farmed lumpfish escaping from net pens needs to be critically assessed, and we argue that it might be beneficial to recover cleaner fish from salmon cages after the production cycle, perhaps using them as broodstock, for export to the Asian food markets or for the production of animal feeds

‘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

Economic stochastic nonlinear model predictive control of a semi-batch polymerization reaction

Batch processes are ubiquitous in the chemical industry and difficult to control, such that nonlinear model predictive control is one of the few promising control techniques. Many chemical process models however are affected by various uncertainties, which can lower the performance and lead to constraint violations. In this paper we propose a framework for output feedback stochastic nonlinear model predictive control (SNMPC) to consider the uncertainties explicitly, which are assumed to follow known probability distributions. Polynomial chaos expansions are employed both for the formulation of the SNMPC algorithm and a nonlinear filter for the estimation of the uncertain parameters online given noisy measurements. The effectiveness of the proposed SNMPC scheme was verified on an extensive case study involving the production of the polymer polypropylene glycol in a semi-batch reactor.publishedVersion© 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd